均一グリッドを作成

目次

均一グリッドを作成#

値の3D NumPy配列から単純な均一グリッドを作成します.

import numpy as np
import pyvista as pv

各軸がXYZデカルト軸に対応する空間データを保持するデータ値の3 D NumPy配列を取ります.この例では,値の3 D NumPy配列をプロットできる3 Dグリッドの空間参照を保持する pyvista.ImageData を作成します.

空間的に参照されるデータの3 D NumPy配列を作成します.これは,グリッドが (20, 5, 10), (nx, ny, nz) であるように空間的に参照さます.

values = np.linspace(0, 10, 1000).reshape((20, 5, 10))
values.shape
(20, 5, 10)

ImageDataの作成

CELLデータに値を注入したいので,グリッドの寸法を shape + 1 に設定します.

grid.dimensions = np.array(values.shape) + 1

空間リファレンスを編集する

grid.origin = (100, 33, 55.6)  # The bottom left corner of the data set
grid.spacing = (1, 5, 2)  # These are the cell sizes along each axis

セルデータにデータを代入します. ImageData オブジェクトのデータは必ずFortranオーダリングを使用して平坦化します.

grid.cell_data["values"] = values.flatten(order="F")
grid
HeaderData Arrays
ImageDataInformation
N Cells1000
N Points1386
X Bounds1.000e+02, 1.200e+02
Y Bounds3.300e+01, 5.800e+01
Z Bounds5.560e+01, 7.560e+01
Dimensions21, 6, 11
Spacing1.000e+00, 5.000e+00, 2.000e+00
N Arrays1
NameFieldTypeN CompMinMax
valuesCellsfloat6410.000e+001.000e+01


さて,グリッドをプロットしてみましょう!

grid.plot(show_edges=True)
c create uniform grid

セルデータが嫌いですか?NumPyの配列を pyvista.ImageData の点データに追加することもできます.初期化時にグリッドの寸法を設定する際に,微妙な違いに注意してください.

# Create the 3D NumPy array of spatially referenced data again.
values = np.linspace(0, 10, 1000).reshape((20, 5, 10))
values.shape
(20, 5, 10)

PyVistaオブジェクトを作成し,上記と同じように属性を設定します.

grid = pv.ImageData()

# Set the grid dimensions to ``shape`` because we want to inject our values on
# the POINT data
grid.dimensions = values.shape

# Edit the spatial reference
grid.origin = (100, 33, 55.6)  # The bottom left corner of the data set
grid.spacing = (1, 5, 2)  # These are the cell sizes along each axis

セルデータにデータ値を追加する

grid.point_data["values"] = values.flatten(order="F")  # Flatten the array!
grid
HeaderData Arrays
ImageDataInformation
N Cells684
N Points1000
X Bounds1.000e+02, 1.190e+02
Y Bounds3.300e+01, 5.300e+01
Z Bounds5.560e+01, 7.360e+01
Dimensions20, 5, 10
Spacing1.000e+00, 5.000e+00, 2.000e+00
N Arrays1
NameFieldTypeN CompMinMax
valuesPointsfloat6410.000e+001.000e+01


さて,グリッドをプロットしてみましょう!

grid.plot(show_edges=True)
c create uniform grid

演習#

それでは,3D NumPyの配列から,あなた自身の pyvista.ImageData を作ってみましょう!

Help on class ImageData in module pyvista.core.grid:

class ImageData(Grid, pyvista.core.filters.image_data.ImageDataFilters, vtkmodules.vtkCommonDataModel.vtkImageData)
 |  ImageData(uinput=None, dimensions=None, spacing=(1.0, 1.0, 1.0), origin=(0.0, 0.0, 0.0), deep=False)
 |
 |  Models datasets with uniform spacing in the three coordinate directions.
 |
 |  Can be initialized in one of several ways:
 |
 |  - Create empty grid
 |  - Initialize from a vtk.vtkImageData object
 |  - Initialize based on dimensions, cell spacing, and origin.
 |
 |  .. versionchanged:: 0.33.0
 |      First argument must now be either a path or
 |      ``vtk.vtkImageData``. Use keyword arguments to specify the
 |      dimensions, spacing, and origin of the uniform grid.
 |
 |  .. versionchanged:: 0.37.0
 |      The ``dims`` parameter has been renamed to ``dimensions``.
 |
 |  Parameters
 |  ----------
 |  uinput : str, vtk.vtkImageData, pyvista.ImageData, optional
 |      Filename or dataset to initialize the uniform grid from.  If
 |      set, remainder of arguments are ignored.
 |
 |  dimensions : sequence[int], optional
 |      Dimensions of the uniform grid.
 |
 |  spacing : sequence[float], default: (1.0, 1.0, 1.0)
 |      Spacing of the uniform grid in each dimension. Must be positive.
 |
 |  origin : sequence[float], default: (0.0, 0.0, 0.0)
 |      Origin of the uniform grid.
 |
 |  deep : bool, default: False
 |      Whether to deep copy a ``vtk.vtkImageData`` object.  Keyword only.
 |
 |  Examples
 |  --------
 |  Create an empty ImageData.
 |
 |  >>> import pyvista as pv
 |  >>> grid = pv.ImageData()
 |
 |  Initialize from a ``vtk.vtkImageData`` object.
 |
 |  >>> import vtk
 |  >>> vtkgrid = vtk.vtkImageData()
 |  >>> grid = pv.ImageData(vtkgrid)
 |
 |  Initialize using just the grid dimensions and default
 |  spacing and origin. These must be keyword arguments.
 |
 |  >>> grid = pv.ImageData(dimensions=(10, 10, 10))
 |
 |  Initialize using dimensions and spacing.
 |
 |  >>> grid = pv.ImageData(
 |  ...     dimensions=(10, 10, 10),
 |  ...     spacing=(2, 1, 5),
 |  ... )
 |
 |  Initialize using dimensions, spacing, and an origin.
 |
 |  >>> grid = pv.ImageData(
 |  ...     dimensions=(10, 10, 10),
 |  ...     spacing=(2, 1, 5),
 |  ...     origin=(10, 35, 50),
 |  ... )
 |
 |  Initialize from another ImageData.
 |
 |  >>> grid = pv.ImageData(
 |  ...     dimensions=(10, 10, 10),
 |  ...     spacing=(2, 1, 5),
 |  ...     origin=(10, 35, 50),
 |  ... )
 |  >>> grid_from_grid = pv.ImageData(grid)
 |  >>> grid_from_grid == grid
 |  True
 |
 |  Method resolution order:
 |      ImageData
 |      Grid
 |      pyvista.core.dataset.DataSet
 |      pyvista.core.filters.image_data.ImageDataFilters
 |      pyvista.core.filters.data_set.DataSetFilters
 |      pyvista.core.dataobject.DataObject
 |      vtkmodules.vtkCommonDataModel.vtkImageData
 |      vtkmodules.vtkCommonDataModel.vtkDataSet
 |      vtkmodules.vtkCommonDataModel.vtkDataObject
 |      vtkmodules.vtkCommonCore.vtkObject
 |      vtkmodules.vtkCommonCore.vtkObjectBase
 |      builtins.object
 |
 |  Methods defined here:
 |
 |  __init__(self, uinput=None, dimensions=None, spacing=(1.0, 1.0, 1.0), origin=(0.0, 0.0, 0.0), deep=False)
 |      Initialize the uniform grid.
 |
 |  __repr__(self)
 |      Return the default representation.
 |
 |  __str__(self)
 |      Return the default str representation.
 |
 |  cast_to_rectilinear_grid(self) -> 'RectilinearGrid'
 |      Cast this uniform grid to a rectilinear grid.
 |
 |      Returns
 |      -------
 |      pyvista.RectilinearGrid
 |          This uniform grid as a rectilinear grid.
 |
 |  cast_to_structured_grid(self) -> 'pyvista.StructuredGrid'
 |      Cast this uniform grid to a structured grid.
 |
 |      Returns
 |      -------
 |      pyvista.StructuredGrid
 |          This grid as a structured grid.
 |
 |  to_tetrahedra(self, tetra_per_cell: 'int' = 5, mixed: 'Sequence[int] | bool' = False, pass_cell_ids: 'bool' = True, pass_data: 'bool' = True, progress_bar: 'bool' = False)
 |      Create a tetrahedral mesh structured grid.
 |
 |      Parameters
 |      ----------
 |      tetra_per_cell : int, default: 5
 |          The number of tetrahedrons to divide each cell into. Can be
 |          either ``5``, ``6``, or ``12``. If ``mixed=True``, this value is
 |          overridden.
 |
 |      mixed : str, bool, sequence, default: False
 |          When set, subdivides some cells into 5 and some cells into 12. Set
 |          to ``True`` to use the active cell scalars of the
 |          :class:`pyvista.RectilinearGrid` to be either 5 or 12 to
 |          determining the number of tetrahedra to generate per cell.
 |
 |          When a sequence, uses these values to subdivide the cells. When a
 |          string uses a cell array rather than the active array to determine
 |          the number of tetrahedra to generate per cell.
 |
 |      pass_cell_ids : bool, default: True
 |          Set to ``True`` to make the tetrahedra have scalar data indicating
 |          which cell they came from in the original
 |          :class:`pyvista.RectilinearGrid`. The name of this array is
 |          ``'vtkOriginalCellIds'`` within the ``cell_data``.
 |
 |      pass_data : bool, default: True
 |          Set to ``True`` to make the tetrahedra mesh have the cell data from
 |          the original :class:`pyvista.RectilinearGrid`.  This uses
 |          ``pass_cell_ids=True`` internally. If ``True``, ``pass_cell_ids``
 |          will also be set to ``True``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          UnstructuredGrid containing the tetrahedral cells.
 |
 |      Examples
 |      --------
 |      Divide a rectangular grid into tetrahedrons. Each cell contains by
 |      default 5 tetrahedrons.
 |
 |      First, create and plot the grid.
 |
 |      >>> import numpy as np
 |      >>> import pyvista as pv
 |      >>> xrng = np.linspace(0, 1, 2)
 |      >>> yrng = np.linspace(0, 1, 2)
 |      >>> zrng = np.linspace(0, 2, 3)
 |      >>> grid = pv.RectilinearGrid(xrng, yrng, zrng)
 |      >>> grid.plot()
 |
 |      Now, generate the tetrahedra plot in the exploded view of the cell.
 |
 |      >>> tet_grid = grid.to_tetrahedra()
 |      >>> tet_grid.explode(factor=0.5).plot(show_edges=True)
 |
 |      Take the same grid but divide the first cell into 5 cells and the other
 |      cell into 12 tetrahedrons per cell.
 |
 |      >>> tet_grid = grid.to_tetrahedra(mixed=[5, 12])
 |      >>> tet_grid.explode(factor=0.5).plot(show_edges=True)
 |
 |  ----------------------------------------------------------------------
 |  Readonly properties defined here:
 |
 |  x
 |      Return all the X points.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(2, 2, 2))
 |      >>> grid.x
 |      array([0., 1., 0., 1., 0., 1., 0., 1.])
 |
 |  y
 |      Return all the Y points.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(2, 2, 2))
 |      >>> grid.y
 |      array([0., 0., 1., 1., 0., 0., 1., 1.])
 |
 |  z
 |      Return all the Z points.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(2, 2, 2))
 |      >>> grid.z
 |      array([0., 0., 0., 0., 1., 1., 1., 1.])
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  extent
 |      Return or set the extent of the ImageData.
 |
 |      The extent is simply the first and last indices for each of the three axes.
 |
 |      Examples
 |      --------
 |      Create a ``ImageData`` and show its extent.
 |
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(10, 10, 10))
 |      >>> grid.extent
 |      (0, 9, 0, 9, 0, 9)
 |
 |      >>> grid.extent = (2, 5, 2, 5, 2, 5)
 |      >>> grid.extent
 |      (2, 5, 2, 5, 2, 5)
 |
 |      Note how this also modifies the grid bounds and dimensions. Since we
 |      use default spacing of 1 here, the bounds match the extent exactly.
 |
 |      >>> grid.bounds
 |      (2.0, 5.0, 2.0, 5.0, 2.0, 5.0)
 |      >>> grid.dimensions
 |      (4, 4, 4)
 |
 |  origin
 |      Return the origin of the grid (bottom southwest corner).
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(5, 5, 5))
 |      >>> grid.origin
 |      (0.0, 0.0, 0.0)
 |
 |      Show how the origin is in the bottom "southwest" corner of the
 |      ImageData.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(grid, show_edges=True)
 |      >>> _ = pl.add_axes_at_origin(ylabel=None)
 |      >>> pl.camera_position = 'xz'
 |      >>> pl.show()
 |
 |      Set the origin to ``(1, 1, 1)`` and show how this shifts the
 |      ImageData.
 |
 |      >>> grid.origin = (1, 1, 1)
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(grid, show_edges=True)
 |      >>> _ = pl.add_axes_at_origin(ylabel=None)
 |      >>> pl.camera_position = 'xz'
 |      >>> pl.show()
 |
 |  points
 |      Build a copy of the implicitly defined points as a numpy array.
 |
 |      Returns
 |      -------
 |      numpy.ndarray
 |          Array of points representing the image data.
 |
 |      Notes
 |      -----
 |      The ``points`` for a :class:`pyvista.ImageData` cannot be set.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(2, 2, 2))
 |      >>> grid.points
 |      array([[0., 0., 0.],
 |             [1., 0., 0.],
 |             [0., 1., 0.],
 |             [1., 1., 0.],
 |             [0., 0., 1.],
 |             [1., 0., 1.],
 |             [0., 1., 1.],
 |             [1., 1., 1.]])
 |
 |  spacing
 |      Return or set the spacing for each axial direction.
 |
 |      Notes
 |      -----
 |      Spacing must be non-negative. While VTK accepts negative
 |      spacing, this results in unexpected behavior. See:
 |      https://github.com/pyvista/pyvista/issues/1967
 |
 |      Examples
 |      --------
 |      Create a 5 x 5 x 5 uniform grid.
 |
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(5, 5, 5))
 |      >>> grid.spacing
 |      (1.0, 1.0, 1.0)
 |      >>> grid.plot(show_edges=True)
 |
 |      Modify the spacing to ``(1, 2, 3)``
 |
 |      >>> grid.spacing = (1, 2, 3)
 |      >>> grid.plot(show_edges=True)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  __annotations__ = {'_WRITERS': 'ClassVar[dict[str, type[_vtk.vtkDataSe...
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from Grid:
 |
 |  __new__(cls, *args, **kwargs)
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from Grid:
 |
 |  dimensions
 |      Return the grid's dimensions.
 |
 |      These are effectively the number of points along each of the
 |      three dataset axes.
 |
 |      Returns
 |      -------
 |      tuple[int]
 |          Dimensions of the grid.
 |
 |      Examples
 |      --------
 |      Create a uniform grid with dimensions ``(1, 2, 3)``.
 |
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(2, 3, 4))
 |      >>> grid.dimensions
 |      (2, 3, 4)
 |      >>> grid.plot(show_edges=True)
 |
 |      Set the dimensions to ``(3, 4, 5)``
 |
 |      >>> grid.dimensions = (3, 4, 5)
 |      >>> grid.plot(show_edges=True)
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyvista.core.dataset.DataSet:
 |
 |  __getattr__(self, item) -> 'Any'
 |      Get attribute from base class if not found.
 |
 |  __getitem__(self, index: 'Iterable[Any] | str') -> 'NumpyArray[float]'
 |      Search both point, cell, and field data for an array.
 |
 |  __setitem__(self, name: 'str', scalars: 'NumpyArray[float] | Sequence[float] | float')
 |      Add/set an array in the point_data, or cell_data accordingly.
 |
 |      It depends on the array's length, or specified mode.
 |
 |  cast_to_pointset(self, pass_cell_data: 'bool' = False) -> 'pyvista.PointSet'
 |      Extract the points of this dataset and return a :class:`pyvista.PointSet`.
 |
 |      Parameters
 |      ----------
 |      pass_cell_data : bool, default: False
 |          Run the :func:`cell_data_to_point_data()
 |          <pyvista.DataSetFilters.cell_data_to_point_data>` filter and pass
 |          cell data fields to the new pointset.
 |
 |      Returns
 |      -------
 |      pyvista.PointSet
 |          Dataset cast into a :class:`pyvista.PointSet`.
 |
 |      Notes
 |      -----
 |      This will produce a deep copy of the points and point/cell data of
 |      the original mesh.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Wavelet()
 |      >>> pointset = mesh.cast_to_pointset()
 |      >>> type(pointset)
 |      <class 'pyvista.core.pointset.PointSet'>
 |
 |  cast_to_poly_points(self, pass_cell_data: 'bool' = False) -> 'pyvista.PolyData'
 |      Extract the points of this dataset and return a :class:`pyvista.PolyData`.
 |
 |      Parameters
 |      ----------
 |      pass_cell_data : bool, default: False
 |          Run the :func:`cell_data_to_point_data()
 |          <pyvista.DataSetFilters.cell_data_to_point_data>` filter and pass
 |          cell data fields to the new pointset.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Dataset cast into a :class:`pyvista.PolyData`.
 |
 |      Notes
 |      -----
 |      This will produce a deep copy of the points and point/cell data of
 |      the original mesh.
 |
 |      Examples
 |      --------
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_uniform()
 |      >>> points = mesh.cast_to_poly_points(pass_cell_data=True)
 |      >>> type(points)
 |      <class 'pyvista.core.pointset.PolyData'>
 |      >>> points.n_arrays
 |      2
 |      >>> points.point_data
 |      pyvista DataSetAttributes
 |      Association     : POINT
 |      Active Scalars  : Spatial Point Data
 |      Active Vectors  : None
 |      Active Texture  : None
 |      Active Normals  : None
 |      Contains arrays :
 |          Spatial Point Data      float64    (1000,)              SCALARS
 |      >>> points.cell_data
 |      pyvista DataSetAttributes
 |      Association     : CELL
 |      Active Scalars  : None
 |      Active Vectors  : None
 |      Active Texture  : None
 |      Active Normals  : None
 |      Contains arrays :
 |          Spatial Cell Data       float64    (1000,)
 |
 |  cast_to_unstructured_grid(self) -> 'pyvista.UnstructuredGrid'
 |      Get a new representation of this object as a :class:`pyvista.UnstructuredGrid`.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Dataset cast into a :class:`pyvista.UnstructuredGrid`.
 |
 |      Examples
 |      --------
 |      Cast a :class:`pyvista.PolyData` to a
 |      :class:`pyvista.UnstructuredGrid`.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> type(mesh)
 |      <class 'pyvista.core.pointset.PolyData'>
 |      >>> grid = mesh.cast_to_unstructured_grid()
 |      >>> type(grid)
 |      <class 'pyvista.core.pointset.UnstructuredGrid'>
 |
 |  cell_neighbors(self, ind: 'int', connections: 'str' = 'points') -> 'list[int]'
 |      Get the cell neighbors of the ind-th cell.
 |
 |      Concrete implementation of vtkDataSet's `GetCellNeighbors
 |      <https://vtk.org/doc/nightly/html/classvtkDataSet.html#ae1ba413c15802ef50d9b1955a66521e4>`_.
 |
 |      Parameters
 |      ----------
 |      ind : int
 |          Cell ID.
 |
 |      connections : str, default: "points"
 |          Describe how the neighbor cell(s) must be connected to the current
 |          cell to be considered as a neighbor.
 |          Can be either ``'points'``, ``'edges'`` or ``'faces'``.
 |
 |      Returns
 |      -------
 |      list[int]
 |          List of neighbor cells IDs for the ind-th cell.
 |
 |      Warnings
 |      --------
 |      For a :class:`pyvista.ExplicitStructuredGrid`, use :func:`pyvista.ExplicitStructuredGrid.neighbors`.
 |
 |      See Also
 |      --------
 |      pyvista.DataSet.cell_neighbors_levels
 |
 |      Examples
 |      --------
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_airplane()
 |
 |      Get the neighbor cell ids that have at least one point in common with
 |      the 0-th cell.
 |
 |      >>> mesh.cell_neighbors(0, "points")
 |      [1, 2, 3, 388, 389, 11, 12, 395, 14, 209, 211, 212]
 |
 |      Get the neighbor cell ids that have at least one edge in common with
 |      the 0-th cell.
 |
 |      >>> mesh.cell_neighbors(0, "edges")
 |      [1, 3, 12]
 |
 |      For unstructured grids with cells of dimension 3 (Tetrahedron for example),
 |      cell neighbors can be defined using faces.
 |
 |      >>> mesh = examples.download_tetrahedron()
 |      >>> mesh.cell_neighbors(0, "faces")
 |      [1, 5, 7]
 |
 |      Show a visual example.
 |
 |      >>> from functools import partial
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere(theta_resolution=10)
 |      >>>
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> pl.link_views()
 |      >>> add_point_labels = partial(
 |      ...     pl.add_point_labels,
 |      ...     text_color="white",
 |      ...     font_size=20,
 |      ...     shape=None,
 |      ...     show_points=False,
 |      ... )
 |      >>>
 |      >>> for i, connection in enumerate(["points", "edges"]):
 |      ...     pl.subplot(0, i)
 |      ...     pl.view_xy()
 |      ...     _ = pl.add_title(
 |      ...         f"{connection.capitalize()} neighbors",
 |      ...         color="red",
 |      ...         shadow=True,
 |      ...         font_size=8,
 |      ...     )
 |      ...
 |      ...     # Add current cell
 |      ...     i_cell = 0
 |      ...     current_cell = mesh.extract_cells(i_cell)
 |      ...     _ = pl.add_mesh(
 |      ...         current_cell, show_edges=True, color="blue"
 |      ...     )
 |      ...     _ = add_point_labels(
 |      ...         current_cell.cell_centers().points,
 |      ...         labels=[f"{i_cell}"],
 |      ...     )
 |      ...
 |      ...     # Add neighbors
 |      ...     ids = mesh.cell_neighbors(i_cell, connection)
 |      ...     cells = mesh.extract_cells(ids)
 |      ...     _ = pl.add_mesh(cells, color="red", show_edges=True)
 |      ...     _ = add_point_labels(
 |      ...         cells.cell_centers().points,
 |      ...         labels=[f"{i}" for i in ids],
 |      ...     )
 |      ...
 |      ...     # Add other cells
 |      ...     ids.append(i_cell)
 |      ...     others = mesh.extract_cells(ids, invert=True)
 |      ...     _ = pl.add_mesh(others, show_edges=True)
 |      ...
 |      >>> pl.show()
 |
 |  cell_neighbors_levels(self, ind: 'int', connections: 'str' = 'points', n_levels: 'int' = 1) -> 'Generator[list[int], None, None]'
 |      Get consecutive levels of cell neighbors.
 |
 |      Parameters
 |      ----------
 |      ind : int
 |          Cell ID.
 |
 |      connections : str, default: "points"
 |          Describe how the neighbor cell(s) must be connected to the current
 |          cell to be considered as a neighbor.
 |          Can be either ``'points'``, ``'edges'`` or ``'faces'``.
 |
 |      n_levels : int, default: 1
 |          Number of levels to search for cell neighbors.
 |          When equal to 1, it is equivalent to :func:`pyvista.DataSet.cell_neighbors`.
 |
 |      Returns
 |      -------
 |      generator[list[int]]
 |          A generator of list of cell IDs for each level.
 |
 |      Warnings
 |      --------
 |      For a :class:`pyvista.ExplicitStructuredGrid`, use :func:`pyvista.ExplicitStructuredGrid.neighbors`.
 |
 |      See Also
 |      --------
 |      pyvista.DataSet.cell_neighbors
 |
 |      Examples
 |      --------
 |      Get the cell neighbors IDs starting from the 0-th cell
 |      up until the third level.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere(theta_resolution=10)
 |      >>> nbr_levels = mesh.cell_neighbors_levels(
 |      ...     0, connections="edges", n_levels=3
 |      ... )
 |      >>> nbr_levels = list(nbr_levels)
 |      >>> nbr_levels[0]
 |      [1, 21, 9]
 |      >>> nbr_levels[1]
 |      [2, 8, 74, 75, 20, 507]
 |      >>> nbr_levels[2]
 |      [128, 129, 3, 453, 7, 77, 23, 506]
 |
 |      Visualize these cells IDs.
 |
 |      >>> from functools import partial
 |      >>> pv.global_theme.color_cycler = [
 |      ...     'red',
 |      ...     'green',
 |      ...     'blue',
 |      ...     'purple',
 |      ... ]
 |      >>> pl = pv.Plotter()
 |      >>>
 |      >>> # Define partial function to add point labels
 |      >>> add_point_labels = partial(
 |      ...     pl.add_point_labels,
 |      ...     text_color="white",
 |      ...     font_size=40,
 |      ...     shape=None,
 |      ...     show_points=False,
 |      ... )
 |      >>>
 |      >>> # Add the 0-th cell to the plotter
 |      >>> cell = mesh.extract_cells(0)
 |      >>> _ = pl.add_mesh(cell, show_edges=True)
 |      >>> _ = add_point_labels(cell.cell_centers().points, labels=["0"])
 |      >>> other_ids = [0]
 |      >>>
 |      >>> # Add the neighbors to the plot
 |      >>> neighbors = mesh.cell_neighbors_levels(
 |      ...     0, connections="edges", n_levels=3
 |      ... )
 |      >>> for i, ids in enumerate(neighbors, start=1):
 |      ...     cells = mesh.extract_cells(ids)
 |      ...     _ = pl.add_mesh(cells, show_edges=True)
 |      ...     _ = add_point_labels(
 |      ...         cells.cell_centers().points, labels=[f"{i}"] * len(ids)
 |      ...     )
 |      ...     other_ids.extend(ids)
 |      ...
 |      >>>
 |      >>> # Add the cell IDs that are not neighbors (ie. the rest of the sphere)
 |      >>> cells = mesh.extract_cells(other_ids, invert=True)
 |      >>> _ = pl.add_mesh(cells, color="white", show_edges=True)
 |      >>>
 |      >>> pl.view_xy()
 |      >>> pl.camera.zoom(6.0)
 |      >>> pl.show()
 |
 |  clear_cell_data(self) -> 'None'
 |      Remove all cell arrays.
 |
 |  clear_data(self) -> 'None'
 |      Remove all arrays from point/cell/field data.
 |
 |      Examples
 |      --------
 |      Clear all arrays from a mesh.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.point_data.keys()
 |      ['Normals']
 |      >>> mesh.clear_data()
 |      >>> mesh.point_data.keys()
 |      []
 |
 |  clear_point_data(self) -> 'None'
 |      Remove all point arrays.
 |
 |      Examples
 |      --------
 |      Clear all point arrays from a mesh.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.point_data.keys()
 |      ['Normals']
 |      >>> mesh.clear_point_data()
 |      >>> mesh.point_data.keys()
 |      []
 |
 |  copy_from(self, mesh: '_vtk.vtkDataSet', deep: 'bool' = True) -> 'None'
 |      Overwrite this dataset inplace with the new dataset's geometries and data.
 |
 |      Parameters
 |      ----------
 |      mesh : vtk.vtkDataSet
 |          The overwriting mesh.
 |
 |      deep : bool, default: True
 |          Whether to perform a deep or shallow copy.
 |
 |      Examples
 |      --------
 |      Create two meshes and overwrite ``mesh_a`` with ``mesh_b``.
 |      Show that ``mesh_a`` is equal to ``mesh_b``.
 |
 |      >>> import pyvista as pv
 |      >>> mesh_a = pv.Sphere()
 |      >>> mesh_b = pv.Cube()
 |      >>> mesh_a.copy_from(mesh_b)
 |      >>> mesh_a == mesh_b
 |      True
 |
 |  copy_meta_from(self, ido: 'DataSet', deep: 'bool' = True) -> 'None'
 |      Copy pyvista meta data onto this object from another object.
 |
 |      Parameters
 |      ----------
 |      ido : pyvista.DataSet
 |          Dataset to copy the metadata from.
 |
 |      deep : bool, default: True
 |          Deep or shallow copy.
 |
 |  find_cells_along_line(self, pointa: 'VectorLike[float]', pointb: 'VectorLike[float]', tolerance: 'float' = 0.0) -> 'NumpyArray[int]'
 |      Find the index of cells whose bounds intersect a line.
 |
 |      Line is defined from ``pointa`` to ``pointb``.
 |
 |      Parameters
 |      ----------
 |      pointa : Vector
 |          Length 3 coordinate of the start of the line.
 |
 |      pointb : Vector
 |          Length 3 coordinate of the end of the line.
 |
 |      tolerance : float, default: 0.0
 |          The absolute tolerance to use to find cells along line.
 |
 |      Returns
 |      -------
 |      numpy.ndarray
 |          Index or indices of the cell(s) whose bounds intersect
 |          the line.
 |
 |      Warnings
 |      --------
 |      This method returns cells whose bounds intersect the line.
 |      This means that the line may not intersect the cell itself.
 |      To obtain cells that intersect the line, use
 |      :func:`pyvista.DataSet.find_cells_intersecting_line`.
 |
 |      See Also
 |      --------
 |      DataSet.find_closest_point
 |      DataSet.find_closest_cell
 |      DataSet.find_containing_cell
 |      DataSet.find_cells_within_bounds
 |      DataSet.find_cells_intersecting_line
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.find_cells_along_line([0.0, 0, 0], [1.0, 0, 0])
 |      array([  86,   87, 1652, 1653])
 |
 |  find_cells_intersecting_line(self, pointa: 'VectorLike[float]', pointb: 'VectorLike[float]', tolerance: 'float' = 0.0) -> 'NumpyArray[int]'
 |      Find the index of cells that intersect a line.
 |
 |      Line is defined from ``pointa`` to ``pointb``.  This
 |      method requires vtk version >=9.2.0.
 |
 |      Parameters
 |      ----------
 |      pointa : sequence[float]
 |          Length 3 coordinate of the start of the line.
 |
 |      pointb : sequence[float]
 |          Length 3 coordinate of the end of the line.
 |
 |      tolerance : float, default: 0.0
 |          The absolute tolerance to use to find cells along line.
 |
 |      Returns
 |      -------
 |      numpy.ndarray
 |          Index or indices of the cell(s) that intersect
 |          the line.
 |
 |      See Also
 |      --------
 |      DataSet.find_closest_point
 |      DataSet.find_closest_cell
 |      DataSet.find_containing_cell
 |      DataSet.find_cells_within_bounds
 |      DataSet.find_cells_along_line
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.find_cells_intersecting_line([0.0, 0, 0], [1.0, 0, 0])
 |      array([  86, 1653])
 |
 |  find_cells_within_bounds(self, bounds: 'BoundsLike') -> 'NumpyArray[int]'
 |      Find the index of cells in this mesh within bounds.
 |
 |      Parameters
 |      ----------
 |      bounds : sequence[float]
 |          Bounding box. The form is: ``[xmin, xmax, ymin, ymax, zmin, zmax]``.
 |
 |      Returns
 |      -------
 |      numpy.ndarray
 |          Index or indices of the cell in this mesh that are closest
 |          to the given point.
 |
 |      See Also
 |      --------
 |      DataSet.find_closest_point
 |      DataSet.find_closest_cell
 |      DataSet.find_containing_cell
 |      DataSet.find_cells_along_line
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> index = mesh.find_cells_within_bounds(
 |      ...     [-2.0, 2.0, -2.0, 2.0, -2.0, 2.0]
 |      ... )
 |
 |  find_closest_cell(self, point: 'VectorLike[float] | MatrixLike[float]', return_closest_point: 'bool' = False) -> 'int | NumpyArray[int] | tuple[int | NumpyArray[int], NumpyArray[int]]'
 |      Find index of closest cell in this mesh to the given point.
 |
 |      Parameters
 |      ----------
 |      point : Vector | Matrix
 |          Coordinates of point to query (length 3) or a
 |          :class:`numpy.ndarray` of ``n`` points with shape ``(n, 3)``.
 |
 |      return_closest_point : bool, default: False
 |          If ``True``, the closest point within a mesh cell to that point is
 |          returned.  This is not necessarily the closest nodal point on the
 |          mesh.  Default is ``False``.
 |
 |      Returns
 |      -------
 |      int or numpy.ndarray
 |          Index or indices of the cell in this mesh that is/are closest
 |          to the given point(s).
 |
 |          .. versionchanged:: 0.35.0
 |             Inputs of shape ``(1, 3)`` now return a :class:`numpy.ndarray`
 |             of shape ``(1,)``.
 |
 |      numpy.ndarray
 |          Point or points inside a cell of the mesh that is/are closest
 |          to the given point(s).  Only returned if
 |          ``return_closest_point=True``.
 |
 |          .. versionchanged:: 0.35.0
 |             Inputs of shape ``(1, 3)`` now return a :class:`numpy.ndarray`
 |             of the same shape.
 |
 |      Warnings
 |      --------
 |      This method may still return a valid cell index even if the point
 |      contains a value like ``numpy.inf`` or ``numpy.nan``.
 |
 |      See Also
 |      --------
 |      DataSet.find_closest_point
 |      DataSet.find_containing_cell
 |      DataSet.find_cells_along_line
 |      DataSet.find_cells_within_bounds
 |
 |      Examples
 |      --------
 |      Find nearest cell on a sphere centered on the
 |      origin to the point ``[0.1, 0.2, 0.3]``.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> point = [0.1, 0.2, 0.3]
 |      >>> index = mesh.find_closest_cell(point)
 |      >>> index
 |      338
 |
 |      Make sure that this cell indeed is the closest to
 |      ``[0.1, 0.2, 0.3]``.
 |
 |      >>> import numpy as np
 |      >>> cell_centers = mesh.cell_centers()
 |      >>> relative_position = cell_centers.points - point
 |      >>> distance = np.linalg.norm(relative_position, axis=1)
 |      >>> np.argmin(distance)
 |      np.int64(338)
 |
 |      Find the nearest cells to several random points that
 |      are centered on the origin.
 |
 |      >>> points = 2 * np.random.default_rng().random((5000, 3)) - 1
 |      >>> indices = mesh.find_closest_cell(points)
 |      >>> indices.shape
 |      (5000,)
 |
 |      For the closest cell, find the point inside the cell that is
 |      closest to the supplied point.  The rectangle is a unit square
 |      with 1 cell and 4 nodal points at the corners in the plane with
 |      ``z`` normal and ``z=0``.  The closest point inside the cell is
 |      not usually at a nodal point.
 |
 |      >>> unit_square = pv.Rectangle()
 |      >>> index, closest_point = unit_square.find_closest_cell(
 |      ...     [0.25, 0.25, 0.5], return_closest_point=True
 |      ... )
 |      >>> closest_point
 |      array([0.25, 0.25, 0.  ])
 |
 |      But, the closest point can be a nodal point, although the index of
 |      that point is not returned.  If the closest nodal point by index is
 |      desired, see :func:`DataSet.find_closest_point`.
 |
 |      >>> index, closest_point = unit_square.find_closest_cell(
 |      ...     [1.0, 1.0, 0.5], return_closest_point=True
 |      ... )
 |      >>> closest_point
 |      array([1., 1., 0.])
 |
 |  find_closest_point(self, point: 'Iterable[float]', n=1) -> 'int'
 |      Find index of closest point in this mesh to the given point.
 |
 |      If wanting to query many points, use a KDTree with scipy or another
 |      library as those implementations will be easier to work with.
 |
 |      See: https://github.com/pyvista/pyvista-support/issues/107
 |
 |      Parameters
 |      ----------
 |      point : sequence[float]
 |          Length 3 coordinate of the point to query.
 |
 |      n : int, optional
 |          If greater than ``1``, returns the indices of the ``n`` closest
 |          points.
 |
 |      Returns
 |      -------
 |      int
 |          The index of the point in this mesh that is closest to the given point.
 |
 |      See Also
 |      --------
 |      DataSet.find_closest_cell
 |      DataSet.find_containing_cell
 |      DataSet.find_cells_along_line
 |      DataSet.find_cells_within_bounds
 |
 |      Examples
 |      --------
 |      Find the index of the closest point to ``(0, 1, 0)``.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> index = mesh.find_closest_point((0, 1, 0))
 |      >>> index
 |      239
 |
 |      Get the coordinate of that point.
 |
 |      >>> mesh.points[index]
 |      pyvista_ndarray([-0.05218758,  0.49653167,  0.02706946], dtype=float32)
 |
 |  find_containing_cell(self, point: 'VectorLike[float] | MatrixLike[float]') -> 'int | NumpyArray[int]'
 |      Find index of a cell that contains the given point.
 |
 |      Parameters
 |      ----------
 |      point : Vector, Matrix
 |          Coordinates of point to query (length 3) or a
 |          :class:`numpy.ndarray` of ``n`` points with shape ``(n, 3)``.
 |
 |      Returns
 |      -------
 |      int or numpy.ndarray
 |          Index or indices of the cell in this mesh that contains
 |          the given point.
 |
 |          .. versionchanged:: 0.35.0
 |             Inputs of shape ``(1, 3)`` now return a :class:`numpy.ndarray`
 |             of shape ``(1,)``.
 |
 |      See Also
 |      --------
 |      DataSet.find_closest_point
 |      DataSet.find_closest_cell
 |      DataSet.find_cells_along_line
 |      DataSet.find_cells_within_bounds
 |
 |      Examples
 |      --------
 |      A unit square with 16 equal sized cells is created and a cell
 |      containing the point ``[0.3, 0.3, 0.0]`` is found.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.ImageData(
 |      ...     dimensions=[5, 5, 1], spacing=[1 / 4, 1 / 4, 0]
 |      ... )
 |      >>> mesh
 |      ImageData...
 |      >>> mesh.find_containing_cell([0.3, 0.3, 0.0])
 |      5
 |
 |      A point outside the mesh domain will return ``-1``.
 |
 |      >>> mesh.find_containing_cell([0.3, 0.3, 1.0])
 |      -1
 |
 |      Find the cells that contain 1000 random points inside the mesh.
 |
 |      >>> import numpy as np
 |      >>> points = np.random.default_rng().random((1000, 3))
 |      >>> indices = mesh.find_containing_cell(points)
 |      >>> indices.shape
 |      (1000,)
 |
 |  flip_normal(self, normal: 'VectorLike[float]', point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Flip mesh about the normal.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      normal : sequence[float]
 |         Normal vector to flip about.
 |
 |      point : sequence[float]
 |          Point to rotate about.  Defaults to center of mesh at
 |          :attr:`center <pyvista.DataSet.center>`.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset flipped about its normal.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> pl.subplot(0, 0)
 |      >>> pl.show_axes()
 |      >>> mesh1 = examples.download_teapot()
 |      >>> _ = pl.add_mesh(mesh1)
 |      >>> pl.subplot(0, 1)
 |      >>> pl.show_axes()
 |      >>> mesh2 = mesh1.flip_normal([1.0, 1.0, 1.0], inplace=False)
 |      >>> _ = pl.add_mesh(mesh2)
 |      >>> pl.show(cpos="xy")
 |
 |  flip_x(self, point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Flip mesh about the x-axis.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      point : sequence[float], optional
 |          Point to rotate about.  Defaults to center of mesh at
 |          :attr:`center <pyvista.DataSet.center>`.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Flipped dataset.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> pl.subplot(0, 0)
 |      >>> pl.show_axes()
 |      >>> mesh1 = examples.download_teapot()
 |      >>> _ = pl.add_mesh(mesh1)
 |      >>> pl.subplot(0, 1)
 |      >>> pl.show_axes()
 |      >>> mesh2 = mesh1.flip_x(inplace=False)
 |      >>> _ = pl.add_mesh(mesh2)
 |      >>> pl.show(cpos="xy")
 |
 |  flip_y(self, point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Flip mesh about the y-axis.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      point : sequence[float], optional
 |          Point to rotate about.  Defaults to center of mesh at
 |          :attr:`center <pyvista.DataSet.center>`.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Flipped dataset.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> pl.subplot(0, 0)
 |      >>> pl.show_axes()
 |      >>> mesh1 = examples.download_teapot()
 |      >>> _ = pl.add_mesh(mesh1)
 |      >>> pl.subplot(0, 1)
 |      >>> pl.show_axes()
 |      >>> mesh2 = mesh1.flip_y(inplace=False)
 |      >>> _ = pl.add_mesh(mesh2)
 |      >>> pl.show(cpos="xy")
 |
 |  flip_z(self, point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Flip mesh about the z-axis.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      point : Vector, optional
 |          Point to rotate about.  Defaults to center of mesh at
 |          :attr:`center <pyvista.DataSet.center>`.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Flipped dataset.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> pl.subplot(0, 0)
 |      >>> pl.show_axes()
 |      >>> mesh1 = examples.download_teapot().rotate_x(90, inplace=False)
 |      >>> _ = pl.add_mesh(mesh1)
 |      >>> pl.subplot(0, 1)
 |      >>> pl.show_axes()
 |      >>> mesh2 = mesh1.flip_z(inplace=False)
 |      >>> _ = pl.add_mesh(mesh2)
 |      >>> pl.show(cpos="xz")
 |
 |  get_array(self, name: 'str', preference: "Literal['cell', 'point', 'field']" = 'cell') -> 'pyvista.pyvista_ndarray'
 |      Search both point, cell and field data for an array.
 |
 |      Parameters
 |      ----------
 |      name : str
 |          Name of the array.
 |
 |      preference : str, default: "cell"
 |          When scalars is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'``, ``'cell'``, or ``'field'``.
 |
 |      Returns
 |      -------
 |      pyvista.pyvista_ndarray
 |          Requested array.
 |
 |      Examples
 |      --------
 |      Create a DataSet with a variety of arrays.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> mesh.clear_data()
 |      >>> mesh.point_data['point-data'] = range(mesh.n_points)
 |      >>> mesh.cell_data['cell-data'] = range(mesh.n_cells)
 |      >>> mesh.field_data['field-data'] = ['a', 'b', 'c']
 |      >>> mesh.array_names
 |      ['point-data', 'field-data', 'cell-data']
 |
 |      Get the point data array.
 |
 |      >>> mesh.get_array('point-data')
 |      pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7])
 |
 |      Get the cell data array.
 |
 |      >>> mesh.get_array('cell-data')
 |      pyvista_ndarray([0, 1, 2, 3, 4, 5])
 |
 |      Get the field data array.
 |
 |      >>> mesh.get_array('field-data')
 |      pyvista_ndarray(['a', 'b', 'c'], dtype='<U1')
 |
 |  get_array_association(self, name: 'str', preference: "Literal['cell', 'point', 'field']" = 'cell') -> 'FieldAssociation'
 |      Get the association of an array.
 |
 |      Parameters
 |      ----------
 |      name : str
 |          Name of the array.
 |
 |      preference : str, default: "cell"
 |          When ``name`` is specified, this is the preferred array
 |          association to search for in the dataset.  Must be either
 |          ``'point'``, ``'cell'``, or ``'field'``.
 |
 |      Returns
 |      -------
 |      pyvista.core.utilities.arrays.FieldAssociation
 |          Field association of the array.
 |
 |      Examples
 |      --------
 |      Create a DataSet with a variety of arrays.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> mesh.clear_data()
 |      >>> mesh.point_data['point-data'] = range(mesh.n_points)
 |      >>> mesh.cell_data['cell-data'] = range(mesh.n_cells)
 |      >>> mesh.field_data['field-data'] = ['a', 'b', 'c']
 |      >>> mesh.array_names
 |      ['point-data', 'field-data', 'cell-data']
 |
 |      Get the point data array association.
 |
 |      >>> mesh.get_array_association('point-data')
 |      <FieldAssociation.POINT: 0>
 |
 |      Get the cell data array association.
 |
 |      >>> mesh.get_array_association('cell-data')
 |      <FieldAssociation.CELL: 1>
 |
 |      Get the field data array association.
 |
 |      >>> mesh.get_array_association('field-data')
 |      <FieldAssociation.NONE: 2>
 |
 |  get_cell(self, index: 'int') -> 'pyvista.Cell'
 |      Return a :class:`pyvista.Cell` object.
 |
 |      Parameters
 |      ----------
 |      index : int
 |          Cell ID.
 |
 |      Returns
 |      -------
 |      pyvista.Cell
 |          The i-th pyvista.Cell.
 |
 |      Notes
 |      -----
 |      Cells returned from this method are deep copies of the original
 |      cells. Changing properties (for example, ``points``) will not affect
 |      the dataset they originated from.
 |
 |      Examples
 |      --------
 |      Get the 0-th cell.
 |
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_airplane()
 |      >>> cell = mesh.get_cell(0)
 |      >>> cell
 |      Cell ...
 |
 |      Get the point ids of the first cell
 |
 |      >>> cell.point_ids
 |      [0, 1, 2]
 |
 |      Get the point coordinates of the first cell
 |
 |      >>> cell.points
 |      array([[897.0,  48.8,  82.3],
 |             [906.6,  48.8,  80.7],
 |             [907.5,  55.5,  83.7]])
 |
 |      For the first cell, get the points associated with the first edge
 |
 |      >>> cell.edges[0].point_ids
 |      [0, 1]
 |
 |      For a Tetrahedron, get the point ids of the last face
 |
 |      >>> mesh = examples.cells.Tetrahedron()
 |      >>> cell = mesh.get_cell(0)
 |      >>> cell.faces[-1].point_ids
 |      [0, 2, 1]
 |
 |  get_data_range(self, arr_var: 'str | NumpyArray[float] | None' = None, preference='cell') -> 'tuple[float, float]'
 |      Get the min and max of a named array.
 |
 |      Parameters
 |      ----------
 |      arr_var : str, np.ndarray, optional
 |          The name of the array to get the range. If ``None``, the
 |          active scalars is used.
 |
 |      preference : str, default: "cell"
 |          When scalars is specified, this is the preferred array type
 |          to search for in the dataset.  Must be either ``'point'``,
 |          ``'cell'``, or ``'field'``.
 |
 |      Returns
 |      -------
 |      tuple
 |          ``(min, max)`` of the named array.
 |
 |  plot(var_item, off_screen=None, full_screen=None, screenshot=None, interactive=True, cpos=None, window_size=None, show_bounds=False, show_axes=None, notebook=None, background=None, text='', return_img=False, eye_dome_lighting=False, volume=False, parallel_projection=False, jupyter_backend=None, return_viewer=False, return_cpos=False, jupyter_kwargs=None, theme=None, anti_aliasing=None, zoom=None, border=False, border_color='k', border_width=2.0, ssao=False, **kwargs)
 |      Plot a PyVista, numpy, or vtk object.
 |
 |      Parameters
 |      ----------
 |      var_item : pyvista.DataSet
 |          See :func:`Plotter.add_mesh <pyvista.Plotter.add_mesh>` for all
 |          supported types.
 |
 |      off_screen : bool, optional
 |          Plots off screen when ``True``.  Helpful for saving
 |          screenshots without a window popping up.  Defaults to the
 |          global setting ``pyvista.OFF_SCREEN``.
 |
 |      full_screen : bool, default: :attr:`pyvista.plotting.themes.Theme.full_screen`
 |          Opens window in full screen.  When enabled, ignores
 |          ``window_size``.
 |
 |      screenshot : str or bool, optional
 |          Saves screenshot to file when enabled.  See:
 |          :func:`Plotter.screenshot() <pyvista.Plotter.screenshot>`.
 |          Default ``False``.
 |
 |          When ``True``, takes screenshot and returns ``numpy`` array of
 |          image.
 |
 |      interactive : bool, default: :attr:`pyvista.plotting.themes.Theme.interactive`
 |          Allows user to pan and move figure.
 |
 |      cpos : list, optional
 |          List of camera position, focal point, and view up.
 |
 |      window_size : sequence, default: :attr:`pyvista.plotting.themes.Theme.window_size`
 |          Window size in pixels.
 |
 |      show_bounds : bool, default: False
 |          Shows mesh bounds when ``True``.
 |
 |      show_axes : bool, default: :attr:`pyvista.plotting.themes._AxesConfig.show`
 |          Shows a vtk axes widget.
 |
 |      notebook : bool, default: :attr:`pyvista.plotting.themes.Theme.notebook`
 |          When ``True``, the resulting plot is placed inline a jupyter
 |          notebook.  Assumes a jupyter console is active.
 |
 |      background : ColorLike, default: :attr:`pyvista.plotting.themes.Theme.background`
 |          Color of the background.
 |
 |      text : str, optional
 |          Adds text at the bottom of the plot.
 |
 |      return_img : bool, default: False
 |          Returns numpy array of the last image rendered.
 |
 |      eye_dome_lighting : bool, optional
 |          Enables eye dome lighting.
 |
 |      volume : bool, default: False
 |          Use the :func:`Plotter.add_volume()
 |          <pyvista.Plotter.add_volume>` method for volume rendering.
 |
 |      parallel_projection : bool, default: False
 |          Enable parallel projection.
 |
 |      jupyter_backend : str, default: :attr:`pyvista.plotting.themes.Theme.jupyter_backend`
 |          Jupyter notebook plotting backend to use.  One of the
 |          following:
 |
 |          * ``'none'`` : Do not display in the notebook.
 |          * ``'static'`` : Display a static figure.
 |          * ``'trame'`` : Display using ``trame``.
 |
 |          This can also be set globally with
 |          :func:`pyvista.set_jupyter_backend`.
 |
 |      return_viewer : bool, default: False
 |          Return the jupyterlab viewer, scene, or display object
 |          when plotting with jupyter notebook.
 |
 |      return_cpos : bool, default: False
 |          Return the last camera position from the render window
 |          when enabled.  Defaults to value in theme settings.
 |
 |      jupyter_kwargs : dict, optional
 |          Keyword arguments for the Jupyter notebook plotting backend.
 |
 |      theme : pyvista.plotting.themes.Theme, optional
 |          Plot-specific theme.
 |
 |      anti_aliasing : str | bool, default: :attr:`pyvista.plotting.themes.Theme.anti_aliasing`
 |          Enable or disable anti-aliasing. If ``True``, uses ``"msaa"``. If False,
 |          disables anti_aliasing. If a string, should be either ``"fxaa"`` or
 |          ``"ssaa"``.
 |
 |      zoom : float, str, optional
 |          Camera zoom.  Either ``'tight'`` or a float. A value greater than 1
 |          is a zoom-in, a value less than 1 is a zoom-out.  Must be greater
 |          than 0.
 |
 |      border : bool, default: False
 |          Draw a border around each render window.
 |
 |      border_color : ColorLike, default: "k"
 |          Either a string, rgb list, or hex color string.  For example:
 |
 |              * ``color='white'``
 |              * ``color='w'``
 |              * ``color=[1.0, 1.0, 1.0]``
 |              * ``color='#FFFFFF'``
 |
 |      border_width : float, default: 2.0
 |          Width of the border in pixels when enabled.
 |
 |      ssao : bool, optional
 |          Enable surface space ambient occlusion (SSAO). See
 |          :func:`Plotter.enable_ssao` for more details.
 |
 |      **kwargs : dict, optional
 |          See :func:`pyvista.Plotter.add_mesh` for additional options.
 |
 |      Returns
 |      -------
 |      cpos : list
 |          List of camera position, focal point, and view up.
 |          Returned only when ``return_cpos=True`` or set in the
 |          default global or plot theme.  Not returned when in a
 |          jupyter notebook and ``return_viewer=True``.
 |
 |      image : np.ndarray
 |          Numpy array of the last image when either ``return_img=True``
 |          or ``screenshot=True`` is set. Not returned when in a
 |          jupyter notebook with ``return_viewer=True``. Optionally
 |          contains alpha values. Sized:
 |
 |          * [Window height x Window width x 3] if the theme sets
 |            ``transparent_background=False``.
 |          * [Window height x Window width x 4] if the theme sets
 |            ``transparent_background=True``.
 |
 |      widget : ipywidgets.Widget
 |          IPython widget when ``return_viewer=True``.
 |
 |      Examples
 |      --------
 |      Plot a simple sphere while showing its edges.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.plot(show_edges=True)
 |
 |      Plot a volume mesh. Color by distance from the center of the
 |      ImageData. Note ``volume=True`` is passed.
 |
 |      >>> import numpy as np
 |      >>> grid = pv.ImageData(
 |      ...     dimensions=(32, 32, 32), spacing=(0.5, 0.5, 0.5)
 |      ... )
 |      >>> grid['data'] = np.linalg.norm(grid.center - grid.points, axis=1)
 |      >>> grid['data'] = np.abs(grid['data'] - grid['data'].max()) ** 3
 |      >>> grid.plot(volume=True)
 |
 |  point_cell_ids(self, ind: 'int') -> 'list[int]'
 |      Get the cell IDs that use the ind-th point.
 |
 |      Implements vtkDataSet's `GetPointCells <https://vtk.org/doc/nightly/html/classvtkDataSet.html#a36d1d8f67ad67adf4d1a9cfb30dade49>`_.
 |
 |      Parameters
 |      ----------
 |      ind : int
 |          Point ID.
 |
 |      Returns
 |      -------
 |      listint]
 |          List of cell IDs using the ind-th point.
 |
 |      Examples
 |      --------
 |      Get the cell ids using the 0-th point.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere(theta_resolution=10)
 |      >>> mesh.point_cell_ids(0)
 |      [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
 |
 |      Plot them.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(mesh, show_edges=True)
 |      >>>
 |      >>> # Label the 0-th point
 |      >>> _ = pl.add_point_labels(
 |      ...     mesh.points[0], ["0"], text_color="blue", font_size=20
 |      ... )
 |      >>>
 |      >>> # Get the cells ids using the 0-th point
 |      >>> ids = mesh.point_cell_ids(0)
 |      >>> cells = mesh.extract_cells(ids)
 |      >>> _ = pl.add_mesh(cells, color="red", show_edges=True)
 |      >>> centers = cells.cell_centers().points
 |      >>> _ = pl.add_point_labels(
 |      ...     centers,
 |      ...     labels=[f"{i}" for i in ids],
 |      ...     text_color="white",
 |      ...     font_size=20,
 |      ...     shape=None,
 |      ...     show_points=False,
 |      ... )
 |      >>>
 |      >>> # Plot the other cells
 |      >>> others = mesh.extract_cells(
 |      ...     [i for i in range(mesh.n_cells) if i not in ids]
 |      ... )
 |      >>> _ = pl.add_mesh(others, show_edges=True)
 |      >>>
 |      >>> pl.camera_position = "yx"
 |      >>> pl.camera.zoom(7.0)
 |      >>> pl.show()
 |
 |  point_is_inside_cell(self, ind: 'int', point: 'VectorLike[float] | MatrixLike[float]') -> 'bool | NumpyArray[np.bool_]'
 |      Return whether one or more points are inside a cell.
 |
 |      .. versionadded:: 0.35.0
 |
 |      Parameters
 |      ----------
 |      ind : int
 |          Cell ID.
 |
 |      point : Matrix
 |          Point or points to query if are inside a cell.
 |
 |      Returns
 |      -------
 |      bool or numpy.ndarray
 |          Whether point(s) is/are inside cell. A single bool is only returned if
 |          the input point has shape ``(3,)``.
 |
 |      Examples
 |      --------
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_hexbeam()
 |      >>> mesh.get_cell(0).bounds
 |      (0.0, 0.5, 0.0, 0.5, 0.0, 0.5)
 |      >>> mesh.point_is_inside_cell(0, [0.2, 0.2, 0.2])
 |      True
 |
 |  point_neighbors(self, ind: 'int') -> 'list[int]'
 |      Get the point neighbors of the ind-th point.
 |
 |      Parameters
 |      ----------
 |      ind : int
 |          Point ID.
 |
 |      Returns
 |      -------
 |      list[int]
 |          List of neighbor points IDs for the ind-th point.
 |
 |      See Also
 |      --------
 |      pyvista.DataSet.point_neighbors_levels
 |
 |      Examples
 |      --------
 |      Get the point neighbors of the 0-th point.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere(theta_resolution=10)
 |      >>> mesh.point_neighbors(0)
 |      [2, 226, 198, 170, 142, 114, 86, 254, 58, 30]
 |
 |      Plot them.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(mesh, show_edges=True)
 |      >>>
 |      >>> # Label the 0-th point
 |      >>> _ = pl.add_point_labels(
 |      ...     mesh.points[0], ["0"], text_color="blue", font_size=40
 |      ... )
 |      >>>
 |      >>> # Get the point neighbors and plot them
 |      >>> neighbors = mesh.point_neighbors(0)
 |      >>> _ = pl.add_point_labels(
 |      ...     mesh.points[neighbors],
 |      ...     labels=[f"{i}" for i in neighbors],
 |      ...     text_color="red",
 |      ...     font_size=40,
 |      ... )
 |      >>> pl.camera_position = "xy"
 |      >>> pl.camera.zoom(7.0)
 |      >>> pl.show()
 |
 |  point_neighbors_levels(self, ind: 'int', n_levels: 'int' = 1) -> 'Generator[list[int], None, None]'
 |      Get consecutive levels of point neighbors.
 |
 |      Parameters
 |      ----------
 |      ind : int
 |          Point ID.
 |
 |      n_levels : int, default: 1
 |          Number of levels to search for point neighbors.
 |          When equal to 1, it is equivalent to :func:`pyvista.DataSet.point_neighbors`.
 |
 |      Returns
 |      -------
 |      generator[list[[int]]
 |          A generator of list of neighbor points IDs for the ind-th point.
 |
 |      See Also
 |      --------
 |      pyvista.DataSet.point_neighbors
 |
 |      Examples
 |      --------
 |      Get the point neighbors IDs starting from the 0-th point
 |      up until the third level.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere(theta_resolution=10)
 |      >>> pt_nbr_levels = mesh.point_neighbors_levels(0, 3)
 |      >>> pt_nbr_levels = list(pt_nbr_levels)
 |      >>> pt_nbr_levels[0]
 |      [2, 226, 198, 170, 142, 114, 86, 30, 58, 254]
 |      >>> pt_nbr_levels[1]
 |      [3, 227, 255, 199, 171, 143, 115, 87, 59, 31]
 |      >>> pt_nbr_levels[2]
 |      [256, 32, 4, 228, 200, 172, 144, 116, 88, 60]
 |
 |      Visualize these points IDs.
 |
 |      >>> from functools import partial
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(mesh, show_edges=True)
 |      >>>
 |      >>> # Define partial function to add point labels
 |      >>> add_point_labels = partial(
 |      ...     pl.add_point_labels,
 |      ...     text_color="white",
 |      ...     font_size=40,
 |      ...     point_size=10,
 |      ... )
 |      >>>
 |      >>> # Add the first point label
 |      >>> _ = add_point_labels(
 |      ...     mesh.points[0], labels=["0"], text_color="blue"
 |      ... )
 |      >>>
 |      >>> # Add the neighbors to the plot
 |      >>> neighbors = mesh.point_neighbors_levels(0, n_levels=3)
 |      >>> for i, ids in enumerate(neighbors, start=1):
 |      ...     _ = add_point_labels(
 |      ...         mesh.points[ids],
 |      ...         labels=[f"{i}"] * len(ids),
 |      ...         text_color="red",
 |      ...     )
 |      ...
 |      >>>
 |      >>> pl.view_xy()
 |      >>> pl.camera.zoom(4.0)
 |      >>> pl.show()
 |
 |  rename_array(self, old_name: 'str', new_name: 'str', preference='cell') -> 'None'
 |      Change array name by searching for the array then renaming it.
 |
 |      Parameters
 |      ----------
 |      old_name : str
 |          Name of the array to rename.
 |
 |      new_name : str
 |          Name to rename the array to.
 |
 |      preference : str, default: "cell"
 |          If there are two arrays of the same name associated with
 |          points, cells, or field data, it will prioritize an array
 |          matching this type.  Can be either ``'cell'``,
 |          ``'field'``, or ``'point'``.
 |
 |      Examples
 |      --------
 |      Create a cube, assign a point array to the mesh named
 |      ``'my_array'``, and rename it to ``'my_renamed_array'``.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> cube = pv.Cube()
 |      >>> cube['my_array'] = range(cube.n_points)
 |      >>> cube.rename_array('my_array', 'my_renamed_array')
 |      >>> cube['my_renamed_array']
 |      pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7])
 |
 |  rotate_vector(self, vector: 'VectorLike[float]', angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Rotate mesh about a vector.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      vector : Vector
 |          Vector to rotate about.
 |
 |      angle : float
 |          Angle to rotate.
 |
 |      point : Vector, default: (0.0, 0.0, 0.0)
 |          Point to rotate about. Defaults to origin.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Rotated dataset.
 |
 |      Examples
 |      --------
 |      Rotate a mesh 30 degrees about the ``(1, 1, 1)`` axis.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> rot = mesh.rotate_vector((1, 1, 1), 30, inplace=False)
 |
 |      Plot the rotated mesh.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(rot)
 |      >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
 |      >>> _ = pl.add_axes_at_origin()
 |      >>> pl.show()
 |
 |  rotate_x(self, angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Rotate mesh about the x-axis.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      angle : float
 |          Angle in degrees to rotate about the x-axis.
 |
 |      point : Vector, default: (0.0, 0.0, 0.0)
 |          Point to rotate about. Defaults to origin.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Rotated dataset.
 |
 |      Examples
 |      --------
 |      Rotate a mesh 30 degrees about the x-axis.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> rot = mesh.rotate_x(30, inplace=False)
 |
 |      Plot the rotated mesh.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(rot)
 |      >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
 |      >>> _ = pl.add_axes_at_origin()
 |      >>> pl.show()
 |
 |  rotate_y(self, angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Rotate mesh about the y-axis.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      angle : float
 |          Angle in degrees to rotate about the y-axis.
 |
 |      point : Vector, default: (0.0, 0.0, 0.0)
 |          Point to rotate about.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are transformed. Otherwise, only
 |          the points, normals and active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Rotated dataset.
 |
 |      Examples
 |      --------
 |      Rotate a cube 30 degrees about the y-axis.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> rot = mesh.rotate_y(30, inplace=False)
 |
 |      Plot the rotated mesh.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(rot)
 |      >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
 |      >>> _ = pl.add_axes_at_origin()
 |      >>> pl.show()
 |
 |  rotate_z(self, angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Rotate mesh about the z-axis.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      angle : float
 |          Angle in degrees to rotate about the z-axis.
 |
 |      point : Vector, default: (0.0, 0.0, 0.0)
 |          Point to rotate about.  Defaults to origin.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Rotated dataset.
 |
 |      Examples
 |      --------
 |      Rotate a mesh 30 degrees about the z-axis.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> rot = mesh.rotate_z(30, inplace=False)
 |
 |      Plot the rotated mesh.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(rot)
 |      >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
 |      >>> _ = pl.add_axes_at_origin()
 |      >>> pl.show()
 |
 |  scale(self, xyz: 'Number | VectorLike[float]', transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Scale the mesh.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      xyz : Number | Vector
 |          A vector sequence defining the scale factors along x, y, and z. If
 |          a scalar, the same uniform scale is used along all three axes.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are transformed. Otherwise, only
 |          the points, normals and active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Scaled dataset.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> pl.subplot(0, 0)
 |      >>> pl.show_axes()
 |      >>> _ = pl.show_grid()
 |      >>> mesh1 = examples.download_teapot()
 |      >>> _ = pl.add_mesh(mesh1)
 |      >>> pl.subplot(0, 1)
 |      >>> pl.show_axes()
 |      >>> _ = pl.show_grid()
 |      >>> mesh2 = mesh1.scale([10.0, 10.0, 10.0], inplace=False)
 |      >>> _ = pl.add_mesh(mesh2)
 |      >>> pl.show(cpos="xy")
 |
 |  set_active_scalars(self, name: 'str | None', preference='cell') -> 'tuple[FieldAssociation, NumpyArray[float] | None]'
 |      Find the scalars by name and appropriately sets it as active.
 |
 |      To deactivate any active scalars, pass ``None`` as the ``name``.
 |
 |      Parameters
 |      ----------
 |      name : str, optional
 |          Name of the scalars array to assign as active.  If
 |          ``None``, deactivates active scalars for both point and
 |          cell data.
 |
 |      preference : str, default: "cell"
 |          If there are two arrays of the same name associated with
 |          points or cells, it will prioritize an array matching this
 |          type.  Can be either ``'cell'`` or ``'point'``.
 |
 |      Returns
 |      -------
 |      pyvista.core.utilities.arrays.FieldAssociation
 |          Association of the scalars matching ``name``.
 |
 |      pyvista_ndarray
 |          An array from the dataset matching ``name``.
 |
 |  set_active_tensors(self, name: 'str | None', preference: 'str' = 'point') -> 'None'
 |      Find the tensors by name and appropriately sets it as active.
 |
 |      To deactivate any active tensors, pass ``None`` as the ``name``.
 |
 |      Parameters
 |      ----------
 |      name : str, optional
 |          Name of the tensors array to assign as active.
 |
 |      preference : str, default: "point"
 |          If there are two arrays of the same name associated with
 |          points, cells, or field data, it will prioritize an array
 |          matching this type.  Can be either ``'cell'``,
 |          ``'field'``, or ``'point'``.
 |
 |  set_active_vectors(self, name: 'str | None', preference: 'str' = 'point') -> 'None'
 |      Find the vectors by name and appropriately sets it as active.
 |
 |      To deactivate any active vectors, pass ``None`` as the ``name``.
 |
 |      Parameters
 |      ----------
 |      name : str, optional
 |          Name of the vectors array to assign as active.
 |
 |      preference : str, default: "point"
 |          If there are two arrays of the same name associated with
 |          points, cells, or field data, it will prioritize an array
 |          matching this type.  Can be either ``'cell'``,
 |          ``'field'``, or ``'point'``.
 |
 |  translate(self, xyz: 'VectorLike[float]', transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
 |      Translate the mesh.
 |
 |      .. note::
 |          See also the notes at :func:`transform()
 |          <DataSetFilters.transform>` which is used by this filter
 |          under the hood.
 |
 |      Parameters
 |      ----------
 |      xyz : Vector
 |          A vector of three floats.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are
 |          transformed. Otherwise, only the points, normals and
 |          active vectors are transformed.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Translated dataset.
 |
 |      Examples
 |      --------
 |      Create a sphere and translate it by ``(2, 1, 2)``.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.center
 |      [0.0, 0.0, 0.0]
 |      >>> trans = mesh.translate((2, 1, 2), inplace=False)
 |      >>> trans.center
 |      [2.0, 1.0, 2.0]
 |
 |  ----------------------------------------------------------------------
 |  Readonly properties inherited from pyvista.core.dataset.DataSet:
 |
 |  active_normals
 |      Return the active normals as an array.
 |
 |      Returns
 |      -------
 |      pyvista_ndarray
 |          Active normals of this dataset.
 |
 |      Notes
 |      -----
 |      If both point and cell normals exist, this returns point
 |      normals by default.
 |
 |      Examples
 |      --------
 |      Compute normals on an example sphere mesh and return the
 |      active normals for the dataset.  Show that this is the same size
 |      as the number of points.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh = mesh.compute_normals()
 |      >>> normals = mesh.active_normals
 |      >>> normals.shape
 |      (842, 3)
 |      >>> mesh.n_points
 |      842
 |
 |  active_scalars
 |      Return the active scalars as an array.
 |
 |      Returns
 |      -------
 |      Optional[pyvista_ndarray]
 |          Active scalars as an array.
 |
 |  active_scalars_info
 |      Return the active scalar's association and name.
 |
 |      Association refers to the data association (e.g. point, cell, or
 |      field) of the active scalars.
 |
 |      Returns
 |      -------
 |      ActiveArrayInfo
 |          The scalars info in an object with namedtuple semantics,
 |          with attributes ``association`` and ``name``.
 |
 |      Notes
 |      -----
 |      If both cell and point scalars are present and neither have
 |      been set active within at the dataset level, point scalars
 |      will be made active.
 |
 |      Examples
 |      --------
 |      Create a mesh, add scalars to the mesh, and return the active
 |      scalars info.  Note how when the scalars are added, they
 |      automatically become the active scalars.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh['Z Height'] = mesh.points[:, 2]
 |      >>> mesh.active_scalars_info
 |      ActiveArrayInfoTuple(association=<FieldAssociation.POINT: 0>, name='Z Height')
 |
 |  active_tensors
 |      Return the active tensors array.
 |
 |      Returns
 |      -------
 |      Optional[np.ndarray]
 |          Active tensors array.
 |
 |  active_tensors_info
 |      Return the active tensor's field and name: [field, name].
 |
 |      Returns
 |      -------
 |      ActiveArrayInfo
 |          Active tensor's field and name: [field, name].
 |
 |  active_vectors
 |      Return the active vectors array.
 |
 |      Returns
 |      -------
 |      Optional[pyvista_ndarray]
 |          Active vectors array.
 |
 |      Examples
 |      --------
 |      Create a mesh, compute the normals inplace, and return the
 |      normals vector array.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> _ = mesh.compute_normals(inplace=True)
 |      >>> mesh.active_vectors  # doctest:+SKIP
 |      pyvista_ndarray([[-2.48721432e-10, -1.08815623e-09, -1.00000000e+00],
 |                       [-2.48721432e-10, -1.08815623e-09,  1.00000000e+00],
 |                       [-1.18888125e-01,  3.40539310e-03, -9.92901802e-01],
 |                       ...,
 |                       [-3.11940581e-01, -6.81432486e-02,  9.47654784e-01],
 |                       [-2.09880397e-01, -4.65070531e-02,  9.76620376e-01],
 |                       [-1.15582108e-01, -2.80492082e-02,  9.92901802e-01]],
 |                      dtype=float32)
 |
 |  active_vectors_info
 |      Return the active vector's association and name.
 |
 |      Association refers to the data association (e.g. point, cell, or
 |      field) of the active vectors.
 |
 |      Returns
 |      -------
 |      ActiveArrayInfo
 |          The vectors info in an object with namedtuple semantics,
 |          with attributes ``association`` and ``name``.
 |
 |      Notes
 |      -----
 |      If both cell and point vectors are present and neither have
 |      been set active within at the dataset level, point vectors
 |      will be made active.
 |
 |      Examples
 |      --------
 |      Create a mesh, compute the normals inplace, set the active
 |      vectors to the normals, and show that the active vectors are
 |      the ``'Normals'`` array associated with points.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> _ = mesh.compute_normals(inplace=True)
 |      >>> mesh.active_vectors_name = 'Normals'
 |      >>> mesh.active_vectors_info
 |      ActiveArrayInfoTuple(association=<FieldAssociation.POINT: 0>, name='Normals')
 |
 |  area
 |      Return the mesh area if 2D.
 |
 |      This will return 0 for meshes with 3D cells.
 |
 |      Returns
 |      -------
 |      float
 |          Total area of the mesh.
 |
 |      Examples
 |      --------
 |      Get the area of a square of size 2x2.
 |      Note 5 points in each direction.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.ImageData(dimensions=(5, 5, 1))
 |      >>> mesh.area
 |      16.0
 |
 |      A mesh with 3D cells does not have an area.  To get
 |      the outer surface area, first extract the surface using
 |      :func:`pyvista.DataSetFilters.extract_surface`.
 |
 |      >>> mesh = pv.ImageData(dimensions=(5, 5, 5))
 |      >>> mesh.area
 |      0.0
 |
 |      Get the area of a sphere. Discretization error results
 |      in slight difference from ``pi``.
 |
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.area
 |      3.13
 |
 |  array_names
 |      Return a list of array names for the dataset.
 |
 |      This makes sure to put the active scalars' name first in the list.
 |
 |      Returns
 |      -------
 |      list[str]
 |          List of array names for the dataset.
 |
 |      Examples
 |      --------
 |      Return the array names for a mesh.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.point_data['my_array'] = range(mesh.n_points)
 |      >>> mesh.array_names
 |      ['my_array', 'Normals']
 |
 |  arrows
 |      Return a glyph representation of the active vector data as arrows.
 |
 |      Arrows will be located at the points of the mesh and
 |      their size will be dependent on the norm of the vector.
 |      Their direction will be the "direction" of the vector
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Active vectors represented as arrows.
 |
 |      Examples
 |      --------
 |      Create a mesh, compute the normals and set them active, and
 |      plot the active vectors.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> mesh_w_normals = mesh.compute_normals()
 |      >>> mesh_w_normals.active_vectors_name = 'Normals'
 |      >>> arrows = mesh_w_normals.arrows
 |      >>> arrows.plot(show_scalar_bar=False)
 |
 |  bounds
 |      Return the bounding box of this dataset.
 |
 |      Returns
 |      -------
 |      BoundsLike
 |          Bounding box of this dataset.
 |          The form is: ``(xmin, xmax, ymin, ymax, zmin, zmax)``.
 |
 |      Examples
 |      --------
 |      Create a cube and return the bounds of the mesh.
 |
 |      >>> import pyvista as pv
 |      >>> cube = pv.Cube()
 |      >>> cube.bounds
 |      (-0.5, 0.5, -0.5, 0.5, -0.5, 0.5)
 |
 |  cell
 |      A generator that provides an easy way to loop over all cells.
 |
 |      To access a single cell, use :func:`pyvista.DataSet.get_cell`.
 |
 |      .. versionchanged:: 0.39.0
 |          Now returns a generator instead of a list.
 |          Use ``get_cell(i)`` instead of ``cell[i]``.
 |
 |      Yields
 |      ------
 |      pyvista.Cell
 |
 |      See Also
 |      --------
 |      pyvista.DataSet.get_cell
 |
 |      Examples
 |      --------
 |      Loop over the cells
 |
 |      >>> import pyvista as pv
 |      >>> # Create a grid with 9 points and 4 cells
 |      >>> mesh = pv.ImageData(dimensions=(3, 3, 1))
 |      >>> for cell in mesh.cell:  # doctest: +SKIP
 |      ...     cell
 |      ...
 |
 |  cell_data
 |      Return cell data as DataSetAttributes.
 |
 |      Returns
 |      -------
 |      DataSetAttributes
 |          Cell data as DataSetAttributes.
 |
 |      Examples
 |      --------
 |      Add cell arrays to a mesh and list the available ``cell_data``.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> mesh = pv.Cube()
 |      >>> mesh.clear_data()
 |      >>> mesh.cell_data['my_array'] = np.random.default_rng().random(
 |      ...     mesh.n_cells
 |      ... )
 |      >>> mesh.cell_data['my_other_array'] = np.arange(mesh.n_cells)
 |      >>> mesh.cell_data
 |      pyvista DataSetAttributes
 |      Association     : CELL
 |      Active Scalars  : my_array
 |      Active Vectors  : None
 |      Active Texture  : None
 |      Active Normals  : None
 |      Contains arrays :
 |          my_array                float64    (6,)                 SCALARS
 |          my_other_array          int64      (6,)
 |
 |      Access an array from ``cell_data``.
 |
 |      >>> mesh.cell_data['my_other_array']
 |      pyvista_ndarray([0, 1, 2, 3, 4, 5])
 |
 |      Or access it directly from the mesh.
 |
 |      >>> mesh['my_array'].shape
 |      (6,)
 |
 |  center
 |      Return the center of the bounding box.
 |
 |      Returns
 |      -------
 |      Vector
 |          Center of the bounding box.
 |
 |      Examples
 |      --------
 |      Get the center of a mesh.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere(center=(1, 2, 0))
 |      >>> mesh.center
 |      [1.0, 2.0, 0.0]
 |
 |  length
 |      Return the length of the diagonal of the bounding box.
 |
 |      Returns
 |      -------
 |      float
 |          Length of the diagonal of the bounding box.
 |
 |      Examples
 |      --------
 |      Get the length of the bounding box of a cube.  This should
 |      match ``3**(1/2)`` since it is the diagonal of a cube that is
 |      ``1 x 1 x 1``.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> mesh.length
 |      1.7320508075688772
 |
 |  n_arrays
 |      Return the number of arrays present in the dataset.
 |
 |      Returns
 |      -------
 |      int
 |         Number of arrays present in the dataset.
 |
 |  n_cells
 |      Return the number of cells in the entire dataset.
 |
 |      Returns
 |      -------
 |      int :
 |           Number of cells in the entire dataset.
 |
 |      Notes
 |      -----
 |      This returns the total number of cells -- for :class:`pyvista.PolyData`
 |      this includes vertices, lines, triangle strips and polygonal faces.
 |
 |      Examples
 |      --------
 |      Create a mesh and return the number of cells in the
 |      mesh.
 |
 |      >>> import pyvista as pv
 |      >>> cube = pv.Cube()
 |      >>> cube.n_cells
 |      6
 |
 |  n_points
 |      Return the number of points in the entire dataset.
 |
 |      Returns
 |      -------
 |      int
 |          Number of points in the entire dataset.
 |
 |      Examples
 |      --------
 |      Create a mesh and return the number of points in the
 |      mesh.
 |
 |      >>> import pyvista as pv
 |      >>> cube = pv.Cube()
 |      >>> cube.n_points
 |      8
 |
 |  number_of_cells
 |      Return the number of cells.
 |
 |      Returns
 |      -------
 |      int :
 |           Number of cells.
 |
 |  number_of_points
 |      Return the number of points.
 |
 |      Returns
 |      -------
 |      int :
 |           Number of points.
 |
 |  point_data
 |      Return point data as DataSetAttributes.
 |
 |      Returns
 |      -------
 |      DataSetAttributes
 |          Point data as DataSetAttributes.
 |
 |      Examples
 |      --------
 |      Add point arrays to a mesh and list the available ``point_data``.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> mesh = pv.Cube()
 |      >>> mesh.clear_data()
 |      >>> mesh.point_data['my_array'] = np.random.default_rng().random(
 |      ...     mesh.n_points
 |      ... )
 |      >>> mesh.point_data['my_other_array'] = np.arange(mesh.n_points)
 |      >>> mesh.point_data
 |      pyvista DataSetAttributes
 |      Association     : POINT
 |      Active Scalars  : my_array
 |      Active Vectors  : None
 |      Active Texture  : None
 |      Active Normals  : None
 |      Contains arrays :
 |          my_array                float64    (8,)                 SCALARS
 |          my_other_array          int64      (8,)
 |
 |      Access an array from ``point_data``.
 |
 |      >>> mesh.point_data['my_other_array']
 |      pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7])
 |
 |      Or access it directly from the mesh.
 |
 |      >>> mesh['my_array'].shape
 |      (8,)
 |
 |  volume
 |      Return the mesh volume.
 |
 |      This will return 0 for meshes with 2D cells.
 |
 |      Returns
 |      -------
 |      float
 |          Total volume of the mesh.
 |
 |      Examples
 |      --------
 |      Get the volume of a cube of size 4x4x4.
 |      Note that there are 5 points in each direction.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.ImageData(dimensions=(5, 5, 5))
 |      >>> mesh.volume
 |      64.0
 |
 |      A mesh with 2D cells has no volume.
 |
 |      >>> mesh = pv.ImageData(dimensions=(5, 5, 1))
 |      >>> mesh.volume
 |      0.0
 |
 |      :class:`pyvista.PolyData` is special as a 2D surface can
 |      enclose a 3D volume. This case uses a different methodology,
 |      see :func:`pyvista.PolyData.volume`.
 |
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.volume
 |      0.51825
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyvista.core.dataset.DataSet:
 |
 |  active_scalars_name
 |      Return the name of the active scalars.
 |
 |      Returns
 |      -------
 |      str
 |          Name of the active scalars.
 |
 |      Examples
 |      --------
 |      Create a mesh, add scalars to the mesh, and return the name of
 |      the active scalars.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh['Z Height'] = mesh.points[:, 2]
 |      >>> mesh.active_scalars_name
 |      'Z Height'
 |
 |  active_t_coords
 |      Return the active texture coordinates on the points.
 |
 |      Returns
 |      -------
 |      Optional[pyvista_ndarray]
 |          Active texture coordinates on the points.
 |
 |  active_tensors_name
 |      Return the name of the active tensor array.
 |
 |      Returns
 |      -------
 |      str
 |          Name of the active tensor array.
 |
 |  active_texture_coordinates
 |      Return the active texture coordinates on the points.
 |
 |      Returns
 |      -------
 |      Optional[pyvista_ndarray]
 |          Active texture coordinates on the points.
 |
 |      Examples
 |      --------
 |      Return the active texture coordinates from the globe example.
 |
 |      >>> from pyvista import examples
 |      >>> globe = examples.load_globe()
 |      >>> globe.active_texture_coordinates
 |      pyvista_ndarray([[0.        , 0.        ],
 |                       [0.        , 0.07142857],
 |                       [0.        , 0.14285714],
 |                       ...,
 |                       [1.        , 0.85714286],
 |                       [1.        , 0.92857143],
 |                       [1.        , 1.        ]])
 |
 |  active_vectors_name
 |      Return the name of the active vectors array.
 |
 |      Returns
 |      -------
 |      str
 |          Name of the active vectors array.
 |
 |      Examples
 |      --------
 |      Create a mesh, compute the normals, set them as active, and
 |      return the name of the active vectors.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh_w_normals = mesh.compute_normals()
 |      >>> mesh_w_normals.active_vectors_name = 'Normals'
 |      >>> mesh_w_normals.active_vectors_name
 |      'Normals'
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyvista.core.filters.image_data.ImageDataFilters:
 |
 |  cells_to_points(self, scalars: 'str | None' = None, *, copy: 'bool' = True)
 |      Re-mesh image data from a cell-based to a point-based representation.
 |
 |      This filter changes how image data is represented. Data represented as cells
 |      at the input is re-meshed into an alternative representation as points at the
 |      output. Only the :class:`~pyvista.ImageData` container is modified so that
 |      the number of input cells equals the number of output points. The re-meshing is
 |      otherwise lossless in the sense that cell data at the input is passed through
 |      unmodified and stored as point data at the output. Any point data at the input is
 |      ignored and is not used by this filter.
 |
 |      To change the image data's representation, the input cell centers are used to
 |      represent the output points. This has the effect of "shrinking" the
 |      input image dimensions by one along each axis (i.e. half the cell width on each
 |      side). For example, an image with 101 points and 100 cells along an axis at the
 |      input will have 100 points and 99 cells at the output. If the input has 1mm
 |      spacing, the axis size will also decrease from 100mm to 99mm.
 |
 |      Since filters may be inherently cell-based (e.g. some :class:`~pyvista.DataSetFilters`)
 |      or may operate on point data exclusively (e.g. most :class:`~pyvista.ImageDataFilters`),
 |      re-meshing enables the same data to be used with either kind of filter while
 |      ensuring the input data to those filters has the appropriate representation.
 |      This filter is also useful when plotting image data to achieve a desired visual
 |      effect, such as plotting images as points instead of as voxel cells.
 |
 |      .. versionadded:: 0.44.0
 |
 |      See Also
 |      --------
 |      points_to_cells
 |          Inverse of this filter to represent points as cells.
 |      :meth:`~pyvista.DataSetFilters.cell_data_to_point_data`
 |          Resample cell data as point data without modifying the container.
 |      :meth:`~pyvista.DataSetFilters.point_data_to_cell_data`
 |          Resample point data as cell data without modifying the container.
 |
 |      Parameters
 |      ----------
 |      scalars : str, optional
 |          Name of cell data scalars to pass through to the output as point data. Use
 |          this parameter to restrict the output to only include the specified array.
 |          By default, all cell data arrays at the input are passed through as point
 |          data at the output.
 |
 |      copy : bool, default: True
 |          Copy the input cell data before associating it with the output point data.
 |          If ``False``, the input and output will both refer to the same data array(s).
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          Image with a point-based representation.
 |
 |      Examples
 |      --------
 |      Load an image with cell data.
 |
 |      >>> from pyvista import examples
 |      >>> image = examples.load_uniform()
 |
 |      Show the current properties and cell arrays of the image.
 |
 |      >>> image
 |      ImageData (...)
 |        N Cells:      729
 |        N Points:     1000
 |        X Bounds:     0.000e+00, 9.000e+00
 |        Y Bounds:     0.000e+00, 9.000e+00
 |        Z Bounds:     0.000e+00, 9.000e+00
 |        Dimensions:   10, 10, 10
 |        Spacing:      1.000e+00, 1.000e+00, 1.000e+00
 |        N Arrays:     2
 |
 |      >>> image.cell_data.keys()
 |      ['Spatial Cell Data']
 |
 |      Re-mesh the cells and cell data as points and point data.
 |
 |      >>> points_image = image.cells_to_points()
 |
 |      Show the properties and point arrays of the re-meshed image.
 |
 |      >>> points_image
 |      ImageData (...)
 |        N Cells:      512
 |        N Points:     729
 |        X Bounds:     5.000e-01, 8.500e+00
 |        Y Bounds:     5.000e-01, 8.500e+00
 |        Z Bounds:     5.000e-01, 8.500e+00
 |        Dimensions:   9, 9, 9
 |        Spacing:      1.000e+00, 1.000e+00, 1.000e+00
 |        N Arrays:     1
 |
 |      >>> points_image.point_data.keys()
 |      ['Spatial Cell Data']
 |
 |      Observe that:
 |
 |      - The input cell array is now a point array
 |      - The output has one less array (the input point data is ignored)
 |      - The dimensions have decreased by one
 |      - The bounds have decreased by half the spacing
 |      - The output N Points equals the input N Cells
 |
 |      See :ref:`image_representations_example` for more examples using this filter.
 |
 |  contour_labeled(self, n_labels: 'int | None' = None, smoothing: 'bool' = False, smoothing_num_iterations: 'int' = 50, smoothing_relaxation_factor: 'float' = 0.5, smoothing_constraint_distance: 'float' = 1, output_mesh_type: "Literal['quads', 'triangles']" = 'quads', output_style: "Literal['default', 'boundary']" = 'default', scalars: 'str | None' = None, progress_bar: 'bool' = False) -> 'pyvista.PolyData'
 |      Generate labeled contours from 3D label maps.
 |
 |      SurfaceNets algorithm is used to extract contours preserving sharp
 |      boundaries for the selected labels from the label maps.
 |      Optionally, the boundaries can be smoothened to reduce the staircase
 |      appearance in case of low resolution input label maps.
 |
 |      This filter requires that the :class:`ImageData` has integer point
 |      scalars, such as multi-label maps generated from image segmentation.
 |
 |      .. note::
 |         Requires ``vtk>=9.3.0``.
 |
 |      Parameters
 |      ----------
 |      n_labels : int, optional
 |          Number of labels to be extracted (all are extracted if None is given).
 |
 |      smoothing : bool, default: False
 |          Apply smoothing to the meshes.
 |
 |      smoothing_num_iterations : int, default: 50
 |          Number of smoothing iterations.
 |
 |      smoothing_relaxation_factor : float, default: 0.5
 |          Relaxation factor of the smoothing.
 |
 |      smoothing_constraint_distance : float, default: 1
 |          Constraint distance of the smoothing.
 |
 |      output_mesh_type : str, default: 'quads'
 |          Type of the output mesh. Must be either ``'quads'``, or ``'triangles'``.
 |
 |      output_style : str, default: 'default'
 |          Style of the output mesh. Must be either ``'default'`` or ``'boundary'``.
 |          When ``'default'`` is specified, the filter produces a mesh with both
 |          interior and exterior polygons. When ``'boundary'`` is selected, only
 |          polygons on the border with the background are produced (without interior
 |          polygons). Note that style ``'selected'`` is currently not implemented.
 |
 |      scalars : str, optional
 |          Name of scalars to process. Defaults to currently active scalars.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          :class:`pyvista.PolyData` Labeled mesh with the segments labeled.
 |
 |      References
 |      ----------
 |      Sarah F. Frisken, SurfaceNets for Multi-Label Segmentations with Preservation
 |      of Sharp Boundaries, Journal of Computer Graphics Techniques (JCGT), vol. 11,
 |      no. 1, 34-54, 2022. Available online http://jcgt.org/published/0011/01/03/
 |
 |      https://www.kitware.com/really-fast-isocontouring/
 |
 |      Examples
 |      --------
 |      See :ref:`contouring_example` for a full example using this filter.
 |
 |      See Also
 |      --------
 |      pyvista.DataSetFilters.contour
 |          Generalized contouring method which uses MarchingCubes or FlyingEdges.
 |
 |      pyvista.DataSetFilters.pack_labels
 |          Function used internally by SurfaceNets to generate contiguous label data.
 |
 |  extract_subset(self, voi, rate=(1, 1, 1), boundary=False, progress_bar=False)
 |      Select piece (e.g., volume of interest).
 |
 |      To use this filter set the VOI ivar which are i-j-k min/max indices
 |      that specify a rectangular region in the data. (Note that these are
 |      0-offset.) You can also specify a sampling rate to subsample the
 |      data.
 |
 |      Typical applications of this filter are to extract a slice from a
 |      volume for image processing, subsampling large volumes to reduce data
 |      size, or extracting regions of a volume with interesting data.
 |
 |      Parameters
 |      ----------
 |      voi : sequence[int]
 |          Length 6 iterable of ints: ``(xmin, xmax, ymin, ymax, zmin, zmax)``.
 |          These bounds specify the volume of interest in i-j-k min/max
 |          indices.
 |
 |      rate : sequence[int], default: (1, 1, 1)
 |          Length 3 iterable of ints: ``(xrate, yrate, zrate)``.
 |
 |      boundary : bool, default: False
 |          Control whether to enforce that the "boundary" of the grid
 |          is output in the subsampling process. This only has effect
 |          when the rate in any direction is not equal to 1. When
 |          this is enabled, the subsampling will always include the
 |          boundary of the grid even though the sample rate is not an
 |          even multiple of the grid dimensions. By default this is
 |          disabled.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          ImageData subset.
 |
 |  fft(self, output_scalars_name=None, progress_bar=False)
 |      Apply a fast Fourier transform (FFT) to the active scalars.
 |
 |      The input can be real or complex data, but the output is always
 |      :attr:`numpy.complex128`. The filter is fastest for images that have
 |      power of two sizes.
 |
 |      The filter uses a butterfly diagram for each prime factor of the
 |      dimension. This makes images with prime number dimensions (i.e. 17x17)
 |      much slower to compute. FFTs of multidimensional meshes (i.e volumes)
 |      are decomposed so that each axis executes serially.
 |
 |      The frequencies of the output assume standard order: along each axis
 |      first positive frequencies are assumed from 0 to the maximum, then
 |      negative frequencies are listed from the largest absolute value to
 |      smallest. This implies that the corners of the grid correspond to low
 |      frequencies, while the center of the grid corresponds to high
 |      frequencies.
 |
 |      Parameters
 |      ----------
 |      output_scalars_name : str, optional
 |          The name of the output scalars. By default, this is the same as the
 |          active scalars of the dataset.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          :class:`pyvista.ImageData` with applied FFT.
 |
 |      See Also
 |      --------
 |      rfft : The reverse transform.
 |      low_pass : Low-pass filtering of FFT output.
 |      high_pass : High-pass filtering of FFT output.
 |
 |      Examples
 |      --------
 |      Apply FFT to an example image.
 |
 |      >>> from pyvista import examples
 |      >>> image = examples.download_moonlanding_image()
 |      >>> fft_image = image.fft()
 |      >>> fft_image.point_data  # doctest:+SKIP
 |      pyvista DataSetAttributes
 |      Association     : POINT
 |      Active Scalars  : PNGImage
 |      Active Vectors  : None
 |      Active Texture  : None
 |      Active Normals  : None
 |      Contains arrays :
 |      PNGImage                complex128 (298620,)          SCALARS
 |
 |      See :ref:`image_fft_example` for a full example using this filter.
 |
 |  gaussian_smooth(self, radius_factor=1.5, std_dev=2.0, scalars=None, progress_bar=False)
 |      Smooth the data with a Gaussian kernel.
 |
 |      Parameters
 |      ----------
 |      radius_factor : float | sequence[float], default: 1.5
 |          Unitless factor to limit the extent of the kernel.
 |
 |      std_dev : float | sequence[float], default: 2.0
 |          Standard deviation of the kernel in pixel units.
 |
 |      scalars : str, optional
 |          Name of scalars to process. Defaults to currently active scalars.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          Uniform grid with smoothed scalars.
 |
 |      Notes
 |      -----
 |      This filter only supports point data. For inputs with cell data, consider
 |      re-meshing the cell data as point data with :meth:`~pyvista.ImageDataFilters.cells_to_points`
 |      or resampling the cell data to point data with :func:`~pyvista.DataSetFilters.cell_data_to_point_data`.
 |
 |      Examples
 |      --------
 |      First, create sample data to smooth. Here, we use
 |      :func:`pyvista.perlin_noise() <pyvista.core.utilities.features.perlin_noise>`
 |      to create meaningful data.
 |
 |      >>> import numpy as np
 |      >>> import pyvista as pv
 |      >>> noise = pv.perlin_noise(0.1, (2, 5, 8), (0, 0, 0))
 |      >>> grid = pv.sample_function(
 |      ...     noise, [0, 1, 0, 1, 0, 1], dim=(20, 20, 20)
 |      ... )
 |      >>> grid.plot(show_scalar_bar=False)
 |
 |      Next, smooth the sample data.
 |
 |      >>> smoothed = grid.gaussian_smooth()
 |      >>> smoothed.plot(show_scalar_bar=False)
 |
 |      See :ref:`gaussian_smoothing_example` for a full example using this filter.
 |
 |  high_pass(self, x_cutoff, y_cutoff, z_cutoff, order=1, output_scalars_name=None, progress_bar=False)
 |      Perform a Butterworth high pass filter in the frequency domain.
 |
 |      This filter requires that the :class:`ImageData` have a complex point
 |      scalars, usually generated after the :class:`ImageData` has been
 |      converted to the frequency domain by a :func:`ImageDataFilters.fft`
 |      filter.
 |
 |      A :func:`ImageDataFilters.rfft` filter can be used to convert the
 |      output back into the spatial domain. This filter attenuates low
 |      frequency components.  Input and output are complex arrays with
 |      datatype :attr:`numpy.complex128`.
 |
 |      The frequencies of the input assume standard order: along each axis
 |      first positive frequencies are assumed from 0 to the maximum, then
 |      negative frequencies are listed from the largest absolute value to
 |      smallest. This implies that the corners of the grid correspond to low
 |      frequencies, while the center of the grid corresponds to high
 |      frequencies.
 |
 |      Parameters
 |      ----------
 |      x_cutoff : float
 |          The cutoff frequency for the x-axis.
 |
 |      y_cutoff : float
 |          The cutoff frequency for the y-axis.
 |
 |      z_cutoff : float
 |          The cutoff frequency for the z-axis.
 |
 |      order : int, default: 1
 |          The order of the cutoff curve. Given from the equation
 |          ``1/(1 + (cutoff/freq(i, j))**(2*order))``.
 |
 |      output_scalars_name : str, optional
 |          The name of the output scalars. By default, this is the same as the
 |          active scalars of the dataset.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          :class:`pyvista.ImageData` with the applied high pass filter.
 |
 |      See Also
 |      --------
 |      fft : Direct fast Fourier transform.
 |      rfft : Reverse fast Fourier transform.
 |      low_pass : Low-pass filtering of FFT output.
 |
 |      Examples
 |      --------
 |      See :ref:`image_fft_perlin_example` for a full example using this filter.
 |
 |  image_dilate_erode(self, dilate_value=1.0, erode_value=0.0, kernel_size=(3, 3, 3), scalars=None, progress_bar=False)
 |      Dilates one value and erodes another.
 |
 |      ``image_dilate_erode`` will dilate one value and erode another. It uses
 |      an elliptical footprint, and only erodes/dilates on the boundary of the
 |      two values. The filter is restricted to the X, Y, and Z axes for now.
 |      It can degenerate to a 2 or 1-dimensional filter by setting the kernel
 |      size to 1 for a specific axis.
 |
 |      Parameters
 |      ----------
 |      dilate_value : float, default: 1.0
 |          Dilate value in the dataset.
 |
 |      erode_value : float, default: 0.0
 |          Erode value in the dataset.
 |
 |      kernel_size : sequence[int], default: (3, 3, 3)
 |          Determines the size of the kernel along the three axes.
 |
 |      scalars : str, optional
 |          Name of scalars to process. Defaults to currently active scalars.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          Dataset that has been dilated/eroded on the boundary of the specified scalars.
 |
 |      Notes
 |      -----
 |      This filter only supports point data. For inputs with cell data, consider
 |      re-meshing the cell data as point data with :meth:`~pyvista.ImageDataFilters.cells_to_points`
 |      or resampling the cell data to point data with :func:`~pyvista.DataSetFilters.cell_data_to_point_data`.
 |
 |      Examples
 |      --------
 |      Demonstrate image dilate/erode on an example dataset. First, plot
 |      the example dataset with the active scalars.
 |
 |      >>> from pyvista import examples
 |      >>> uni = examples.load_uniform()
 |      >>> uni.plot()
 |
 |      Now, plot the image threshold with ``threshold=[400, 600]``. Note how
 |      values within the threshold are 1 and outside are 0.
 |
 |      >>> ithresh = uni.image_threshold([400, 600])
 |      >>> ithresh.plot()
 |
 |      Note how there is a hole in the thresholded image. Apply a dilation/
 |      erosion filter with a large kernel to fill that hole in.
 |
 |      >>> idilate = ithresh.image_dilate_erode(kernel_size=[5, 5, 5])
 |      >>> idilate.plot()
 |
 |  image_threshold(self, threshold, in_value=1.0, out_value=0.0, scalars=None, preference='point', progress_bar=False)
 |      Apply a threshold to scalar values in a uniform grid.
 |
 |      If a single value is given for threshold, scalar values above or equal
 |      to the threshold are ``'in'`` and scalar values below the threshold are ``'out'``.
 |      If two values are given for threshold (sequence) then values equal to
 |      or between the two values are ``'in'`` and values outside the range are ``'out'``.
 |
 |      If ``None`` is given for ``in_value``, scalars that are ``'in'`` will not be replaced.
 |      If ``None`` is given for ``out_value``, scalars that are ``'out'`` will not be replaced.
 |
 |      Warning: applying this filter to cell data will send the output to a
 |      new point array with the same name, overwriting any existing point data
 |      array with the same name.
 |
 |      Parameters
 |      ----------
 |      threshold : float or sequence[float]
 |          Single value or (min, max) to be used for the data threshold.  If
 |          a sequence, then length must be 2. Threshold(s) for deciding which
 |          cells/points are ``'in'`` or ``'out'`` based on scalar data.
 |
 |      in_value : float, default: 1.0
 |          Scalars that match the threshold criteria for ``'in'`` will be replaced with this.
 |
 |      out_value : float, default: 0.0
 |          Scalars that match the threshold criteria for ``'out'`` will be replaced with this.
 |
 |      scalars : str, optional
 |          Name of scalars to process. Defaults to currently active scalars.
 |
 |      preference : str, default: "point"
 |          When scalars is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'`` or ``'cell'``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          Dataset with the specified scalars thresholded.
 |
 |      See Also
 |      --------
 |      :meth:`~pyvista.DataSetFilters.threshold`
 |
 |      Examples
 |      --------
 |      Demonstrate image threshold on an example dataset. First, plot
 |      the example dataset with the active scalars.
 |
 |      >>> from pyvista import examples
 |      >>> uni = examples.load_uniform()
 |      >>> uni.plot()
 |
 |      Now, plot the image threshold with ``threshold=100``. Note how
 |      values above the threshold are 1 and below are 0.
 |
 |      >>> ithresh = uni.image_threshold(100)
 |      >>> ithresh.plot()
 |
 |      See :ref:`image_representations_example` for more examples using this filter.
 |
 |  low_pass(self, x_cutoff, y_cutoff, z_cutoff, order=1, output_scalars_name=None, progress_bar=False)
 |      Perform a Butterworth low pass filter in the frequency domain.
 |
 |      This filter requires that the :class:`ImageData` have a complex point
 |      scalars, usually generated after the :class:`ImageData` has been
 |      converted to the frequency domain by a :func:`ImageDataFilters.fft`
 |      filter.
 |
 |      A :func:`ImageDataFilters.rfft` filter can be used to convert the
 |      output back into the spatial domain. This filter attenuates high
 |      frequency components.  Input and output are complex arrays with
 |      datatype :attr:`numpy.complex128`.
 |
 |      The frequencies of the input assume standard order: along each axis
 |      first positive frequencies are assumed from 0 to the maximum, then
 |      negative frequencies are listed from the largest absolute value to
 |      smallest. This implies that the corners of the grid correspond to low
 |      frequencies, while the center of the grid corresponds to high
 |      frequencies.
 |
 |      Parameters
 |      ----------
 |      x_cutoff : float
 |          The cutoff frequency for the x-axis.
 |
 |      y_cutoff : float
 |          The cutoff frequency for the y-axis.
 |
 |      z_cutoff : float
 |          The cutoff frequency for the z-axis.
 |
 |      order : int, default: 1
 |          The order of the cutoff curve. Given from the equation
 |          ``1 + (cutoff/freq(i, j))**(2*order)``.
 |
 |      output_scalars_name : str, optional
 |          The name of the output scalars. By default, this is the same as the
 |          active scalars of the dataset.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          :class:`pyvista.ImageData` with the applied low pass filter.
 |
 |      See Also
 |      --------
 |      fft : Direct fast Fourier transform.
 |      rfft : Reverse fast Fourier transform.
 |      high_pass : High-pass filtering of FFT output.
 |
 |      Examples
 |      --------
 |      See :ref:`image_fft_perlin_example` for a full example using this filter.
 |
 |  median_smooth(self, kernel_size=(3, 3, 3), scalars=None, preference='point', progress_bar=False)
 |      Smooth data using a median filter.
 |
 |      The Median filter that replaces each pixel with the median value from a
 |      rectangular neighborhood around that pixel. Neighborhoods can be no
 |      more than 3 dimensional. Setting one axis of the neighborhood
 |      kernelSize to 1 changes the filter into a 2D median.
 |
 |      See `vtkImageMedian3D
 |      <https://vtk.org/doc/nightly/html/classvtkImageMedian3D.html#details>`_
 |      for more details.
 |
 |      Parameters
 |      ----------
 |      kernel_size : sequence[int], default: (3, 3, 3)
 |          Size of the kernel in each dimension (units of voxels), for example
 |          ``(x_size, y_size, z_size)``. Default is a 3D median filter. If you
 |          want to do a 2D median filter, set the size to 1 in the dimension
 |          you don't want to filter over.
 |
 |      scalars : str, optional
 |          Name of scalars to process. Defaults to currently active scalars.
 |
 |      preference : str, default: "point"
 |          When scalars is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'`` or ``'cell'``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          Uniform grid with smoothed scalars.
 |
 |      Warnings
 |      --------
 |      Applying this filter to cell data will send the output to a new point
 |      array with the same name, overwriting any existing point data array
 |      with the same name.
 |
 |      Examples
 |      --------
 |      First, create sample data to smooth. Here, we use
 |      :func:`pyvista.perlin_noise() <pyvista.core.utilities.features.perlin_noise>`
 |      to create meaningful data.
 |
 |      >>> import numpy as np
 |      >>> import pyvista as pv
 |      >>> noise = pv.perlin_noise(0.1, (2, 5, 8), (0, 0, 0))
 |      >>> grid = pv.sample_function(
 |      ...     noise, [0, 1, 0, 1, 0, 1], dim=(20, 20, 20)
 |      ... )
 |      >>> grid.plot(show_scalar_bar=False)
 |
 |      Next, smooth the sample data.
 |
 |      >>> smoothed = grid.median_smooth(kernel_size=(10, 10, 10))
 |      >>> smoothed.plot(show_scalar_bar=False)
 |
 |  pad_image(self, pad_value: "float | VectorLike[float] | Literal['wrap', 'mirror']" = 0.0, *, pad_size: 'int | VectorLike[int]' = 1, pad_singleton_dims: 'bool' = False, scalars: 'str | None' = None, pad_all_scalars: 'bool' = False, progress_bar=False) -> 'pyvista.ImageData'
 |      Enlarge an image by padding its boundaries with new points.
 |
 |      .. versionadded:: 0.44.0
 |
 |      Padded points may be mirrored, wrapped, or filled with a constant value. By
 |      default, all boundaries of the image are padded with a single constant value.
 |
 |      This filter is designed to work with 1D, 2D, or 3D image data and will only pad
 |      non-singleton dimensions unless otherwise specified.
 |
 |      Parameters
 |      ----------
 |      pad_value : float | sequence[float] | 'mirror' | 'wrap', default : 0.0
 |          Padding value(s) given to new points outside the original image extent.
 |          Specify:
 |
 |          - a number: New points are filled with the specified constant value.
 |          - a vector: New points are filled with the specified multi-component vector.
 |          - ``'wrap'``: New points are filled by wrapping around the padding axis.
 |          - ``'mirror'``: New points are filled by mirroring the padding axis.
 |
 |      pad_size : int | sequence[int], default : 1
 |          Number of points to add to the image boundaries. Specify:
 |
 |          - A single value to pad all boundaries equally.
 |          - Two values, one for each ``(X, Y)`` axis, to apply symmetric padding to
 |            each axis independently.
 |          - Three values, one for each ``(X, Y, Z)`` axis, to apply symmetric padding
 |            to each axis independently.
 |          - Four values, one for each ``(-X, +X, -Y, +Y)`` boundary, to apply
 |            padding to each boundary independently.
 |          - Six values, one for each ``(-X, +X, -Y, +Y, -Z, +Z)`` boundary, to apply
 |            padding to each boundary independently.
 |
 |          .. note::
 |              The pad size for singleton dimensions is set to ``0`` by default, even
 |              if non-zero pad sizes are specified for these axes with this parameter.
 |              Set ``pad_singleton_dims`` to ``True`` to override this behavior and
 |              enable padding any or all dimensions.
 |
 |      pad_singleton_dims : bool, default : False
 |          Control whether to pad singleton dimensions. By default, only non-singleton
 |          dimensions are padded, which means that 1D or 2D inputs will remain 1D or
 |          2D after padding. Set this to ``True`` to enable padding any or all
 |          dimensions.
 |
 |      scalars : str, optional
 |          Name of scalars to pad. Defaults to currently active scalars. Unless
 |          ``pad_all_scalars`` is ``True``, only the specified ``scalars`` are included
 |          in the output.
 |
 |      pad_all_scalars : bool, default: False
 |          Pad all point data scalars and include them in the output. This is useful
 |          for padding images with multiple scalars. If ``False``, only the specified
 |          ``scalars`` are padded.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          Padded image.
 |
 |      Examples
 |      --------
 |      Pad a grayscale image with a 100-pixel wide border. The padding is black
 |      (i.e. has a value of ``0``) by default.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>>
 |      >>> gray_image = examples.download_moonlanding_image()
 |      >>> gray_image.dimensions
 |      (630, 474, 1)
 |      >>> padded = gray_image.pad_image(pad_size=100)
 |      >>> padded.dimensions
 |      (830, 674, 1)
 |
 |      Plot the image. To show grayscale images correctly, we define a custom plotting
 |      method.
 |
 |      >>> def grayscale_image_plotter(image):
 |      ...     import vtk
 |      ...
 |      ...     actor = vtk.vtkImageActor()
 |      ...     actor.GetMapper().SetInputData(image)
 |      ...     actor.GetProperty().SetInterpolationTypeToNearest()
 |      ...     plot = pv.Plotter()
 |      ...     plot.add_actor(actor)
 |      ...     plot.view_xy()
 |      ...     plot.camera.tight()
 |      ...     return plot
 |      ...
 |      >>>
 |      >>> plot = grayscale_image_plotter(padded)
 |      >>> plot.show()
 |
 |      Pad only the x-axis with a white border.
 |
 |      >>> padded = gray_image.pad_image(pad_value=255, pad_size=(200, 0))
 |      >>> plot = grayscale_image_plotter(padded)
 |      >>> plot.show()
 |
 |      Pad with wrapping.
 |
 |      >>> padded = gray_image.pad_image('wrap', pad_size=100)
 |      >>> plot = grayscale_image_plotter(padded)
 |      >>> plot.show()
 |
 |      Pad with mirroring.
 |
 |      >>> padded = gray_image.pad_image('mirror', pad_size=100)
 |      >>> plot = grayscale_image_plotter(padded)
 |      >>> plot.show()
 |
 |      Pad a color image using multi-component color vectors. Here, RGBA values are
 |      used.
 |
 |      >>> color_image = examples.load_logo()
 |      >>> red = (255, 0, 0, 255)  # RGBA
 |      >>> padded = color_image.pad_image(pad_value=red, pad_size=200)
 |      >>>
 |      >>> plot_kwargs = dict(
 |      ...     cpos='xy', zoom='tight', rgb=True, show_axes=False
 |      ... )
 |      >>> padded.plot(**plot_kwargs)
 |
 |      Pad each edge of the image separately with a different color.
 |
 |      >>> orange = pv.Color('orange').int_rgba
 |      >>> purple = pv.Color('purple').int_rgba
 |      >>> blue = pv.Color('blue').int_rgba
 |      >>> green = pv.Color('green').int_rgba
 |      >>>
 |      >>> padded = color_image.pad_image(orange, pad_size=(100, 0, 0, 0))
 |      >>> padded = padded.pad_image(purple, pad_size=(0, 100, 0, 0))
 |      >>> padded = padded.pad_image(blue, pad_size=(0, 0, 100, 0))
 |      >>> padded = padded.pad_image(green, pad_size=(0, 0, 0, 100))
 |      >>>
 |      >>> padded.plot(**plot_kwargs)
 |
 |  points_to_cells(self, scalars: 'str | None' = None, *, copy: 'bool' = True)
 |      Re-mesh image data from a point-based to a cell-based representation.
 |
 |      This filter changes how image data is represented. Data represented as points
 |      at the input is re-meshed into an alternative representation as cells at the
 |      output. Only the :class:`~pyvista.ImageData` container is modified so that
 |      the number of input points equals the number of output cells. The re-meshing is
 |      otherwise lossless in the sense that point data at the input is passed through
 |      unmodified and stored as cell data at the output. Any cell data at the input is
 |      ignored and is not used by this filter.
 |
 |      To change the image data's representation, the input points are used to
 |      represent the centers of the output cells. This has the effect of "growing" the
 |      input image dimensions by one along each axis (i.e. half the cell width on each
 |      side). For example, an image with 100 points and 99 cells along an axis at the
 |      input will have 101 points and 100 cells at the output. If the input has 1mm
 |      spacing, the axis size will also increase from 99mm to 100mm.
 |
 |      Since filters may be inherently cell-based (e.g. some :class:`~pyvista.DataSetFilters`)
 |      or may operate on point data exclusively (e.g. most :class:`~pyvista.ImageDataFilters`),
 |      re-meshing enables the same data to be used with either kind of filter while
 |      ensuring the input data to those filters has the appropriate representation.
 |      This filter is also useful when plotting image data to achieve a desired visual
 |      effect, such as plotting images as voxel cells instead of as points.
 |
 |      .. versionadded:: 0.44.0
 |
 |      See Also
 |      --------
 |      cells_to_points
 |          Inverse of this filter to represent cells as points.
 |      :meth:`~pyvista.DataSetFilters.point_data_to_cell_data`
 |          Resample point data as cell data without modifying the container.
 |      :meth:`~pyvista.DataSetFilters.cell_data_to_point_data`
 |          Resample cell data as point data without modifying the container.
 |
 |      Parameters
 |      ----------
 |      scalars : str, optional
 |          Name of point data scalars to pass through to the output as cell data. Use
 |          this parameter to restrict the output to only include the specified array.
 |          By default, all point data arrays at the input are passed through as cell
 |          data at the output.
 |
 |      copy : bool, default: True
 |          Copy the input point data before associating it with the output cell data.
 |          If ``False``, the input and output will both refer to the same data array(s).
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          Image with a cell-based representation.
 |
 |      Examples
 |      --------
 |      Load an image with point data.
 |
 |      >>> from pyvista import examples
 |      >>> image = examples.load_uniform()
 |
 |      Show the current properties and point arrays of the image.
 |
 |      >>> image
 |      ImageData (...)
 |        N Cells:      729
 |        N Points:     1000
 |        X Bounds:     0.000e+00, 9.000e+00
 |        Y Bounds:     0.000e+00, 9.000e+00
 |        Z Bounds:     0.000e+00, 9.000e+00
 |        Dimensions:   10, 10, 10
 |        Spacing:      1.000e+00, 1.000e+00, 1.000e+00
 |        N Arrays:     2
 |
 |      >>> image.point_data.keys()
 |      ['Spatial Point Data']
 |
 |      Re-mesh the points and point data as cells and cell data.
 |
 |      >>> cells_image = image.points_to_cells()
 |
 |      Show the properties and cell arrays of the re-meshed image.
 |
 |      >>> cells_image
 |      ImageData (...)
 |        N Cells:      1000
 |        N Points:     1331
 |        X Bounds:     -5.000e-01, 9.500e+00
 |        Y Bounds:     -5.000e-01, 9.500e+00
 |        Z Bounds:     -5.000e-01, 9.500e+00
 |        Dimensions:   11, 11, 11
 |        Spacing:      1.000e+00, 1.000e+00, 1.000e+00
 |        N Arrays:     1
 |
 |      >>> cells_image.cell_data.keys()
 |      ['Spatial Point Data']
 |
 |      Observe that:
 |
 |      - The input point array is now a cell array
 |      - The output has one less array (the input cell data is ignored)
 |      - The dimensions have increased by one
 |      - The bounds have increased by half the spacing
 |      - The output N Cells equals the input N Points
 |
 |      See :ref:`image_representations_example` for more examples using this filter.
 |
 |  rfft(self, output_scalars_name=None, progress_bar=False)
 |      Apply a reverse fast Fourier transform (RFFT) to the active scalars.
 |
 |      The input can be real or complex data, but the output is always
 |      :attr:`numpy.complex128`. The filter is fastest for images that have power
 |      of two sizes.
 |
 |      The filter uses a butterfly diagram for each prime factor of the
 |      dimension. This makes images with prime number dimensions (i.e. 17x17)
 |      much slower to compute. FFTs of multidimensional meshes (i.e volumes)
 |      are decomposed so that each axis executes serially.
 |
 |      The frequencies of the input assume standard order: along each axis
 |      first positive frequencies are assumed from 0 to the maximum, then
 |      negative frequencies are listed from the largest absolute value to
 |      smallest. This implies that the corners of the grid correspond to low
 |      frequencies, while the center of the grid corresponds to high
 |      frequencies.
 |
 |      Parameters
 |      ----------
 |      output_scalars_name : str, optional
 |          The name of the output scalars. By default, this is the same as the
 |          active scalars of the dataset.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.ImageData
 |          :class:`pyvista.ImageData` with the applied reverse FFT.
 |
 |      See Also
 |      --------
 |      fft : The direct transform.
 |      low_pass : Low-pass filtering of FFT output.
 |      high_pass : High-pass filtering of FFT output.
 |
 |      Examples
 |      --------
 |      Apply reverse FFT to an example image.
 |
 |      >>> from pyvista import examples
 |      >>> image = examples.download_moonlanding_image()
 |      >>> fft_image = image.fft()
 |      >>> image_again = fft_image.rfft()
 |      >>> image_again.point_data  # doctest:+SKIP
 |      pyvista DataSetAttributes
 |      Association     : POINT
 |      Active Scalars  : PNGImage
 |      Active Vectors  : None
 |      Active Texture  : None
 |      Active Normals  : None
 |      Contains arrays :
 |          PNGImage                complex128 (298620,)            SCALARS
 |
 |      See :ref:`image_fft_example` for a full example using this filter.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyvista.core.filters.data_set.DataSetFilters:
 |
 |  __add__(self, dataset)
 |      Combine this mesh with another into a :class:`pyvista.UnstructuredGrid`.
 |
 |  __iadd__(self, dataset)
 |      Merge another mesh into this one if possible.
 |
 |      "If possible" means that ``self`` is a :class:`pyvista.UnstructuredGrid`.
 |      Otherwise we have to return a new object, and the attempted in-place
 |      merge will raise.
 |
 |  align(self, target, max_landmarks=100, max_mean_distance=1e-05, max_iterations=500, check_mean_distance=True, start_by_matching_centroids=True, return_matrix=False)
 |      Align a dataset to another.
 |
 |      Uses the iterative closest point algorithm to align the points of the
 |      two meshes.  See the VTK class `vtkIterativeClosestPointTransform
 |      <https://vtk.org/doc/nightly/html/classvtkIterativeClosestPointTransform.html>`_
 |
 |      Parameters
 |      ----------
 |      target : pyvista.DataSet
 |          The target dataset to align to.
 |
 |      max_landmarks : int, default: 100
 |          The maximum number of landmarks.
 |
 |      max_mean_distance : float, default: 1e-5
 |          The maximum mean distance for convergence.
 |
 |      max_iterations : int, default: 500
 |          The maximum number of iterations.
 |
 |      check_mean_distance : bool, default: True
 |          Whether to check the mean distance for convergence.
 |
 |      start_by_matching_centroids : bool, default: True
 |          Whether to start the alignment by matching centroids. Default is True.
 |
 |      return_matrix : bool, default: False
 |          Return the transform matrix as well as the aligned mesh.
 |
 |      Returns
 |      -------
 |      aligned : pyvista.DataSet
 |          The dataset aligned to the target mesh.
 |
 |      matrix : numpy.ndarray
 |          Transform matrix to transform the input dataset to the target dataset.
 |
 |      Examples
 |      --------
 |      Create a cylinder, translate it, and use iterative closest point to
 |      align mesh to its original position.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> source = pv.Cylinder(resolution=30).triangulate().subdivide(1)
 |      >>> transformed = source.rotate_y(20).translate([-0.75, -0.5, 0.5])
 |      >>> aligned = transformed.align(source)
 |      >>> _, closest_points = aligned.find_closest_cell(
 |      ...     source.points, return_closest_point=True
 |      ... )
 |      >>> dist = np.linalg.norm(source.points - closest_points, axis=1)
 |
 |      Visualize the source, transformed, and aligned meshes.
 |
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> _ = pl.add_text('Before Alignment')
 |      >>> _ = pl.add_mesh(
 |      ...     source, style='wireframe', opacity=0.5, line_width=2
 |      ... )
 |      >>> _ = pl.add_mesh(transformed)
 |      >>> pl.subplot(0, 1)
 |      >>> _ = pl.add_text('After Alignment')
 |      >>> _ = pl.add_mesh(
 |      ...     source, style='wireframe', opacity=0.5, line_width=2
 |      ... )
 |      >>> _ = pl.add_mesh(
 |      ...     aligned,
 |      ...     scalars=dist,
 |      ...     scalar_bar_args={
 |      ...         'title': 'Distance to Source',
 |      ...         'fmt': '%.1E',
 |      ...     },
 |      ... )
 |      >>> pl.show()
 |
 |      Show that the mean distance between the source and the target is
 |      nearly zero.
 |
 |      >>> np.abs(dist).mean()  # doctest:+SKIP
 |      9.997635192915073e-05
 |
 |  cell_centers(self, vertex=True, progress_bar=False)
 |      Generate points at the center of the cells in this dataset.
 |
 |      These points can be used for placing glyphs or vectors.
 |
 |      Parameters
 |      ----------
 |      vertex : bool, default: True
 |          Enable or disable the generation of vertex cells.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Polydata where the points are the cell centers of the
 |          original dataset.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Plane()
 |      >>> mesh.point_data.clear()
 |      >>> centers = mesh.cell_centers()
 |      >>> pl = pv.Plotter()
 |      >>> actor = pl.add_mesh(mesh, show_edges=True)
 |      >>> actor = pl.add_points(
 |      ...     centers,
 |      ...     render_points_as_spheres=True,
 |      ...     color='red',
 |      ...     point_size=20,
 |      ... )
 |      >>> pl.show()
 |
 |      See :ref:`cell_centers_example` for more examples using this filter.
 |
 |  cell_data_to_point_data(self, pass_cell_data=False, progress_bar=False)
 |      Transform cell data into point data.
 |
 |      Point data are specified per node and cell data specified
 |      within cells.  Optionally, the input point data can be passed
 |      through to the output.
 |
 |      The method of transformation is based on averaging the data
 |      values of all cells using a particular point. Optionally, the
 |      input cell data can be passed through to the output as well.
 |
 |      Parameters
 |      ----------
 |      pass_cell_data : bool, default: False
 |          If enabled, pass the input cell data through to the output.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with the point data transformed into cell data.
 |          Return type matches input.
 |
 |      See Also
 |      --------
 |      point_data_to_cell_data
 |          Similar transformation applied to point data.
 |      :meth:`~pyvista.ImageDataFilters.cells_to_points`
 |          Re-mesh :class:`~pyvista.ImageData` to a points-based representation.
 |
 |      Examples
 |      --------
 |      First compute the face area of the example airplane mesh and
 |      show the cell values.  This is to show discrete cell data.
 |
 |      >>> from pyvista import examples
 |      >>> surf = examples.load_airplane()
 |      >>> surf = surf.compute_cell_sizes(length=False, volume=False)
 |      >>> surf.plot(scalars='Area')
 |
 |      These cell scalars can be applied to individual points to
 |      effectively smooth out the cell data onto the points.
 |
 |      >>> from pyvista import examples
 |      >>> surf = examples.load_airplane()
 |      >>> surf = surf.compute_cell_sizes(length=False, volume=False)
 |      >>> surf = surf.cell_data_to_point_data()
 |      >>> surf.plot(scalars='Area')
 |
 |  clip(self, normal='x', origin=None, invert=True, value=0.0, inplace=False, return_clipped=False, progress_bar=False, crinkle=False)
 |      Clip a dataset by a plane by specifying the origin and normal.
 |
 |      If no parameters are given the clip will occur in the center
 |      of that dataset.
 |
 |      Parameters
 |      ----------
 |      normal : tuple(float) or str, default: 'x'
 |          Length 3 tuple for the normal vector direction. Can also
 |          be specified as a string conventional direction such as
 |          ``'x'`` for ``(1, 0, 0)`` or ``'-x'`` for ``(-1, 0, 0)``, etc.
 |
 |      origin : sequence[float], optional
 |          The center ``(x, y, z)`` coordinate of the plane on which the clip
 |          occurs. The default is the center of the dataset.
 |
 |      invert : bool, default: True
 |          Flag on whether to flip/invert the clip.
 |
 |      value : float, default: 0.0
 |          Set the clipping value along the normal direction.
 |
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      return_clipped : bool, default: False
 |          Return both unclipped and clipped parts of the dataset.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      crinkle : bool, default: False
 |          Crinkle the clip by extracting the entire cells along the
 |          clip. This adds the ``"cell_ids"`` array to the ``cell_data``
 |          attribute that tracks the original cell IDs of the original
 |          dataset.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData or tuple[pyvista.PolyData]
 |          Clipped mesh when ``return_clipped=False``,
 |          otherwise a tuple containing the unclipped and clipped datasets.
 |
 |      Examples
 |      --------
 |      Clip a cube along the +X direction.  ``triangulate`` is used as
 |      the cube is initially composed of quadrilateral faces and
 |      subdivide only works on triangles.
 |
 |      >>> import pyvista as pv
 |      >>> cube = pv.Cube().triangulate().subdivide(3)
 |      >>> clipped_cube = cube.clip()
 |      >>> clipped_cube.plot()
 |
 |      Clip a cube in the +Z direction.  This leaves half a cube
 |      below the XY plane.
 |
 |      >>> import pyvista as pv
 |      >>> cube = pv.Cube().triangulate().subdivide(3)
 |      >>> clipped_cube = cube.clip('z')
 |      >>> clipped_cube.plot()
 |
 |      See :ref:`clip_with_surface_example` for more examples using this filter.
 |
 |  clip_box(self, bounds=None, invert=True, factor=0.35, progress_bar=False, merge_points=True, crinkle=False)
 |      Clip a dataset by a bounding box defined by the bounds.
 |
 |      If no bounds are given, a corner of the dataset bounds will be removed.
 |
 |      Parameters
 |      ----------
 |      bounds : sequence[float], optional
 |          Length 6 sequence of floats: ``(xmin, xmax, ymin, ymax, zmin, zmax)``.
 |          Length 3 sequence of floats: distances from the min coordinate of
 |          of the input mesh. Single float value: uniform distance from the
 |          min coordinate. Length 12 sequence of length 3 sequence of floats:
 |          a plane collection (normal, center, ...).
 |          :class:`pyvista.PolyData`: if a poly mesh is passed that represents
 |          a box with 6 faces that all form a standard box, then planes will
 |          be extracted from the box to define the clipping region.
 |
 |      invert : bool, default: True
 |          Flag on whether to flip/invert the clip.
 |
 |      factor : float, default: 0.35
 |          If bounds are not given this is the factor along each axis to
 |          extract the default box.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      merge_points : bool, default: True
 |          If ``True``, coinciding points of independently defined mesh
 |          elements will be merged.
 |
 |      crinkle : bool, default: False
 |          Crinkle the clip by extracting the entire cells along the
 |          clip. This adds the ``"cell_ids"`` array to the ``cell_data``
 |          attribute that tracks the original cell IDs of the original
 |          dataset.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Clipped dataset.
 |
 |      Examples
 |      --------
 |      Clip a corner of a cube.  The bounds of a cube are normally
 |      ``[-0.5, 0.5, -0.5, 0.5, -0.5, 0.5]``, and this removes 1/8 of
 |      the cube's surface.
 |
 |      >>> import pyvista as pv
 |      >>> cube = pv.Cube().triangulate().subdivide(3)
 |      >>> clipped_cube = cube.clip_box([0, 1, 0, 1, 0, 1])
 |      >>> clipped_cube.plot()
 |
 |      See :ref:`clip_with_plane_box_example` for more examples using this filter.
 |
 |  clip_scalar(self, scalars=None, invert=True, value=0.0, inplace=False, progress_bar=False, both=False)
 |      Clip a dataset by a scalar.
 |
 |      Parameters
 |      ----------
 |      scalars : str, optional
 |          Name of scalars to clip on.  Defaults to currently active scalars.
 |
 |      invert : bool, default: True
 |          Flag on whether to flip/invert the clip.  When ``True``,
 |          only the mesh below ``value`` will be kept.  When
 |          ``False``, only values above ``value`` will be kept.
 |
 |      value : float, default: 0.0
 |          Set the clipping value.
 |
 |      inplace : bool, default: False
 |          Update mesh in-place.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      both : bool, default: False
 |          If ``True``, also returns the complementary clipped mesh.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData or tuple
 |          Clipped dataset if ``both=False``.  If ``both=True`` then
 |          returns a tuple of both clipped datasets.
 |
 |      Examples
 |      --------
 |      Remove the part of the mesh with "sample_point_scalars" above 100.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> dataset = examples.load_hexbeam()
 |      >>> clipped = dataset.clip_scalar(
 |      ...     scalars="sample_point_scalars", value=100
 |      ... )
 |      >>> clipped.plot()
 |
 |      Get clipped meshes corresponding to the portions of the mesh above and below 100.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> dataset = examples.load_hexbeam()
 |      >>> _below, _above = dataset.clip_scalar(
 |      ...     scalars="sample_point_scalars", value=100, both=True
 |      ... )
 |
 |      Remove the part of the mesh with "sample_point_scalars" below 100.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> dataset = examples.load_hexbeam()
 |      >>> clipped = dataset.clip_scalar(
 |      ...     scalars="sample_point_scalars", value=100, invert=False
 |      ... )
 |      >>> clipped.plot()
 |
 |  clip_surface(self, surface, invert=True, value=0.0, compute_distance=False, progress_bar=False, crinkle=False)
 |      Clip any mesh type using a :class:`pyvista.PolyData` surface mesh.
 |
 |      This will return a :class:`pyvista.UnstructuredGrid` of the clipped
 |      mesh. Geometry of the input dataset will be preserved where possible.
 |      Geometries near the clip intersection will be triangulated/tessellated.
 |
 |      Parameters
 |      ----------
 |      surface : pyvista.PolyData
 |          The ``PolyData`` surface mesh to use as a clipping
 |          function.  If this input mesh is not a :class`pyvista.PolyData`,
 |          the external surface will be extracted.
 |
 |      invert : bool, default: True
 |          Flag on whether to flip/invert the clip.
 |
 |      value : float, default: 0.0
 |          Set the clipping value of the implicit function (if
 |          clipping with implicit function) or scalar value (if
 |          clipping with scalars).
 |
 |      compute_distance : bool, default: False
 |          Compute the implicit distance from the mesh onto the input
 |          dataset.  A new array called ``'implicit_distance'`` will
 |          be added to the output clipped mesh.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      crinkle : bool, default: False
 |          Crinkle the clip by extracting the entire cells along the
 |          clip. This adds the ``"cell_ids"`` array to the ``cell_data``
 |          attribute that tracks the original cell IDs of the original
 |          dataset.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Clipped surface.
 |
 |      Examples
 |      --------
 |      Clip a cube with a sphere.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere(center=(-0.4, -0.4, -0.4))
 |      >>> cube = pv.Cube().triangulate().subdivide(3)
 |      >>> clipped = cube.clip_surface(sphere)
 |      >>> clipped.plot(show_edges=True, cpos='xy', line_width=3)
 |
 |      See :ref:`clip_with_surface_example` for more examples using
 |      this filter.
 |
 |  compute_boundary_mesh_quality(self, *, progress_bar=False)
 |      Compute metrics on the boundary faces of a mesh.
 |
 |      The metrics that can be computed on the boundary faces of the mesh and are:
 |
 |      - Distance from cell center to face center
 |      - Distance from cell center to face plane
 |      - Angle of faces plane normal and cell center to face center vector
 |
 |      Parameters
 |      ----------
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with the computed metrics on the boundary faces of a mesh.
 |          ``cell_data`` as the ``"CellQuality"`` array.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> mesh = examples.download_can_crushed_vtu()
 |      >>> cqual = mesh.compute_boundary_mesh_quality()
 |      >>> plotter = pv.Plotter(shape=(2, 2))
 |      >>> _ = plotter.add_mesh(mesh, show_edges=True)
 |      >>> plotter.subplot(1, 0)
 |      >>> _ = plotter.add_mesh(
 |      ...     cqual, scalars="DistanceFromCellCenterToFaceCenter"
 |      ... )
 |      >>> plotter.subplot(0, 1)
 |      >>> _ = plotter.add_mesh(
 |      ...     cqual, scalars="DistanceFromCellCenterToFacePlane"
 |      ... )
 |      >>> plotter.subplot(1, 1)
 |      >>> _ = plotter.add_mesh(
 |      ...     cqual,
 |      ...     scalars="AngleFaceNormalAndCellCenterToFaceCenterVector",
 |      ... )
 |      >>> plotter.show()
 |
 |  compute_cell_quality(self, quality_measure='scaled_jacobian', null_value=-1.0, progress_bar=False)
 |      Compute a function of (geometric) quality for each cell of a mesh.
 |
 |      The per-cell quality is added to the mesh's cell data, in an
 |      array named ``"CellQuality"``. Cell types not supported by this
 |      filter or undefined quality of supported cell types will have an
 |      entry of -1.
 |
 |      Defaults to computing the scaled Jacobian.
 |
 |      Options for cell quality measure:
 |
 |      - ``'area'``
 |      - ``'aspect_beta'``
 |      - ``'aspect_frobenius'``
 |      - ``'aspect_gamma'``
 |      - ``'aspect_ratio'``
 |      - ``'collapse_ratio'``
 |      - ``'condition'``
 |      - ``'diagonal'``
 |      - ``'dimension'``
 |      - ``'distortion'``
 |      - ``'jacobian'``
 |      - ``'max_angle'``
 |      - ``'max_aspect_frobenius'``
 |      - ``'max_edge_ratio'``
 |      - ``'med_aspect_frobenius'``
 |      - ``'min_angle'``
 |      - ``'oddy'``
 |      - ``'radius_ratio'``
 |      - ``'relative_size_squared'``
 |      - ``'scaled_jacobian'``
 |      - ``'shape'``
 |      - ``'shape_and_size'``
 |      - ``'shear'``
 |      - ``'shear_and_size'``
 |      - ``'skew'``
 |      - ``'stretch'``
 |      - ``'taper'``
 |      - ``'volume'``
 |      - ``'warpage'``
 |
 |      Notes
 |      -----
 |      There is a `discussion about shape option <https://github.com/pyvista/pyvista/discussions/6143>`_.
 |
 |      Parameters
 |      ----------
 |      quality_measure : str, default: 'scaled_jacobian'
 |          The cell quality measure to use.
 |
 |      null_value : float, default: -1.0
 |          Float value for undefined quality. Undefined quality are qualities
 |          that could be addressed by this filter but is not well defined for
 |          the particular geometry of cell in question, e.g. a volume query
 |          for a triangle. Undefined quality will always be undefined.
 |          The default value is -1.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with the computed mesh quality in the
 |          ``cell_data`` as the ``"CellQuality"`` array.
 |
 |      Examples
 |      --------
 |      Compute and plot the minimum angle of a sample sphere mesh.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere(theta_resolution=20, phi_resolution=20)
 |      >>> cqual = sphere.compute_cell_quality('min_angle')
 |      >>> cqual.plot(show_edges=True)
 |
 |      See the :ref:`mesh_quality_example` for more examples using this filter.
 |
 |  compute_cell_sizes(self, length=True, area=True, volume=True, progress_bar=False, vertex_count=False)
 |      Compute sizes for 0D (vertex count), 1D (length), 2D (area) and 3D (volume) cells.
 |
 |      Parameters
 |      ----------
 |      length : bool, default: True
 |          Specify whether or not to compute the length of 1D cells.
 |
 |      area : bool, default: True
 |          Specify whether or not to compute the area of 2D cells.
 |
 |      volume : bool, default: True
 |          Specify whether or not to compute the volume of 3D cells.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      vertex_count : bool, default: False
 |          Specify whether or not to compute sizes for vertex and polyvertex cells (0D cells).
 |          The computed value is the number of points in the cell.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with `cell_data` containing the ``"VertexCount"``,
 |          ``"Length"``, ``"Area"``, and ``"Volume"`` arrays if set
 |          in the parameters.  Return type matches input.
 |
 |      Notes
 |      -----
 |      If cells do not have a dimension (for example, the length of
 |      hexahedral cells), the corresponding array will be all zeros.
 |
 |      Examples
 |      --------
 |      Compute the face area of the example airplane mesh.
 |
 |      >>> from pyvista import examples
 |      >>> surf = examples.load_airplane()
 |      >>> surf = surf.compute_cell_sizes(length=False, volume=False)
 |      >>> surf.plot(show_edges=True, scalars='Area')
 |
 |  compute_derivative(self, scalars=None, gradient=True, divergence=None, vorticity=None, qcriterion=None, faster=False, preference='point', progress_bar=False)
 |      Compute derivative-based quantities of point/cell scalar field.
 |
 |      Utilize ``vtkGradientFilter`` to compute derivative-based quantities,
 |      such as gradient, divergence, vorticity, and Q-criterion, of the
 |      selected point or cell scalar field.
 |
 |      Parameters
 |      ----------
 |      scalars : str, optional
 |          String name of the scalars array to use when computing the
 |          derivative quantities.  Defaults to the active scalars in
 |          the dataset.
 |
 |      gradient : bool | str, default: True
 |          Calculate gradient. If a string is passed, the string will be used
 |          for the resulting array name. Otherwise, array name will be
 |          ``'gradient'``. Default ``True``.
 |
 |      divergence : bool | str, optional
 |          Calculate divergence. If a string is passed, the string will be
 |          used for the resulting array name. Otherwise, default array name
 |          will be ``'divergence'``.
 |
 |      vorticity : bool | str, optional
 |          Calculate vorticity. If a string is passed, the string will be used
 |          for the resulting array name. Otherwise, default array name will be
 |          ``'vorticity'``.
 |
 |      qcriterion : bool | str, optional
 |          Calculate qcriterion. If a string is passed, the string will be
 |          used for the resulting array name. Otherwise, default array name
 |          will be ``'qcriterion'``.
 |
 |      faster : bool, default: False
 |          Use faster algorithm for computing derivative quantities. Result is
 |          less accurate and performs fewer derivative calculations,
 |          increasing computation speed. The error will feature smoothing of
 |          the output and possibly errors at boundaries. Option has no effect
 |          if DataSet is not :class:`pyvista.UnstructuredGrid`.
 |
 |      preference : str, default: "point"
 |          Data type preference. Either ``'point'`` or ``'cell'``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with calculated derivative.
 |
 |      Examples
 |      --------
 |      First, plot the random hills dataset with the active elevation
 |      scalars.  These scalars will be used for the derivative
 |      calculations.
 |
 |      >>> from pyvista import examples
 |      >>> hills = examples.load_random_hills()
 |      >>> hills.plot(smooth_shading=True)
 |
 |      Compute and plot the gradient of the active scalars.
 |
 |      >>> from pyvista import examples
 |      >>> hills = examples.load_random_hills()
 |      >>> deriv = hills.compute_derivative()
 |      >>> deriv.plot(scalars='gradient')
 |
 |      See the :ref:`gradients_example` for more examples using this filter.
 |
 |  compute_implicit_distance(self, surface, inplace=False)
 |      Compute the implicit distance from the points to a surface.
 |
 |      This filter will compute the implicit distance from all of the
 |      nodes of this mesh to a given surface. This distance will be
 |      added as a point array called ``'implicit_distance'``.
 |
 |      Nodes of this mesh which are interior to the input surface
 |      geometry have a negative distance, and nodes on the exterior
 |      have a positive distance. Nodes which intersect the input
 |      surface has a distance of zero.
 |
 |      Parameters
 |      ----------
 |      surface : pyvista.DataSet
 |          The surface used to compute the distance.
 |
 |      inplace : bool, default: False
 |          If ``True``, a new scalar array will be added to the
 |          ``point_data`` of this mesh and the modified mesh will
 |          be returned. Otherwise a copy of this mesh is returned
 |          with that scalar field added.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset containing the ``'implicit_distance'`` array in
 |          ``point_data``.
 |
 |      Examples
 |      --------
 |      Compute the distance between all the points on a sphere and a
 |      plane.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere(radius=0.35)
 |      >>> plane = pv.Plane()
 |      >>> _ = sphere.compute_implicit_distance(plane, inplace=True)
 |      >>> dist = sphere['implicit_distance']
 |      >>> type(dist)
 |      <class 'pyvista.core.pyvista_ndarray.pyvista_ndarray'>
 |
 |      Plot these distances as a heatmap. Note how distances above the
 |      plane are positive, and distances below the plane are negative.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(
 |      ...     sphere, scalars='implicit_distance', cmap='bwr'
 |      ... )
 |      >>> _ = pl.add_mesh(plane, color='w', style='wireframe')
 |      >>> pl.show()
 |
 |      We can also compute the distance from all the points on the
 |      plane to the sphere.
 |
 |      >>> _ = plane.compute_implicit_distance(sphere, inplace=True)
 |
 |      Again, we can plot these distances as a heatmap. Note how
 |      distances inside the sphere are negative and distances outside
 |      the sphere are positive.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(
 |      ...     plane,
 |      ...     scalars='implicit_distance',
 |      ...     cmap='bwr',
 |      ...     clim=[-0.35, 0.35],
 |      ... )
 |      >>> _ = pl.add_mesh(sphere, color='w', style='wireframe')
 |      >>> pl.show()
 |
 |      See :ref:`clip_with_surface_example` and
 |      :ref:`voxelize_surface_mesh_example` for more examples using
 |      this filter.
 |
 |  connectivity(self, extraction_mode: "Literal['all', 'largest', 'specified', 'cell_seed', 'point_seed', 'closest']" = 'all', variable_input=None, scalar_range=None, scalars=None, label_regions=True, region_ids=None, point_ids=None, cell_ids=None, closest_point=None, inplace=False, progress_bar=False, **kwargs)
 |      Find and label connected regions.
 |
 |      This filter extracts cell regions based on a specified connectivity
 |      criterion. The extraction criterion can be controlled with
 |      ``extraction_mode`` to extract the largest region or the closest
 |      region to a seed point, for example.
 |
 |      In general, cells are considered to be connected if they
 |      share a point. However, if a ``scalar_range`` is provided, cells
 |      must also have at least one point with scalar values in the
 |      specified range to be considered connected.
 |
 |      See :ref:`connectivity_example` and :ref:`volumetric_example` for
 |      more examples using this filter.
 |
 |      .. versionadded:: 0.43.0
 |
 |         * New extraction modes: ``'specified'``, ``'cell_seed'``, ``'point_seed'``,
 |           and ``'closest'``.
 |         * Extracted regions are now sorted in descending order by
 |           cell count.
 |         * Region connectivity can be controlled using ``scalar_range``.
 |
 |      .. deprecated:: 0.43.0
 |         Parameter ``largest`` is deprecated. Use ``'largest'`` or
 |         ``extraction_mode='largest'`` instead.
 |
 |      Parameters
 |      ----------
 |      extraction_mode : str, default: "all"
 |          * ``'all'``: Extract all connected regions.
 |          * ``'largest'`` : Extract the largest connected region (by cell
 |            count).
 |          * ``'specified'``: Extract specific region IDs. Use ``region_ids``
 |            to specify the region IDs to extract.
 |          * ``'cell_seed'``: Extract all regions sharing the specified cell
 |            ids. Use ``cell_ids`` to specify the cell ids.
 |          * ``'point_seed'`` : Extract all regions sharing the specified
 |            point ids. Use ``point_ids`` to specify the point ids.
 |          * ``'closest'`` : Extract the region closest to the specified
 |            point. Use ``closest_point`` to specify the point.
 |
 |      variable_input : float | sequence[float], optional
 |          The convenience parameter used for specifying any required input
 |          values for some values of ``extraction_mode``. Setting
 |          ``variable_input`` is equivalent to setting:
 |
 |          * ``'region_ids'`` if mode is ``'specified'``.
 |          * ``'cell_ids'`` if mode is ``'cell_seed'``.
 |          * ``'point_ids'`` if mode is ``'point_seed'``.
 |          * ``'closest_point'`` if mode is ``'closest'``.
 |
 |          It has no effect if the mode is ``'all'`` or ``'largest'``.
 |
 |      scalar_range : sequence[float], optional
 |          Scalar range in the form ``[min, max]``. If set, the connectivity is
 |          restricted to cells with at least one point with scalar values in
 |          the specified range.
 |
 |      scalars : str, optional
 |          Name of scalars to use if ``scalar_range`` is specified. Defaults
 |          to currently active scalars.
 |
 |          .. note::
 |             This filter requires point scalars to determine region
 |             connectivity. If cell scalars are provided, they are first
 |             converted to point scalars with :func:`cell_data_to_point_data`
 |             before applying the filter. The converted point scalars are
 |             removed from the output after applying the filter.
 |
 |      label_regions : bool, default: True
 |          If ``True``, ``'RegionId'`` point and cell scalar arrays are stored.
 |          Each region is assigned a unique ID. IDs are zero-indexed and are
 |          assigned by region cell count in descending order (i.e. the largest
 |          region has ID ``0``).
 |
 |      region_ids : sequence[int], optional
 |          Region ids to extract. Only used if ``extraction_mode`` is
 |          ``specified``.
 |
 |      point_ids : sequence[int], optional
 |          Point ids to use as seeds. Only used if ``extraction_mode`` is
 |          ``point_seed``.
 |
 |      cell_ids : sequence[int], optional
 |          Cell ids to use as seeds. Only used if ``extraction_mode`` is
 |          ``cell_seed``.
 |
 |      closest_point : sequence[int], optional
 |          Point coordinates in ``(x, y, z)``. Only used if
 |          ``extraction_mode`` is ``closest``.
 |
 |      inplace : bool, default: False
 |          If ``True`` the mesh is updated in-place, otherwise a copy
 |          is returned. A copy is always returned if the input type is
 |          not ``pyvista.PolyData`` or ``pyvista.UnstructuredGrid``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar.
 |
 |      **kwargs : dict, optional
 |          Used for handling deprecated parameters.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with labeled connected regions. Return type is
 |          ``pyvista.PolyData`` if input type is ``pyvista.PolyData`` and
 |          ``pyvista.UnstructuredGrid`` otherwise.
 |
 |      See Also
 |      --------
 |      extract_largest, split_bodies, threshold, extract_values
 |
 |      Examples
 |      --------
 |      Create a single mesh with three disconnected regions where each
 |      region has a different cell count.
 |
 |      >>> import pyvista as pv
 |      >>> large = pv.Sphere(
 |      ...     center=(-4, 0, 0), phi_resolution=40, theta_resolution=40
 |      ... )
 |      >>> medium = pv.Sphere(
 |      ...     center=(-2, 0, 0), phi_resolution=15, theta_resolution=15
 |      ... )
 |      >>> small = pv.Sphere(
 |      ...     center=(0, 0, 0), phi_resolution=7, theta_resolution=7
 |      ... )
 |      >>> mesh = large + medium + small
 |
 |      Plot their connectivity.
 |
 |      >>> conn = mesh.connectivity('all')
 |      >>> conn.plot(cmap=['red', 'green', 'blue'], show_edges=True)
 |
 |      Restrict connectivity to a scalar range.
 |
 |      >>> mesh['y_coordinates'] = mesh.points[:, 1]
 |      >>> conn = mesh.connectivity('all', scalar_range=[-1, 0])
 |      >>> conn.plot(cmap=['red', 'green', 'blue'], show_edges=True)
 |
 |      Extract the region closest to the origin.
 |
 |      >>> conn = mesh.connectivity('closest', (0, 0, 0))
 |      >>> conn.plot(color='blue', show_edges=True)
 |
 |      Extract a region using a cell ID ``100`` as a seed.
 |
 |      >>> conn = mesh.connectivity('cell_seed', 100)
 |      >>> conn.plot(color='green', show_edges=True)
 |
 |      Extract the largest region.
 |
 |      >>> conn = mesh.connectivity('largest')
 |      >>> conn.plot(color='red', show_edges=True)
 |
 |      Extract the largest and smallest regions by specifying their
 |      region IDs. Note that the region IDs of the output differ from
 |      the specified IDs since the input has three regions but the output
 |      only has two.
 |
 |      >>> large_id = 0  # largest always has ID '0'
 |      >>> small_id = 2  # smallest has ID 'N-1' with N=3 regions
 |      >>> conn = mesh.connectivity('specified', (small_id, large_id))
 |      >>> conn.plot(cmap=['red', 'blue'], show_edges=True)
 |
 |  contour(self, isosurfaces=10, scalars=None, compute_normals=False, compute_gradients=False, compute_scalars=True, rng=None, preference='point', method='contour', progress_bar=False)
 |      Contour an input self by an array.
 |
 |      ``isosurfaces`` can be an integer specifying the number of
 |      isosurfaces in the data range or a sequence of values for
 |      explicitly setting the isosurfaces.
 |
 |      Parameters
 |      ----------
 |      isosurfaces : int | sequence[float], optional
 |          Number of isosurfaces to compute across valid data range or a
 |          sequence of float values to explicitly use as the isosurfaces.
 |
 |      scalars : str | array_like[float], optional
 |          Name or array of scalars to threshold on. If this is an array, the
 |          output of this filter will save them as ``"Contour Data"``.
 |          Defaults to currently active scalars.
 |
 |      compute_normals : bool, default: False
 |          Compute normals for the dataset.
 |
 |      compute_gradients : bool, default: False
 |          Compute gradients for the dataset.
 |
 |      compute_scalars : bool, default: True
 |          Preserves the scalar values that are being contoured.
 |
 |      rng : sequence[float], optional
 |          If an integer number of isosurfaces is specified, this is
 |          the range over which to generate contours. Default is the
 |          scalars array's full data range.
 |
 |      preference : str, default: "point"
 |          When ``scalars`` is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'`` or ``'cell'``.
 |
 |      method : str, default:  "contour"
 |          Specify to choose which vtk filter is used to create the contour.
 |          Must be one of ``'contour'``, ``'marching_cubes'`` and
 |          ``'flying_edges'``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Contoured surface.
 |
 |      Examples
 |      --------
 |      Generate contours for the random hills dataset.
 |
 |      >>> from pyvista import examples
 |      >>> hills = examples.load_random_hills()
 |      >>> contours = hills.contour()
 |      >>> contours.plot(line_width=5)
 |
 |      Generate the surface of a mobius strip using flying edges.
 |
 |      >>> import pyvista as pv
 |      >>> a = 0.4
 |      >>> b = 0.1
 |      >>> def f(x, y, z):
 |      ...     xx = x * x
 |      ...     yy = y * y
 |      ...     zz = z * z
 |      ...     xyz = x * y * z
 |      ...     xx_yy = xx + yy
 |      ...     a_xx = a * xx
 |      ...     b_yy = b * yy
 |      ...     return (
 |      ...         (xx_yy + 1) * (a_xx + b_yy)
 |      ...         + zz * (b * xx + a * yy)
 |      ...         - 2 * (a - b) * xyz
 |      ...         - a * b * xx_yy
 |      ...     ) ** 2 - 4 * (xx + yy) * (a_xx + b_yy - xyz * (a - b)) ** 2
 |      ...
 |      >>> n = 100
 |      >>> x_min, y_min, z_min = -1.35, -1.7, -0.65
 |      >>> grid = pv.ImageData(
 |      ...     dimensions=(n, n, n),
 |      ...     spacing=(
 |      ...         abs(x_min) / n * 2,
 |      ...         abs(y_min) / n * 2,
 |      ...         abs(z_min) / n * 2,
 |      ...     ),
 |      ...     origin=(x_min, y_min, z_min),
 |      ... )
 |      >>> x, y, z = grid.points.T
 |      >>> values = f(x, y, z)
 |      >>> out = grid.contour(
 |      ...     1,
 |      ...     scalars=values,
 |      ...     rng=[0, 0],
 |      ...     method='flying_edges',
 |      ... )
 |      >>> out.plot(color='lightblue', smooth_shading=True)
 |
 |      See :ref:`common_filter_example` or
 |      :ref:`marching_cubes_example` for more examples using this
 |      filter.
 |
 |  ctp(self, pass_cell_data=False, progress_bar=False, **kwargs)
 |      Transform cell data into point data.
 |
 |      Point data are specified per node and cell data specified
 |      within cells.  Optionally, the input point data can be passed
 |      through to the output.
 |
 |      This method is an alias for
 |      :func:`pyvista.DataSetFilters.cell_data_to_point_data`.
 |
 |      Parameters
 |      ----------
 |      pass_cell_data : bool, default: False
 |          If enabled, pass the input cell data through to the output.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      **kwargs : dict, optional
 |          Deprecated keyword argument ``pass_cell_arrays``.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with the cell data transformed into point data.
 |          Return type matches input.
 |
 |  decimate_boundary(self, target_reduction=0.5, progress_bar=False)
 |      Return a decimated version of a triangulation of the boundary.
 |
 |      Only the outer surface of the input dataset will be considered.
 |
 |      Parameters
 |      ----------
 |      target_reduction : float, default: 0.5
 |          Fraction of the original mesh to remove.
 |          TargetReduction is set to ``0.9``, this filter will try to reduce
 |          the data set to 10% of its original size and will remove 90%
 |          of the input triangles.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Decimated boundary.
 |
 |      Examples
 |      --------
 |      See the :ref:`linked_views_example` example.
 |
 |  delaunay_3d(self, alpha=0.0, tol=0.001, offset=2.5, progress_bar=False)
 |      Construct a 3D Delaunay triangulation of the mesh.
 |
 |      This filter can be used to generate a 3D tetrahedral mesh from
 |      a surface or scattered points.  If you want to create a
 |      surface from a point cloud, see
 |      :func:`pyvista.PolyDataFilters.reconstruct_surface`.
 |
 |      Parameters
 |      ----------
 |      alpha : float, default: 0.0
 |          Distance value to control output of this filter. For a
 |          non-zero alpha value, only vertices, edges, faces, or
 |          tetrahedra contained within the circumsphere (of radius
 |          alpha) will be output. Otherwise, only tetrahedra will be
 |          output.
 |
 |      tol : float, default: 0.001
 |          Tolerance to control discarding of closely spaced points.
 |          This tolerance is specified as a fraction of the diagonal
 |          length of the bounding box of the points.
 |
 |      offset : float, default: 2.5
 |          Multiplier to control the size of the initial, bounding
 |          Delaunay triangulation.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          UnstructuredGrid containing the Delaunay triangulation.
 |
 |      Examples
 |      --------
 |      Generate a 3D Delaunay triangulation of a surface mesh of a
 |      sphere and plot the interior edges generated.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere(theta_resolution=5, phi_resolution=5)
 |      >>> grid = sphere.delaunay_3d()
 |      >>> edges = grid.extract_all_edges()
 |      >>> edges.plot(line_width=5, color='k')
 |
 |  elevation(self, low_point=None, high_point=None, scalar_range=None, preference='point', set_active=True, progress_bar=False)
 |      Generate scalar values on a dataset.
 |
 |      The scalar values lie within a user specified range, and are
 |      generated by computing a projection of each dataset point onto
 |      a line.  The line can be oriented arbitrarily.  A typical
 |      example is to generate scalars based on elevation or height
 |      above a plane.
 |
 |      .. warning::
 |         This will create a scalars array named ``'Elevation'`` on the
 |         point data of the input dataset and overwrite the array
 |         named ``'Elevation'`` if present.
 |
 |      Parameters
 |      ----------
 |      low_point : sequence[float], optional
 |          The low point of the projection line in 3D space. Default is bottom
 |          center of the dataset. Otherwise pass a length 3 sequence.
 |
 |      high_point : sequence[float], optional
 |          The high point of the projection line in 3D space. Default is top
 |          center of the dataset. Otherwise pass a length 3 sequence.
 |
 |      scalar_range : str | sequence[float], optional
 |          The scalar range to project to the low and high points on the line
 |          that will be mapped to the dataset. If None given, the values will
 |          be computed from the elevation (Z component) range between the
 |          high and low points. Min and max of a range can be given as a length
 |          2 sequence. If ``str``, name of scalar array present in the
 |          dataset given, the valid range of that array will be used.
 |
 |      preference : str, default: "point"
 |          When an array name is specified for ``scalar_range``, this is the
 |          preferred array type to search for in the dataset.
 |          Must be either ``'point'`` or ``'cell'``.
 |
 |      set_active : bool, default: True
 |          A boolean flag on whether or not to set the new
 |          ``'Elevation'`` scalar as the active scalars array on the
 |          output dataset.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset containing elevation scalars in the
 |          ``"Elevation"`` array in ``point_data``.
 |
 |      Examples
 |      --------
 |      Generate the "elevation" scalars for a sphere mesh.  This is
 |      simply the height in Z from the XY plane.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere()
 |      >>> sphere_elv = sphere.elevation()
 |      >>> sphere_elv.plot(smooth_shading=True)
 |
 |      Access the first 4 elevation scalars.  This is a point-wise
 |      array containing the "elevation" of each point.
 |
 |      >>> sphere_elv['Elevation'][:4]  # doctest:+SKIP
 |      array([-0.5       ,  0.5       , -0.49706897, -0.48831028], dtype=float32)
 |
 |      See :ref:`common_filter_example` for more examples using this filter.
 |
 |  explode(self, factor=0.1)
 |      Push each individual cell away from the center of the dataset.
 |
 |      Parameters
 |      ----------
 |      factor : float, default: 0.1
 |          How much each cell will move from the center of the dataset
 |          relative to its distance from it. Increase this number to push the
 |          cells farther away.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          UnstructuredGrid containing the exploded cells.
 |
 |      Notes
 |      -----
 |      This is similar to :func:`shrink <pyvista.DataSetFilters.shrink>`
 |      except that it does not change the size of the cells.
 |
 |      Examples
 |      --------
 |      >>> import numpy as np
 |      >>> import pyvista as pv
 |      >>> xrng = np.linspace(0, 1, 3)
 |      >>> yrng = np.linspace(0, 2, 4)
 |      >>> zrng = np.linspace(0, 3, 5)
 |      >>> grid = pv.RectilinearGrid(xrng, yrng, zrng)
 |      >>> exploded = grid.explode()
 |      >>> exploded.plot(show_edges=True)
 |
 |  extract_all_edges(self, use_all_points=False, clear_data=False, progress_bar=False)
 |      Extract all the internal/external edges of the dataset as PolyData.
 |
 |      This produces a full wireframe representation of the input dataset.
 |
 |      Parameters
 |      ----------
 |      use_all_points : bool, default: False
 |          Indicates whether all of the points of the input mesh should exist
 |          in the output. When ``True``, point numbering does not change and
 |          a threaded approach is used, which avoids the use of a point locator
 |          and is quicker.
 |
 |          By default this is set to ``False``, and unused points are omitted
 |          from the output.
 |
 |          This parameter can only be set to ``True`` with ``vtk==9.1.0`` or newer.
 |
 |      clear_data : bool, default: False
 |          Clear any point, cell, or field data. This is useful
 |          if wanting to strictly extract the edges.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Edges extracted from the dataset.
 |
 |      Examples
 |      --------
 |      Extract the edges of a sample unstructured grid and plot the edges.
 |      Note how it plots interior edges.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> hex_beam = pv.read(examples.hexbeamfile)
 |      >>> edges = hex_beam.extract_all_edges()
 |      >>> edges.plot(line_width=5, color='k')
 |
 |      See :ref:`cell_centers_example` for more examples using this filter.
 |
 |  extract_cells(self, ind, invert=False, progress_bar=False)
 |      Return a subset of the grid.
 |
 |      Parameters
 |      ----------
 |      ind : sequence[int]
 |          Numpy array of cell indices to be extracted.
 |
 |      invert : bool, default: False
 |          Invert the selection.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      See Also
 |      --------
 |      extract_points, extract_values
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Subselected grid.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> grid = pv.read(examples.hexbeamfile)
 |      >>> subset = grid.extract_cells(range(20))
 |      >>> subset.n_cells
 |      20
 |      >>> pl = pv.Plotter()
 |      >>> actor = pl.add_mesh(
 |      ...     grid, style='wireframe', line_width=5, color='black'
 |      ... )
 |      >>> actor = pl.add_mesh(subset, color='grey')
 |      >>> pl.show()
 |
 |  extract_cells_by_type(self, cell_types, progress_bar=False)
 |      Extract cells of a specified type.
 |
 |      Given an input dataset and a list of cell types, produce an output
 |      dataset containing only cells of the specified type(s). Note that if
 |      the input dataset is homogeneous (e.g., all cells are of the same type)
 |      and the cell type is one of the cells specified, then the input dataset
 |      is shallow copied to the output.
 |
 |      The type of output dataset is always the same as the input type. Since
 |      structured types of data (i.e., :class:`pyvista.ImageData`,
 |      :class:`pyvista.StructuredGrid`, :class`pyvista.RectilnearGrid`)
 |      are all composed of a cell of the same
 |      type, the output is either empty, or a shallow copy of the input.
 |      Unstructured data (:class:`pyvista.UnstructuredGrid`,
 |      :class:`pyvista.PolyData`) input may produce a subset of the input data
 |      (depending on the selected cell types).
 |
 |      Parameters
 |      ----------
 |      cell_types :  int | sequence[int]
 |          The cell types to extract. Must be a single or list of integer cell
 |          types. See :class:`pyvista.CellType`.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with the extracted cells. Type is the same as the input.
 |
 |      Notes
 |      -----
 |      Unlike :func:`pyvista.DataSetFilters.extract_cells` which always
 |      produces a :class:`pyvista.UnstructuredGrid` output, this filter
 |      produces the same output type as input type.
 |
 |      Examples
 |      --------
 |      Create an unstructured grid with both hexahedral and tetrahedral
 |      cells and then extract each individual cell type.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> beam = examples.load_hexbeam()
 |      >>> beam = beam.translate([1, 0, 0])
 |      >>> ugrid = beam + examples.load_tetbeam()
 |      >>> hex_cells = ugrid.extract_cells_by_type(pv.CellType.HEXAHEDRON)
 |      >>> tet_cells = ugrid.extract_cells_by_type(pv.CellType.TETRA)
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> _ = pl.add_text('Extracted Hexahedron cells')
 |      >>> _ = pl.add_mesh(hex_cells, show_edges=True)
 |      >>> pl.subplot(0, 1)
 |      >>> _ = pl.add_text('Extracted Tetrahedron cells')
 |      >>> _ = pl.add_mesh(tet_cells, show_edges=True)
 |      >>> pl.show()
 |
 |  extract_feature_edges(self, feature_angle=30.0, boundary_edges=True, non_manifold_edges=True, feature_edges=True, manifold_edges=True, clear_data=False, progress_bar=False)
 |      Extract edges from the surface of the mesh.
 |
 |      If the given mesh is not PolyData, the external surface of the given
 |      mesh is extracted and used.
 |
 |      From vtk documentation, the edges are one of the following:
 |
 |          1) Boundary (used by one polygon) or a line cell.
 |          2) Non-manifold (used by three or more polygons).
 |          3) Feature edges (edges used by two triangles and whose
 |             dihedral angle > feature_angle).
 |          4) Manifold edges (edges used by exactly two polygons).
 |
 |      Parameters
 |      ----------
 |      feature_angle : float, default: 30.0
 |          Feature angle (in degrees) used to detect sharp edges on
 |          the mesh. Used only when ``feature_edges=True``.
 |
 |      boundary_edges : bool, default: True
 |          Extract the boundary edges.
 |
 |      non_manifold_edges : bool, default: True
 |          Extract non-manifold edges.
 |
 |      feature_edges : bool, default: True
 |          Extract edges exceeding ``feature_angle``.
 |
 |      manifold_edges : bool, default: True
 |          Extract manifold edges.
 |
 |      clear_data : bool, default: False
 |          Clear any point, cell, or field data. This is useful
 |          if wanting to strictly extract the edges.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Extracted edges.
 |
 |      Examples
 |      --------
 |      Extract the edges from an unstructured grid.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> hex_beam = pv.read(examples.hexbeamfile)
 |      >>> feat_edges = hex_beam.extract_feature_edges()
 |      >>> feat_edges.clear_data()  # clear array data for plotting
 |      >>> feat_edges.plot(line_width=10)
 |
 |      See the :ref:`extract_edges_example` for more examples using this filter.
 |
 |  extract_geometry(self, extent: 'Sequence[float] | None' = None, progress_bar=False)
 |      Extract the outer surface of a volume or structured grid dataset.
 |
 |      This will extract all 0D, 1D, and 2D cells producing the
 |      boundary faces of the dataset.
 |
 |      .. note::
 |          This tends to be less efficient than :func:`extract_surface`.
 |
 |      Parameters
 |      ----------
 |      extent : sequence[float], optional
 |          Specify a ``(xmin, xmax, ymin, ymax, zmin, zmax)`` bounding box to
 |          clip data.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Surface of the dataset.
 |
 |      Examples
 |      --------
 |      Extract the surface of a sample unstructured grid.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> hex_beam = pv.read(examples.hexbeamfile)
 |      >>> hex_beam.extract_geometry()
 |      PolyData (...)
 |        N Cells:    88
 |        N Points:   90
 |        N Strips:   0
 |        X Bounds:   0.000e+00, 1.000e+00
 |        Y Bounds:   0.000e+00, 1.000e+00
 |        Z Bounds:   0.000e+00, 5.000e+00
 |        N Arrays:   3
 |
 |      See :ref:`surface_smoothing_example` for more examples using this filter.
 |
 |  extract_largest(self, inplace=False, progress_bar=False)
 |      Extract largest connected set in mesh.
 |
 |      Can be used to reduce residues obtained when generating an
 |      isosurface.  Works only if residues are not connected (share
 |      at least one point with) the main component of the image.
 |
 |      Parameters
 |      ----------
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Largest connected set in the dataset.  Return type matches input.
 |
 |      Examples
 |      --------
 |      Join two meshes together, extract the largest, and plot it.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere() + pv.Cube()
 |      >>> largest = mesh.extract_largest()
 |      >>> largest.plot()
 |
 |      See :ref:`connectivity_example` and :ref:`volumetric_example` for
 |      more examples using this filter.
 |
 |      .. seealso::
 |          :func:`pyvista.DataSetFilters.connectivity`
 |
 |  extract_points(self, ind, adjacent_cells=True, include_cells=True, progress_bar=False)
 |      Return a subset of the grid (with cells) that contains any of the given point indices.
 |
 |      Parameters
 |      ----------
 |      ind : sequence[int]
 |          Sequence of point indices to be extracted.
 |
 |      adjacent_cells : bool, default: True
 |          If ``True``, extract the cells that contain at least one of
 |          the extracted points. If ``False``, extract the cells that
 |          contain exclusively points from the extracted points list.
 |          Has no effect if ``include_cells`` is ``False``.
 |
 |      include_cells : bool, default: True
 |          Specifies if the cells shall be returned or not.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      See Also
 |      --------
 |      extract_cells, extract_values
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Subselected grid.
 |
 |      Examples
 |      --------
 |      Extract all the points of a sphere with a Z coordinate greater than 0
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere()
 |      >>> extracted = sphere.extract_points(
 |      ...     sphere.points[:, 2] > 0, include_cells=False
 |      ... )
 |      >>> extracted.clear_data()  # clear for plotting
 |      >>> extracted.plot()
 |
 |  extract_surface(self, pass_pointid=True, pass_cellid=True, nonlinear_subdivision=1, progress_bar=False)
 |      Extract surface mesh of the grid.
 |
 |      Parameters
 |      ----------
 |      pass_pointid : bool, default: True
 |          Adds a point array ``"vtkOriginalPointIds"`` that
 |          identifies which original points these surface points
 |          correspond to.
 |
 |      pass_cellid : bool, default: True
 |          Adds a cell array ``"vtkOriginalCellIds"`` that
 |          identifies which original cells these surface cells
 |          correspond to.
 |
 |      nonlinear_subdivision : int, default: 1
 |          If the input is an unstructured grid with nonlinear faces,
 |          this parameter determines how many times the face is
 |          subdivided into linear faces.
 |
 |          If 0, the output is the equivalent of its linear
 |          counterpart (and the midpoints determining the nonlinear
 |          interpolation are discarded). If 1 (the default), the
 |          nonlinear face is triangulated based on the midpoints. If
 |          greater than 1, the triangulated pieces are recursively
 |          subdivided to reach the desired subdivision. Setting the
 |          value to greater than 1 may cause some point data to not
 |          be passed even if no nonlinear faces exist. This option
 |          has no effect if the input is not an unstructured grid.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Surface mesh of the grid.
 |
 |      Warnings
 |      --------
 |      Both ``"vtkOriginalPointIds"`` and ``"vtkOriginalCellIds"`` may be
 |      affected by other VTK operations. See `issue 1164
 |      <https://github.com/pyvista/pyvista/issues/1164>`_ for
 |      recommendations on tracking indices across operations.
 |
 |      Examples
 |      --------
 |      Extract the surface of an UnstructuredGrid.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> grid = examples.load_hexbeam()
 |      >>> surf = grid.extract_surface()
 |      >>> type(surf)
 |      <class 'pyvista.core.pointset.PolyData'>
 |      >>> surf["vtkOriginalPointIds"]
 |      pyvista_ndarray([ 0,  2, 36, 27,  7,  8, 81,  1, 18,  4, 54,  3,  6, 45,
 |                       72,  5, 63,  9, 35, 44, 11, 16, 89, 17, 10, 26, 62, 13,
 |                       12, 53, 80, 15, 14, 71, 19, 37, 55, 20, 38, 56, 21, 39,
 |                       57, 22, 40, 58, 23, 41, 59, 24, 42, 60, 25, 43, 61, 28,
 |                       82, 29, 83, 30, 84, 31, 85, 32, 86, 33, 87, 34, 88, 46,
 |                       73, 47, 74, 48, 75, 49, 76, 50, 77, 51, 78, 52, 79, 64,
 |                       65, 66, 67, 68, 69, 70])
 |      >>> surf["vtkOriginalCellIds"]
 |      pyvista_ndarray([ 0,  0,  0,  1,  1,  1,  3,  3,  3,  2,  2,  2, 36, 36,
 |                       36, 37, 37, 37, 39, 39, 39, 38, 38, 38,  5,  5,  9,  9,
 |                       13, 13, 17, 17, 21, 21, 25, 25, 29, 29, 33, 33,  4,  4,
 |                        8,  8, 12, 12, 16, 16, 20, 20, 24, 24, 28, 28, 32, 32,
 |                        7,  7, 11, 11, 15, 15, 19, 19, 23, 23, 27, 27, 31, 31,
 |                       35, 35,  6,  6, 10, 10, 14, 14, 18, 18, 22, 22, 26, 26,
 |                       30, 30, 34, 34])
 |
 |      Note that in the "vtkOriginalCellIds" array, the same original cells
 |      appears multiple times since this array represents the original cell of
 |      each surface cell extracted.
 |
 |      See the :ref:`extract_surface_example` for more examples using this filter.
 |
 |  extract_values(self, values: 'None | (float | VectorLike[float] | MatrixLike[float] | dict[str, float] | dict[float, str])' = None, *, ranges: 'None | (VectorLike[float] | MatrixLike[float] | dict[str, VectorLike[float]] | dict[tuple[float, float], str])' = None, scalars: 'str | None' = None, preference: "Literal['point', 'cell']" = 'point', component_mode: "Literal['any', 'all', 'multi'] | int" = 'all', invert: 'bool' = False, adjacent_cells: 'bool' = True, include_cells: 'bool | None' = None, split: 'bool' = False, pass_point_ids: 'bool' = True, pass_cell_ids: 'bool' = True, progress_bar: 'bool' = False)
 |      Return a subset of the mesh based on the value(s) of point or cell data.
 |
 |      Points and cells may be extracted with a single value, multiple values, a range
 |      of values, or any mix of values and ranges. This enables threshold-like
 |      filtering of data in a discontinuous manner to extract a single label or groups
 |      of labels from categorical data, or to extract multiple regions from continuous
 |      data. Extracted values may optionally be split into separate meshes.
 |
 |      This filter operates on point data and cell data distinctly:
 |
 |      **Point data**
 |
 |          All cells with at least one point with the specified value(s) are returned.
 |          Optionally, set ``adjacent_cells`` to ``False`` to only extract points from
 |          cells where all points in the cell strictly have the specified value(s).
 |          In these cases, a point is only included in the output if that point is part
 |          of an extracted cell.
 |
 |          Alternatively, set ``include_cells`` to ``False`` to exclude cells from
 |          the operation completely and extract all points with a specified value.
 |
 |      **Cell Data**
 |
 |          Only the cells (and their points) with the specified values(s) are included
 |          in the output.
 |
 |      Internally, :meth:`~pyvista.DataSetFilters.extract_points` is called to extract
 |      points for point data, and :meth:`~pyvista.DataSetFilters.extract_cells` is
 |      called to extract cells for cell data.
 |
 |      By default, two arrays are included with the output: ``'vtkOriginalPointIds'``
 |      and ``'vtkOriginalCellIds'``. These arrays can be used to link the filtered
 |      points or cells directly to the input.
 |
 |      .. versionadded:: 0.44
 |
 |      Parameters
 |      ----------
 |      values : number | array_like | dict, optional
 |          Value(s) to extract. Can be a number, an iterable of numbers, or a dictionary
 |          with numeric entries. For ``dict`` inputs, either its keys or values may be
 |          numeric, and the other field must be strings. The numeric field is used as
 |          the input for this parameter, and if ``split`` is ``True``, the string field
 |          is used to set the block names of the returned :class:`~pyvista.MultiBlock`.
 |
 |          .. note::
 |              When extracting multi-component values with ``component_mode=multi``,
 |              each value is specified as a multi-component scalar. In this case,
 |              ``values`` can be a single vector or an array of row vectors.
 |
 |      ranges : array_like | dict, optional
 |          Range(s) of values to extract. Can be a single range (i.e. a sequence of
 |          two numbers in the form ``[lower, upper]``), a sequence of ranges, or a
 |          dictionary with range entries. Any combination of ``values`` and ``ranges``
 |          may be specified together. The endpoints of the ranges are included in the
 |          extraction. Ranges cannot be set when ``component_mode=multi``.
 |
 |          For ``dict`` inputs, either its keys or values may be numeric, and the other
 |          field must be strings. The numeric field is used as the input for this
 |          parameter, and if ``split`` is ``True``, the string field is used to set the
 |          block names of the returned :class:`~pyvista.MultiBlock`.
 |
 |          .. note::
 |              Use ``+/-`` infinity to specify an unlimited bound, e.g.:
 |
 |              - ``[0, float('inf')]`` to extract values greater than or equal to zero.
 |              - ``[float('-inf'), 0]`` to extract values less than or equal to zero.
 |
 |      scalars : str, optional
 |          Name of scalars to extract with. Defaults to currently active scalars.
 |
 |      preference : str, default: 'point'
 |          When ``scalars`` is specified, this is the preferred array type to search
 |          for in the dataset.  Must be either ``'point'`` or ``'cell'``.
 |
 |      component_mode : int | 'any' | 'all' | 'multi', default: 'all'
 |          Specify the component(s) to use when ``scalars`` is a multi-component array.
 |          Has no effect when the scalars have a single component. Must be one of:
 |
 |          - number: specify the component number as a 0-indexed integer. The selected
 |            component must have the specified value(s).
 |          - ``'any'``: any single component can have the specified value(s).
 |          - ``'all'``: all individual components must have the specified values(s).
 |          - ``'multi'``: the entire multi-component item must have the specified value.
 |
 |      invert : bool, default: False
 |          Invert the extraction values. If ``True`` extract the points (with cells)
 |          which do *not* have the specified values.
 |
 |      adjacent_cells : bool, default: True
 |          If ``True``, include cells (and their points) that contain at least one of
 |          the extracted points. If ``False``, only include cells that contain
 |          exclusively points from the extracted points list. Has no effect if
 |          ``include_cells`` is ``False``. Has no effect when extracting values from
 |          cell data.
 |
 |      include_cells : bool, default: None
 |          Specify if cells shall be used for extraction or not. If ``False``, points
 |          with the specified values are extracted regardless of their cell
 |          connectivity, and all cells at the output will be vertex cells (one for each
 |          point.) Has no effect when extracting values from cell data.
 |
 |          By default, this value is ``True`` if the input has at least one cell and
 |          ``False`` otherwise.
 |
 |      split : bool, default: False
 |          If ``True``, each value in ``values`` and each range in ``range`` is
 |          extracted independently and returned as a :class:`~pyvista.MultiBlock`.
 |          The number of blocks returned equals the number of input values and ranges.
 |          The blocks may be named if a dictionary is used as input. See ``values``
 |          and ``ranges`` for details.
 |
 |          .. note::
 |              Output blocks may contain empty meshes if no values meet the extraction
 |              criteria. This can impact plotting since empty meshes cannot be plotted
 |              by default. Use :meth:`pyvista.MultiBlock.clean` on the output to remove
 |              empty meshes, or set ``pv.global_theme.allow_empty_mesh = True`` to
 |              enable plotting empty meshes.
 |
 |      pass_point_ids : bool, default: True
 |          Add a point array ``'vtkOriginalPointIds'`` that identifies the original
 |          points the extracted points correspond to.
 |
 |      pass_cell_ids : bool, default: True
 |          Add a cell array ``'vtkOriginalCellIds'`` that identifies the original cells
 |          the extracted cells correspond to.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      See Also
 |      --------
 |      split_values, extract_points, extract_cells, threshold, partition
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid or pyvista.MultiBlock
 |          An extracted mesh or a composite of extracted meshes, depending on ``split``.
 |
 |      Examples
 |      --------
 |      Extract a single value from a grid's point data.
 |
 |      >>> import numpy as np
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_uniform()
 |      >>> extracted = mesh.extract_values(0)
 |
 |      Plot extracted values. Since adjacent cells are included by default, points with
 |      values other than ``0`` are included in the output.
 |
 |      >>> extracted.get_data_range()
 |      (np.float64(0.0), np.float64(81.0))
 |      >>> extracted.plot()
 |
 |      Set ``include_cells=False`` to only extract points. The output scalars now
 |      strictly contain zeros.
 |
 |      >>> extracted = mesh.extract_values(0, include_cells=False)
 |      >>> extracted.get_data_range()
 |      (np.float64(0.0), np.float64(0.0))
 |      >>> extracted.plot(render_points_as_spheres=True, point_size=100)
 |
 |      Use ``ranges`` to extract values from a grid's point data in range.
 |
 |      Here, we use ``+/-`` infinity to extract all values of ``100`` or less.
 |
 |      >>> extracted = mesh.extract_values(ranges=[-np.inf, 100])
 |      >>> extracted.plot()
 |
 |      Extract every third cell value from cell data.
 |
 |      >>> mesh = examples.load_hexbeam()
 |      >>> lower, upper = mesh.get_data_range()
 |      >>> step = 3
 |      >>> extracted = mesh.extract_values(
 |      ...     range(lower, upper, step)  # values 0, 3, 6, ...
 |      ... )
 |
 |      Plot result and show an outline of the input for context.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(extracted)
 |      >>> _ = pl.add_mesh(mesh.extract_all_edges())
 |      >>> pl.show()
 |
 |      Any combination of values and ranges may be specified.
 |
 |      E.g. extract a single value and two ranges, and split the result into separate
 |      blocks of a MultiBlock.
 |
 |      >>> extracted = mesh.extract_values(
 |      ...     values=18, ranges=[[0, 8], [29, 40]], split=True
 |      ... )
 |      >>> extracted
 |      MultiBlock (...)
 |        N Blocks    3
 |        X Bounds    0.000, 1.000
 |        Y Bounds    0.000, 1.000
 |        Z Bounds    0.000, 5.000
 |      >>> extracted.plot(multi_colors=True)
 |
 |      Extract values from multi-component scalars.
 |
 |      First, create a point cloud with a 3-component RGB color array.
 |
 |      >>> rng = np.random.default_rng(seed=1)
 |      >>> points = rng.random((30, 3))
 |      >>> colors = rng.random((30, 3))
 |      >>> point_cloud = pv.PointSet(points)
 |      >>> point_cloud['colors'] = colors
 |      >>> plot_kwargs = dict(
 |      ...     render_points_as_spheres=True, point_size=50, rgb=True
 |      ... )
 |      >>> point_cloud.plot(**plot_kwargs)
 |
 |      Extract values from a single component.
 |
 |      E.g. extract points with a strong red component (i.e. > 0.8).
 |
 |      >>> extracted = point_cloud.extract_values(
 |      ...     ranges=[0.8, 1.0], component_mode=0
 |      ... )
 |      >>> extracted.plot(**plot_kwargs)
 |
 |      Extract values from all components.
 |
 |      E.g. extract points where all RGB components are dark (i.e. < 0.5).
 |
 |      >>> extracted = point_cloud.extract_values(
 |      ...     ranges=[0.0, 0.5], component_mode='all'
 |      ... )
 |      >>> extracted.plot(**plot_kwargs)
 |
 |      Extract specific multi-component values.
 |
 |      E.g. round the scalars to create binary RGB components, and extract only green
 |      and blue components.
 |
 |      >>> point_cloud['colors'] = np.round(point_cloud['colors'])
 |      >>> green = [0, 1, 0]
 |      >>> blue = [0, 0, 1]
 |      >>>
 |      >>> extracted = point_cloud.extract_values(
 |      ...     values=[blue, green],
 |      ...     component_mode='multi',
 |      ... )
 |      >>> extracted.plot(**plot_kwargs)
 |
 |      Use the original IDs returned by the extraction to modify the original point
 |      cloud.
 |
 |      For example, change the color of the blue and green points to yellow.
 |
 |      >>> point_ids = extracted['vtkOriginalPointIds']
 |      >>> yellow = [1, 1, 0]
 |      >>> point_cloud['colors'][point_ids] = yellow
 |      >>> point_cloud.plot(**plot_kwargs)
 |
 |  glyph(self, orient=True, scale=True, factor=1.0, geom=None, indices=None, tolerance=None, absolute=False, clamping=False, rng=None, color_mode='scale', progress_bar=False)
 |      Copy a geometric representation (called a glyph) to the input dataset.
 |
 |      The glyph may be oriented along the input vectors, and it may
 |      be scaled according to scalar data or vector
 |      magnitude. Passing a table of glyphs to choose from based on
 |      scalars or vector magnitudes is also supported.  The arrays
 |      used for ``orient`` and ``scale`` must be either both point data
 |      or both cell data.
 |
 |      Parameters
 |      ----------
 |      orient : bool | str, default: True
 |          If ``True``, use the active vectors array to orient the glyphs.
 |          If string, the vector array to use to orient the glyphs.
 |          If ``False``, the glyphs will not be orientated.
 |
 |      scale : bool | str | sequence[float], default: True
 |          If ``True``, use the active scalars to scale the glyphs.
 |          If string, the scalar array to use to scale the glyphs.
 |          If ``False``, the glyphs will not be scaled.
 |
 |      factor : float, default: 1.0
 |          Scale factor applied to scaling array.
 |
 |      geom : vtk.vtkDataSet or tuple(vtk.vtkDataSet), optional
 |          The geometry to use for the glyph. If missing, an arrow glyph
 |          is used. If a sequence, the datasets inside define a table of
 |          geometries to choose from based on scalars or vectors. In this
 |          case a sequence of numbers of the same length must be passed as
 |          ``indices``. The values of the range (see ``rng``) affect lookup
 |          in the table.
 |
 |      indices : sequence[float], optional
 |          Specifies the index of each glyph in the table for lookup in case
 |          ``geom`` is a sequence. If given, must be the same length as
 |          ``geom``. If missing, a default value of ``range(len(geom))`` is
 |          used. Indices are interpreted in terms of the scalar range
 |          (see ``rng``). Ignored if ``geom`` has length 1.
 |
 |      tolerance : float, optional
 |          Specify tolerance in terms of fraction of bounding box length.
 |          Float value is between 0 and 1. Default is None. If ``absolute``
 |          is ``True`` then the tolerance can be an absolute distance.
 |          If ``None``, points merging as a preprocessing step is disabled.
 |
 |      absolute : bool, default: False
 |          Control if ``tolerance`` is an absolute distance or a fraction.
 |
 |      clamping : bool, default: False
 |          Turn on/off clamping of "scalar" values to range.
 |
 |      rng : sequence[float], optional
 |          Set the range of values to be considered by the filter
 |          when scalars values are provided.
 |
 |      color_mode : str, optional, default: ``'scale'``
 |          If ``'scale'`` , color the glyphs by scale.
 |          If ``'scalar'`` , color the glyphs by scalar.
 |          If ``'vector'`` , color the glyphs by vector.
 |
 |          .. versionadded:: 0.44
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Glyphs at either the cell centers or points.
 |
 |      Examples
 |      --------
 |      Create arrow glyphs oriented by vectors and scaled by scalars.
 |      Factor parameter is used to reduce the size of the arrows.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_random_hills()
 |      >>> arrows = mesh.glyph(
 |      ...     scale="Normals", orient="Normals", tolerance=0.05
 |      ... )
 |      >>> pl = pv.Plotter()
 |      >>> actor = pl.add_mesh(arrows, color="black")
 |      >>> actor = pl.add_mesh(
 |      ...     mesh,
 |      ...     scalars="Elevation",
 |      ...     cmap="terrain",
 |      ...     show_scalar_bar=False,
 |      ... )
 |      >>> pl.show()
 |
 |      See :ref:`glyph_example` and :ref:`glyph_table_example` for more
 |      examples using this filter.
 |
 |  integrate_data(self, progress_bar=False)
 |      Integrate point and cell data.
 |
 |      Area or volume is also provided in point data.
 |
 |      This filter uses the VTK `vtkIntegrateAttributes
 |      <https://vtk.org/doc/nightly/html/classvtkIntegrateAttributes.html>`_
 |      and requires VTK v9.1.0 or newer.
 |
 |      Parameters
 |      ----------
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Mesh with 1 point and 1 vertex cell with integrated data in point
 |          and cell data.
 |
 |      Examples
 |      --------
 |      Integrate data on a sphere mesh.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> sphere = pv.Sphere(theta_resolution=100, phi_resolution=100)
 |      >>> sphere.point_data["data"] = 2 * np.ones(sphere.n_points)
 |      >>> integrated = sphere.integrate_data()
 |
 |      There is only 1 point and cell, so access the only value.
 |
 |      >>> integrated["Area"][0]
 |      np.float64(3.14)
 |      >>> integrated["data"][0]
 |      np.float64(6.28)
 |
 |      See the :ref:`integrate_example` for more examples using this filter.
 |
 |  interpolate(self, target, sharpness=2.0, radius=1.0, strategy='null_value', null_value=0.0, n_points=None, pass_cell_data=True, pass_point_data=True, progress_bar=False)
 |      Interpolate values onto this mesh from a given dataset.
 |
 |      The ``target`` dataset is typically a point cloud. Only point data from
 |      the ``target`` mesh will be interpolated onto points of this mesh. Whether
 |      preexisting point and cell data of this mesh are preserved in the
 |      output can be customized with the ``pass_point_data`` and
 |      ``pass_cell_data`` parameters.
 |
 |      This uses a Gaussian interpolation kernel. Use the ``sharpness`` and
 |      ``radius`` parameters to adjust this kernel. You can also switch this
 |      kernel to use an N closest points approach.
 |
 |      If the cell topology is more useful for interpolating, e.g. from a
 |      discretized FEM or CFD simulation, use
 |      :func:`pyvista.DataSetFilters.sample` instead.
 |
 |      Parameters
 |      ----------
 |      target : pyvista.DataSet
 |          The vtk data object to sample from. Point and cell arrays from
 |          this object are interpolated onto this mesh.
 |
 |      sharpness : float, default: 2.0
 |          Set the sharpness (i.e., falloff) of the Gaussian kernel. As the
 |          sharpness increases the effects of distant points are reduced.
 |
 |      radius : float, optional
 |          Specify the radius within which the basis points must lie.
 |
 |      strategy : str, default: "null_value"
 |          Specify a strategy to use when encountering a "null" point during
 |          the interpolation process. Null points occur when the local
 |          neighborhood (of nearby points to interpolate from) is empty. If
 |          the strategy is set to ``'mask_points'``, then an output array is
 |          created that marks points as being valid (=1) or null (invalid =0)
 |          (and the NullValue is set as well). If the strategy is set to
 |          ``'null_value'``, then the output data value(s) are set to the
 |          ``null_value`` (specified in the output point data). Finally, the
 |          strategy ``'closest_point'`` is to simply use the closest point to
 |          perform the interpolation.
 |
 |      null_value : float, default: 0.0
 |          Specify the null point value. When a null point is encountered
 |          then all components of each null tuple are set to this value.
 |
 |      n_points : int, optional
 |          If given, specifies the number of the closest points used to form
 |          the interpolation basis. This will invalidate the radius argument
 |          in favor of an N closest points approach. This typically has poorer
 |          results.
 |
 |      pass_cell_data : bool, default: True
 |          Preserve input mesh's original cell data arrays.
 |
 |      pass_point_data : bool, default: True
 |          Preserve input mesh's original point data arrays.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Interpolated dataset.  Return type matches input.
 |
 |      See Also
 |      --------
 |      pyvista.DataSetFilters.sample
 |
 |      Examples
 |      --------
 |      Interpolate the values of 5 points onto a sample plane.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> rng = np.random.default_rng(7)
 |      >>> point_cloud = rng.random((5, 3))
 |      >>> point_cloud[:, 2] = 0
 |      >>> point_cloud -= point_cloud.mean(0)
 |      >>> pdata = pv.PolyData(point_cloud)
 |      >>> pdata['values'] = rng.random(5)
 |      >>> plane = pv.Plane()
 |      >>> plane.clear_data()
 |      >>> plane = plane.interpolate(pdata, sharpness=3)
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(
 |      ...     pdata, render_points_as_spheres=True, point_size=50
 |      ... )
 |      >>> _ = pl.add_mesh(plane, style='wireframe', line_width=5)
 |      >>> pl.show()
 |
 |      See :ref:`interpolate_example` for more examples using this filter.
 |
 |  merge(self, grid=None, merge_points=True, tolerance=0.0, inplace=False, main_has_priority=True, progress_bar=False)
 |      Join one or many other grids to this grid.
 |
 |      Grid is updated in-place by default.
 |
 |      Can be used to merge points of adjacent cells when no grids
 |      are input.
 |
 |      .. note::
 |         The ``+`` operator between two meshes uses this filter with
 |         the default parameters. When the target mesh is already a
 |         :class:`pyvista.UnstructuredGrid`, in-place merging via
 |         ``+=`` is similarly possible.
 |
 |      Parameters
 |      ----------
 |      grid : vtk.UnstructuredGrid or list of vtk.UnstructuredGrids, optional
 |          Grids to merge to this grid.
 |
 |      merge_points : bool, default: True
 |          Points in exactly the same location will be merged between
 |          the two meshes. Warning: this can leave degenerate point data.
 |
 |      tolerance : float, default: 0.0
 |          The absolute tolerance to use to find coincident points when
 |          ``merge_points=True``.
 |
 |      inplace : bool, default: False
 |          Updates grid inplace when True if the input type is an
 |          :class:`pyvista.UnstructuredGrid`.
 |
 |      main_has_priority : bool, default: True
 |          When this parameter is true and merge_points is true,
 |          the arrays of the merging grids will be overwritten
 |          by the original main mesh.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Merged grid.
 |
 |      Notes
 |      -----
 |      When two or more grids are joined, the type and name of each
 |      array must match or the arrays will be ignored and not
 |      included in the final merged mesh.
 |
 |      Examples
 |      --------
 |      Merge three separate spheres into a single mesh.
 |
 |      >>> import pyvista as pv
 |      >>> sphere_a = pv.Sphere(center=(1, 0, 0))
 |      >>> sphere_b = pv.Sphere(center=(0, 1, 0))
 |      >>> sphere_c = pv.Sphere(center=(0, 0, 1))
 |      >>> merged = sphere_a.merge([sphere_b, sphere_c])
 |      >>> merged.plot()
 |
 |  outline(self, generate_faces=False, progress_bar=False)
 |      Produce an outline of the full extent for the input dataset.
 |
 |      Parameters
 |      ----------
 |      generate_faces : bool, default: False
 |          Generate solid faces for the box. This is disabled by default.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Mesh containing an outline of the original dataset.
 |
 |      Examples
 |      --------
 |      Generate and plot the outline of a sphere.  This is
 |      effectively the ``(x, y, z)`` bounds of the mesh.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere()
 |      >>> outline = sphere.outline()
 |      >>> pv.plot([sphere, outline], line_width=5)
 |
 |      See :ref:`common_filter_example` for more examples using this filter.
 |
 |  outline_corners(self, factor=0.2, progress_bar=False)
 |      Produce an outline of the corners for the input dataset.
 |
 |      Parameters
 |      ----------
 |      factor : float, default: 0.2
 |          Controls the relative size of the corners to the length of
 |          the corresponding bounds.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Mesh containing outlined corners.
 |
 |      Examples
 |      --------
 |      Generate and plot the corners of a sphere.  This is
 |      effectively the ``(x, y, z)`` bounds of the mesh.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere()
 |      >>> corners = sphere.outline_corners(factor=0.1)
 |      >>> pv.plot([sphere, corners], line_width=5)
 |
 |  pack_labels(self, sort=False, scalars=None, preference='point', output_scalars=None, progress_bar=False, inplace=False)
 |      Renumber labeled data such that labels are contiguous.
 |
 |      This filter renumbers scalar label data of any type with ``N`` labels
 |      such that the output labels are contiguous from ``[0, N)``. The
 |      output may optionally be sorted by label count.
 |
 |      The output array ``'packed_labels'`` is added to the output by default,
 |      and is automatically set as the active scalars.
 |
 |      See Also
 |      --------
 |      sort_labels
 |          Similar function with ``sort=True`` by default.
 |
 |      Notes
 |      -----
 |      This filter uses ``vtkPackLabels`` as the underlying method which
 |      requires VTK version 9.3 or higher. If ``vtkPackLabels`` is not
 |      available, packing is done with ``NumPy`` instead which may be
 |      slower. For best performance, consider upgrading VTK.
 |
 |      .. versionadded:: 0.43
 |
 |      Parameters
 |      ----------
 |      sort : bool, default: False
 |          Whether to sort the output by label count in descending order
 |          (i.e. from largest to smallest).
 |
 |      scalars : str, optional
 |          Name of scalars to pack. Defaults to currently active scalars.
 |
 |      preference : str, default: "point"
 |          When ``scalars`` is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'`` or ``'cell'``.
 |
 |      output_scalars : str, None
 |          Name of the packed output scalars. By default, the output is
 |          saved to ``'packed_labels'``.
 |
 |      progress_bar : bool, default: False
 |          If ``True``, display a progress bar. Has no effect if VTK
 |          version is lower than 9.3.
 |
 |      inplace : bool, default: False
 |          If ``True``, the mesh is updated in-place.
 |
 |      Returns
 |      -------
 |      pyvista.Dataset
 |          Dataset with packed labels.
 |
 |      Examples
 |      --------
 |      Pack segmented image labels.
 |
 |      Load non-contiguous image labels
 |
 |      >>> from pyvista import examples
 |      >>> import numpy as np
 |      >>> image_labels = examples.load_frog_tissues()
 |
 |      Show range of labels
 |
 |      >>> image_labels.get_data_range()
 |      (np.uint8(0), np.uint8(29))
 |
 |      Find 'gaps' in the labels
 |
 |      >>> label_numbers = np.unique(image_labels.active_scalars)
 |      >>> label_max = np.max(label_numbers)
 |      >>> missing_labels = set(range(label_max)) - set(label_numbers)
 |      >>> len(missing_labels)
 |      4
 |
 |      Pack labels to remove gaps
 |
 |      >>> packed_labels = image_labels.pack_labels()
 |
 |      Show range of packed labels
 |
 |      >>> packed_labels.get_data_range()
 |      (np.uint8(0), np.uint8(25))
 |
 |  partition(self, n_partitions, generate_global_id=False, as_composite=True)
 |      Break down input dataset into a requested number of partitions.
 |
 |      Cells on boundaries are uniquely assigned to each partition without duplication.
 |
 |      It uses a kdtree implementation that builds balances the cell
 |      centers among a requested number of partitions. The current implementation
 |      only supports power-of-2 target partition. If a non-power of two value
 |      is specified for ``n_partitions``, then the load balancing simply
 |      uses the power-of-two greater than the requested value
 |
 |      For more details, see `vtkRedistributeDataSetFilter
 |      <https://vtk.org/doc/nightly/html/classvtkRedistributeDataSetFilter.html>`_.
 |
 |      Parameters
 |      ----------
 |      n_partitions : int
 |          Specify the number of partitions to split the input dataset
 |          into. Current implementation results in a number of partitions equal
 |          to the power of 2 greater than or equal to the chosen value.
 |
 |      generate_global_id : bool, default: False
 |          Generate global cell ids if ``None`` are present in the input.  If
 |          global cell ids are present in the input then this flag is
 |          ignored.
 |
 |          This is stored as ``"vtkGlobalCellIds"`` within the ``cell_data``
 |          of the output dataset(s).
 |
 |      as_composite : bool, default: False
 |          Return the partitioned dataset as a :class:`pyvista.MultiBlock`.
 |
 |      See Also
 |      --------
 |      split_bodies, extract_values
 |
 |      Returns
 |      -------
 |      pyvista.MultiBlock or pyvista.UnstructuredGrid
 |          UnStructuredGrid if ``as_composite=False`` and MultiBlock when ``True``.
 |
 |      Examples
 |      --------
 |      Partition a simple ImageData into a :class:`pyvista.MultiBlock`
 |      containing each partition.
 |
 |      >>> import pyvista as pv
 |      >>> grid = pv.ImageData(dimensions=(5, 5, 5))
 |      >>> out = grid.partition(4, as_composite=True)
 |      >>> out.plot(multi_colors=True, show_edges=True)
 |
 |      Partition of the Stanford bunny.
 |
 |      >>> from pyvista import examples
 |      >>> mesh = examples.download_bunny()
 |      >>> out = mesh.partition(4, as_composite=True)
 |      >>> out.plot(multi_colors=True, cpos='xy')
 |
 |  plot_over_circular_arc(self, pointa, pointb, center, resolution=None, scalars=None, title=None, ylabel=None, figsize=None, figure=True, show=True, tolerance=None, fname=None, progress_bar=False)
 |      Sample a dataset along a circular arc and plot it.
 |
 |      Plot the variables of interest in 2D where the X-axis is
 |      distance from Point A and the Y-axis is the variable of
 |      interest. Note that this filter returns ``None``.
 |
 |      Parameters
 |      ----------
 |      pointa : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      pointb : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      center : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      resolution : int, optional
 |          Number of pieces to divide the circular arc into. Defaults
 |          to number of cells in the input mesh. Must be a positive
 |          integer.
 |
 |      scalars : str, optional
 |          The string name of the variable in the input dataset to
 |          probe. The active scalar is used by default.
 |
 |      title : str, optional
 |          The string title of the ``matplotlib`` figure.
 |
 |      ylabel : str, optional
 |          The string label of the Y-axis. Defaults to the variable name.
 |
 |      figsize : tuple(int), optional
 |          The size of the new figure.
 |
 |      figure : bool, default: True
 |          Flag on whether or not to create a new figure.
 |
 |      show : bool, default: True
 |          Shows the ``matplotlib`` figure when ``True``.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is
 |          in a cell of the input.  If not given, tolerance is
 |          automatically generated.
 |
 |      fname : str, optional
 |          Save the figure this file name when set.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Examples
 |      --------
 |      Sample a dataset along a high resolution circular arc and plot.
 |
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_uniform()
 |      >>> a = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[5]]
 |      >>> b = [mesh.bounds[1], mesh.bounds[2], mesh.bounds[4]]
 |      >>> center = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[4]]
 |      >>> mesh.plot_over_circular_arc(
 |      ...     a, b, center, resolution=1000, show=False
 |      ... )  # doctest:+SKIP
 |
 |  plot_over_circular_arc_normal(self, center, resolution=None, normal=None, polar=None, angle=None, scalars=None, title=None, ylabel=None, figsize=None, figure=True, show=True, tolerance=None, fname=None, progress_bar=False)
 |      Sample a dataset along a resolution circular arc defined by a normal and polar vector and plot it.
 |
 |      Plot the variables of interest in 2D where the X-axis is
 |      distance from Point A and the Y-axis is the variable of
 |      interest. Note that this filter returns ``None``.
 |
 |      Parameters
 |      ----------
 |      center : sequence[int]
 |          Location in ``[x, y, z]``.
 |
 |      resolution : int, optional
 |          Number of pieces to divide circular arc into. Defaults to
 |          number of cells in the input mesh. Must be a positive
 |          integer.
 |
 |      normal : sequence[float], optional
 |          The normal vector to the plane of the arc.  By default it
 |          points in the positive Z direction.
 |
 |      polar : sequence[float], optional
 |          Starting point of the arc in polar coordinates.  By
 |          default it is the unit vector in the positive x direction.
 |
 |      angle : float, optional
 |          Arc length (in degrees), beginning at the polar vector.  The
 |          direction is counterclockwise.  By default it is 360.
 |
 |      scalars : str, optional
 |          The string name of the variable in the input dataset to
 |          probe. The active scalar is used by default.
 |
 |      title : str, optional
 |          The string title of the `matplotlib` figure.
 |
 |      ylabel : str, optional
 |          The string label of the Y-axis. Defaults to variable name.
 |
 |      figsize : tuple(int), optional
 |          The size of the new figure.
 |
 |      figure : bool, optional
 |          Flag on whether or not to create a new figure.
 |
 |      show : bool, default: True
 |          Shows the matplotlib figure.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is
 |          in a cell of the input.  If not given, tolerance is
 |          automatically generated.
 |
 |      fname : str, optional
 |          Save the figure this file name when set.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Examples
 |      --------
 |      Sample a dataset along a high resolution circular arc and plot.
 |
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_uniform()
 |      >>> normal = normal = [0, 0, 1]
 |      >>> polar = [0, 9, 0]
 |      >>> angle = 90
 |      >>> center = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[4]]
 |      >>> mesh.plot_over_circular_arc_normal(
 |      ...     center, polar=polar, angle=angle
 |      ... )  # doctest:+SKIP
 |
 |  plot_over_line(self, pointa, pointb, resolution=None, scalars=None, title=None, ylabel=None, figsize=None, figure=True, show=True, tolerance=None, fname=None, progress_bar=False)
 |      Sample a dataset along a high resolution line and plot.
 |
 |      Plot the variables of interest in 2D using matplotlib where the
 |      X-axis is distance from Point A and the Y-axis is the variable
 |      of interest. Note that this filter returns ``None``.
 |
 |      Parameters
 |      ----------
 |      pointa : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      pointb : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      resolution : int, optional
 |          Number of pieces to divide line into. Defaults to number of cells
 |          in the input mesh. Must be a positive integer.
 |
 |      scalars : str, optional
 |          The string name of the variable in the input dataset to probe. The
 |          active scalar is used by default.
 |
 |      title : str, optional
 |          The string title of the matplotlib figure.
 |
 |      ylabel : str, optional
 |          The string label of the Y-axis. Defaults to variable name.
 |
 |      figsize : tuple(int), optional
 |          The size of the new figure.
 |
 |      figure : bool, default: True
 |          Flag on whether or not to create a new figure.
 |
 |      show : bool, default: True
 |          Shows the matplotlib figure.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is in a
 |          cell of the input.  If not given, tolerance is automatically generated.
 |
 |      fname : str, optional
 |          Save the figure this file name when set.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Examples
 |      --------
 |      See the :ref:`plot_over_line_example` example.
 |
 |  point_data_to_cell_data(self, pass_point_data=False, progress_bar=False)
 |      Transform point data into cell data.
 |
 |      Point data are specified per node and cell data specified within cells.
 |      Optionally, the input point data can be passed through to the output.
 |
 |      Parameters
 |      ----------
 |      pass_point_data : bool, default: False
 |          If enabled, pass the input point data through to the output.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with the point data transformed into cell data.
 |          Return type matches input.
 |
 |      See Also
 |      --------
 |      cell_data_to_point_data
 |          Similar transformation applied to cell data.
 |      :meth:`~pyvista.ImageDataFilters.points_to_cells`
 |          Re-mesh :class:`~pyvista.ImageData` to a cells-based representation.
 |
 |      Examples
 |      --------
 |      Color cells by their z coordinates.  First, create point
 |      scalars based on z-coordinates of a sample sphere mesh.  Then
 |      convert this point data to cell data.  Use a low resolution
 |      sphere for emphasis of cell valued data.
 |
 |      First, plot these values as point values to show the
 |      difference between point and cell data.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere(theta_resolution=10, phi_resolution=10)
 |      >>> sphere['Z Coordinates'] = sphere.points[:, 2]
 |      >>> sphere.plot()
 |
 |      Now, convert these values to cell data and then plot it.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere(theta_resolution=10, phi_resolution=10)
 |      >>> sphere['Z Coordinates'] = sphere.points[:, 2]
 |      >>> sphere = sphere.point_data_to_cell_data()
 |      >>> sphere.plot()
 |
 |  ptc(self, pass_point_data=False, progress_bar=False, **kwargs)
 |      Transform point data into cell data.
 |
 |      Point data are specified per node and cell data specified
 |      within cells.  Optionally, the input point data can be passed
 |      through to the output.
 |
 |      This method is an alias for
 |      :func:`pyvista.DataSetFilters.point_data_to_cell_data`.
 |
 |      Parameters
 |      ----------
 |      pass_point_data : bool, default: False
 |          If enabled, pass the input point data through to the output.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      **kwargs : dict, optional
 |          Deprecated keyword argument ``pass_point_arrays``.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with the point data transformed into cell data.
 |          Return type matches input.
 |
 |  reflect(self, normal, point=None, inplace=False, transform_all_input_vectors=False, progress_bar=False)
 |      Reflect a dataset across a plane.
 |
 |      Parameters
 |      ----------
 |      normal : array_like[float]
 |          Normal direction for reflection.
 |
 |      point : array_like[float]
 |          Point which, along with ``normal``, defines the reflection
 |          plane. If not specified, this is the origin.
 |
 |      inplace : bool, default: False
 |          When ``True``, modifies the dataset inplace.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all input vectors are transformed. Otherwise,
 |          only the points, normals and active vectors are transformed.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Reflected dataset.  Return type matches input.
 |
 |      Examples
 |      --------
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_airplane()
 |      >>> mesh = mesh.reflect((0, 0, 1), point=(0, 0, -100))
 |      >>> mesh.plot(show_edges=True)
 |
 |      See the :ref:`reflect_example` for more examples using this filter.
 |
 |  sample(self, target, tolerance=None, pass_cell_data=True, pass_point_data=True, categorical=False, progress_bar=False, locator=None, pass_field_data=True, mark_blank=True, snap_to_closest_point=False)
 |      Resample array data from a passed mesh onto this mesh.
 |
 |      For `mesh1.sample(mesh2)`, the arrays from `mesh2` are sampled onto
 |      the points of `mesh1`.  This function interpolates within an
 |      enclosing cell.  This contrasts with
 |      :function`pyvista.DataSetFilters.interpolate` that uses a distance
 |      weighting for nearby points.  If there is cell topology, `sample` is
 |      usually preferred.
 |
 |      The point data 'vtkValidPointMask' stores whether the point could be sampled
 |      with a value of 1 meaning successful sampling. And a value of 0 means
 |      unsuccessful.
 |
 |      This uses :class:`vtk.vtkResampleWithDataSet`.
 |
 |      Parameters
 |      ----------
 |      target : pyvista.DataSet
 |          The vtk data object to sample from - point and cell arrays from
 |          this object are sampled onto the nodes of the ``dataset`` mesh.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is
 |          in a cell of the input.  If not given, tolerance is
 |          automatically generated.
 |
 |      pass_cell_data : bool, default: True
 |          Preserve source mesh's original cell data arrays.
 |
 |      pass_point_data : bool, default: True
 |          Preserve source mesh's original point data arrays.
 |
 |      categorical : bool, default: False
 |          Control whether the source point data is to be treated as
 |          categorical. If the data is categorical, then the resultant data
 |          will be determined by a nearest neighbor interpolation scheme.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      locator : vtkAbstractCellLocator or str, optional
 |          Prototype cell locator to perform the ``FindCell()``
 |          operation.  Default uses the DataSet ``FindCell`` method.
 |          Valid strings with mapping to vtk cell locators are
 |
 |              * 'cell' - vtkCellLocator
 |              * 'cell_tree' - vtkCellTreeLocator
 |              * 'obb_tree' - vtkOBBTree
 |              * 'static_cell' - vtkStaticCellLocator
 |
 |      pass_field_data : bool, default: True
 |          Preserve source mesh's original field data arrays.
 |
 |      mark_blank : bool, default: True
 |          Whether to mark blank points and cells in "vtkGhostType".
 |
 |      snap_to_closest_point : bool, default: False
 |          Whether to snap to cell with closest point if no cell is found. Useful
 |          when sampling from data with vertex cells. Requires vtk >=9.3.0.
 |
 |          .. versionadded:: 0.43
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset containing resampled data.
 |
 |      See Also
 |      --------
 |      pyvista.DataSetFilters.interpolate
 |
 |      Examples
 |      --------
 |      Resample data from another dataset onto a sphere.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> mesh = pv.Sphere(center=(4.5, 4.5, 4.5), radius=4.5)
 |      >>> data_to_probe = examples.load_uniform()
 |      >>> result = mesh.sample(data_to_probe)
 |      >>> result.plot(scalars="Spatial Point Data")
 |
 |      If sampling from a set of points represented by a ``(n, 3)``
 |      shaped ``numpy.ndarray``, they need to be converted to a
 |      PyVista DataSet, e.g. :class:`pyvista.PolyData`, first.
 |
 |      >>> import numpy as np
 |      >>> points = np.array([[1.5, 5.0, 6.2], [6.7, 4.2, 8.0]])
 |      >>> mesh = pv.PolyData(points)
 |      >>> result = mesh.sample(data_to_probe)
 |      >>> result["Spatial Point Data"]
 |      pyvista_ndarray([ 46.5 , 225.12])
 |
 |      See :ref:`resampling_example` for more examples using this filter.
 |
 |  sample_over_circular_arc(self, pointa, pointb, center, resolution=None, tolerance=None, progress_bar=False)
 |      Sample a dataset over a circular arc.
 |
 |      Parameters
 |      ----------
 |      pointa : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      pointb : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      center : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      resolution : int, optional
 |          Number of pieces to divide circular arc into. Defaults to
 |          number of cells in the input mesh. Must be a positive
 |          integer.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is
 |          in a cell of the input.  If not given, tolerance is
 |          automatically generated.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Arc containing the sampled data.
 |
 |      Examples
 |      --------
 |      Sample a dataset over a circular arc and plot it.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> uniform = examples.load_uniform()
 |      >>> uniform["height"] = uniform.points[:, 2]
 |      >>> pointa = [
 |      ...     uniform.bounds[1],
 |      ...     uniform.bounds[2],
 |      ...     uniform.bounds[5],
 |      ... ]
 |      >>> pointb = [
 |      ...     uniform.bounds[1],
 |      ...     uniform.bounds[3],
 |      ...     uniform.bounds[4],
 |      ... ]
 |      >>> center = [
 |      ...     uniform.bounds[1],
 |      ...     uniform.bounds[2],
 |      ...     uniform.bounds[4],
 |      ... ]
 |      >>> sampled_arc = uniform.sample_over_circular_arc(
 |      ...     pointa, pointb, center
 |      ... )
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(uniform, style='wireframe')
 |      >>> _ = pl.add_mesh(sampled_arc, line_width=10)
 |      >>> pl.show_axes()
 |      >>> pl.show()
 |
 |  sample_over_circular_arc_normal(self, center, resolution=None, normal=None, polar=None, angle=None, tolerance=None, progress_bar=False)
 |      Sample a dataset over a circular arc defined by a normal and polar vector and plot it.
 |
 |      The number of segments composing the polyline is controlled by
 |      setting the object resolution.
 |
 |      Parameters
 |      ----------
 |      center : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      resolution : int, optional
 |          Number of pieces to divide circular arc into. Defaults to
 |          number of cells in the input mesh. Must be a positive
 |          integer.
 |
 |      normal : sequence[float], optional
 |          The normal vector to the plane of the arc.  By default it
 |          points in the positive Z direction.
 |
 |      polar : sequence[float], optional
 |          Starting point of the arc in polar coordinates.  By
 |          default it is the unit vector in the positive x direction.
 |
 |      angle : float, optional
 |          Arc length (in degrees), beginning at the polar vector.  The
 |          direction is counterclockwise.  By default it is 360.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is
 |          in a cell of the input.  If not given, tolerance is
 |          automatically generated.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Sampled Dataset.
 |
 |      Examples
 |      --------
 |      Sample a dataset over a circular arc.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> uniform = examples.load_uniform()
 |      >>> uniform["height"] = uniform.points[:, 2]
 |      >>> normal = [0, 0, 1]
 |      >>> polar = [0, 9, 0]
 |      >>> center = [
 |      ...     uniform.bounds[1],
 |      ...     uniform.bounds[2],
 |      ...     uniform.bounds[5],
 |      ... ]
 |      >>> arc = uniform.sample_over_circular_arc_normal(
 |      ...     center, normal=normal, polar=polar
 |      ... )
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(uniform, style='wireframe')
 |      >>> _ = pl.add_mesh(arc, line_width=10)
 |      >>> pl.show_axes()
 |      >>> pl.show()
 |
 |  sample_over_line(self, pointa, pointb, resolution=None, tolerance=None, progress_bar=False)
 |      Sample a dataset onto a line.
 |
 |      Parameters
 |      ----------
 |      pointa : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      pointb : sequence[float]
 |          Location in ``[x, y, z]``.
 |
 |      resolution : int, optional
 |          Number of pieces to divide line into. Defaults to number of cells
 |          in the input mesh. Must be a positive integer.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is in a
 |          cell of the input.  If not given, tolerance is automatically generated.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Line object with sampled data from dataset.
 |
 |      Examples
 |      --------
 |      Sample over a plane that is interpolating a point cloud.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> rng = np.random.default_rng(12)
 |      >>> point_cloud = rng.random((5, 3))
 |      >>> point_cloud[:, 2] = 0
 |      >>> point_cloud -= point_cloud.mean(0)
 |      >>> pdata = pv.PolyData(point_cloud)
 |      >>> pdata['values'] = rng.random(5)
 |      >>> plane = pv.Plane()
 |      >>> plane.clear_data()
 |      >>> plane = plane.interpolate(pdata, sharpness=3.5)
 |      >>> sample = plane.sample_over_line((-0.5, -0.5, 0), (0.5, 0.5, 0))
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(
 |      ...     pdata, render_points_as_spheres=True, point_size=50
 |      ... )
 |      >>> _ = pl.add_mesh(sample, scalars='values', line_width=10)
 |      >>> _ = pl.add_mesh(plane, scalars='values', style='wireframe')
 |      >>> pl.show()
 |
 |  sample_over_multiple_lines(self, points, tolerance=None, progress_bar=False)
 |      Sample a dataset onto a multiple lines.
 |
 |      Parameters
 |      ----------
 |      points : array_like[float]
 |          List of points defining multiple lines.
 |
 |      tolerance : float, optional
 |          Tolerance used to compute whether a point in the source is in a
 |          cell of the input.  If not given, tolerance is automatically generated.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Line object with sampled data from dataset.
 |
 |      Examples
 |      --------
 |      Sample over a plane that is interpolating a point cloud.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> rng = np.random.default_rng(12)
 |      >>> point_cloud = rng.random((5, 3))
 |      >>> point_cloud[:, 2] = 0
 |      >>> point_cloud -= point_cloud.mean(0)
 |      >>> pdata = pv.PolyData(point_cloud)
 |      >>> pdata['values'] = rng.random(5)
 |      >>> plane = pv.Plane()
 |      >>> plane.clear_data()
 |      >>> plane = plane.interpolate(pdata, sharpness=3.5)
 |      >>> sample = plane.sample_over_multiple_lines(
 |      ...     [[-0.5, -0.5, 0], [0.5, -0.5, 0], [0.5, 0.5, 0]]
 |      ... )
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(
 |      ...     pdata, render_points_as_spheres=True, point_size=50
 |      ... )
 |      >>> _ = pl.add_mesh(sample, scalars='values', line_width=10)
 |      >>> _ = pl.add_mesh(plane, scalars='values', style='wireframe')
 |      >>> pl.show()
 |
 |  select_enclosed_points(self, surface, tolerance=0.001, inside_out=False, check_surface=True, progress_bar=False)
 |      Mark points as to whether they are inside a closed surface.
 |
 |      This evaluates all the input points to determine whether they are in an
 |      enclosed surface. The filter produces a (0,1) mask
 |      (in the form of a vtkDataArray) that indicates whether points are
 |      outside (mask value=0) or inside (mask value=1) a provided surface.
 |      (The name of the output vtkDataArray is ``"SelectedPoints"``.)
 |
 |      This filter produces and output data array, but does not modify the
 |      input dataset. If you wish to extract cells or poinrs, various
 |      threshold filters are available (i.e., threshold the output array).
 |
 |      .. warning::
 |         The filter assumes that the surface is closed and
 |         manifold. A boolean flag can be set to force the filter to
 |         first check whether this is true. If ``False`` and not manifold,
 |         an error will be raised.
 |
 |      Parameters
 |      ----------
 |      surface : pyvista.PolyData
 |          Set the surface to be used to test for containment. This must be a
 |          :class:`pyvista.PolyData` object.
 |
 |      tolerance : float, default: 0.001
 |          The tolerance on the intersection. The tolerance is expressed as a
 |          fraction of the bounding box of the enclosing surface.
 |
 |      inside_out : bool, default: False
 |          By default, points inside the surface are marked inside or sent
 |          to the output. If ``inside_out`` is ``True``, then the points
 |          outside the surface are marked inside.
 |
 |      check_surface : bool, default: True
 |          Specify whether to check the surface for closure. When ``True``, the
 |          algorithm first checks to see if the surface is closed and
 |          manifold. If the surface is not closed and manifold, a runtime
 |          error is raised.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Mesh containing the ``point_data['SelectedPoints']`` array.
 |
 |      Examples
 |      --------
 |      Determine which points on a plane are inside a manifold sphere
 |      surface mesh.  Extract these points using the
 |      :func:`DataSetFilters.extract_points` filter and then plot them.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere()
 |      >>> plane = pv.Plane()
 |      >>> selected = plane.select_enclosed_points(sphere)
 |      >>> pts = plane.extract_points(
 |      ...     selected['SelectedPoints'].view(bool),
 |      ...     adjacent_cells=False,
 |      ... )
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(sphere, style='wireframe')
 |      >>> _ = pl.add_points(pts, color='r')
 |      >>> pl.show()
 |
 |  separate_cells(self)
 |      Return a copy of the dataset with separated cells with no shared points.
 |
 |      This method may be useful when datasets have scalars that need to be
 |      associated to each point of each cell rather than either each cell or
 |      just the points of the dataset.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          UnstructuredGrid with isolated cells.
 |
 |      Examples
 |      --------
 |      Load the example hex beam and separate its cells. This increases the
 |      total number of points in the dataset since points are no longer
 |      shared.
 |
 |      >>> from pyvista import examples
 |      >>> grid = examples.load_hexbeam()
 |      >>> grid.n_points
 |      99
 |      >>> sep_grid = grid.separate_cells()
 |      >>> sep_grid.n_points
 |      320
 |
 |      See the :ref:`point_cell_scalars_example` for a more detailed example
 |      using this filter.
 |
 |  shrink(self, shrink_factor=1.0, progress_bar=False)
 |      Shrink the individual faces of a mesh.
 |
 |      This filter shrinks the individual faces of a mesh rather than
 |      scaling the entire mesh.
 |
 |      Parameters
 |      ----------
 |      shrink_factor : float, default: 1.0
 |          Fraction of shrink for each cell. Default does not modify the
 |          faces.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with shrunk faces.  Return type matches input.
 |
 |      Examples
 |      --------
 |      First, plot the original cube.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Cube()
 |      >>> mesh.plot(show_edges=True, line_width=5)
 |
 |      Now, plot the mesh with shrunk faces.
 |
 |      >>> shrunk = mesh.shrink(0.5)
 |      >>> shrunk.clear_data()  # cleans up plot
 |      >>> shrunk.plot(show_edges=True, line_width=5)
 |
 |  slice(self, normal='x', origin=None, generate_triangles=False, contour=False, progress_bar=False)
 |      Slice a dataset by a plane at the specified origin and normal vector orientation.
 |
 |      If no origin is specified, the center of the input dataset will be used.
 |
 |      Parameters
 |      ----------
 |      normal : sequence[float] | str, default: 'x'
 |          Length 3 tuple for the normal vector direction. Can also be
 |          specified as a string conventional direction such as ``'x'`` for
 |          ``(1, 0, 0)`` or ``'-x'`` for ``(-1, 0, 0)``, etc.
 |
 |      origin : sequence[float], optional
 |          The center ``(x, y, z)`` coordinate of the plane on which
 |          the slice occurs.
 |
 |      generate_triangles : bool, default: False
 |          If this is enabled (``False`` by default), the output will
 |          be triangles. Otherwise the output will be the intersection
 |          polygons.
 |
 |      contour : bool, default: False
 |          If ``True``, apply a ``contour`` filter after slicing.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Sliced dataset.
 |
 |      Examples
 |      --------
 |      Slice the surface of a sphere.
 |
 |      >>> import pyvista as pv
 |      >>> sphere = pv.Sphere()
 |      >>> slice_x = sphere.slice(normal='x')
 |      >>> slice_y = sphere.slice(normal='y')
 |      >>> slice_z = sphere.slice(normal='z')
 |      >>> slices = slice_x + slice_y + slice_z
 |      >>> slices.plot(line_width=5)
 |
 |      See :ref:`slice_example` for more examples using this filter.
 |
 |  slice_along_axis(self, n=5, axis='x', tolerance=None, generate_triangles=False, contour=False, bounds=None, center=None, progress_bar=False)
 |      Create many slices of the input dataset along a specified axis.
 |
 |      Parameters
 |      ----------
 |      n : int, default: 5
 |          The number of slices to create.
 |
 |      axis : str | int, default: 'x'
 |          The axis to generate the slices along. Perpendicular to the
 |          slices. Can be string name (``'x'``, ``'y'``, or ``'z'``) or
 |          axis index (``0``, ``1``, or ``2``).
 |
 |      tolerance : float, optional
 |          The tolerance to the edge of the dataset bounds to create
 |          the slices. The ``n`` slices are placed equidistantly with
 |          an absolute padding of ``tolerance`` inside each side of the
 |          ``bounds`` along the specified axis. Defaults to 1% of the
 |          ``bounds`` along the specified axis.
 |
 |      generate_triangles : bool, default: False
 |          When ``True``, the output will be triangles. Otherwise the output
 |          will be the intersection polygons.
 |
 |      contour : bool, default: False
 |          If ``True``, apply a ``contour`` filter after slicing.
 |
 |      bounds : sequence[float], optional
 |          A 6-length sequence overriding the bounds of the mesh.
 |          The bounds along the specified axis define the extent
 |          where slices are taken.
 |
 |      center : sequence[float], optional
 |          A 3-length sequence specifying the position of the line
 |          along which slices are taken. Defaults to the center of
 |          the mesh.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Sliced dataset.
 |
 |      Examples
 |      --------
 |      Slice the random hills dataset in the X direction.
 |
 |      >>> from pyvista import examples
 |      >>> hills = examples.load_random_hills()
 |      >>> slices = hills.slice_along_axis(n=10)
 |      >>> slices.plot(line_width=5)
 |
 |      Slice the random hills dataset in the Z direction.
 |
 |      >>> from pyvista import examples
 |      >>> hills = examples.load_random_hills()
 |      >>> slices = hills.slice_along_axis(n=10, axis='z')
 |      >>> slices.plot(line_width=5)
 |
 |      See :ref:`slice_example` for more examples using this filter.
 |
 |  slice_along_line(self, line, generate_triangles=False, contour=False, progress_bar=False)
 |      Slice a dataset using a polyline/spline as the path.
 |
 |      This also works for lines generated with :func:`pyvista.Line`.
 |
 |      Parameters
 |      ----------
 |      line : pyvista.PolyData
 |          A PolyData object containing one single PolyLine cell.
 |
 |      generate_triangles : bool, default: False
 |          When ``True``, the output will be triangles. Otherwise the output
 |          will be the intersection polygons.
 |
 |      contour : bool, default: False
 |          If ``True``, apply a ``contour`` filter after slicing.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Sliced dataset.
 |
 |      Examples
 |      --------
 |      Slice the random hills dataset along a circular arc.
 |
 |      >>> import numpy as np
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> hills = examples.load_random_hills()
 |      >>> center = np.array(hills.center)
 |      >>> point_a = center + np.array([5, 0, 0])
 |      >>> point_b = center + np.array([-5, 0, 0])
 |      >>> arc = pv.CircularArc(point_a, point_b, center, resolution=100)
 |      >>> line_slice = hills.slice_along_line(arc)
 |
 |      Plot the circular arc and the hills mesh.
 |
 |      >>> pl = pv.Plotter()
 |      >>> _ = pl.add_mesh(hills, smooth_shading=True, style='wireframe')
 |      >>> _ = pl.add_mesh(
 |      ...     line_slice,
 |      ...     line_width=10,
 |      ...     render_lines_as_tubes=True,
 |      ...     color='k',
 |      ... )
 |      >>> _ = pl.add_mesh(arc, line_width=10, color='grey')
 |      >>> pl.show()
 |
 |      See :ref:`slice_example` for more examples using this filter.
 |
 |  slice_implicit(self, implicit_function, generate_triangles=False, contour=False, progress_bar=False)
 |      Slice a dataset by a VTK implicit function.
 |
 |      Parameters
 |      ----------
 |      implicit_function : vtk.vtkImplicitFunction
 |          Specify the implicit function to perform the cutting.
 |
 |      generate_triangles : bool, default: False
 |          If this is enabled (``False`` by default), the output will
 |          be triangles. Otherwise the output will be the intersection
 |          polygons. If the cutting function is not a plane, the
 |          output will be 3D polygons, which might be nice to look at
 |          but hard to compute with downstream.
 |
 |      contour : bool, default: False
 |          If ``True``, apply a ``contour`` filter after slicing.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Sliced dataset.
 |
 |      Examples
 |      --------
 |      Slice the surface of a sphere.
 |
 |      >>> import pyvista as pv
 |      >>> import vtk
 |      >>> sphere = vtk.vtkSphere()
 |      >>> sphere.SetRadius(10)
 |      >>> mesh = pv.Wavelet()
 |      >>> slice = mesh.slice_implicit(sphere)
 |      >>> slice.plot(show_edges=True, line_width=5)
 |
 |      >>> cylinder = vtk.vtkCylinder()
 |      >>> cylinder.SetRadius(10)
 |      >>> mesh = pv.Wavelet()
 |      >>> slice = mesh.slice_implicit(cylinder)
 |      >>> slice.plot(show_edges=True, line_width=5)
 |
 |  slice_orthogonal(self, x=None, y=None, z=None, generate_triangles=False, contour=False, progress_bar=False)
 |      Create three orthogonal slices through the dataset on the three cartesian planes.
 |
 |      Yields a MutliBlock dataset of the three slices.
 |
 |      Parameters
 |      ----------
 |      x : float, optional
 |          The X location of the YZ slice.
 |
 |      y : float, optional
 |          The Y location of the XZ slice.
 |
 |      z : float, optional
 |          The Z location of the XY slice.
 |
 |      generate_triangles : bool, default: False
 |          When ``True``, the output will be triangles. Otherwise the output
 |          will be the intersection polygons.
 |
 |      contour : bool, default: False
 |          If ``True``, apply a ``contour`` filter after slicing.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Sliced dataset.
 |
 |      Examples
 |      --------
 |      Slice the random hills dataset with three orthogonal planes.
 |
 |      >>> from pyvista import examples
 |      >>> hills = examples.load_random_hills()
 |      >>> slices = hills.slice_orthogonal(contour=False)
 |      >>> slices.plot(line_width=5)
 |
 |      See :ref:`slice_example` for more examples using this filter.
 |
 |  sort_labels(self, scalars=None, preference='point', output_scalars=None, progress_bar=False, inplace=False)
 |      Sort labeled data by number of points or cells.
 |
 |      This filter renumbers scalar label data of any type with ``N`` labels
 |      such that the output labels are contiguous from ``[0, N)`` and
 |      sorted in descending order from largest to smallest (by label count).
 |      I.e., the largest label will have a value of ``0`` and the smallest
 |      label will have a value of ``N-1``.
 |
 |      The filter is a convenience method for :func:`pyvista.DataSetFilters.pack_labels`
 |      with ``sort=True``.
 |
 |      Parameters
 |      ----------
 |      scalars : str, optional
 |          Name of scalars to sort. Defaults to currently active scalars.
 |
 |      preference : str, default: "point"
 |          When ``scalars`` is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'`` or ``'cell'``.
 |
 |      output_scalars : str, None
 |          Name of the sorted output scalars. By default, the output is
 |          saved to ``'packed_labels'``.
 |
 |      progress_bar : bool, default: False
 |          If ``True``, display a progress bar. Has no effect if VTK
 |          version is lower than 9.3.
 |
 |      inplace : bool, default: False
 |          If ``True``, the mesh is updated in-place.
 |
 |      Returns
 |      -------
 |      pyvista.Dataset
 |          Dataset with sorted labels.
 |
 |      Examples
 |      --------
 |      Sort segmented image labels.
 |
 |      Load image labels
 |
 |      >>> from pyvista import examples
 |      >>> import numpy as np
 |      >>> image_labels = examples.load_frog_tissues()
 |
 |      Show label info for first four labels
 |
 |      >>> label_number, label_size = np.unique(
 |      ...     image_labels['MetaImage'], return_counts=True
 |      ... )
 |      >>> label_number[:4]
 |      pyvista_ndarray([0, 1, 2, 3], dtype=uint8)
 |      >>> label_size[:4]
 |      array([30805713,    35279,    19172,    38129])
 |
 |      Sort labels
 |
 |      >>> sorted_labels = image_labels.sort_labels()
 |
 |      Show sorted label info for the four largest labels. Note
 |      the difference in label size after sorting.
 |
 |      >>> sorted_label_number, sorted_label_size = np.unique(
 |      ...     sorted_labels["packed_labels"], return_counts=True
 |      ... )
 |      >>> sorted_label_number[:4]
 |      pyvista_ndarray([0, 1, 2, 3], dtype=uint8)
 |      >>> sorted_label_size[:4]
 |      array([30805713,   438052,   204672,   133880])
 |
 |  split_bodies(self, label=False, progress_bar=False)
 |      Find, label, and split connected bodies/volumes.
 |
 |      This splits different connected bodies into blocks in a
 |      :class:`pyvista.MultiBlock` dataset.
 |
 |      Parameters
 |      ----------
 |      label : bool, default: False
 |          A flag on whether to keep the ID arrays given by the
 |          ``connectivity`` filter.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      See Also
 |      --------
 |      extract_values, partition, connectivity
 |
 |      Returns
 |      -------
 |      pyvista.MultiBlock
 |          MultiBlock with a split bodies.
 |
 |      Examples
 |      --------
 |      Split a uniform grid thresholded to be non-connected.
 |
 |      >>> from pyvista import examples
 |      >>> dataset = examples.load_uniform()
 |      >>> _ = dataset.set_active_scalars('Spatial Cell Data')
 |      >>> threshed = dataset.threshold_percent([0.15, 0.50], invert=True)
 |      >>> bodies = threshed.split_bodies()
 |      >>> len(bodies)
 |      2
 |
 |      See :ref:`split_vol` for more examples using this filter.
 |
 |  split_values(self, values: 'None | (float | VectorLike[float] | MatrixLike[float] | dict[str, float] | dict[float, str])' = None, *, ranges: 'None | (VectorLike[float] | MatrixLike[float] | dict[str, VectorLike[float]] | dict[tuple[float, float], str])' = None, scalars: 'str | None' = None, preference: "Literal['point', 'cell']" = 'point', component_mode: "Literal['any', 'all', 'multi'] | int" = 'all', **kwargs)
 |      Split mesh into separate sub-meshes using point or cell data.
 |
 |      By default, this filter generates a separate mesh for each unique value in the
 |      data array and combines them as blocks in a :class:`~pyvista.MultiBlock`
 |      dataset. Optionally, specific values and/or ranges of values may be specified to
 |      control which values to split from the input.
 |
 |      This filter is a convenience method for :meth:`~pyvista.DataSetFilter.extract_values`
 |      with ``split`` set to ``True`` by default. Refer to that filter's documentation
 |      for more details.
 |
 |      .. versionadded:: 0.44
 |
 |      Parameters
 |      ----------
 |      values : number | array_like | dict, optional
 |          Value(s) to extract. Can be a number, an iterable of numbers, or a dictionary
 |          with numeric entries. For ``dict`` inputs, either its keys or values may be
 |          numeric, and the other field must be strings. The numeric field is used as
 |          the input for this parameter, and if ``split`` is ``True``, the string field
 |          is used to set the block names of the returned :class:`~pyvista.MultiBlock`.
 |
 |          .. note::
 |              When extracting multi-component values with ``component_mode=multi``,
 |              each value is specified as a multi-component scalar. In this case,
 |              ``values`` can be a single vector or an array of row vectors.
 |
 |      ranges : array_like | dict, optional
 |          Range(s) of values to extract. Can be a single range (i.e. a sequence of
 |          two numbers in the form ``[lower, upper]``), a sequence of ranges, or a
 |          dictionary with range entries. Any combination of ``values`` and ``ranges``
 |          may be specified together. The endpoints of the ranges are included in the
 |          extraction. Ranges cannot be set when ``component_mode=multi``.
 |
 |          For ``dict`` inputs, either its keys or values may be numeric, and the other
 |          field must be strings. The numeric field is used as the input for this
 |          parameter, and if ``split`` is ``True``, the string field is used to set the
 |          block names of the returned :class:`~pyvista.MultiBlock`.
 |
 |          .. note::
 |              Use ``+/-`` infinity to specify an unlimited bound, e.g.:
 |
 |              - ``[0, float('inf')]`` to extract values greater than or equal to zero.
 |              - ``[float('-inf'), 0]`` to extract values less than or equal to zero.
 |
 |      scalars : str, optional
 |          Name of scalars to extract with. Defaults to currently active scalars.
 |
 |      preference : str, default: 'point'
 |          When ``scalars`` is specified, this is the preferred array type to search
 |          for in the dataset.  Must be either ``'point'`` or ``'cell'``.
 |
 |      component_mode : int | 'any' | 'all' | 'multi', default: 'all'
 |          Specify the component(s) to use when ``scalars`` is a multi-component array.
 |          Has no effect when the scalars have a single component. Must be one of:
 |
 |          - number: specify the component number as a 0-indexed integer. The selected
 |            component must have the specified value(s).
 |          - ``'any'``: any single component can have the specified value(s).
 |          - ``'all'``: all individual components must have the specified values(s).
 |          - ``'multi'``: the entire multi-component item must have the specified value.
 |
 |      **kwargs : dict, optional
 |          Additional keyword arguments passed to :meth:`~pyvista.DataSetFilter.extract_values`.
 |
 |      See Also
 |      --------
 |      extract_values, split_bodies, partition
 |
 |      Returns
 |      -------
 |      pyvista.MultiBlock
 |          Composite of split meshes with :class:`pyvista.UnstructuredGrid` blocks.
 |
 |      Examples
 |      --------
 |      Load image with labeled regions.
 |
 |      >>> import numpy as np
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> image = examples.load_channels()
 |      >>> np.unique(image.active_scalars)
 |      pyvista_ndarray([0, 1, 2, 3, 4])
 |
 |      Split the image into its separate regions. Here, we also remove the first
 |      region for visualization.
 |
 |      >>> multiblock = image.split_values()
 |      >>> _ = multiblock.pop(0)  # Remove first region
 |
 |      Plot the regions.
 |
 |      >>> plot = pv.Plotter()
 |      >>> _ = plot.add_composite(multiblock, multi_colors=True)
 |      >>> _ = plot.show_grid()
 |      >>> plot.show()
 |
 |      Note that the block names are generic by default.
 |
 |      >>> multiblock.keys()
 |      ['Block-01', 'Block-02', 'Block-03', 'Block-04']
 |
 |      To name the output blocks, use a dictionary as input instead.
 |
 |      Here, we also explicitly omit the region with ``0`` values from the input
 |      instead of removing it from the output.
 |
 |      >>> labels = dict(region1=1, region2=2, region3=3, region4=4)
 |      >>>
 |      >>> multiblock = image.split_values(labels)
 |      >>> multiblock.keys()
 |      ['region1', 'region2', 'region3', 'region4']
 |
 |      Plot the regions as separate meshes using the labels instead of plotting
 |      the MultiBlock directly.
 |
 |      Clear scalar data so we can color each mesh using a single color
 |      >>> _ = [block.clear_data() for block in multiblock]
 |      >>>
 |      >>> plot = pv.Plotter()
 |      >>> plot.set_color_cycler('default')
 |      >>> _ = [
 |      ...     plot.add_mesh(block, label=label)
 |      ...     for block, label in zip(multiblock, labels)
 |      ... ]
 |      >>> _ = plot.add_legend()
 |      >>> plot.show()
 |
 |  streamlines(self, vectors=None, source_center=None, source_radius=None, n_points=100, start_position=None, return_source=False, pointa=None, pointb=None, progress_bar=False, **kwargs)
 |      Integrate a vector field to generate streamlines.
 |
 |      The default behavior uses a sphere as the source - set its
 |      location and radius via the ``source_center`` and
 |      ``source_radius`` keyword arguments.  ``n_points`` defines the
 |      number of starting points on the sphere surface.
 |      Alternatively, a line source can be used by specifying
 |      ``pointa`` and ``pointb``.  ``n_points`` again defines the
 |      number of points on the line.
 |
 |      You can retrieve the source by specifying
 |      ``return_source=True``.
 |
 |      Optional keyword parameters from
 |      :func:`pyvista.DataSetFilters.streamlines_from_source` can be
 |      used here to control the generation of streamlines.
 |
 |      Parameters
 |      ----------
 |      vectors : str, optional
 |          The string name of the active vector field to integrate across.
 |
 |      source_center : sequence[float], optional
 |          Length 3 tuple of floats defining the center of the source
 |          particles. Defaults to the center of the dataset.
 |
 |      source_radius : float, optional
 |          Float radius of the source particle cloud. Defaults to one-tenth of
 |          the diagonal of the dataset's spatial extent.
 |
 |      n_points : int, default: 100
 |          Number of particles present in source sphere or line.
 |
 |      start_position : sequence[float], optional
 |          A single point.  This will override the sphere point source.
 |
 |      return_source : bool, default: False
 |          Return the source particles as :class:`pyvista.PolyData` as well as the
 |          streamlines. This will be the second value returned if ``True``.
 |
 |      pointa, pointb : sequence[float], optional
 |          The coordinates of a start and end point for a line source. This
 |          will override the sphere and start_position point source.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      **kwargs : dict, optional
 |          See :func:`pyvista.DataSetFilters.streamlines_from_source`.
 |
 |      Returns
 |      -------
 |      streamlines : pyvista.PolyData
 |          This produces polylines as the output, with each cell
 |          (i.e., polyline) representing a streamline. The attribute values
 |          associated with each streamline are stored in the cell data, whereas
 |          those associated with streamline-points are stored in the point data.
 |
 |      source : pyvista.PolyData
 |          The points of the source are the seed points for the streamlines.
 |          Only returned if ``return_source=True``.
 |
 |      Examples
 |      --------
 |      See the :ref:`streamlines_example` example.
 |
 |  streamlines_evenly_spaced_2D(self, vectors=None, start_position=None, integrator_type=2, step_length=0.5, step_unit='cl', max_steps=2000, terminal_speed=1e-12, interpolator_type='point', separating_distance=10, separating_distance_ratio=0.5, closed_loop_maximum_distance=0.5, loop_angle=20, minimum_number_of_loop_points=4, compute_vorticity=True, progress_bar=False)
 |      Generate evenly spaced streamlines on a 2D dataset.
 |
 |      This filter only supports datasets that lie on the xy plane, i.e. ``z=0``.
 |      Particular care must be used to choose a `separating_distance`
 |      that do not result in too much memory being utilized.  The
 |      default unit is cell length.
 |
 |      Parameters
 |      ----------
 |      vectors : str, optional
 |          The string name of the active vector field to integrate across.
 |
 |      start_position : sequence[float], optional
 |          The seed point for generating evenly spaced streamlines.
 |          If not supplied, a random position in the dataset is chosen.
 |
 |      integrator_type : {2, 4}, default: 2
 |          The integrator type to be used for streamline generation.
 |          The default is Runge-Kutta2. The recognized solvers are:
 |          RUNGE_KUTTA2 (``2``) and RUNGE_KUTTA4 (``4``).
 |
 |      step_length : float, default: 0.5
 |          Constant Step size used for line integration, expressed in length
 |          units or cell length units (see ``step_unit`` parameter).
 |
 |      step_unit : {'cl', 'l'}, default: "cl"
 |          Uniform integration step unit. The valid unit is now limited to
 |          only LENGTH_UNIT (``'l'``) and CELL_LENGTH_UNIT (``'cl'``).
 |          Default is CELL_LENGTH_UNIT.
 |
 |      max_steps : int, default: 2000
 |          Maximum number of steps for integrating a streamline.
 |
 |      terminal_speed : float, default: 1e-12
 |          Terminal speed value, below which integration is terminated.
 |
 |      interpolator_type : str, optional
 |          Set the type of the velocity field interpolator to locate cells
 |          during streamline integration either by points or cells.
 |          The cell locator is more robust then the point locator. Options
 |          are ``'point'`` or ``'cell'`` (abbreviations of ``'p'`` and ``'c'``
 |          are also supported).
 |
 |      separating_distance : float, default: 10
 |          The distance between streamlines expressed in ``step_unit``.
 |
 |      separating_distance_ratio : float, default: 0.5
 |          Streamline integration is stopped if streamlines are closer than
 |          ``SeparatingDistance*SeparatingDistanceRatio`` to other streamlines.
 |
 |      closed_loop_maximum_distance : float, default: 0.5
 |          The distance between points on a streamline to determine a
 |          closed loop.
 |
 |      loop_angle : float, default: 20
 |          The maximum angle in degrees between points to determine a closed loop.
 |
 |      minimum_number_of_loop_points : int, default: 4
 |          The minimum number of points before which a closed loop will
 |          be determined.
 |
 |      compute_vorticity : bool, default: True
 |          Vorticity computation at streamline points. Necessary for generating
 |          proper stream-ribbons using the ``vtkRibbonFilter``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          This produces polylines as the output, with each cell
 |          (i.e., polyline) representing a streamline. The attribute
 |          values associated with each streamline are stored in the
 |          cell data, whereas those associated with streamline-points
 |          are stored in the point data.
 |
 |      Examples
 |      --------
 |      Plot evenly spaced streamlines for cylinder in a crossflow.
 |      This dataset is a multiblock dataset, and the fluid velocity is in the
 |      first block.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> mesh = examples.download_cylinder_crossflow()
 |      >>> streams = mesh[0].streamlines_evenly_spaced_2D(
 |      ...     start_position=(4, 0.1, 0.0),
 |      ...     separating_distance=3,
 |      ...     separating_distance_ratio=0.2,
 |      ... )
 |      >>> plotter = pv.Plotter()
 |      >>> _ = plotter.add_mesh(
 |      ...     streams.tube(radius=0.02), scalars="vorticity_mag"
 |      ... )
 |      >>> plotter.view_xy()
 |      >>> plotter.show()
 |
 |      See :ref:`2d_streamlines_example` for more examples using this filter.
 |
 |  streamlines_from_source(self, source, vectors=None, integrator_type=45, integration_direction='both', surface_streamlines=False, initial_step_length=0.5, step_unit='cl', min_step_length=0.01, max_step_length=1.0, max_steps=2000, terminal_speed=1e-12, max_error=1e-06, max_time=None, compute_vorticity=True, rotation_scale=1.0, interpolator_type='point', progress_bar=False)
 |      Generate streamlines of vectors from the points of a source mesh.
 |
 |      The integration is performed using a specified integrator, by default
 |      Runge-Kutta2. This supports integration through any type of dataset.
 |      If the dataset contains 2D cells like polygons or triangles and the
 |      ``surface_streamlines`` parameter is used, the integration is constrained
 |      to lie on the surface defined by 2D cells.
 |
 |      Parameters
 |      ----------
 |      source : pyvista.DataSet
 |          The points of the source provide the starting points of the
 |          streamlines.  This will override both sphere and line sources.
 |
 |      vectors : str, optional
 |          The string name of the active vector field to integrate across.
 |
 |      integrator_type : {45, 2, 4}, default: 45
 |          The integrator type to be used for streamline generation.
 |          The default is Runge-Kutta45. The recognized solvers are:
 |          RUNGE_KUTTA2 (``2``),  RUNGE_KUTTA4 (``4``), and RUNGE_KUTTA45
 |          (``45``). Options are ``2``, ``4``, or ``45``.
 |
 |      integration_direction : str, default: "both"
 |          Specify whether the streamline is integrated in the upstream or
 |          downstream directions (or both). Options are ``'both'``,
 |          ``'backward'``, or ``'forward'``.
 |
 |      surface_streamlines : bool, default: False
 |          Compute streamlines on a surface.
 |
 |      initial_step_length : float, default: 0.5
 |          Initial step size used for line integration, expressed ib length
 |          unitsL or cell length units (see ``step_unit`` parameter).
 |          either the starting size for an adaptive integrator, e.g., RK45, or
 |          the constant / fixed size for non-adaptive ones, i.e., RK2 and RK4).
 |
 |      step_unit : {'cl', 'l'}, default: "cl"
 |          Uniform integration step unit. The valid unit is now limited to
 |          only LENGTH_UNIT (``'l'``) and CELL_LENGTH_UNIT (``'cl'``).
 |          Default is CELL_LENGTH_UNIT.
 |
 |      min_step_length : float, default: 0.01
 |          Minimum step size used for line integration, expressed in length or
 |          cell length units. Only valid for an adaptive integrator, e.g., RK45.
 |
 |      max_step_length : float, default: 1.0
 |          Maximum step size used for line integration, expressed in length or
 |          cell length units. Only valid for an adaptive integrator, e.g., RK45.
 |
 |      max_steps : int, default: 2000
 |          Maximum number of steps for integrating a streamline.
 |
 |      terminal_speed : float, default: 1e-12
 |          Terminal speed value, below which integration is terminated.
 |
 |      max_error : float, 1e-6
 |          Maximum error tolerated throughout streamline integration.
 |
 |      max_time : float, optional
 |          Specify the maximum length of a streamline expressed in LENGTH_UNIT.
 |
 |      compute_vorticity : bool, default: True
 |          Vorticity computation at streamline points. Necessary for generating
 |          proper stream-ribbons using the ``vtkRibbonFilter``.
 |
 |      rotation_scale : float, default: 1.0
 |          This can be used to scale the rate with which the streamribbons
 |          twist.
 |
 |      interpolator_type : str, default: "point"
 |          Set the type of the velocity field interpolator to locate cells
 |          during streamline integration either by points or cells.
 |          The cell locator is more robust then the point locator. Options
 |          are ``'point'`` or ``'cell'`` (abbreviations of ``'p'`` and ``'c'``
 |          are also supported).
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Streamlines. This produces polylines as the output, with
 |          each cell (i.e., polyline) representing a streamline. The
 |          attribute values associated with each streamline are
 |          stored in the cell data, whereas those associated with
 |          streamline-points are stored in the point data.
 |
 |      Examples
 |      --------
 |      See the :ref:`streamlines_example` example.
 |
 |  surface_indices(self, progress_bar=False)
 |      Return the surface indices of a grid.
 |
 |      Parameters
 |      ----------
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      numpy.ndarray
 |          Indices of the surface points.
 |
 |      Examples
 |      --------
 |      Return the first 10 surface indices of an UnstructuredGrid.
 |
 |      >>> from pyvista import examples
 |      >>> grid = examples.load_hexbeam()
 |      >>> ind = grid.surface_indices()
 |      >>> ind[:10]  # doctest:+SKIP
 |      pyvista_ndarray([ 0,  2, 36, 27,  7,  8, 81,  1, 18,  4])
 |
 |  tessellate(self, max_n_subdivide=3, merge_points=True, progress_bar=False)
 |      Tessellate a mesh.
 |
 |      This filter approximates nonlinear FEM-like elements with linear
 |      simplices. The output mesh will have geometry and any fields specified
 |      as attributes in the input mesh's point data. The attribute's copy
 |      flags are honored, except for normals.
 |
 |      For more details see `vtkTessellatorFilter <https://vtk.org/doc/nightly/html/classvtkTessellatorFilter.html#details>`_.
 |
 |      Parameters
 |      ----------
 |      max_n_subdivide : int, default: 3
 |          Maximum number of subdivisions.
 |
 |      merge_points : bool, default: True
 |          The adaptive tessellation will output vertices that are not shared among cells,
 |          even where they should be. This can be corrected to some extent.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset with tessellated mesh.  Return type matches input.
 |
 |      Examples
 |      --------
 |      First, plot the high order FEM-like elements.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> points = np.array(
 |      ...     [
 |      ...         [0.0, 0.0, 0.0],
 |      ...         [2.0, 0.0, 0.0],
 |      ...         [1.0, 2.0, 0.0],
 |      ...         [1.0, 0.5, 0.0],
 |      ...         [1.5, 1.5, 0.0],
 |      ...         [0.5, 1.5, 0.0],
 |      ...     ]
 |      ... )
 |      >>> cells = np.array([6, 0, 1, 2, 3, 4, 5])
 |      >>> cell_types = np.array([69])
 |      >>> mesh = pv.UnstructuredGrid(cells, cell_types, points)
 |      >>> mesh.plot(show_edges=True, line_width=5)
 |
 |      Now, plot the tessellated mesh.
 |
 |      >>> tessellated = mesh.tessellate()
 |      >>> tessellated.clear_data()  # cleans up plot
 |      >>> tessellated.plot(show_edges=True, line_width=5)
 |
 |  texture_map_to_plane(self, origin=None, point_u=None, point_v=None, inplace=False, name='Texture Coordinates', use_bounds=False, progress_bar=False)
 |      Texture map this dataset to a user defined plane.
 |
 |      This is often used to define a plane to texture map an image
 |      to this dataset.  The plane defines the spatial reference and
 |      extent of that image.
 |
 |      Parameters
 |      ----------
 |      origin : sequence[float], optional
 |          Length 3 iterable of floats defining the XYZ coordinates of the
 |          bottom left corner of the plane.
 |
 |      point_u : sequence[float], optional
 |          Length 3 iterable of floats defining the XYZ coordinates of the
 |          bottom right corner of the plane.
 |
 |      point_v : sequence[float], optional
 |          Length 3 iterable of floats defining the XYZ coordinates of the
 |          top left corner of the plane.
 |
 |      inplace : bool, default: False
 |          If ``True``, the new texture coordinates will be added to this
 |          dataset. If ``False``, a new dataset is returned with the texture
 |          coordinates.
 |
 |      name : str, default: "Texture Coordinates"
 |          The string name to give the new texture coordinates if applying
 |          the filter inplace.
 |
 |      use_bounds : bool, default: False
 |          Use the bounds to set the mapping plane by default (bottom plane
 |          of the bounding box).
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Original dataset with texture coordinates if
 |          ``inplace=True``, otherwise a copied dataset.
 |
 |      Examples
 |      --------
 |      See :ref:`topo_map_example`
 |
 |  texture_map_to_sphere(self, center=None, prevent_seam=True, inplace=False, name='Texture Coordinates', progress_bar=False)
 |      Texture map this dataset to a user defined sphere.
 |
 |      This is often used to define a sphere to texture map an image
 |      to this dataset. The sphere defines the spatial reference and
 |      extent of that image.
 |
 |      Parameters
 |      ----------
 |      center : sequence[float], optional
 |          Length 3 iterable of floats defining the XYZ coordinates of the
 |          center of the sphere. If ``None``, this will be automatically
 |          calculated.
 |
 |      prevent_seam : bool, default: True
 |          Control how the texture coordinates are generated.  If
 |          set, the s-coordinate ranges from 0 to 1 and 1 to 0
 |          corresponding to the theta angle variation between 0 to
 |          180 and 180 to 0 degrees.  Otherwise, the s-coordinate
 |          ranges from 0 to 1 between 0 to 360 degrees.
 |
 |      inplace : bool, default: False
 |          If ``True``, the new texture coordinates will be added to
 |          the dataset inplace. If ``False`` (default), a new dataset
 |          is returned with the texture coordinates.
 |
 |      name : str, default: "Texture Coordinates"
 |          The string name to give the new texture coordinates if applying
 |          the filter inplace.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Dataset containing the texture mapped to a sphere.  Return
 |          type matches input.
 |
 |      Examples
 |      --------
 |      See :ref:`texture_example`.
 |
 |  threshold(self, value=None, scalars=None, invert=False, continuous=False, preference='cell', all_scalars=False, component_mode='all', component=0, method='upper', progress_bar=False)
 |      Apply a ``vtkThreshold`` filter to the input dataset.
 |
 |      This filter will apply a ``vtkThreshold`` filter to the input
 |      dataset and return the resulting object. This extracts cells
 |      where the scalar value in each cell satisfies the threshold
 |      criterion.  If ``scalars`` is ``None``, the input's active
 |      scalars array is used.
 |
 |      .. warning::
 |         Thresholding is inherently a cell operation, even though it can use
 |         associated point data for determining whether to keep a cell. In
 |         other words, whether or not a given point is included after
 |         thresholding depends on whether that point is part of a cell that
 |         is kept after thresholding.
 |
 |         Please also note the default ``preference`` choice for CELL data
 |         over POINT data. This is contrary to most other places in PyVista's
 |         API where the preference typically defaults to POINT data. We chose
 |         to prefer CELL data here so that if thresholding by a named array
 |         that exists for both the POINT and CELL data, this filter will
 |         default to the CELL data array while performing the CELL-wise
 |         operation.
 |
 |      Parameters
 |      ----------
 |      value : float | sequence[float], optional
 |          Single value or ``(min, max)`` to be used for the data threshold. If
 |          a sequence, then length must be 2. If no value is specified, the
 |          non-NaN data range will be used to remove any NaN values.
 |          Please reference the ``method`` parameter for how single values
 |          are handled.
 |
 |      scalars : str, optional
 |          Name of scalars to threshold on. Defaults to currently active scalars.
 |
 |      invert : bool, default: False
 |          Invert the threshold results. That is, cells that would have been
 |          in the output with this option off are excluded, while cells that
 |          would have been excluded from the output are included.
 |
 |      continuous : bool, default: False
 |          When True, the continuous interval [minimum cell scalar,
 |          maximum cell scalar] will be used to intersect the threshold bound,
 |          rather than the set of discrete scalar values from the vertices.
 |
 |      preference : str, default: 'cell'
 |          When ``scalars`` is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'`` or ``'cell'``. Throughout PyVista, the preference
 |          is typically ``'point'`` but since the threshold filter is a
 |          cell-wise operation, we prefer cell data for thresholding
 |          operations.
 |
 |      all_scalars : bool, default: False
 |          If using scalars from point data, all
 |          points in a cell must satisfy the threshold when this
 |          value is ``True``.  When ``False``, any point of the cell
 |          with a scalar value satisfying the threshold criterion
 |          will extract the cell. Has no effect when using cell data.
 |
 |      component_mode : {'selected', 'all', 'any'}
 |          The method to satisfy the criteria for the threshold of
 |          multicomponent scalars.  'selected' (default)
 |          uses only the ``component``.  'all' requires all
 |          components to meet criteria.  'any' is when
 |          any component satisfies the criteria.
 |
 |      component : int, default: 0
 |          When using ``component_mode='selected'``, this sets
 |          which component to threshold on.
 |
 |      method : str, default: 'upper'
 |          Set the threshold method for single-values, defining which
 |          threshold bounds to use. If the ``value`` is a range, this
 |          parameter will be ignored, extracting data between the two
 |          values. For single values, ``'lower'`` will extract data
 |          lower than the  ``value``. ``'upper'`` will extract data
 |          larger than the ``value``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      See Also
 |      --------
 |      threshold_percent, :meth:`~pyvista.ImageDataFilters.image_threshold`, extract_values
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Dataset containing geometry that meets the threshold requirements.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> volume = np.zeros([10, 10, 10])
 |      >>> volume[:3] = 1
 |      >>> vol = pv.wrap(volume)
 |      >>> threshed = vol.threshold(0.1)
 |      >>> threshed
 |      UnstructuredGrid (...)
 |        N Cells:    243
 |        N Points:   400
 |        X Bounds:   0.000e+00, 3.000e+00
 |        Y Bounds:   0.000e+00, 9.000e+00
 |        Z Bounds:   0.000e+00, 9.000e+00
 |        N Arrays:   1
 |
 |      Apply the threshold filter to Perlin noise.  First generate
 |      the structured grid.
 |
 |      >>> import pyvista as pv
 |      >>> noise = pv.perlin_noise(0.1, (1, 1, 1), (0, 0, 0))
 |      >>> grid = pv.sample_function(
 |      ...     noise, [0, 1.0, -0, 1.0, 0, 1.0], dim=(20, 20, 20)
 |      ... )
 |      >>> grid.plot(
 |      ...     cmap='gist_earth_r',
 |      ...     show_scalar_bar=True,
 |      ...     show_edges=False,
 |      ... )
 |
 |      Next, apply the threshold.
 |
 |      >>> import pyvista as pv
 |      >>> noise = pv.perlin_noise(0.1, (1, 1, 1), (0, 0, 0))
 |      >>> grid = pv.sample_function(
 |      ...     noise, [0, 1.0, -0, 1.0, 0, 1.0], dim=(20, 20, 20)
 |      ... )
 |      >>> threshed = grid.threshold(value=0.02)
 |      >>> threshed.plot(
 |      ...     cmap='gist_earth_r',
 |      ...     show_scalar_bar=False,
 |      ...     show_edges=True,
 |      ... )
 |
 |      See :ref:`common_filter_example` for more examples using this filter.
 |
 |  threshold_percent(self, percent=0.5, scalars=None, invert=False, continuous=False, preference='cell', method='upper', progress_bar=False)
 |      Threshold the dataset by a percentage of its range on the active scalars array.
 |
 |      .. warning::
 |         Thresholding is inherently a cell operation, even though it can use
 |         associated point data for determining whether to keep a cell. In
 |         other words, whether or not a given point is included after
 |         thresholding depends on whether that point is part of a cell that
 |         is kept after thresholding.
 |
 |      Parameters
 |      ----------
 |      percent : float | sequence[float], optional
 |          The percentage in the range ``(0, 1)`` to threshold. If value is
 |          out of 0 to 1 range, then it will be divided by 100 and checked to
 |          be in that range.
 |
 |      scalars : str, optional
 |          Name of scalars to threshold on. Defaults to currently active scalars.
 |
 |      invert : bool, default: False
 |          Invert the threshold results. That is, cells that would have been
 |          in the output with this option off are excluded, while cells that
 |          would have been excluded from the output are included.
 |
 |      continuous : bool, default: False
 |          When True, the continuous interval [minimum cell scalar,
 |          maximum cell scalar] will be used to intersect the threshold bound,
 |          rather than the set of discrete scalar values from the vertices.
 |
 |      preference : str, default: 'cell'
 |          When ``scalars`` is specified, this is the preferred array
 |          type to search for in the dataset.  Must be either
 |          ``'point'`` or ``'cell'``. Throughout PyVista, the preference
 |          is typically ``'point'`` but since the threshold filter is a
 |          cell-wise operation, we prefer cell data for thresholding
 |          operations.
 |
 |      method : str, default: 'upper'
 |          Set the threshold method for single-values, defining which
 |          threshold bounds to use. If the ``value`` is a range, this
 |          parameter will be ignored, extracting data between the two
 |          values. For single values, ``'lower'`` will extract data
 |          lower than the  ``value``. ``'upper'`` will extract data
 |          larger than the ``value``.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.UnstructuredGrid
 |          Dataset containing geometry that meets the threshold requirements.
 |
 |      Examples
 |      --------
 |      Apply a 50% threshold filter.
 |
 |      >>> import pyvista as pv
 |      >>> noise = pv.perlin_noise(0.1, (2, 2, 2), (0, 0, 0))
 |      >>> grid = pv.sample_function(
 |      ...     noise, [0, 1.0, -0, 1.0, 0, 1.0], dim=(30, 30, 30)
 |      ... )
 |      >>> threshed = grid.threshold_percent(0.5)
 |      >>> threshed.plot(
 |      ...     cmap='gist_earth_r',
 |      ...     show_scalar_bar=False,
 |      ...     show_edges=True,
 |      ... )
 |
 |      Apply a 80% threshold filter.
 |
 |      >>> threshed = grid.threshold_percent(0.8)
 |      >>> threshed.plot(
 |      ...     cmap='gist_earth_r',
 |      ...     show_scalar_bar=False,
 |      ...     show_edges=True,
 |      ... )
 |
 |      See :ref:`common_filter_example` for more examples using a similar filter.
 |
 |  transform(self: '_vtk.vtkDataSet', trans: '_vtk.vtkMatrix4x4 | _vtk.vtkTransform | NumpyArray[float]', transform_all_input_vectors=False, inplace=True, progress_bar=False)
 |      Transform this mesh with a 4x4 transform.
 |
 |      .. warning::
 |          When using ``transform_all_input_vectors=True``, there is
 |          no distinction in VTK between vectors and arrays with
 |          three components.  This may be an issue if you have scalar
 |          data with three components (e.g. RGB data).  This will be
 |          improperly transformed as if it was vector data rather
 |          than scalar data.  One possible (albeit ugly) workaround
 |          is to store the three components as separate scalar
 |          arrays.
 |
 |      .. warning::
 |          In general, transformations give non-integer results. This
 |          method converts integer-typed vector data to float before
 |          performing the transformation. This applies to the points
 |          array, as well as any vector-valued data that is affected
 |          by the transformation. To prevent subtle bugs arising from
 |          in-place transformations truncating the result to integers,
 |          this conversion always applies to the input mesh.
 |
 |      Parameters
 |      ----------
 |      trans : vtk.vtkMatrix4x4, vtk.vtkTransform, or numpy.ndarray
 |          Accepts a vtk transformation object or a 4x4
 |          transformation matrix.
 |
 |      transform_all_input_vectors : bool, default: False
 |          When ``True``, all arrays with three components are
 |          transformed. Otherwise, only the normals and vectors are
 |          transformed.  See the warning for more details.
 |
 |      inplace : bool, default: False
 |          When ``True``, modifies the dataset inplace.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Transformed dataset.  Return type matches input unless
 |          input dataset is a :class:`pyvista.ImageData`, in which
 |          case the output datatype is a :class:`pyvista.StructuredGrid`.
 |
 |      Examples
 |      --------
 |      Translate a mesh by ``(50, 100, 200)``.
 |
 |      >>> import numpy as np
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_airplane()
 |
 |      Here a 4x4 :class:`numpy.ndarray` is used, but
 |      ``vtk.vtkMatrix4x4`` and ``vtk.vtkTransform`` are also
 |      accepted.
 |
 |      >>> transform_matrix = np.array(
 |      ...     [
 |      ...         [1, 0, 0, 50],
 |      ...         [0, 1, 0, 100],
 |      ...         [0, 0, 1, 200],
 |      ...         [0, 0, 0, 1],
 |      ...     ]
 |      ... )
 |      >>> transformed = mesh.transform(transform_matrix)
 |      >>> transformed.plot(show_edges=True)
 |
 |  triangulate(self, inplace=False, progress_bar=False)
 |      Return an all triangle mesh.
 |
 |      More complex polygons will be broken down into triangles.
 |
 |      Parameters
 |      ----------
 |      inplace : bool, default: False
 |          Updates mesh in-place.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          Mesh containing only triangles.
 |
 |      Examples
 |      --------
 |      Generate a mesh with quadrilateral faces.
 |
 |      >>> import pyvista as pv
 |      >>> plane = pv.Plane()
 |      >>> plane.point_data.clear()
 |      >>> plane.plot(show_edges=True, line_width=5)
 |
 |      Convert it to an all triangle mesh.
 |
 |      >>> mesh = plane.triangulate()
 |      >>> mesh.plot(show_edges=True, line_width=5)
 |
 |  warp_by_scalar(self, scalars=None, factor=1.0, normal=None, inplace=False, progress_bar=False, **kwargs)
 |      Warp the dataset's points by a point data scalars array's values.
 |
 |      This modifies point coordinates by moving points along point
 |      normals by the scalar amount times the scale factor.
 |
 |      Parameters
 |      ----------
 |      scalars : str, optional
 |          Name of scalars to warp by. Defaults to currently active scalars.
 |
 |      factor : float, default: 1.0
 |          A scaling factor to increase the scaling effect. Alias
 |          ``scale_factor`` also accepted - if present, overrides ``factor``.
 |
 |      normal : sequence, optional
 |          User specified normal. If given, data normals will be
 |          ignored and the given normal will be used to project the
 |          warp.
 |
 |      inplace : bool, default: False
 |          If ``True``, the points of the given dataset will be updated.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      **kwargs : dict, optional
 |          Accepts ``scale_factor`` instead of ``factor``.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Warped Dataset.  Return type matches input.
 |
 |      Examples
 |      --------
 |      First, plot the unwarped mesh.
 |
 |      >>> from pyvista import examples
 |      >>> mesh = examples.download_st_helens()
 |      >>> mesh.plot(cmap='gist_earth', show_scalar_bar=False)
 |
 |      Now, warp the mesh by the ``'Elevation'`` scalars.
 |
 |      >>> warped = mesh.warp_by_scalar('Elevation')
 |      >>> warped.plot(cmap='gist_earth', show_scalar_bar=False)
 |
 |      See :ref:`surface_normal_example` for more examples using this filter.
 |
 |  warp_by_vector(self, vectors=None, factor=1.0, inplace=False, progress_bar=False)
 |      Warp the dataset's points by a point data vectors array's values.
 |
 |      This modifies point coordinates by moving points along point
 |      vectors by the local vector times the scale factor.
 |
 |      A classical application of this transform is to visualize
 |      eigenmodes in mechanics.
 |
 |      Parameters
 |      ----------
 |      vectors : str, optional
 |          Name of vector to warp by. Defaults to currently active vector.
 |
 |      factor : float, default: 1.0
 |          A scaling factor that multiplies the vectors to warp by. Can
 |          be used to enhance the warping effect.
 |
 |      inplace : bool, default: False
 |          If ``True``, the function will update the mesh in-place.
 |
 |      progress_bar : bool, default: False
 |          Display a progress bar to indicate progress.
 |
 |      Returns
 |      -------
 |      pyvista.PolyData
 |          The warped mesh resulting from the operation.
 |
 |      Examples
 |      --------
 |      Warp a sphere by vectors.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> sphere = examples.load_sphere_vectors()
 |      >>> warped = sphere.warp_by_vector()
 |      >>> pl = pv.Plotter(shape=(1, 2))
 |      >>> pl.subplot(0, 0)
 |      >>> actor = pl.add_text("Before warp")
 |      >>> actor = pl.add_mesh(sphere, color='white')
 |      >>> pl.subplot(0, 1)
 |      >>> actor = pl.add_text("After warp")
 |      >>> actor = pl.add_mesh(warped, color='white')
 |      >>> pl.show()
 |
 |      See :ref:`warp_by_vectors_example` and :ref:`eigenmodes_example` for
 |      more examples using this filter.
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyvista.core.filters.data_set.DataSetFilters:
 |
 |  __dict__
 |      dictionary for instance variables (if defined)
 |
 |  __weakref__
 |      list of weak references to the object (if defined)
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyvista.core.dataobject.DataObject:
 |
 |  __eq__(self, other: 'object') -> 'bool'
 |      Test equivalency between data objects.
 |
 |  __getstate__(self)
 |      Support pickle by serializing the VTK object data to something which can be pickled natively.
 |
 |      The format of the serialized VTK object data depends on `pyvista.PICKLE_FORMAT` (case-insensitive).
 |      - If `pyvista.PICKLE_FORMAT == 'xml'`, the data is serialized as an XML-formatted string.
 |      - If `pyvista.PICKLE_FORMAT == 'legacy'`, the data is serialized to bytes in VTK's binary format.
 |
 |  __setstate__(self, state)
 |      Support unpickle.
 |
 |  add_field_data(self, array: 'NumpyArray[float]', name: 'str', deep: 'bool' = True)
 |      Add field data.
 |
 |      Use field data when size of the data you wish to associate
 |      with the dataset does not match the number of points or cells
 |      of the dataset.
 |
 |      Parameters
 |      ----------
 |      array : sequence
 |          Array of data to add to the dataset as a field array.
 |
 |      name : str
 |          Name to assign the field array.
 |
 |      deep : bool, default: True
 |          Perform a deep copy of the data when adding it to the
 |          dataset.
 |
 |      Examples
 |      --------
 |      Add field data to a PolyData dataset.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.add_field_data(np.arange(10), 'my-field-data')
 |      >>> mesh['my-field-data']
 |      pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
 |
 |      Add field data to a ImageData dataset.
 |
 |      >>> mesh = pv.ImageData(dimensions=(2, 2, 1))
 |      >>> mesh.add_field_data(
 |      ...     ['I could', 'write', 'notes', 'here'], 'my-field-data'
 |      ... )
 |      >>> mesh['my-field-data']
 |      pyvista_ndarray(['I could', 'write', 'notes', 'here'], dtype='<U7')
 |
 |      Add field data to a MultiBlock dataset.
 |
 |      >>> blocks = pv.MultiBlock()
 |      >>> blocks.append(pv.Sphere())
 |      >>> blocks["cube"] = pv.Cube(center=(0, 0, -1))
 |      >>> blocks.add_field_data([1, 2, 3], 'my-field-data')
 |      >>> blocks.field_data['my-field-data']
 |      pyvista_ndarray([1, 2, 3])
 |
 |  clear_field_data(self) -> 'None'
 |      Remove all field data.
 |
 |      Examples
 |      --------
 |      Add field data to a PolyData dataset and then remove it.
 |
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.field_data['my-field-data'] = range(10)
 |      >>> len(mesh.field_data)
 |      1
 |      >>> mesh.clear_field_data()
 |      >>> len(mesh.field_data)
 |      0
 |
 |  copy(self, deep=True)
 |      Return a copy of the object.
 |
 |      Parameters
 |      ----------
 |      deep : bool, default: True
 |          When ``True`` makes a full copy of the object.  When
 |          ``False``, performs a shallow copy where the points, cell,
 |          and data arrays are references to the original object.
 |
 |      Returns
 |      -------
 |      pyvista.DataSet
 |          Deep or shallow copy of the input.  Type is identical to
 |          the input.
 |
 |      Examples
 |      --------
 |      Create and make a deep copy of a PolyData object.
 |
 |      >>> import pyvista as pv
 |      >>> mesh_a = pv.Sphere()
 |      >>> mesh_b = mesh_a.copy()
 |      >>> mesh_a == mesh_b
 |      True
 |
 |  copy_attributes(self, dataset: '_vtk.vtkDataSet') -> 'None'
 |      Copy the data attributes of the input dataset object.
 |
 |      Parameters
 |      ----------
 |      dataset : pyvista.DataSet
 |          Dataset to copy the data attributes from.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> source = pv.ImageData(dimensions=(10, 10, 5))
 |      >>> source = source.compute_cell_sizes()
 |      >>> target = pv.ImageData(dimensions=(10, 10, 5))
 |      >>> target.copy_attributes(source)
 |      >>> target.plot(scalars='Volume', show_edges=True)
 |
 |  copy_structure(self, dataset: '_vtk.vtkDataSet') -> 'None'
 |      Copy the structure (geometry and topology) of the input dataset object.
 |
 |      Parameters
 |      ----------
 |      dataset : vtk.vtkDataSet
 |          Dataset to copy the geometry and topology from.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> source = pv.ImageData(dimensions=(10, 10, 5))
 |      >>> target = pv.ImageData()
 |      >>> target.copy_structure(source)
 |      >>> target.plot(show_edges=True)
 |
 |  deep_copy(self, to_copy: '_vtk.vtkDataObject') -> 'None'
 |      Overwrite this data object with another data object as a deep copy.
 |
 |      Parameters
 |      ----------
 |      to_copy : pyvista.DataObject or vtk.vtkDataObject
 |          Data object to perform a deep copy from.
 |
 |  head(self, display=True, html=None)
 |      Return the header stats of this dataset.
 |
 |      If in IPython, this will be formatted to HTML. Otherwise
 |      returns a console friendly string.
 |
 |      Parameters
 |      ----------
 |      display : bool, default: True
 |          Display this header in iPython.
 |
 |      html : bool, optional
 |          Generate the output as HTML.
 |
 |      Returns
 |      -------
 |      str
 |          Header statistics.
 |
 |  save(self, filename: 'Path | str', binary: 'bool' = True, texture: 'NumpyArray[np.uint8] | str | None' = None) -> 'None'
 |      Save this vtk object to file.
 |
 |      Parameters
 |      ----------
 |      filename : str, pathlib.Path
 |          Filename of output file. Writer type is inferred from
 |          the extension of the filename.
 |
 |      binary : bool, default: True
 |          If ``True``, write as binary.  Otherwise, write as ASCII.
 |
 |      texture : str, np.ndarray, optional
 |          Write a single texture array to file when using a PLY
 |          file.  Texture array must be a 3 or 4 component array with
 |          the datatype ``np.uint8``.  Array may be a cell array or a
 |          point array, and may also be a string if the array already
 |          exists in the PolyData.
 |
 |          If a string is provided, the texture array will be saved
 |          to disk as that name.  If an array is provided, the
 |          texture array will be saved as ``'RGBA'``
 |
 |          .. note::
 |             This feature is only available when saving PLY files.
 |
 |      Notes
 |      -----
 |      Binary files write much faster than ASCII and have a smaller
 |      file size.
 |
 |  shallow_copy(self, to_copy: '_vtk.vtkDataObject') -> 'None'
 |      Shallow copy the given mesh to this mesh.
 |
 |      Parameters
 |      ----------
 |      to_copy : pyvista.DataObject or vtk.vtkDataObject
 |          Data object to perform a shallow copy from.
 |
 |  ----------------------------------------------------------------------
 |  Readonly properties inherited from pyvista.core.dataobject.DataObject:
 |
 |  actual_memory_size
 |      Return the actual size of the dataset object.
 |
 |      Returns
 |      -------
 |      int
 |          The actual size of the dataset object in kibibytes (1024
 |          bytes).
 |
 |      Examples
 |      --------
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_airplane()
 |      >>> mesh.actual_memory_size  # doctest:+SKIP
 |      93
 |
 |  field_data
 |      Return FieldData as DataSetAttributes.
 |
 |      Use field data when size of the data you wish to associate
 |      with the dataset does not match the number of points or cells
 |      of the dataset.
 |
 |      Returns
 |      -------
 |      DataSetAttributes
 |          FieldData as DataSetAttributes.
 |
 |      Examples
 |      --------
 |      Add field data to a PolyData dataset and then return it.
 |
 |      >>> import pyvista as pv
 |      >>> import numpy as np
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.field_data['my-field-data'] = np.arange(10)
 |      >>> mesh.field_data['my-field-data']
 |      pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
 |
 |  memory_address
 |      Get address of the underlying VTK C++ object.
 |
 |      Returns
 |      -------
 |      str
 |          Memory address formatted as ``'Addr=%p'``.
 |
 |      Examples
 |      --------
 |      >>> import pyvista as pv
 |      >>> mesh = pv.Sphere()
 |      >>> mesh.memory_address
 |      'Addr=...'
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyvista.core.dataobject.DataObject:
 |
 |  user_dict
 |      Set or return a user-specified data dictionary.
 |
 |      The dictionary is stored as a JSON-serialized string as part of the mesh's
 |      field data. Unlike regular field data, which requires values to be stored
 |      as an array, the user dict provides a mapping for scalar values.
 |
 |      Since the user dict is stored as field data, it is automatically saved
 |      with the mesh when it is saved in a compatible file format (e.g. ``'.vtk'``).
 |      Any saved metadata is automatically de-serialized by PyVista whenever
 |      the user dict is accessed again. Since the data is stored as JSON, it
 |      may also be easily retrieved or read by other programs.
 |
 |      Any JSON-serializable values are permitted by the user dict, i.e. values
 |      can have type ``dict``, ``list``, ``tuple``, ``str``, ``int``, ``float``,
 |      ``bool``, or ``None``. Storing NumPy arrays is not directly supported, but
 |      these may be cast beforehand to a supported type, e.g. by calling ``tolist()``
 |      on the array.
 |
 |      To completely remove the user dict string from the dataset's field data,
 |      set its value to ``None``.
 |
 |      .. note::
 |
 |          The user dict is a convenience property and is intended for metadata storage.
 |          It has an inefficient dictionary implementation and should only be used to
 |          store a small number of infrequently-accessed keys with relatively small
 |          values. It should not be used to store frequently accessed array data
 |          with many entries (a regular field data array should be used instead).
 |
 |      .. warning::
 |
 |          Field data is typically passed-through by dataset filters, and therefore
 |          the user dict's items can generally be expected to persist and remain
 |          unchanged in the output of filtering methods. However, this behavior is
 |          not guaranteed, as it's possible that some filters may modify or clear
 |          field data. Use with caution.
 |
 |      Returns
 |      -------
 |      UserDict
 |          JSON-serialized dict-like object which is subclassed from :py:class:`collections.UserDict`.
 |
 |      Examples
 |      --------
 |      Load a mesh.
 |
 |      >>> import pyvista as pv
 |      >>> from pyvista import examples
 |      >>> mesh = examples.load_ant()
 |
 |      Add data to the user dict. The contents are serialized as JSON.
 |
 |      >>> mesh.user_dict['name'] = 'ant'
 |      >>> mesh.user_dict
 |      {"name": "ant"}
 |
 |      Alternatively, set the user dict from an existing dict.
 |
 |      >>> mesh.user_dict = dict(name='ant')
 |
 |      The user dict can be updated like a regular dict.
 |
 |      >>> mesh.user_dict.update(
 |      ...     {
 |      ...         'num_legs': 6,
 |      ...         'body_parts': ['head', 'thorax', 'abdomen'],
 |      ...     }
 |      ... )
 |      >>> mesh.user_dict
 |      {"name": "ant", "num_legs": 6, "body_parts": ["head", "thorax", "abdomen"]}
 |
 |      Data in the user dict is stored as field data.
 |
 |      >>> mesh.field_data
 |      pyvista DataSetAttributes
 |      Association     : NONE
 |      Contains arrays :
 |          _PYVISTA_USER_DICT      str        "{"name": "ant",..."
 |
 |      Since it's field data, the user dict can be saved to file along with the
 |      mesh and retrieved later.
 |
 |      >>> mesh.save('ant.vtk')
 |      >>> mesh_from_file = pv.read('ant.vtk')
 |      >>> mesh_from_file.user_dict
 |      {"name": "ant", "num_legs": 6, "body_parts": ["head", "thorax", "abdomen"]}
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from pyvista.core.dataobject.DataObject:
 |
 |  __hash__ = None
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from vtkmodules.vtkCommonDataModel.vtkImageData:
 |
 |  AllocateScalars(...)
 |      AllocateScalars(self, dataType:int, numComponents:int) -> None
 |      C++: virtual void AllocateScalars(int dataType, int numComponents)
 |      AllocateScalars(self, pipeline_info:vtkInformation) -> None
 |      C++: virtual void AllocateScalars(vtkInformation *pipeline_info)
 |
 |      Allocate the point scalars for this dataset. The data type
 |      determines the type of the array (VTK_FLOAT, VTK_INT etc.) where
 |      as numComponents determines its number of components.
 |
 |  ComputeBounds(...)
 |      ComputeBounds(self) -> None
 |      C++: void ComputeBounds() override;
 |
 |      Compute the data bounding box from data points. THIS METHOD IS
 |      NOT THREAD SAFE.
 |
 |  ComputeCellId(...)
 |      ComputeCellId(self, ijk:[int, int, int]) -> int
 |      C++: virtual vtkIdType ComputeCellId(int ijk[3])
 |
 |      Given a location in structured coordinates (i-j-k), return the
 |      cell id.
 |
 |  ComputeIndexToPhysicalMatrix(...)
 |      ComputeIndexToPhysicalMatrix(origin:(float, float, float),
 |          spacing:(float, float, float), direction:(float, float, float,
 |           float, float, float, float, float, float), result:[float,
 |          float, float, float, float, float, float, float, float, float,
 |           float, float, float, float, float, float]) -> None
 |      C++: static void ComputeIndexToPhysicalMatrix(
 |          double const origin[3], double const spacing[3],
 |          double const direction[9], double result[16])
 |
 |  ComputeInternalExtent(...)
 |      ComputeInternalExtent(self, intExt:[int, ...], tgtExt:[int, ...],
 |          bnds:[int, ...]) -> None
 |      C++: void ComputeInternalExtent(int *intExt, int *tgtExt,
 |          int *bnds)
 |
 |      Given how many pixel are required on a side for boundary
 |      conditions (in bnds), the target extent to traverse, compute the
 |      internal extent (the extent for this ImageData that does not
 |      suffer from any boundary conditions) and place it in intExt
 |
 |  ComputePointId(...)
 |      ComputePointId(self, ijk:[int, int, int]) -> int
 |      C++: virtual vtkIdType ComputePointId(int ijk[3])
 |
 |      Given a location in structured coordinates (i-j-k), return the
 |      point id.
 |
 |  ComputeStructuredCoordinates(...)
 |      ComputeStructuredCoordinates(self, x:(float, float, float),
 |          ijk:[int, int, int], pcoords:[float, float, float]) -> int
 |      C++: virtual int ComputeStructuredCoordinates(const double x[3],
 |          int ijk[3], double pcoords[3])
 |
 |      Convenience function computes the structured coordinates for a
 |      point x[3]. The voxel is specified by the array ijk[3], and the
 |      parametric coordinates in the cell are specified with pcoords[3].
 |      The function returns a 0 if the point x is outside of the volume,
 |      and a 1 if inside the volume.
 |
 |  CopyAndCastFrom(...)
 |      CopyAndCastFrom(self, inData:vtkImageData, extent:[int, int, int,
 |          int, int, int]) -> None
 |      C++: virtual void CopyAndCastFrom(vtkImageData *inData,
 |          int extent[6])
 |      CopyAndCastFrom(self, inData:vtkImageData, x0:int, x1:int, y0:int,
 |           y1:int, z0:int, z1:int) -> None
 |      C++: virtual void CopyAndCastFrom(vtkImageData *inData, int x0,
 |          int x1, int y0, int y1, int z0, int z1)
 |
 |      This method is passed a input and output region, and executes the
 |      filter algorithm to fill the output from the input. It just
 |      executes a switch statement to call the correct function for the
 |      regions data types.
 |
 |  CopyInformationFromPipeline(...)
 |      CopyInformationFromPipeline(self, information:vtkInformation)
 |          -> None
 |      C++: void CopyInformationFromPipeline(vtkInformation *information)
 |           override;
 |
 |      Override these to handle origin, spacing, scalar type, and scalar
 |      number of components.  See vtkDataObject for details.
 |
 |  CopyInformationToPipeline(...)
 |      CopyInformationToPipeline(self, information:vtkInformation)
 |          -> None
 |      C++: void CopyInformationToPipeline(vtkInformation *information)
 |          override;
 |
 |      Copy information from this data object to the pipeline
 |      information. This is used by the vtkTrivialProducer that is
 |      created when someone calls SetInputData() to connect the image to
 |      a pipeline.
 |
 |  CopyStructure(...)
 |      CopyStructure(self, ds:vtkDataSet) -> None
 |      C++: void CopyStructure(vtkDataSet *ds) override;
 |
 |      Copy the geometric and topological structure of an input image
 |      data object.
 |
 |  Crop(...)
 |      Crop(self, updateExtent:(int, ...)) -> None
 |      C++: void Crop(const int *updateExtent) override;
 |
 |      Reallocates and copies to set the Extent to updateExtent. This is
 |      used internally when the exact extent is requested, and the
 |      source generated more than the update extent.
 |
 |  DeepCopy(...)
 |      DeepCopy(self, src:vtkDataObject) -> None
 |      C++: void DeepCopy(vtkDataObject *src) override;
 |
 |      The goal of the method is to copy the complete data from src into
 |      this object. The implementation is delegated to the differenent
 |      subclasses. If you want to copy the data up to the array pointers
 |      only, @see ShallowCopy.
 |
 |      This method deep copy the field data and copy the internal
 |      structure.
 |
 |  ExtendedNew(...)
 |      ExtendedNew() -> vtkImageData
 |      C++: static vtkImageData *ExtendedNew()
 |
 |  FindAndGetCell(...)
 |      FindAndGetCell(self, x:[float, float, float], cell:vtkCell,
 |          cellId:int, tol2:float, subId:int, pcoords:[float, float,
 |          float], weights:[float, ...]) -> vtkCell
 |      C++: vtkCell *FindAndGetCell(double x[3], vtkCell *cell,
 |          vtkIdType cellId, double tol2, int &subId, double pcoords[3],
 |          double *weights) override;
 |
 |      Locate the cell that contains a point and return the cell. Also
 |      returns the subcell id, parametric coordinates and weights for
 |      subsequent interpolation. This method combines the derived class
 |      methods int FindCell and vtkCell *GetCell. Derived classes may
 |      provide a more efficient implementation. See for example
 |      vtkStructuredPoints. THIS METHOD IS NOT THREAD SAFE.
 |
 |  FindCell(...)
 |      FindCell(self, x:[float, float, float], cell:vtkCell, cellId:int,
 |          tol2:float, subId:int, pcoords:[float, float, float],
 |          weights:[float, ...]) -> int
 |      C++: vtkIdType FindCell(double x[3], vtkCell *cell,
 |          vtkIdType cellId, double tol2, int &subId, double pcoords[3],
 |          double *weights) override;
 |      FindCell(self, x:[float, float, float], cell:vtkCell,
 |          gencell:vtkGenericCell, cellId:int, tol2:float, subId:int,
 |          pcoords:[float, float, float], weights:[float, ...]) -> int
 |      C++: vtkIdType FindCell(double x[3], vtkCell *cell,
 |          vtkGenericCell *gencell, vtkIdType cellId, double tol2,
 |          int &subId, double pcoords[3], double *weights) override;
 |
 |      Locate cell based on global coordinate x and tolerance squared.
 |      If cell and cellId is non-nullptr, then search starts from this
 |      cell and looks at immediate neighbors.  Returns cellId >= 0 if
 |      inside, < 0 otherwise.  The parametric coordinates are provided
 |      in pcoords[3]. The interpolation weights are returned in
 |      weights[]. (The number of weights is equal to the number of
 |      points in the found cell). Tolerance is used to control how close
 |      the point is to be considered "in" the cell. THIS METHOD IS NOT
 |      THREAD SAFE.
 |
 |  FindPoint(...)
 |      FindPoint(self, x:float, y:float, z:float) -> int
 |      C++: virtual vtkIdType FindPoint(double x, double y, double z)
 |      FindPoint(self, x:[float, float, float]) -> int
 |      C++: vtkIdType FindPoint(double x[3]) override;
 |
 |      Locate the closest point to the global coordinate x. Return the
 |      point id. If point id < 0; then no point found. (This may arise
 |      when point is outside of dataset.) THIS METHOD IS THREAD SAFE IF
 |      FIRST CALLED FROM A SINGLE THREAD AND THE DATASET IS NOT MODIFIED
 |
 |  GetActualMemorySize(...)
 |      GetActualMemorySize(self) -> int
 |      C++: unsigned long GetActualMemorySize() override;
 |
 |      Return the actual size of the data in kibibytes (1024 bytes).
 |      This number is valid only after the pipeline has updated. The
 |      memory size returned is guaranteed to be greater than or equal to
 |      the memory required to represent the data (e.g., extra space in
 |      arrays, etc. are not included in the return value). THIS METHOD
 |      IS THREAD SAFE.
 |
 |  GetArrayIncrements(...)
 |      GetArrayIncrements(self, array:vtkDataArray, increments:[int, int,
 |           int]) -> None
 |      C++: void GetArrayIncrements(vtkDataArray *array,
 |          vtkIdType increments[3])
 |
 |      Since various arrays have different number of components, the
 |      will have different increments.
 |
 |  GetArrayPointer(...)
 |      GetArrayPointer(self, array:vtkDataArray, coordinates:[int, int,
 |          int]) -> Pointer
 |      C++: void *GetArrayPointer(vtkDataArray *array,
 |          int coordinates[3])
 |
 |  GetArrayPointerForExtent(...)
 |      GetArrayPointerForExtent(self, array:vtkDataArray, extent:[int,
 |          int, int, int, int, int]) -> Pointer
 |      C++: void *GetArrayPointerForExtent(vtkDataArray *array,
 |          int extent[6])
 |
 |      These are convenience methods for getting a pointer from any
 |      filed array.  It is a start at expanding image filters to process
 |      any array (not just scalars).
 |
 |  GetAxisUpdateExtent(...)
 |      GetAxisUpdateExtent(self, axis:int, min:int, max:int,
 |          updateExtent:(int, ...)) -> None
 |      C++: virtual void GetAxisUpdateExtent(int axis, int &min,
 |          int &max, const int *updateExtent)
 |
 |  GetCell(...)
 |      GetCell(self, cellId:int) -> vtkCell
 |      C++: vtkCell *GetCell(vtkIdType cellId) override;
 |      GetCell(self, i:int, j:int, k:int) -> vtkCell
 |      C++: vtkCell *GetCell(int i, int j, int k) override;
 |      GetCell(self, cellId:int, cell:vtkGenericCell) -> None
 |      C++: void GetCell(vtkIdType cellId, vtkGenericCell *cell)
 |          override;
 |
 |      Get cell with cellId such that: 0 <= cellId < NumberOfCells. The
 |      returned vtkCell is an object owned by this instance, hence the
 |      return value must not be deleted by the caller.
 |
 |      @warning Repeat calls to this function for different face ids
 |          will change
 |      the data stored in the internal member object whose pointer is
 |      returned by this function.
 |
 |      @warning THIS METHOD IS NOT THREAD SAFE. For a thread-safe
 |          version, please use
 |      void GetCell(vtkIdType cellId, vtkGenericCell* cell).
 |
 |  GetCellBounds(...)
 |      GetCellBounds(self, cellId:int, bounds:[float, float, float,
 |          float, float, float]) -> None
 |      C++: void GetCellBounds(vtkIdType cellId, double bounds[6])
 |          override;
 |
 |      Get the bounds of the cell with cellId such that: 0 <= cellId <
 |      NumberOfCells. A subclass may be able to determine the bounds of
 |      cell without using an expensive GetCell() method. A default
 |      implementation is provided that actually uses a GetCell() call.
 |      This is to ensure the method is available to all datasets.
 |      Subclasses should override this method to provide an efficient
 |      implementation. THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A
 |      SINGLE THREAD AND THE DATASET IS NOT MODIFIED
 |
 |  GetCellDims(...)
 |      GetCellDims(self, cellDims:[int, int, int]) -> None
 |      C++: void GetCellDims(int cellDims[3])
 |
 |      Given the node dimensions of this grid instance, this method
 |      computes the node dimensions. The value in each dimension can
 |      will have a lowest value of "1" such that computing the total
 |      number of cells can be achieved by simply by
 |      cellDims[0]*cellDims[1]*cellDims[2].
 |
 |  GetCellNeighbors(...)
 |      GetCellNeighbors(self, cellId:int, ptIds:vtkIdList,
 |          cellIds:vtkIdList) -> None
 |      C++: void GetCellNeighbors(vtkIdType cellId, vtkIdList *ptIds,
 |          vtkIdList *cellIds) override;
 |      GetCellNeighbors(self, cellId:int, ptIds:vtkIdList,
 |          cellIds:vtkIdList, seedLoc:[int, ...]) -> None
 |      C++: void GetCellNeighbors(vtkIdType cellId, vtkIdList *ptIds,
 |          vtkIdList *cellIds, int *seedLoc)
 |
 |      Topological inquiry to get all cells using list of points
 |      exclusive of cell specified (e.g., cellId). Note that the list
 |      consists of only cells that use ALL the points provided. THIS
 |      METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND
 |      THE DATASET IS NOT MODIFIED
 |
 |  GetCellPoints(...)
 |      GetCellPoints(self, cellId:int, ptIds:vtkIdList) -> None
 |      C++: void GetCellPoints(vtkIdType cellId, vtkIdList *ptIds)
 |          override;
 |      GetCellPoints(self, cellId:int, npts:int, pts:(int, ...),
 |          ptIds:vtkIdList) -> None
 |      C++: virtual void GetCellPoints(vtkIdType cellId, vtkIdType &npts,
 |           vtkIdType const *&pts, vtkIdList *ptIds)
 |
 |      Topological inquiry to get points defining cell. THIS METHOD IS
 |      THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND THE DATASET
 |      IS NOT MODIFIED
 |
 |  GetCellSize(...)
 |      GetCellSize(self, cellId:int) -> int
 |      C++: vtkIdType GetCellSize(vtkIdType cellId) override;
 |
 |      Get the size of cell with cellId such that: 0 <= cellId <
 |      NumberOfCells. THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A
 |      SINGLE THREAD AND THE DATASET IS NOT MODIFIED
 |
 |      @warning This method MUST be overridden for performance reasons.
 |      Default implementation is very unefficient.
 |
 |  GetCellType(...)
 |      GetCellType(self, cellId:int) -> int
 |      C++: int GetCellType(vtkIdType cellId) override;
 |
 |      Get type of cell with cellId such that: 0 <= cellId <
 |      NumberOfCells. THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A
 |      SINGLE THREAD AND THE DATASET IS NOT MODIFIED
 |
 |  GetContinuousIncrements(...)
 |      GetContinuousIncrements(self, extent:[int, int, int, int, int,
 |          int], incX:int, incY:int, incZ:int) -> None
 |      C++: virtual void GetContinuousIncrements(int extent[6],
 |          vtkIdType &incX, vtkIdType &incY, vtkIdType &incZ)
 |      GetContinuousIncrements(self, scalars:vtkDataArray, extent:[int,
 |          int, int, int, int, int], incX:int, incY:int, incZ:int)
 |          -> None
 |      C++: virtual void GetContinuousIncrements(vtkDataArray *scalars,
 |          int extent[6], vtkIdType &incX, vtkIdType &incY,
 |          vtkIdType &incZ)
 |
 |      Different ways to get the increments for moving around the data.
 |      incX is always returned with 0.  incY is returned with the
 |      increment needed to move from the end of one X scanline of data
 |      to the start of the next line.  incZ is filled in with the
 |      increment needed to move from the end of one image to the start
 |      of the next.  The proper way to use these values is to for a loop
 |      over Z, Y, X, C, incrementing the pointer by 1 after each
 |      component.  When the end of the component is reached, the pointer
 |      is set to the beginning of the next pixel, thus incX is properly
 |      set to 0. The first form of GetContinuousIncrements uses the
 |      active scalar field while the second form allows the scalar array
 |      to be passed in.
 |
 |  GetData(...)
 |      GetData(info:vtkInformation) -> vtkImageData
 |      C++: static vtkImageData *GetData(vtkInformation *info)
 |      GetData(v:vtkInformationVector, i:int=0) -> vtkImageData
 |      C++: static vtkImageData *GetData(vtkInformationVector *v,
 |          int i=0)
 |
 |      Retrieve an instance of this class from an information object.
 |
 |  GetDataDimension(...)
 |      GetDataDimension(self) -> int
 |      C++: virtual int GetDataDimension()
 |
 |      Return the dimensionality of the data.
 |
 |  GetDataObjectType(...)
 |      GetDataObjectType(self) -> int
 |      C++: int GetDataObjectType() override;
 |
 |      Return what type of dataset this is.
 |
 |  GetDimensions(...)
 |      GetDimensions(self) -> (int, int, int)
 |      C++: virtual int *GetDimensions()
 |      GetDimensions(self, dims:[int, int, int]) -> None
 |      C++: virtual void GetDimensions(int dims[3])
 |      GetDimensions(self, dims:[int, int, int]) -> None
 |      C++: virtual void GetDimensions(vtkIdType dims[3])
 |
 |      Get dimensions of this structured points dataset. It is the
 |      number of points on each axis. Dimensions are computed from
 |      Extents during this call.
 |      \warning Non thread-safe, use second signature if you want it to
 |          be.
 |
 |  GetDirectionMatrix(...)
 |      GetDirectionMatrix(self) -> vtkMatrix3x3
 |      C++: virtual vtkMatrix3x3 *GetDirectionMatrix()
 |
 |      Set/Get the direction transform of the dataset. The direction
 |      matrix is a 3x3 transformation matrix supporting scaling and
 |      rotation.
 |
 |  GetExtent(...)
 |      GetExtent(self) -> (int, int, int, int, int, int)
 |      C++: virtual int *GetExtent()
 |
 |  GetExtentType(...)
 |      GetExtentType(self) -> int
 |      C++: int GetExtentType() override;
 |
 |      The extent type is a 3D extent
 |
 |  GetIncrements(...)
 |      GetIncrements(self) -> (int, int, int)
 |      C++: virtual vtkIdType *GetIncrements()
 |      GetIncrements(self, incX:int, incY:int, incZ:int) -> None
 |      C++: virtual void GetIncrements(vtkIdType &incX, vtkIdType &incY,
 |          vtkIdType &incZ)
 |      GetIncrements(self, inc:[int, int, int]) -> None
 |      C++: virtual void GetIncrements(vtkIdType inc[3])
 |      GetIncrements(self, scalars:vtkDataArray) -> (int, int, int)
 |      C++: virtual vtkIdType *GetIncrements(vtkDataArray *scalars)
 |      GetIncrements(self, scalars:vtkDataArray, incX:int, incY:int,
 |          incZ:int) -> None
 |      C++: virtual void GetIncrements(vtkDataArray *scalars,
 |          vtkIdType &incX, vtkIdType &incY, vtkIdType &incZ)
 |      GetIncrements(self, scalars:vtkDataArray, inc:[int, int, int])
 |          -> None
 |      C++: virtual void GetIncrements(vtkDataArray *scalars,
 |          vtkIdType inc[3])
 |
 |      Different ways to get the increments for moving around the data.
 |      GetIncrements() calls ComputeIncrements() to ensure the
 |      increments are up to date.  The first three methods compute the
 |      increments based on the active scalar field while the next three,
 |      the scalar field is passed in.
 |
 |      Note that all methods which do not have the increments passed in
 |      are not thread-safe. When working on a given `vtkImageData`
 |      instance on multiple threads, each thread should use the `inc*`
 |      overloads to compute the increments to avoid racing with other
 |      threads.
 |
 |  GetIndexToPhysicalMatrix(...)
 |      GetIndexToPhysicalMatrix(self) -> vtkMatrix4x4
 |      C++: virtual vtkMatrix4x4 *GetIndexToPhysicalMatrix()
 |
 |      Get the transformation matrix from the index space to the
 |      physical space coordinate system of the dataset. The transform is
 |      a 4 by 4 matrix.
 |
 |  GetMaxCellSize(...)
 |      GetMaxCellSize(self) -> int
 |      C++: int GetMaxCellSize() override;
 |
 |      Convenience method returns largest cell size in dataset. This is
 |      generally used to allocate memory for supporting data structures.
 |      THIS METHOD IS THREAD SAFE
 |
 |  GetNumberOfCells(...)
 |      GetNumberOfCells(self) -> int
 |      C++: vtkIdType GetNumberOfCells() override;
 |
 |      Standard vtkDataSet API methods. See vtkDataSet for more
 |      information.
 |      \warning If GetCell(int,int,int) gets overridden in a subclass,
 |          it is
 |      necessary to override GetCell(vtkIdType) in that class as well
 |      since vtkImageData::GetCell(vtkIdType) will always call
 |      vkImageData::GetCell(int,int,int)
 |
 |  GetNumberOfGenerationsFromBase(...)
 |      GetNumberOfGenerationsFromBase(self, type:str) -> int
 |      C++: vtkIdType GetNumberOfGenerationsFromBase(const char *type)
 |          override;
 |
 |      Given the name of a base class of this class type, return the
 |      distance of inheritance between this class type and the named
 |      class (how many generations of inheritance are there between this
 |      class and the named class). If the named class is not in this
 |      class's inheritance tree, return a negative value. Valid
 |      responses will always be nonnegative. This method works in
 |      combination with vtkTypeMacro found in vtkSetGet.h.
 |
 |  GetNumberOfGenerationsFromBaseType(...)
 |      GetNumberOfGenerationsFromBaseType(type:str) -> int
 |      C++: static vtkIdType GetNumberOfGenerationsFromBaseType(
 |          const char *type)
 |
 |      Given a the name of a base class of this class type, return the
 |      distance of inheritance between this class type and the named
 |      class (how many generations of inheritance are there between this
 |      class and the named class). If the named class is not in this
 |      class's inheritance tree, return a negative value. Valid
 |      responses will always be nonnegative. This method works in
 |      combination with vtkTypeMacro found in vtkSetGet.h.
 |
 |  GetNumberOfPoints(...)
 |      GetNumberOfPoints(self) -> int
 |      C++: vtkIdType GetNumberOfPoints() override;
 |
 |      Determine the number of points composing the dataset. THIS METHOD
 |      IS THREAD SAFE
 |
 |  GetNumberOfScalarComponents(...)
 |      GetNumberOfScalarComponents(meta_data:vtkInformation) -> int
 |      C++: static int GetNumberOfScalarComponents(
 |          vtkInformation *meta_data)
 |      GetNumberOfScalarComponents(self) -> int
 |      C++: int GetNumberOfScalarComponents()
 |
 |  GetOrigin(...)
 |      GetOrigin(self) -> (float, float, float)
 |      C++: virtual double *GetOrigin()
 |
 |      Set/Get the origin of the dataset. The origin is the position in
 |      world coordinates of the point of extent (0,0,0). This point does
 |      not have to be part of the dataset, in other words, the dataset
 |      extent does not have to start at (0,0,0) and the origin can be
 |      outside of the dataset bounding box. The origin plus spacing
 |      determine the position in space of the points.
 |
 |  GetPhysicalToIndexMatrix(...)
 |      GetPhysicalToIndexMatrix(self) -> vtkMatrix4x4
 |      C++: virtual vtkMatrix4x4 *GetPhysicalToIndexMatrix()
 |
 |      Get the transformation matrix from the physical space to the
 |      index space coordinate system of the dataset. The transform is a
 |      4 by 4 matrix.
 |
 |  GetPoint(...)
 |      GetPoint(self, ptId:int) -> (float, float, float)
 |      C++: double *GetPoint(vtkIdType ptId) override;
 |      GetPoint(self, id:int, x:[float, float, float]) -> None
 |      C++: void GetPoint(vtkIdType id, double x[3]) override;
 |
 |      Get point coordinates with ptId such that: 0 <= ptId <
 |      NumberOfPoints. THIS METHOD IS NOT THREAD SAFE.
 |
 |  GetPointCells(...)
 |      GetPointCells(self, ptId:int, cellIds:vtkIdList) -> None
 |      C++: void GetPointCells(vtkIdType ptId, vtkIdList *cellIds)
 |          override;
 |
 |      Topological inquiry to get cells using point. THIS METHOD IS
 |      THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND THE DATASET
 |      IS NOT MODIFIED
 |
 |  GetPointGradient(...)
 |      GetPointGradient(self, i:int, j:int, k:int, s:vtkDataArray,
 |          g:[float, float, float]) -> None
 |      C++: virtual void GetPointGradient(int i, int j, int k,
 |          vtkDataArray *s, double g[3])
 |
 |      Given structured coordinates (i,j,k) for a point in a structured
 |      point dataset, compute the gradient vector from the scalar data
 |      at that point. The scalars s are the scalars from which the
 |      gradient is to be computed. This method will treat structured
 |      point datasets of any dimension.
 |
 |  GetScalarComponentAsDouble(...)
 |      GetScalarComponentAsDouble(self, x:int, y:int, z:int,
 |          component:int) -> float
 |      C++: virtual double GetScalarComponentAsDouble(int x, int y,
 |          int z, int component)
 |
 |  GetScalarComponentAsFloat(...)
 |      GetScalarComponentAsFloat(self, x:int, y:int, z:int,
 |          component:int) -> float
 |      C++: virtual float GetScalarComponentAsFloat(int x, int y, int z,
 |          int component)
 |
 |      For access to data from wrappers
 |
 |  GetScalarIndex(...)
 |      GetScalarIndex(self, coordinates:[int, int, int]) -> int
 |      C++: virtual vtkIdType GetScalarIndex(int coordinates[3])
 |      GetScalarIndex(self, x:int, y:int, z:int) -> int
 |      C++: virtual vtkIdType GetScalarIndex(int x, int y, int z)
 |
 |  GetScalarIndexForExtent(...)
 |      GetScalarIndexForExtent(self, extent:[int, int, int, int, int,
 |          int]) -> int
 |      C++: virtual vtkIdType GetScalarIndexForExtent(int extent[6])
 |
 |      Access the index for the scalar data
 |
 |  GetScalarPointer(...)
 |      GetScalarPointer(self, coordinates:[int, int, int]) -> Pointer
 |      C++: virtual void *GetScalarPointer(int coordinates[3])
 |      GetScalarPointer(self, x:int, y:int, z:int) -> Pointer
 |      C++: virtual void *GetScalarPointer(int x, int y, int z)
 |      GetScalarPointer(self) -> Pointer
 |      C++: virtual void *GetScalarPointer()
 |
 |  GetScalarPointerForExtent(...)
 |      GetScalarPointerForExtent(self, extent:[int, int, int, int, int,
 |          int]) -> Pointer
 |      C++: virtual void *GetScalarPointerForExtent(int extent[6])
 |
 |      Access the native pointer for the scalar data
 |
 |  GetScalarSize(...)
 |      GetScalarSize(self, meta_data:vtkInformation) -> int
 |      C++: virtual int GetScalarSize(vtkInformation *meta_data)
 |      GetScalarSize(self) -> int
 |      C++: virtual int GetScalarSize()
 |
 |      Get the size of the scalar type in bytes.
 |
 |  GetScalarType(...)
 |      GetScalarType(meta_data:vtkInformation) -> int
 |      C++: static int GetScalarType(vtkInformation *meta_data)
 |      GetScalarType(self) -> int
 |      C++: int GetScalarType()
 |
 |  GetScalarTypeAsString(...)
 |      GetScalarTypeAsString(self) -> str
 |      C++: const char *GetScalarTypeAsString()
 |
 |  GetScalarTypeMax(...)
 |      GetScalarTypeMax(self, meta_data:vtkInformation) -> float
 |      C++: virtual double GetScalarTypeMax(vtkInformation *meta_data)
 |      GetScalarTypeMax(self) -> float
 |      C++: virtual double GetScalarTypeMax()
 |
 |  GetScalarTypeMin(...)
 |      GetScalarTypeMin(self, meta_data:vtkInformation) -> float
 |      C++: virtual double GetScalarTypeMin(vtkInformation *meta_data)
 |      GetScalarTypeMin(self) -> float
 |      C++: virtual double GetScalarTypeMin()
 |
 |      These returns the minimum and maximum values the ScalarType can
 |      hold without overflowing.
 |
 |  GetSpacing(...)
 |      GetSpacing(self) -> (float, float, float)
 |      C++: virtual double *GetSpacing()
 |
 |      Set the spacing (width,height,length) of the cubical cells that
 |      compose the data set.
 |
 |  GetTupleIndex(...)
 |      GetTupleIndex(self, array:vtkDataArray, coordinates:[int, int,
 |          int]) -> int
 |      C++: vtkIdType GetTupleIndex(vtkDataArray *array,
 |          int coordinates[3])
 |
 |      Given a data array and a coordinate, return the index of the
 |      tuple in the array corresponding to that coordinate.
 |
 |      This method is analogous to GetArrayPointer(), but it conforms to
 |      the API of vtkGenericDataArray.
 |
 |  GetVoxelGradient(...)
 |      GetVoxelGradient(self, i:int, j:int, k:int, s:vtkDataArray,
 |          g:vtkDataArray) -> None
 |      C++: virtual void GetVoxelGradient(int i, int j, int k,
 |          vtkDataArray *s, vtkDataArray *g)
 |
 |      Given structured coordinates (i,j,k) for a voxel cell, compute
 |      the eight gradient values for the voxel corners. The order in
 |      which the gradient vectors are arranged corresponds to the
 |      ordering of the voxel points. Gradient vector is computed by
 |      central differences (except on edges of volume where forward
 |      difference is used). The scalars s are the scalars from which the
 |      gradient is to be computed. This method will treat only 3D
 |      structured point datasets (i.e., volumes).
 |
 |  HasAnyBlankCells(...)
 |      HasAnyBlankCells(self) -> bool
 |      C++: bool HasAnyBlankCells() override;
 |
 |      Returns 1 if there is any visibility constraint on the cells, 0
 |      otherwise.
 |
 |  HasAnyBlankPoints(...)
 |      HasAnyBlankPoints(self) -> bool
 |      C++: bool HasAnyBlankPoints() override;
 |
 |      Returns 1 if there is any visibility constraint on the points, 0
 |      otherwise.
 |
 |  HasNumberOfScalarComponents(...)
 |      HasNumberOfScalarComponents(meta_data:vtkInformation) -> bool
 |      C++: static bool HasNumberOfScalarComponents(
 |          vtkInformation *meta_data)
 |
 |  HasScalarType(...)
 |      HasScalarType(meta_data:vtkInformation) -> bool
 |      C++: static bool HasScalarType(vtkInformation *meta_data)
 |
 |  Initialize(...)
 |      Initialize(self) -> None
 |      C++: void Initialize() override;
 |
 |      Restore data object to initial state.
 |
 |  IsA(...)
 |      IsA(self, type:str) -> int
 |      C++: vtkTypeBool IsA(const char *type) override;
 |
 |      Return 1 if this class is the same type of (or a subclass of) the
 |      named class. Returns 0 otherwise. This method works in
 |      combination with vtkTypeMacro found in vtkSetGet.h.
 |
 |  IsCellVisible(...)
 |      IsCellVisible(self, cellId:int) -> int
 |      C++: unsigned char IsCellVisible(vtkIdType cellId)
 |
 |      Return non-zero value if specified point is visible. These
 |      methods should be called only after the dimensions of the grid
 |      are set.
 |
 |  IsPointVisible(...)
 |      IsPointVisible(self, ptId:int) -> int
 |      C++: unsigned char IsPointVisible(vtkIdType ptId)
 |
 |      Return non-zero value if specified point is visible. These
 |      methods should be called only after the dimensions of the grid
 |      are set.
 |
 |  IsTypeOf(...)
 |      IsTypeOf(type:str) -> int
 |      C++: static vtkTypeBool IsTypeOf(const char *type)
 |
 |      Return 1 if this class type is the same type of (or a subclass
 |      of) the named class. Returns 0 otherwise. This method works in
 |      combination with vtkTypeMacro found in vtkSetGet.h.
 |
 |  NewInstance(...)
 |      NewInstance(self) -> vtkImageData
 |      C++: vtkImageData *NewInstance()
 |
 |  PrepareForNewData(...)
 |      PrepareForNewData(self) -> None
 |      C++: void PrepareForNewData() override;
 |
 |      make the output data ready for new data to be inserted. For most
 |      objects we just call Initialize. But for image data we leave the
 |      old data in case the memory can be reused.
 |
 |  SafeDownCast(...)
 |      SafeDownCast(o:vtkObjectBase) -> vtkImageData
 |      C++: static vtkImageData *SafeDownCast(vtkObjectBase *o)
 |
 |  SetAxisUpdateExtent(...)
 |      SetAxisUpdateExtent(self, axis:int, min:int, max:int,
 |          updateExtent:(int, ...), axisUpdateExtent:[int, ...]) -> None
 |      C++: virtual void SetAxisUpdateExtent(int axis, int min, int max,
 |          const int *updateExtent, int *axisUpdateExtent)
 |
 |      Set / Get the extent on just one axis
 |
 |  SetDimensions(...)
 |      SetDimensions(self, i:int, j:int, k:int) -> None
 |      C++: virtual void SetDimensions(int i, int j, int k)
 |      SetDimensions(self, dims:(int, int, int)) -> None
 |      C++: virtual void SetDimensions(const int dims[3])
 |
 |      Same as SetExtent(0, i-1, 0, j-1, 0, k-1)
 |
 |  SetDirectionMatrix(...)
 |      SetDirectionMatrix(self, m:vtkMatrix3x3) -> None
 |      C++: virtual void SetDirectionMatrix(vtkMatrix3x3 *m)
 |      SetDirectionMatrix(self, elements:(float, float, float, float,
 |          float, float, float, float, float)) -> None
 |      C++: virtual void SetDirectionMatrix(const double elements[9])
 |      SetDirectionMatrix(self, e00:float, e01:float, e02:float,
 |          e10:float, e11:float, e12:float, e20:float, e21:float,
 |          e22:float) -> None
 |      C++: virtual void SetDirectionMatrix(double e00, double e01,
 |          double e02, double e10, double e11, double e12, double e20,
 |          double e21, double e22)
 |
 |  SetExtent(...)
 |      SetExtent(self, extent:[int, int, int, int, int, int]) -> None
 |      C++: virtual void SetExtent(int extent[6])
 |      SetExtent(self, x1:int, x2:int, y1:int, y2:int, z1:int, z2:int)
 |          -> None
 |      C++: virtual void SetExtent(int x1, int x2, int y1, int y2,
 |          int z1, int z2)
 |
 |      Set/Get the extent. On each axis, the extent is defined by the
 |      index of the first point and the index of the last point.  The
 |      extent should be set before the "Scalars" are set or allocated.
 |      The Extent is stored in the order (X, Y, Z). The dataset extent
 |      does not have to start at (0,0,0). (0,0,0) is just the extent of
 |      the origin. The first point (the one with Id=0) is at extent
 |      (Extent[0],Extent[2],Extent[4]). As for any dataset, a data array
 |      on point data starts at Id=0.
 |
 |  SetNumberOfScalarComponents(...)
 |      SetNumberOfScalarComponents(n:int, meta_data:vtkInformation)
 |          -> None
 |      C++: static void SetNumberOfScalarComponents(int n,
 |          vtkInformation *meta_data)
 |
 |      Set/Get the number of scalar components for points. As with the
 |      SetScalarType method this is setting pipeline info.
 |
 |  SetOrigin(...)
 |      SetOrigin(self, i:float, j:float, k:float) -> None
 |      C++: virtual void SetOrigin(double i, double j, double k)
 |      SetOrigin(self, ijk:(float, float, float)) -> None
 |      C++: virtual void SetOrigin(const double ijk[3])
 |
 |  SetScalarComponentFromDouble(...)
 |      SetScalarComponentFromDouble(self, x:int, y:int, z:int,
 |          component:int, v:float) -> None
 |      C++: virtual void SetScalarComponentFromDouble(int x, int y,
 |          int z, int component, double v)
 |
 |  SetScalarComponentFromFloat(...)
 |      SetScalarComponentFromFloat(self, x:int, y:int, z:int,
 |          component:int, v:float) -> None
 |      C++: virtual void SetScalarComponentFromFloat(int x, int y, int z,
 |           int component, float v)
 |
 |  SetScalarType(...)
 |      SetScalarType(__a:int, meta_data:vtkInformation) -> None
 |      C++: static void SetScalarType(int, vtkInformation *meta_data)
 |
 |  SetSpacing(...)
 |      SetSpacing(self, i:float, j:float, k:float) -> None
 |      C++: virtual void SetSpacing(double i, double j, double k)
 |      SetSpacing(self, ijk:(float, float, float)) -> None
 |      C++: virtual void SetSpacing(const double ijk[3])
 |
 |  ShallowCopy(...)
 |      ShallowCopy(self, src:vtkDataObject) -> None
 |      C++: void ShallowCopy(vtkDataObject *src) override;
 |
 |      Shallow and Deep copy.
 |
 |  TransformContinuousIndexToPhysicalPoint(...)
 |      TransformContinuousIndexToPhysicalPoint(self, i:float, j:float,
 |          k:float, xyz:[float, float, float]) -> None
 |      C++: virtual void TransformContinuousIndexToPhysicalPoint(
 |          double i, double j, double k, double xyz[3])
 |      TransformContinuousIndexToPhysicalPoint(self, ijk:(float, float,
 |          float), xyz:[float, float, float]) -> None
 |      C++: virtual void TransformContinuousIndexToPhysicalPoint(
 |          const double ijk[3], double xyz[3])
 |      TransformContinuousIndexToPhysicalPoint(i:float, j:float, k:float,
 |           origin:(float, float, float), spacing:(float, float, float),
 |          direction:(float, float, float, float, float, float, float,
 |          float, float), xyz:[float, float, float]) -> None
 |      C++: static void TransformContinuousIndexToPhysicalPoint(double i,
 |           double j, double k, double const origin[3],
 |          double const spacing[3], double const direction[9],
 |          double xyz[3])
 |
 |      Convert coordinates from index space (ijk) to physical space
 |      (xyz).
 |
 |  TransformIndexToPhysicalPoint(...)
 |      TransformIndexToPhysicalPoint(self, i:int, j:int, k:int,
 |          xyz:[float, float, float]) -> None
 |      C++: virtual void TransformIndexToPhysicalPoint(int i, int j,
 |          int k, double xyz[3])
 |      TransformIndexToPhysicalPoint(self, ijk:(int, int, int),
 |          xyz:[float, float, float]) -> None
 |      C++: virtual void TransformIndexToPhysicalPoint(const int ijk[3],
 |          double xyz[3])
 |
 |  TransformPhysicalNormalToContinuousIndex(...)
 |      TransformPhysicalNormalToContinuousIndex(self, xyz:(float, float,
 |          float), ijk:[float, float, float]) -> None
 |      C++: virtual void TransformPhysicalNormalToContinuousIndex(
 |          const double xyz[3], double ijk[3])
 |
 |      Convert normal from physical space (xyz) to index space (ijk).
 |
 |  TransformPhysicalPlaneToContinuousIndex(...)
 |      TransformPhysicalPlaneToContinuousIndex(self, pplane:(float,
 |          float, float, float), iplane:[float, float, float, float])
 |          -> None
 |      C++: virtual void TransformPhysicalPlaneToContinuousIndex(
 |          double const pplane[4], double iplane[4])
 |
 |      Convert a plane from physical to a continuous index. The plane is
 |      represented as n(x-xo)=0; or using a four component normal:
 |      pplane=( nx,ny,nz,-(n(x0)) ).
 |
 |  TransformPhysicalPointToContinuousIndex(...)
 |      TransformPhysicalPointToContinuousIndex(self, x:float, y:float,
 |          z:float, ijk:[float, float, float]) -> None
 |      C++: virtual void TransformPhysicalPointToContinuousIndex(
 |          double x, double y, double z, double ijk[3])
 |      TransformPhysicalPointToContinuousIndex(self, xyz:(float, float,
 |          float), ijk:[float, float, float]) -> None
 |      C++: virtual void TransformPhysicalPointToContinuousIndex(
 |          const double xyz[3], double ijk[3])
 |
 |      Convert coordinates from physical space (xyz) to index space
 |      (ijk).
 |
 |  __delattr__(self, name, /)
 |      Implement delattr(self, name).
 |
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |
 |  __setattr__(self, name, value, /)
 |      Implement setattr(self, name, value).
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from vtkmodules.vtkCommonDataModel.vtkImageData:
 |
 |  __this__
 |      Pointer to the C++ object.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from vtkmodules.vtkCommonDataModel.vtkImageData:
 |
 |  __vtkname__ = 'vtkImageData'
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from vtkmodules.vtkCommonDataModel.vtkDataSet:
 |
 |  AllocateCellGhostArray(...)
 |      AllocateCellGhostArray(self) -> vtkUnsignedCharArray
 |      C++: vtkUnsignedCharArray *AllocateCellGhostArray()
 |
 |      Allocate ghost array for cells.
 |
 |  AllocatePointGhostArray(...)
 |      AllocatePointGhostArray(self) -> vtkUnsignedCharArray
 |      C++: vtkUnsignedCharArray *AllocatePointGhostArray()
 |
 |      Allocate ghost array for points.
 |
 |  CheckAttributes(...)
 |      CheckAttributes(self) -> int
 |      C++: int CheckAttributes()
 |
 |      This method checks to see if the cell and point attributes match
 |      the geometry.  Many filters will crash if the number of tuples in
 |      an array is less than the number of points/cells. This method
 |      returns 1 if there is a mismatch, and 0 if everything is ok.  It
 |      prints an error if an array is too short, and a warning if an
 |      array is too long.
 |
 |  CopyAttributes(...)
 |      CopyAttributes(self, ds:vtkDataSet) -> None
 |      C++: virtual void CopyAttributes(vtkDataSet *ds)
 |
 |      Copy the attributes associated with the specified dataset to this
 |      instance of vtkDataSet. THIS METHOD IS NOT THREAD SAFE.
 |
 |  GenerateGhostArray(...)
 |      GenerateGhostArray(self, zeroExt:[int, int, int, int, int, int])
 |          -> None
 |      C++: virtual void GenerateGhostArray(int zeroExt[6])
 |      GenerateGhostArray(self, zeroExt:[int, int, int, int, int, int],
 |          cellOnly:bool) -> None
 |      C++: virtual void GenerateGhostArray(int zeroExt[6],
 |          bool cellOnly)
 |
 |      Normally called by pipeline executives or algorithms only. This
 |      method computes the ghost arrays for a given dataset. The zeroExt
 |      argument specifies the extent of the region which ghost type = 0.
 |
 |  GetAttributesAsFieldData(...)
 |      GetAttributesAsFieldData(self, type:int) -> vtkFieldData
 |      C++: vtkFieldData *GetAttributesAsFieldData(int type) override;
 |
 |      Returns the attributes of the data object as a vtkFieldData. This
 |      returns non-null values in all the same cases as GetAttributes,
 |      in addition to the case of FIELD, which will return the field
 |      data for any vtkDataObject subclass.
 |
 |  GetBounds(...)
 |      GetBounds(self) -> (float, float, float, float, float, float)
 |      C++: double *GetBounds()
 |      GetBounds(self, bounds:[float, float, float, float, float, float])
 |           -> None
 |      C++: void GetBounds(double bounds[6])
 |
 |      Return a pointer to the geometry bounding box in the form
 |      (xmin,xmax, ymin,ymax, zmin,zmax). THIS METHOD IS NOT THREAD
 |      SAFE.
 |
 |  GetCellData(...)
 |      GetCellData(self) -> vtkCellData
 |      C++: vtkCellData *GetCellData()
 |
 |      Return a pointer to this dataset's cell data. THIS METHOD IS
 |      THREAD SAFE
 |
 |  GetCellGhostArray(...)
 |      GetCellGhostArray(self) -> vtkUnsignedCharArray
 |      C++: vtkUnsignedCharArray *GetCellGhostArray()
 |
 |      Get the array that defines the ghost type of each cell. We cache
 |      the pointer to the array to save a lookup involving string
 |      comparisons
 |
 |  GetCellNumberOfFaces(...)
 |      GetCellNumberOfFaces(self, cellId:int, cellType:int,
 |          cell:vtkGenericCell) -> int
 |      C++: int GetCellNumberOfFaces(vtkIdType cellId,
 |          unsigned char &cellType, vtkGenericCell *cell)
 |
 |      Get the number of faces of a cell.
 |
 |      Most of the times extracting the number of faces requires only
 |      extracting the cell type. However, for some cell types, the
 |      number of faces is not constant. For example, a vtkPolyhedron
 |      cell can have a different number of faces for each cell. That's
 |      why this method requires the cell id and the dataset.
 |
 |  GetCellTypes(...)
 |      GetCellTypes(self, types:vtkCellTypes) -> None
 |      C++: virtual void GetCellTypes(vtkCellTypes *types)
 |
 |      Get a list of types of cells in a dataset. The list consists of
 |      an array of types (not necessarily in any order), with a single
 |      entry per type. For example a dataset 5 triangles, 3 lines, and
 |      100 hexahedra would result a list of three entries, corresponding
 |      to the types VTK_TRIANGLE, VTK_LINE, and VTK_HEXAHEDRON. THIS
 |      METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND
 |      THE DATASET IS NOT MODIFIED
 |
 |  GetCenter(...)
 |      GetCenter(self) -> (float, float, float)
 |      C++: double *GetCenter()
 |      GetCenter(self, center:[float, float, float]) -> None
 |      C++: void GetCenter(double center[3])
 |
 |      Get the center of the bounding box. THIS METHOD IS NOT THREAD
 |      SAFE.
 |
 |  GetGhostArray(...)
 |      GetGhostArray(self, type:int) -> vtkUnsignedCharArray
 |      C++: vtkUnsignedCharArray *GetGhostArray(int type) override;
 |
 |      Returns the ghost array for the given type (point or cell). Takes
 |      advantage of the cache with the pointer to the array to save a
 |      string comparison.
 |
 |  GetLength(...)
 |      GetLength(self) -> float
 |      C++: double GetLength()
 |
 |      Return the length of the diagonal of the bounding box. THIS
 |      METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND
 |      THE DATASET IS NOT MODIFIED
 |
 |  GetLength2(...)
 |      GetLength2(self) -> float
 |      C++: double GetLength2()
 |
 |      Return the squared length of the diagonal of the bounding box.
 |      THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD
 |      AND THE DATASET IS NOT MODIFIED
 |
 |  GetMTime(...)
 |      GetMTime(self) -> int
 |      C++: vtkMTimeType GetMTime() override;
 |
 |      Datasets are composite objects and need to check each part for
 |      MTime THIS METHOD IS THREAD SAFE
 |
 |  GetNumberOfElements(...)
 |      GetNumberOfElements(self, type:int) -> int
 |      C++: vtkIdType GetNumberOfElements(int type) override;
 |
 |      Get the number of elements for a specific attribute type (POINT,
 |      CELL, etc.).
 |
 |  GetPointData(...)
 |      GetPointData(self) -> vtkPointData
 |      C++: vtkPointData *GetPointData()
 |
 |      Return a pointer to this dataset's point data. THIS METHOD IS
 |      THREAD SAFE
 |
 |  GetPointGhostArray(...)
 |      GetPointGhostArray(self) -> vtkUnsignedCharArray
 |      C++: vtkUnsignedCharArray *GetPointGhostArray()
 |
 |      Gets the array that defines the ghost type of each point. We
 |      cache the pointer to the array to save a lookup involving string
 |      comparisons
 |
 |  GetScalarRange(...)
 |      GetScalarRange(self, range:[float, float]) -> None
 |      C++: virtual void GetScalarRange(double range[2])
 |      GetScalarRange(self) -> (float, float)
 |      C++: double *GetScalarRange()
 |
 |      Convenience method to get the range of the first component (and
 |      only the first component) of any scalars in the data set.  If the
 |      data has both point data and cell data, it returns the (min/max)
 |      range of combined point and cell data.  If there are no point or
 |      cell scalars the method will return (0,1).  Note: It might be
 |      necessary to call Update to create or refresh the scalars before
 |      calling this method. THIS METHOD IS THREAD SAFE IF FIRST CALLED
 |      FROM A SINGLE THREAD AND THE DATASET IS NOT MODIFIED
 |
 |  HasAnyGhostCells(...)
 |      HasAnyGhostCells(self) -> bool
 |      C++: bool HasAnyGhostCells()
 |
 |      Returns 1 if there are any ghost cells 0 otherwise.
 |
 |  HasAnyGhostPoints(...)
 |      HasAnyGhostPoints(self) -> bool
 |      C++: bool HasAnyGhostPoints()
 |
 |      Returns 1 if there are any ghost points 0 otherwise.
 |
 |  NewCellIterator(...)
 |      NewCellIterator(self) -> vtkCellIterator
 |      C++: virtual vtkCellIterator *NewCellIterator()
 |
 |      Return an iterator that traverses the cells in this data set.
 |
 |  SetCellOrderAndRationalWeights(...)
 |      SetCellOrderAndRationalWeights(self, cellId:int,
 |          cell:vtkGenericCell) -> None
 |      C++: void SetCellOrderAndRationalWeights(vtkIdType cellId,
 |          vtkGenericCell *cell)
 |
 |  Squeeze(...)
 |      Squeeze(self) -> None
 |      C++: virtual void Squeeze()
 |
 |      Reclaim any extra memory used to store data. THIS METHOD IS NOT
 |      THREAD SAFE.
 |
 |  UpdateCellGhostArrayCache(...)
 |      UpdateCellGhostArrayCache(self) -> None
 |      C++: void UpdateCellGhostArrayCache()
 |
 |      Updates the pointer to the cell ghost array.
 |
 |  UpdatePointGhostArrayCache(...)
 |      UpdatePointGhostArrayCache(self) -> None
 |      C++: void UpdatePointGhostArrayCache()
 |
 |      Updates the pointer to the point ghost array.
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from vtkmodules.vtkCommonDataModel.vtkDataSet:
 |
 |  CELL_DATA_FIELD = 2
 |
 |  DATA_OBJECT_FIELD = 0
 |
 |  FieldDataType = <class 'vtkmodules.vtkCommonDataModel.vtkDataSet.Field...
 |
 |  POINT_DATA_FIELD = 1
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from vtkmodules.vtkCommonDataModel.vtkDataObject:
 |
 |  ALL_PIECES_EXTENT(...)
 |      ALL_PIECES_EXTENT() -> vtkInformationIntegerVectorKey
 |      C++: static vtkInformationIntegerVectorKey *ALL_PIECES_EXTENT()
 |
 |  BOUNDING_BOX(...)
 |      BOUNDING_BOX() -> vtkInformationDoubleVectorKey
 |      C++: static vtkInformationDoubleVectorKey *BOUNDING_BOX()
 |
 |  CELL_DATA_VECTOR(...)
 |      CELL_DATA_VECTOR() -> vtkInformationInformationVectorKey
 |      C++: static vtkInformationInformationVectorKey *CELL_DATA_VECTOR()
 |
 |  DATA_EXTENT(...)
 |      DATA_EXTENT() -> vtkInformationIntegerPointerKey
 |      C++: static vtkInformationIntegerPointerKey *DATA_EXTENT()
 |
 |  DATA_EXTENT_TYPE(...)
 |      DATA_EXTENT_TYPE() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *DATA_EXTENT_TYPE()
 |
 |  DATA_NUMBER_OF_GHOST_LEVELS(...)
 |      DATA_NUMBER_OF_GHOST_LEVELS() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *DATA_NUMBER_OF_GHOST_LEVELS(
 |          )
 |
 |  DATA_NUMBER_OF_PIECES(...)
 |      DATA_NUMBER_OF_PIECES() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *DATA_NUMBER_OF_PIECES()
 |
 |  DATA_OBJECT(...)
 |      DATA_OBJECT() -> vtkInformationDataObjectKey
 |      C++: static vtkInformationDataObjectKey *DATA_OBJECT()
 |
 |  DATA_PIECE_NUMBER(...)
 |      DATA_PIECE_NUMBER() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *DATA_PIECE_NUMBER()
 |
 |  DATA_TIME_STEP(...)
 |      DATA_TIME_STEP() -> vtkInformationDoubleKey
 |      C++: static vtkInformationDoubleKey *DATA_TIME_STEP()
 |
 |  DATA_TYPE_NAME(...)
 |      DATA_TYPE_NAME() -> vtkInformationStringKey
 |      C++: static vtkInformationStringKey *DATA_TYPE_NAME()
 |
 |  DIRECTION(...)
 |      DIRECTION() -> vtkInformationDoubleVectorKey
 |      C++: static vtkInformationDoubleVectorKey *DIRECTION()
 |
 |  DataHasBeenGenerated(...)
 |      DataHasBeenGenerated(self) -> None
 |      C++: void DataHasBeenGenerated()
 |
 |      This method is called by the source when it executes to generate
 |      data. It is sort of the opposite of ReleaseData. It sets the
 |      DataReleased flag to 0, and sets a new UpdateTime.
 |
 |  EDGE_DATA_VECTOR(...)
 |      EDGE_DATA_VECTOR() -> vtkInformationInformationVectorKey
 |      C++: static vtkInformationInformationVectorKey *EDGE_DATA_VECTOR()
 |
 |  FIELD_ACTIVE_ATTRIBUTE(...)
 |      FIELD_ACTIVE_ATTRIBUTE() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *FIELD_ACTIVE_ATTRIBUTE()
 |
 |  FIELD_ARRAY_TYPE(...)
 |      FIELD_ARRAY_TYPE() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *FIELD_ARRAY_TYPE()
 |
 |  FIELD_ASSOCIATION(...)
 |      FIELD_ASSOCIATION() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *FIELD_ASSOCIATION()
 |
 |  FIELD_ATTRIBUTE_TYPE(...)
 |      FIELD_ATTRIBUTE_TYPE() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *FIELD_ATTRIBUTE_TYPE()
 |
 |  FIELD_NAME(...)
 |      FIELD_NAME() -> vtkInformationStringKey
 |      C++: static vtkInformationStringKey *FIELD_NAME()
 |
 |  FIELD_NUMBER_OF_COMPONENTS(...)
 |      FIELD_NUMBER_OF_COMPONENTS() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *FIELD_NUMBER_OF_COMPONENTS()
 |
 |  FIELD_NUMBER_OF_TUPLES(...)
 |      FIELD_NUMBER_OF_TUPLES() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *FIELD_NUMBER_OF_TUPLES()
 |
 |  FIELD_OPERATION(...)
 |      FIELD_OPERATION() -> vtkInformationIntegerKey
 |      C++: static vtkInformationIntegerKey *FIELD_OPERATION()
 |
 |  FIELD_RANGE(...)
 |      FIELD_RANGE() -> vtkInformationDoubleVectorKey
 |      C++: static vtkInformationDoubleVectorKey *FIELD_RANGE()
 |
 |  GetActiveFieldInformation(...)
 |      GetActiveFieldInformation(info:vtkInformation,
 |          fieldAssociation:int, attributeType:int) -> vtkInformation
 |      C++: static vtkInformation *GetActiveFieldInformation(
 |          vtkInformation *info, int fieldAssociation, int attributeType)
 |
 |      Return the information object within the input information
 |      object's field data corresponding to the specified association
 |      (FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS) and
 |      attribute (SCALARS, VECTORS, NORMALS, TCOORDS, or TENSORS)
 |
 |  GetAssociationTypeAsString(...)
 |      GetAssociationTypeAsString(associationType:int) -> str
 |      C++: static const char *GetAssociationTypeAsString(
 |          int associationType)
 |
 |      Given an integer association type, this static method returns a
 |      string type for the attribute (i.e. associationType = 0: returns
 |      "Points").
 |
 |  GetAssociationTypeFromString(...)
 |      GetAssociationTypeFromString(associationName:str) -> int
 |      C++: static int GetAssociationTypeFromString(
 |          const char *associationName)
 |
 |      Given a string association name, this static method returns an
 |      integer association type for the attribute (i.e. associationName
 |      = "Points": returns 0).
 |
 |  GetAttributeTypeForArray(...)
 |      GetAttributeTypeForArray(self, arr:vtkAbstractArray) -> int
 |      C++: virtual int GetAttributeTypeForArray(vtkAbstractArray *arr)
 |
 |      Retrieves the attribute type that an array came from. This is
 |      useful for obtaining which attribute type a input array to an
 |      algorithm came from (retrieved from
 |      GetInputAbstractArrayToProcesss).
 |
 |  GetAttributes(...)
 |      GetAttributes(self, type:int) -> vtkDataSetAttributes
 |      C++: virtual vtkDataSetAttributes *GetAttributes(int type)
 |
 |      Returns the attributes of the data object of the specified
 |      attribute type. The type may be:  POINT  - Defined in vtkDataSet
 |      subclasses. CELL   - Defined in vtkDataSet subclasses. VERTEX -
 |      Defined in vtkGraph subclasses. EDGE   - Defined in vtkGraph
 |      subclasses. ROW    - Defined in vtkTable.  The other attribute
 |      type, FIELD, will return nullptr since field data is stored as a
 |      vtkFieldData instance, not a vtkDataSetAttributes instance. To
 |      retrieve field data, use GetAttributesAsFieldData.
 |
 |      @warning This method NEEDS to be
 |      overridden in subclasses to work as documented. If not, it
 |      returns nullptr for any type but FIELD.
 |
 |  GetDataReleased(...)
 |      GetDataReleased(self) -> int
 |      C++: virtual vtkTypeBool GetDataReleased()
 |
 |      Get the flag indicating the data has been released.
 |
 |  GetFieldData(...)
 |      GetFieldData(self) -> vtkFieldData
 |      C++: virtual vtkFieldData *GetFieldData()
 |
 |  GetGlobalReleaseDataFlag(...)
 |      GetGlobalReleaseDataFlag() -> int
 |      C++: static vtkTypeBool GetGlobalReleaseDataFlag()
 |
 |  GetInformation(...)
 |      GetInformation(self) -> vtkInformation
 |      C++: virtual vtkInformation *GetInformation()
 |
 |      Set/Get the information object associated with this data object.
 |
 |  GetNamedFieldInformation(...)
 |      GetNamedFieldInformation(info:vtkInformation,
 |          fieldAssociation:int, name:str) -> vtkInformation
 |      C++: static vtkInformation *GetNamedFieldInformation(
 |          vtkInformation *info, int fieldAssociation, const char *name)
 |
 |      Return the information object within the input information
 |      object's field data corresponding to the specified association
 |      (FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS) and name.
 |
 |  GetUpdateTime(...)
 |      GetUpdateTime(self) -> int
 |      C++: vtkMTimeType GetUpdateTime()
 |
 |      Used by Threaded ports to determine if they should initiate an
 |      asynchronous update (still in development).
 |
 |  GlobalReleaseDataFlagOff(...)
 |      GlobalReleaseDataFlagOff(self) -> None
 |      C++: void GlobalReleaseDataFlagOff()
 |
 |  GlobalReleaseDataFlagOn(...)
 |      GlobalReleaseDataFlagOn(self) -> None
 |      C++: void GlobalReleaseDataFlagOn()
 |
 |  ORIGIN(...)
 |      ORIGIN() -> vtkInformationDoubleVectorKey
 |      C++: static vtkInformationDoubleVectorKey *ORIGIN()
 |
 |  PIECE_EXTENT(...)
 |      PIECE_EXTENT() -> vtkInformationIntegerVectorKey
 |      C++: static vtkInformationIntegerVectorKey *PIECE_EXTENT()
 |
 |  POINT_DATA_VECTOR(...)
 |      POINT_DATA_VECTOR() -> vtkInformationInformationVectorKey
 |      C++: static vtkInformationInformationVectorKey *POINT_DATA_VECTOR(
 |          )
 |
 |  ReleaseData(...)
 |      ReleaseData(self) -> None
 |      C++: void ReleaseData()
 |
 |      Release data back to system to conserve memory resource. Used
 |      during visualization network execution.  Releasing this data does
 |      not make down-stream data invalid.
 |
 |  RemoveNamedFieldInformation(...)
 |      RemoveNamedFieldInformation(info:vtkInformation,
 |          fieldAssociation:int, name:str) -> None
 |      C++: static void RemoveNamedFieldInformation(vtkInformation *info,
 |           int fieldAssociation, const char *name)
 |
 |      Remove the info associated with an array
 |
 |  SIL(...)
 |      SIL() -> vtkInformationDataObjectKey
 |      C++: static vtkInformationDataObjectKey *SIL()
 |
 |  SPACING(...)
 |      SPACING() -> vtkInformationDoubleVectorKey
 |      C++: static vtkInformationDoubleVectorKey *SPACING()
 |
 |  SetActiveAttribute(...)
 |      SetActiveAttribute(info:vtkInformation, fieldAssociation:int,
 |          attributeName:str, attributeType:int) -> vtkInformation
 |      C++: static vtkInformation *SetActiveAttribute(
 |          vtkInformation *info, int fieldAssociation,
 |          const char *attributeName, int attributeType)
 |
 |      Set the named array to be the active field for the specified type
 |      (SCALARS, VECTORS, NORMALS, TCOORDS, or TENSORS) and association
 |      (FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS).  Returns
 |      the active field information object and creates on entry if one
 |      not found.
 |
 |  SetActiveAttributeInfo(...)
 |      SetActiveAttributeInfo(info:vtkInformation, fieldAssociation:int,
 |          attributeType:int, name:str, arrayType:int, numComponents:int,
 |           numTuples:int) -> None
 |      C++: static void SetActiveAttributeInfo(vtkInformation *info,
 |          int fieldAssociation, int attributeType, const char *name,
 |          int arrayType, int numComponents, int numTuples)
 |
 |      Set the name, array type, number of components, and number of
 |      tuples within the passed information object for the active
 |      attribute of type attributeType (in specified association,
 |      FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS).  If there
 |      is not an active attribute of the specified type, an entry in the
 |      information object is created.  If arrayType, numComponents, or
 |      numTuples equal to -1, or name=nullptr the value is not changed.
 |
 |  SetFieldData(...)
 |      SetFieldData(self, __a:vtkFieldData) -> None
 |      C++: virtual void SetFieldData(vtkFieldData *)
 |
 |      Assign or retrieve a general field data to this data object.
 |
 |  SetGlobalReleaseDataFlag(...)
 |      SetGlobalReleaseDataFlag(val:int) -> None
 |      C++: static void SetGlobalReleaseDataFlag(vtkTypeBool val)
 |
 |      Turn on/off flag to control whether every object releases its
 |      data after being used by a filter.
 |
 |  SetInformation(...)
 |      SetInformation(self, __a:vtkInformation) -> None
 |      C++: virtual void SetInformation(vtkInformation *)
 |
 |  SetPointDataActiveScalarInfo(...)
 |      SetPointDataActiveScalarInfo(info:vtkInformation, arrayType:int,
 |          numComponents:int) -> None
 |      C++: static void SetPointDataActiveScalarInfo(
 |          vtkInformation *info, int arrayType, int numComponents)
 |
 |      Convenience version of previous method for use (primarily) by the
 |      Imaging filters. If arrayType or numComponents == -1, the value
 |      is not changed.
 |
 |  VERTEX_DATA_VECTOR(...)
 |      VERTEX_DATA_VECTOR() -> vtkInformationInformationVectorKey
 |      C++: static vtkInformationInformationVectorKey *VERTEX_DATA_VECTOR(
 |          )
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes inherited from vtkmodules.vtkCommonDataModel.vtkDataObject:
 |
 |  AttributeTypes = <class 'vtkmodules.vtkCommonDataModel.vtkDataObject.A...
 |
 |  CELL = 1
 |
 |  EDGE = 5
 |
 |  FIELD = 2
 |
 |  FIELD_ASSOCIATION_CELLS = 1
 |
 |  FIELD_ASSOCIATION_EDGES = 5
 |
 |  FIELD_ASSOCIATION_NONE = 2
 |
 |  FIELD_ASSOCIATION_POINTS = 0
 |
 |  FIELD_ASSOCIATION_POINTS_THEN_CELLS = 3
 |
 |  FIELD_ASSOCIATION_ROWS = 6
 |
 |  FIELD_ASSOCIATION_VERTICES = 4
 |
 |  FIELD_OPERATION_MODIFIED = 2
 |
 |  FIELD_OPERATION_PRESERVED = 0
 |
 |  FIELD_OPERATION_REINTERPOLATED = 1
 |
 |  FIELD_OPERATION_REMOVED = 3
 |
 |  FieldAssociations = <class 'vtkmodules.vtkCommonDataModel.vtkDataObjec...
 |
 |  FieldOperations = <class 'vtkmodules.vtkCommonDataModel.vtkDataObject....
 |
 |  NUMBER_OF_ASSOCIATIONS = 7
 |
 |  NUMBER_OF_ATTRIBUTE_TYPES = 7
 |
 |  POINT = 0
 |
 |  POINT_THEN_CELL = 3
 |
 |  ROW = 6
 |
 |  VERTEX = 4
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from vtkmodules.vtkCommonCore.vtkObject:
 |
 |  AddObserver(...)
 |      AddObserver(self, event:int, command:Callback, priority:float=0.0) -> int
 |      C++: unsigned long AddObserver(const char* event,
 |          vtkCommand* command, float priority=0.0f)
 |
 |      Add an event callback command(o:vtkObject, event:int) for an event type.
 |      Returns a handle that can be used with RemoveEvent(event:int).
 |
 |  BreakOnError(...)
 |      BreakOnError() -> None
 |      C++: static void BreakOnError()
 |
 |      This method is called when vtkErrorMacro executes. It allows the
 |      debugger to break on error.
 |
 |  DebugOff(...)
 |      DebugOff(self) -> None
 |      C++: virtual void DebugOff()
 |
 |      Turn debugging output off.
 |
 |  DebugOn(...)
 |      DebugOn(self) -> None
 |      C++: virtual void DebugOn()
 |
 |      Turn debugging output on.
 |
 |  GetCommand(...)
 |      GetCommand(self, tag:int) -> vtkCommand
 |      C++: vtkCommand *GetCommand(unsigned long tag)
 |
 |  GetDebug(...)
 |      GetDebug(self) -> bool
 |      C++: bool GetDebug()
 |
 |      Get the value of the debug flag.
 |
 |  GetGlobalWarningDisplay(...)
 |      GetGlobalWarningDisplay() -> int
 |      C++: static vtkTypeBool GetGlobalWarningDisplay()
 |
 |  GetObjectDescription(...)
 |      GetObjectDescription(self) -> str
 |      C++: std::string GetObjectDescription() override;
 |
 |      The object description printed in messages and PrintSelf output.
 |      To be used only for reporting purposes.
 |
 |  GetObjectName(...)
 |      GetObjectName(self) -> str
 |      C++: virtual std::string GetObjectName()
 |
 |  GlobalWarningDisplayOff(...)
 |      GlobalWarningDisplayOff() -> None
 |      C++: static void GlobalWarningDisplayOff()
 |
 |  GlobalWarningDisplayOn(...)
 |      GlobalWarningDisplayOn() -> None
 |      C++: static void GlobalWarningDisplayOn()
 |
 |  HasObserver(...)
 |      HasObserver(self, event:int, __b:vtkCommand) -> int
 |      C++: vtkTypeBool HasObserver(unsigned long event, vtkCommand *)
 |      HasObserver(self, event:str, __b:vtkCommand) -> int
 |      C++: vtkTypeBool HasObserver(const char *event, vtkCommand *)
 |      HasObserver(self, event:int) -> int
 |      C++: vtkTypeBool HasObserver(unsigned long event)
 |      HasObserver(self, event:str) -> int
 |      C++: vtkTypeBool HasObserver(const char *event)
 |
 |  InvokeEvent(...)
 |      InvokeEvent(self, event:int, callData:Any) -> int
 |      C++: int InvokeEvent(unsigned long event, void* callData)
 |      InvokeEvent(self, event:str, callData:Any) -> int
 |      C++: int InvokeEvent(const char* event, void* callData)
 |      InvokeEvent(self, event:int) -> int
 |      C++: int InvokeEvent(unsigned long event)
 |      InvokeEvent(self, event:str) -> int
 |      C++: int InvokeEvent(const char* event)
 |
 |      This method invokes an event and returns whether the event was
 |      aborted or not. If the event was aborted, the return value is 1,
 |      otherwise it is 0.
 |
 |  Modified(...)
 |      Modified(self) -> None
 |      C++: virtual void Modified()
 |
 |      Update the modification time for this object. Many filters rely
 |      on the modification time to determine if they need to recompute
 |      their data. The modification time is a unique monotonically
 |      increasing unsigned long integer.
 |
 |  RemoveAllObservers(...)
 |      RemoveAllObservers(self) -> None
 |      C++: void RemoveAllObservers()
 |
 |  RemoveObserver(...)
 |      RemoveObserver(self, __a:vtkCommand) -> None
 |      C++: void RemoveObserver(vtkCommand *)
 |      RemoveObserver(self, tag:int) -> None
 |      C++: void RemoveObserver(unsigned long tag)
 |
 |  RemoveObservers(...)
 |      RemoveObservers(self, event:int, __b:vtkCommand) -> None
 |      C++: void RemoveObservers(unsigned long event, vtkCommand *)
 |      RemoveObservers(self, event:str, __b:vtkCommand) -> None
 |      C++: void RemoveObservers(const char *event, vtkCommand *)
 |      RemoveObservers(self, event:int) -> None
 |      C++: void RemoveObservers(unsigned long event)
 |      RemoveObservers(self, event:str) -> None
 |      C++: void RemoveObservers(const char *event)
 |
 |  SetDebug(...)
 |      SetDebug(self, debugFlag:bool) -> None
 |      C++: void SetDebug(bool debugFlag)
 |
 |      Set the value of the debug flag. A true value turns debugging on.
 |
 |  SetGlobalWarningDisplay(...)
 |      SetGlobalWarningDisplay(val:int) -> None
 |      C++: static void SetGlobalWarningDisplay(vtkTypeBool val)
 |
 |      This is a global flag that controls whether any debug, warning or
 |      error messages are displayed.
 |
 |  SetObjectName(...)
 |      SetObjectName(self, objectName:str) -> None
 |      C++: virtual void SetObjectName(const std::string &objectName)
 |
 |      Set/get the name of this object for reporting purposes. The name
 |      appears in warning and debug messages and in the Print output.
 |      Setting the object name does not change the MTime and does not
 |      invoke a ModifiedEvent. Derived classes implementing copying
 |      methods are expected not to copy the ObjectName.
 |
 |  ----------------------------------------------------------------------
 |  Methods inherited from vtkmodules.vtkCommonCore.vtkObjectBase:
 |
 |  FastDelete(...)
 |      FastDelete(self) -> None
 |      C++: virtual void FastDelete()
 |
 |      Delete a reference to this object.  This version will not invoke
 |      garbage collection and can potentially leak the object if it is
 |      part of a reference loop.  Use this method only when it is known
 |      that the object has another reference and would not be collected
 |      if a full garbage collection check were done.
 |
 |  GetAddressAsString(...)
 |      GetAddressAsString(self, classname:str) -> str
 |
 |      Get address of C++ object in format 'Addr=%p' after casting to
 |      the specified type.  This method is obsolete, you can get the
 |      same information from o.__this__.
 |
 |  GetClassName(...)
 |      GetClassName(self) -> str
 |      C++: const char *GetClassName()
 |
 |      Return the class name as a string.
 |
 |  GetIsInMemkind(...)
 |      GetIsInMemkind(self) -> bool
 |      C++: bool GetIsInMemkind()
 |
 |      A local state flag that remembers whether this object lives in
 |      the normal or extended memory space.
 |
 |  GetReferenceCount(...)
 |      GetReferenceCount(self) -> int
 |      C++: int GetReferenceCount()
 |
 |      Return the current reference count of this object.
 |
 |  GetUsingMemkind(...)
 |      GetUsingMemkind() -> bool
 |      C++: static bool GetUsingMemkind()
 |
 |      A global state flag that controls whether vtkObjects are
 |      constructed in the usual way (the default) or within the extended
 |      memory space.
 |
 |  InitializeObjectBase(...)
 |      InitializeObjectBase(self) -> None
 |      C++: void InitializeObjectBase()
 |
 |  Register(...)
 |      Register(self, o:vtkObjectBase)
 |      C++: virtual void Register(vtkObjectBase *o)
 |
 |      Increase the reference count by 1.
 |
 |  SetMemkindDirectory(...)
 |      SetMemkindDirectory(directoryname:str) -> None
 |      C++: static void SetMemkindDirectory(const char *directoryname)
 |
 |      The name of a directory, ideally mounted -o dax, to memory map an
 |      extended memory space within. This must be called before any
 |      objects are constructed in the extended space. It can not be
 |      changed once setup.
 |
 |  SetReferenceCount(...)
 |      SetReferenceCount(self, __a:int) -> None
 |      C++: void SetReferenceCount(int)
 |
 |      Sets the reference count. (This is very dangerous, use with
 |      care.)
 |
 |  UnRegister(...)
 |      UnRegister(self, o:vtkObjectBase)
 |      C++: virtual void UnRegister(vtkObjectBase* o)
 |
 |      Decrease the reference count (release by another object). This
 |      has the same effect as invoking Delete() (i.e., it reduces the
 |      reference count by 1).
 |
 |  UsesGarbageCollector(...)
 |      UsesGarbageCollector(self) -> bool
 |      C++: virtual bool UsesGarbageCollector()
 |
 |      Indicate whether the class uses `vtkGarbageCollector` or not.
 |
 |      Most classes will not need to do this, but if the class
 |      participates in a strongly-connected reference count cycle,
 |      participation can resolve these cycles.
 |
 |      If overriding this method to return true, the `ReportReferences`
 |      method should be overridden to report references that may be in
 |      cycles.
 |
 |  ----------------------------------------------------------------------
 |  Class methods inherited from vtkmodules.vtkCommonCore.vtkObjectBase:
 |
 |  override(...) from builtins.type
 |      This method can be used to override a VTK class with a Python subclass.
 |      The class type passed to override will afterwards be instantiated
 |      instead of the type override is called on.
 |      For example,
 |
 |      class foo(vtk.vtkPoints):
 |        pass
 |      vtk.vtkPoints.override(foo)
 |
 |      will lead to foo being instantied everytime vtkPoints() is called.
 |      The main objective of this functionality is to enable developers to
 |      extend VTK classes with more pythonic subclasses that contain
 |      convenience functionality.

numpy.random.random() を使って3Dデータを生成します.ここでは,ご自分の3D numpy配列を自由に使ってください.

arr = np.random.random((100, 100, 100))
arr.shape
(100, 100, 100)

クラス :pyvista.ImageData を作成します.

注釈

おそらく Fortran-ordering で配列を ravel する必要があります: arr.ravel(order="F") とします.

vol = pv.ImageData()
vol.dimensions = arr.shape
vol["array"] = arr.ravel(order="F")

ImageDataのプロット

c create uniform grid

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PyVista には ImageData を使う例がいくつかあります.

ボリュームレンダリング の詳細については,PyVistaのドキュメントを参照してください

以下にデータセットの例を挙げます:

from pyvista import examples

vol = examples.download_knee_full()

p = pv.Plotter()
p.add_volume(vol, cmap="bone", opacity="sigmoid")
p.show()
c create uniform grid
vol = pv.Wavelet()
vol.plot(volume=True)
c create uniform grid
Open In Colab

Total running time of the script: (0 minutes 6.328 seconds)

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