# Creating a Uniform Grid#

Create a simple uniform grid from a 3D NumPy array of values.

```import numpy as np
import pyvista as pv
```

Take a 3D NumPy array of data values that holds some spatial data where each axis corresponds to the XYZ cartesian axes. This example will create a `pyvista.ImageData` that will hold the spatial reference for a 3D grid by which a 3D NumPy array of values can be plotted against.

Create the 3D NumPy array of spatially referenced data. This is spatially referenced such that the grid is `(20, 5, 10)` `(nx, ny, nz)`.

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

Create the ImageData

```grid = pv.ImageData()
```

Set the grid dimensions to `shape + 1` because we want to inject our values on the CELL data.

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

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
```

Assign the data to the cell data. Be sure to flatten the data for `ImageData` objects using Fortran ordering.

```grid.cell_data["values"] = values.flatten(order="F")
grid
```
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

Now plot the grid!

```grid.plot(show_edges=True)
```

Don’t like cell data? You could also add the NumPy array to the point data of a `pyvista.ImageData`. Take note of the subtle difference when setting the grid dimensions upon initialization.

```# 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)
```

Create the PyVista object and set the same attributes as earlier.

```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
```

Add the data values to the cell data

```grid.point_data["values"] = values.flatten(order="F")  # Flatten the array!
grid
```
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

Now plot the grid!

```grid.plot(show_edges=True)
```

## Exercise#

Now create your own `pyvista.ImageData` from a 3D NumPy array!

```help(pv.ImageData)
```
```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)
|
|  ----------------------------------------------------------------------
|
|  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.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.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'
|
|  __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
|      >>> 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`.
|
|      --------
|      pyvista.DataSet.cell_neighbors_levels
|
|      Examples
|      --------
|      >>> from pyvista import examples
|
|      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.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))
|      ...     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()
|      ...         f"{connection.capitalize()} neighbors",
|      ...         color="red",
|      ...         font_size=8,
|      ...     )
|      ...
|      ...     # Add current cell
|      ...     i_cell = 0
|      ...     current_cell = mesh.extract_cells(i_cell)
|      ...         current_cell, show_edges=True, color="blue"
|      ...     )
|      ...         current_cell.cell_centers().points,
|      ...         labels=[f"{i_cell}"],
|      ...     )
|      ...
|      ...     ids = mesh.cell_neighbors(i_cell, connection)
|      ...     cells = mesh.extract_cells(ids)
|      ...     _ = pl.add_mesh(cells, color="red", show_edges=True)
|      ...         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`.
|
|      --------
|      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
|      ...     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)
|      ...         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`.
|
|      --------
|      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.
|
|      --------
|      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.
|
|      --------
|      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``.
|
|      --------
|      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.
|
|      --------
|      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,)``.
|
|      --------
|      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::
|          <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
|
|      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()
|      >>> pl.subplot(0, 1)
|      >>> pl.show_axes()
|      >>> mesh2 = mesh1.flip_normal([1.0, 1.0, 1.0], inplace=False)
|      >>> 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::
|          <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
|
|      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()
|      >>> pl.subplot(0, 1)
|      >>> pl.show_axes()
|      >>> mesh2 = mesh1.flip_x(inplace=False)
|      >>> 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::
|          <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
|
|      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()
|      >>> pl.subplot(0, 1)
|      >>> pl.show_axes()
|      >>> mesh2 = mesh1.flip_y(inplace=False)
|      >>> 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::
|          <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
|
|      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()
|      >>> pl.subplot(0, 1)
|      >>> pl.show_axes()
|      >>> mesh2 = mesh1.flip_z(inplace=False)
|      >>> 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
|      >>> 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
|          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
|          <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
|
|      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.
|
|
|      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
|      ...     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
|      ...     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.
|
|
|      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.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.
|
|      --------
|      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
|      ...     mesh.points[0], ["0"], text_color="blue", font_size=40
|      ... )
|      >>>
|      >>> # Get the point neighbors and plot them
|      >>> neighbors = mesh.point_neighbors(0)
|      ...     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.
|
|      --------
|      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
|      ...     text_color="white",
|      ...     font_size=40,
|      ...     point_size=10,
|      ... )
|      >>>
|      >>> # Add the first point label
|      ...     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):
|      ...         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::
|          <DataSetFilters.transform>` which is used by this filter
|          under the hood.
|
|      Parameters
|      ----------
|      vector : Vector
|
|      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
|
|      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(mesh, style='wireframe', line_width=3)
|      >>> 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::
|          <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
|
|      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(mesh, style='wireframe', line_width=3)
|      >>> 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::
|          <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)
|
|      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
|
|      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(mesh, style='wireframe', line_width=3)
|      >>> 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::
|          <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
|
|      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(mesh, style='wireframe', line_width=3)
|      >>> pl.show()
|
|  scale(self, xyz: 'Number | VectorLike[float]', transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
|      Scale the mesh.
|
|      .. note::
|          <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
|
|      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()
|      >>> pl.subplot(0, 1)
|      >>> pl.show_axes()
|      >>> _ = pl.show_grid()
|      >>> mesh2 = mesh1.scale([10.0, 10.0, 10.0], inplace=False)
|      >>> 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::
|          <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
|
|      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
|
|      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
|
|      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
|
|      --------
|      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.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.
|
|
|      --------
|      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
|
|      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.
|
|      --------
|      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.
|
|      --------
|      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
|      >>> 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.
|
|      --------
|      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.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.
|
|      --------
|      :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.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.
|
|      --------
|      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.
|
|
|      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
|
|      progress_bar : bool, default: False
|          Display a progress bar to indicate progress.
|
|      Returns
|      -------
|      pyvista.ImageData
|
|      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.dimensions
|      (630, 474, 1)
|      (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.view_xy()
|      ...     plot.camera.tight()
|      ...     return plot
|      ...
|      >>>
|      >>> plot.show()
|
|      Pad only the x-axis with a white border.
|
|      >>> plot.show()
|
|
|      >>> plot.show()
|
|
|      >>> plot.show()
|
|      Pad a color image using multi-component color vectors. Here, RGBA values are
|      used.
|
|      >>> red = (255, 0, 0, 255)  # RGBA
|      >>>
|      >>> plot_kwargs = dict(
|      ...     cpos='xy', zoom='tight', rgb=True, show_axes=False
|      ... )
|
|      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
|      >>>
|      >>>
|
|  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.
|
|
|      --------
|      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
|
|      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.
|
|      --------
|      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
|      >>> 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:
|
|      Combine this mesh with another into a :class:`pyvista.UnstructuredGrid`.
|
|      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')
|      ...     source, style='wireframe', opacity=0.5, line_width=2
|      ... )
|      >>> pl.subplot(0, 1)
|      >>> _ = pl.add_text('After Alignment')
|      ...     source, style='wireframe', opacity=0.5, line_width=2
|      ... )
|      ...     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)
|      ...     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.
|
|      --------
|      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 = 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 = 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
|
|      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
|      >>> 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
|      >>> _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
|      >>> 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
|      >>> cqual = mesh.compute_boundary_mesh_quality()
|      >>> plotter = pv.Plotter(shape=(2, 2))
|      >>> _ = plotter.add_mesh(mesh, show_edges=True)
|      >>> plotter.subplot(1, 0)
|      ...     cqual, scalars="DistanceFromCellCenterToFaceCenter"
|      ... )
|      >>> plotter.subplot(0, 1)
|      ...     cqual, scalars="DistanceFromCellCenterToFacePlane"
|      ... )
|      >>> plotter.subplot(1, 1)
|      ...     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'``
|      - ``'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 = 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
|
|      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
|
|      Compute and plot the gradient of the active scalars.
|
|      >>> from pyvista import examples
|      >>> deriv = hills.compute_derivative()
|
|      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
|      >>> 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()
|      ...     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()
|      ...     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.
|
|
|         * 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
|
|      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.
|
|      --------
|      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
|      >>> 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',
|      ... )
|
|      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
|      --------
|
|  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()
|
|      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
|      >>> 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.
|
|      --------
|      extract_points, extract_values
|
|      Returns
|      -------
|      pyvista.UnstructuredGrid
|          Subselected grid.
|
|      Examples
|      --------
|      >>> import pyvista as pv
|      >>> from pyvista import examples
|      >>> subset = grid.extract_cells(range(20))
|      >>> subset.n_cells
|      20
|      >>> pl = pv.Plotter()
|      ...     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 = 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
|      >>> 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.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
|
|      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.
|
|      --------
|      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
|      >>> 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.
|
|
|      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.
|
|      --------
|      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
|      >>> 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.
|
|      >>> 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.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.
|
|
|      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
|      >>> arrows = mesh.glyph(
|      ...     scale="Normals", orient="Normals", tolerance=0.05
|      ... )
|      >>> pl = pv.Plotter()
|      >>> actor = pl.add_mesh(arrows, color="black")
|      ...     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
|
|      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.
|
|          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.
|
|      --------
|      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()
|      ...     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.
|
|      --------
|      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.
|
|
|      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.
|
|
|      >>> from pyvista import examples
|      >>> import numpy as np
|
|      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`.
|
|      --------
|      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
|      >>> 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
|      >>> 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
|      >>> 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.
|
|      --------
|      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 = 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.
|
|
|      Returns
|      -------
|      pyvista.DataSet
|          Dataset containing resampled data.
|
|      --------
|      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)
|      >>> 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["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["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()
|      ...     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()
|      ...     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
|      (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),
|      ... )
|      >>> 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.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
|      >>> 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
|      >>> 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
|      >>> 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()
|      ...     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()
|      >>> mesh = pv.Wavelet()
|      >>> slice = mesh.slice_implicit(sphere)
|      >>> slice.plot(show_edges=True, line_width=5)
|
|      >>> cylinder = vtk.vtkCylinder()
|      >>> 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
|      >>> 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.
|
|
|      >>> from pyvista import examples
|      >>> import numpy as np
|
|      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.
|
|      --------
|      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.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.
|
|
|      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`.
|
|      --------
|      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
|      >>> 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')
|      >>> _ = [
|      ...     for block, label in zip(multiblock, labels)
|      ... ]
|      >>> 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.
|
|          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
|      >>> 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.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
|      >>> 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.
|
|      --------
|      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
|
|      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
|
|      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.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
|      >>> 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)
|
|      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['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))
|      ...     ['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.
|
|      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
|
|  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.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])
|
|      Get address of the underlying VTK C++ object.
|
|      Returns
|      -------
|      str
|
|      Examples
|      --------
|      >>> import pyvista as pv
|      >>> mesh = pv.Sphere()
|
|  ----------------------------------------------------------------------
|  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
|      --------
|
|      >>> import pyvista as pv
|      >>> from pyvista import examples
|
|      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.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
|
|  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
|
|  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
|
|  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.
|
|      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.
|
|
|      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`
|      overloads to compute the increments to avoid racing with other
|
|  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
|
|  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(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)
|
|
|  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(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
|
|  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
|
|  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
|
|  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
|      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
|
|  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(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.
|
|
|      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.
```

Generate example 3D data using `numpy.random.random()`. Feel free to use your own 3D numpy array here.

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

Create the `pyvista.ImageData`.

Note

You will likely need to `ravel` the array with Fortran-ordering: `arr.ravel(order="F")`

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

Plot the ImageData

```vol.plot()
```

## Example#

PyVista has several examples that use `ImageData`.

See the PyVista documentation for further details on Volume Rendering

Here’s one of these example datasets:

```from pyvista import examples

```vol = pv.Wavelet()