Note
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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
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
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) from pyvista.core.filters.rectilinear_grid.RectilinearGridFilters
| Create a tetrahedral mesh structured grid.
|
| Parameters
| ----------
| tetra_per_cell : int, default: 5
| The number of tetrahedrons to divide each cell into. Can be
| either ``5``, ``6``, or ``12``. If ``mixed=True``, this value is
| overridden.
|
| mixed : str, bool, sequence, default: False
| When set, subdivides some cells into 5 and some cells into 12. Set
| to ``True`` to use the active cell scalars of the
| :class:`pyvista.RectilinearGrid` to be either 5 or 12 to
| determining the number of tetrahedra to generate per cell.
|
| When a sequence, uses these values to subdivide the cells. When a
| string uses a cell array rather than the active array to determine
| the number of tetrahedra to generate per cell.
|
| pass_cell_ids : bool, default: True
| Set to ``True`` to make the tetrahedra have scalar data indicating
| which cell they came from in the original
| :class:`pyvista.RectilinearGrid`. The name of this array is
| ``'vtkOriginalCellIds'`` within the ``cell_data``.
|
| pass_data : bool, default: True
| Set to ``True`` to make the tetrahedra mesh have the cell data from
| the original :class:`pyvista.RectilinearGrid`. This uses
| ``pass_cell_ids=True`` internally. If ``True``, ``pass_cell_ids``
| will also be set to ``True``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| UnstructuredGrid containing the tetrahedral cells.
|
| Examples
| --------
| Divide a rectangular grid into tetrahedrons. Each cell contains by
| default 5 tetrahedrons.
|
| First, create and plot the grid.
|
| >>> import numpy as np
| >>> import pyvista as pv
| >>> xrng = np.linspace(0, 1, 2)
| >>> yrng = np.linspace(0, 1, 2)
| >>> zrng = np.linspace(0, 2, 3)
| >>> grid = pv.RectilinearGrid(xrng, yrng, zrng)
| >>> grid.plot()
|
| Now, generate the tetrahedra plot in the exploded view of the cell.
|
| >>> tet_grid = grid.to_tetrahedra()
| >>> tet_grid.explode(factor=0.5).plot(show_edges=True)
|
| Take the same grid but divide the first cell into 5 cells and the other
| cell into 12 tetrahedrons per cell.
|
| >>> tet_grid = grid.to_tetrahedra(mixed=[5, 12])
| >>> tet_grid.explode(factor=0.5).plot(show_edges=True)
|
| ----------------------------------------------------------------------
| Readonly properties defined here:
|
| x
| Return all the X points.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(2, 2, 2))
| >>> grid.x
| array([0., 1., 0., 1., 0., 1., 0., 1.])
|
| y
| Return all the Y points.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(2, 2, 2))
| >>> grid.y
| array([0., 0., 1., 1., 0., 0., 1., 1.])
|
| z
| Return all the Z points.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(2, 2, 2))
| >>> grid.z
| array([0., 0., 0., 0., 1., 1., 1., 1.])
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| extent
| Return or set the extent of the ImageData.
|
| The extent is simply the first and last indices for each of the three axes.
|
| Examples
| --------
| Create a ``ImageData`` and show its extent.
|
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(10, 10, 10))
| >>> grid.extent
| (0, 9, 0, 9, 0, 9)
|
| >>> grid.extent = (2, 5, 2, 5, 2, 5)
| >>> grid.extent
| (2, 5, 2, 5, 2, 5)
|
| Note how this also modifies the grid bounds and dimensions. Since we
| use default spacing of 1 here, the bounds match the extent exactly.
|
| >>> grid.bounds
| (2.0, 5.0, 2.0, 5.0, 2.0, 5.0)
| >>> grid.dimensions
| (4, 4, 4)
|
| origin
| Return the origin of the grid (bottom southwest corner).
|
| Examples
| --------
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(5, 5, 5))
| >>> grid.origin
| (0.0, 0.0, 0.0)
|
| Show how the origin is in the bottom "southwest" corner of the
| ImageData.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(grid, show_edges=True)
| >>> _ = pl.add_axes_at_origin(ylabel=None)
| >>> pl.camera_position = 'xz'
| >>> pl.show()
|
| Set the origin to ``(1, 1, 1)`` and show how this shifts the
| ImageData.
|
| >>> grid.origin = (1, 1, 1)
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(grid, show_edges=True)
| >>> _ = pl.add_axes_at_origin(ylabel=None)
| >>> pl.camera_position = 'xz'
| >>> pl.show()
|
| points
| Build a copy of the implicitly defined points as a numpy array.
|
| Returns
| -------
| numpy.ndarray
| Array of points representing the image data.
|
| Notes
| -----
| The ``points`` for a :class:`pyvista.ImageData` cannot be set.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(2, 2, 2))
| >>> grid.points
| array([[0., 0., 0.],
| [1., 0., 0.],
| [0., 1., 0.],
| [1., 1., 0.],
| [0., 0., 1.],
| [1., 0., 1.],
| [0., 1., 1.],
| [1., 1., 1.]])
|
| spacing
| Return or set the spacing for each axial direction.
|
| Notes
| -----
| Spacing must be non-negative. While VTK accepts negative
| spacing, this results in unexpected behavior. See:
| https://github.com/pyvista/pyvista/issues/1967
|
| Examples
| --------
| Create a 5 x 5 x 5 uniform grid.
|
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(5, 5, 5))
| >>> grid.spacing
| (1.0, 1.0, 1.0)
| >>> grid.plot(show_edges=True)
|
| Modify the spacing to ``(1, 2, 3)``
|
| >>> grid.spacing = (1, 2, 3)
| >>> grid.plot(show_edges=True)
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __annotations__ = {'_WRITERS': 'ClassVar[dict[str, type[_vtk.vtkDataSe...
|
| ----------------------------------------------------------------------
| Methods inherited from Grid:
|
| __new__(cls, *args, **kwargs) from pyvista.core.utilities.misc.abstract_class.<locals>
|
| ----------------------------------------------------------------------
| Data descriptors inherited from Grid:
|
| dimensions
| Return the grid's dimensions.
|
| These are effectively the number of points along each of the
| three dataset axes.
|
| Returns
| -------
| tuple[int]
| Dimensions of the grid.
|
| Examples
| --------
| Create a uniform grid with dimensions ``(1, 2, 3)``.
|
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(2, 3, 4))
| >>> grid.dimensions
| (2, 3, 4)
| >>> grid.plot(show_edges=True)
|
| Set the dimensions to ``(3, 4, 5)``
|
| >>> grid.dimensions = (3, 4, 5)
| >>> grid.plot(show_edges=True)
|
| ----------------------------------------------------------------------
| Methods inherited from pyvista.core.dataset.DataSet:
|
| __getattr__(self, item) -> 'Any'
| Get attribute from base class if not found.
|
| __getitem__(self, index: 'Iterable[Any] | str') -> 'NumpyArray[float]'
| Search both point, cell, and field data for an array.
|
| __setitem__(self, name: 'str', scalars: 'NumpyArray[float] | Sequence[float] | float')
| Add/set an array in the point_data, or cell_data accordingly.
|
| It depends on the array's length, or specified mode.
|
| cast_to_pointset(self, pass_cell_data: 'bool' = False) -> 'pyvista.PointSet'
| Extract the points of this dataset and return a :class:`pyvista.PointSet`.
|
| Parameters
| ----------
| pass_cell_data : bool, default: False
| Run the :func:`cell_data_to_point_data()
| <pyvista.DataSetFilters.cell_data_to_point_data>` filter and pass
| cell data fields to the new pointset.
|
| Returns
| -------
| pyvista.PointSet
| Dataset cast into a :class:`pyvista.PointSet`.
|
| Notes
| -----
| This will produce a deep copy of the points and point/cell data of
| the original mesh.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> mesh = pv.Wavelet()
| >>> pointset = mesh.cast_to_pointset()
| >>> type(pointset)
| <class 'pyvista.core.pointset.PointSet'>
|
| cast_to_poly_points(self, pass_cell_data: 'bool' = False) -> 'pyvista.PolyData'
| Extract the points of this dataset and return a :class:`pyvista.PolyData`.
|
| Parameters
| ----------
| pass_cell_data : bool, default: False
| Run the :func:`cell_data_to_point_data()
| <pyvista.DataSetFilters.cell_data_to_point_data>` filter and pass
| cell data fields to the new pointset.
|
| Returns
| -------
| pyvista.PolyData
| Dataset cast into a :class:`pyvista.PolyData`.
|
| Notes
| -----
| This will produce a deep copy of the points and point/cell data of
| the original mesh.
|
| Examples
| --------
| >>> from pyvista import examples
| >>> mesh = examples.load_uniform()
| >>> points = mesh.cast_to_poly_points(pass_cell_data=True)
| >>> type(points)
| <class 'pyvista.core.pointset.PolyData'>
| >>> points.n_arrays
| 2
| >>> points.point_data
| pyvista DataSetAttributes
| Association : POINT
| Active Scalars : Spatial Point Data
| Active Vectors : None
| Active Texture : None
| Active Normals : None
| Contains arrays :
| Spatial Point Data float64 (1000,) SCALARS
| >>> points.cell_data
| pyvista DataSetAttributes
| Association : CELL
| Active Scalars : None
| Active Vectors : None
| Active Texture : None
| Active Normals : None
| Contains arrays :
| Spatial Cell Data float64 (1000,)
|
| cast_to_unstructured_grid(self) -> 'pyvista.UnstructuredGrid'
| Get a new representation of this object as a :class:`pyvista.UnstructuredGrid`.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Dataset cast into a :class:`pyvista.UnstructuredGrid`.
|
| Examples
| --------
| Cast a :class:`pyvista.PolyData` to a
| :class:`pyvista.UnstructuredGrid`.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> type(mesh)
| <class 'pyvista.core.pointset.PolyData'>
| >>> grid = mesh.cast_to_unstructured_grid()
| >>> type(grid)
| <class 'pyvista.core.pointset.UnstructuredGrid'>
|
| cell_neighbors(self, ind: 'int', connections: 'str' = 'points') -> 'list[int]'
| Get the cell neighbors of the ind-th cell.
|
| Concrete implementation of vtkDataSet's `GetCellNeighbors
| <https://vtk.org/doc/nightly/html/classvtkDataSet.html#ae1ba413c15802ef50d9b1955a66521e4>`_.
|
| Parameters
| ----------
| ind : int
| Cell ID.
|
| connections : str, default: "points"
| Describe how the neighbor cell(s) must be connected to the current
| cell to be considered as a neighbor.
| Can be either ``'points'``, ``'edges'`` or ``'faces'``.
|
| Returns
| -------
| list[int]
| List of neighbor cells IDs for the ind-th cell.
|
| Warnings
| --------
| For a :class:`pyvista.ExplicitStructuredGrid`, use :func:`pyvista.ExplicitStructuredGrid.neighbors`.
|
| See Also
| --------
| pyvista.DataSet.cell_neighbors_levels
|
| Examples
| --------
| >>> from pyvista import examples
| >>> mesh = examples.load_airplane()
|
| Get the neighbor cell ids that have at least one point in common with
| the 0-th cell.
|
| >>> mesh.cell_neighbors(0, "points")
| [1, 2, 3, 388, 389, 11, 12, 395, 14, 209, 211, 212]
|
| Get the neighbor cell ids that have at least one edge in common with
| the 0-th cell.
|
| >>> mesh.cell_neighbors(0, "edges")
| [1, 3, 12]
|
| For unstructured grids with cells of dimension 3 (Tetrahedron for example),
| cell neighbors can be defined using faces.
|
| >>> mesh = examples.download_tetrahedron()
| >>> mesh.cell_neighbors(0, "faces")
| [1, 5, 7]
|
| Show a visual example.
|
| >>> from functools import partial
| >>> import pyvista as pv
| >>> mesh = pv.Sphere(theta_resolution=10)
| >>>
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> pl.link_views()
| >>> add_point_labels = partial(
| ... pl.add_point_labels,
| ... text_color="white",
| ... font_size=20,
| ... shape=None,
| ... show_points=False,
| ... )
| >>>
| >>> for i, connection in enumerate(["points", "edges"]):
| ... pl.subplot(0, i)
| ... pl.view_xy()
| ... _ = pl.add_title(
| ... f"{connection.capitalize()} neighbors",
| ... color="red",
| ... shadow=True,
| ... font_size=8,
| ... )
| ...
| ... # Add current cell
| ... i_cell = 0
| ... current_cell = mesh.extract_cells(i_cell)
| ... _ = pl.add_mesh(
| ... current_cell, show_edges=True, color="blue"
| ... )
| ... _ = add_point_labels(
| ... current_cell.cell_centers().points,
| ... labels=[f"{i_cell}"],
| ... )
| ...
| ... # Add neighbors
| ... ids = mesh.cell_neighbors(i_cell, connection)
| ... cells = mesh.extract_cells(ids)
| ... _ = pl.add_mesh(cells, color="red", show_edges=True)
| ... _ = add_point_labels(
| ... cells.cell_centers().points,
| ... labels=[f"{i}" for i in ids],
| ... )
| ...
| ... # Add other cells
| ... ids.append(i_cell)
| ... others = mesh.extract_cells(ids, invert=True)
| ... _ = pl.add_mesh(others, show_edges=True)
| ...
| >>> pl.show()
|
| cell_neighbors_levels(self, ind: 'int', connections: 'str' = 'points', n_levels: 'int' = 1) -> 'Generator[list[int], None, None]'
| Get consecutive levels of cell neighbors.
|
| Parameters
| ----------
| ind : int
| Cell ID.
|
| connections : str, default: "points"
| Describe how the neighbor cell(s) must be connected to the current
| cell to be considered as a neighbor.
| Can be either ``'points'``, ``'edges'`` or ``'faces'``.
|
| n_levels : int, default: 1
| Number of levels to search for cell neighbors.
| When equal to 1, it is equivalent to :func:`pyvista.DataSet.cell_neighbors`.
|
| Returns
| -------
| generator[list[int]]
| A generator of list of cell IDs for each level.
|
| Warnings
| --------
| For a :class:`pyvista.ExplicitStructuredGrid`, use :func:`pyvista.ExplicitStructuredGrid.neighbors`.
|
| See Also
| --------
| pyvista.DataSet.cell_neighbors
|
| Examples
| --------
| Get the cell neighbors IDs starting from the 0-th cell
| up until the third level.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere(theta_resolution=10)
| >>> nbr_levels = mesh.cell_neighbors_levels(
| ... 0, connections="edges", n_levels=3
| ... )
| >>> nbr_levels = list(nbr_levels)
| >>> nbr_levels[0]
| [1, 21, 9]
| >>> nbr_levels[1]
| [2, 8, 74, 75, 20, 507]
| >>> nbr_levels[2]
| [128, 129, 3, 453, 7, 77, 23, 506]
|
| Visualize these cells IDs.
|
| >>> from functools import partial
| >>> pv.global_theme.color_cycler = [
| ... 'red',
| ... 'green',
| ... 'blue',
| ... 'purple',
| ... ]
| >>> pl = pv.Plotter()
| >>>
| >>> # Define partial function to add point labels
| >>> add_point_labels = partial(
| ... pl.add_point_labels,
| ... text_color="white",
| ... font_size=40,
| ... shape=None,
| ... show_points=False,
| ... )
| >>>
| >>> # Add the 0-th cell to the plotter
| >>> cell = mesh.extract_cells(0)
| >>> _ = pl.add_mesh(cell, show_edges=True)
| >>> _ = add_point_labels(cell.cell_centers().points, labels=["0"])
| >>> other_ids = [0]
| >>>
| >>> # Add the neighbors to the plot
| >>> neighbors = mesh.cell_neighbors_levels(
| ... 0, connections="edges", n_levels=3
| ... )
| >>> for i, ids in enumerate(neighbors, start=1):
| ... cells = mesh.extract_cells(ids)
| ... _ = pl.add_mesh(cells, show_edges=True)
| ... _ = add_point_labels(
| ... cells.cell_centers().points, labels=[f"{i}"] * len(ids)
| ... )
| ... other_ids.extend(ids)
| ...
| >>>
| >>> # Add the cell IDs that are not neighbors (ie. the rest of the sphere)
| >>> cells = mesh.extract_cells(other_ids, invert=True)
| >>> _ = pl.add_mesh(cells, color="white", show_edges=True)
| >>>
| >>> pl.view_xy()
| >>> pl.camera.zoom(6.0)
| >>> pl.show()
|
| clear_cell_data(self) -> 'None'
| Remove all cell arrays.
|
| clear_data(self) -> 'None'
| Remove all arrays from point/cell/field data.
|
| Examples
| --------
| Clear all arrays from a mesh.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> mesh = pv.Sphere()
| >>> mesh.point_data.keys()
| ['Normals']
| >>> mesh.clear_data()
| >>> mesh.point_data.keys()
| []
|
| clear_point_data(self) -> 'None'
| Remove all point arrays.
|
| Examples
| --------
| Clear all point arrays from a mesh.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> mesh = pv.Sphere()
| >>> mesh.point_data.keys()
| ['Normals']
| >>> mesh.clear_point_data()
| >>> mesh.point_data.keys()
| []
|
| copy_from(self, mesh: '_vtk.vtkDataSet', deep: 'bool' = True) -> 'None'
| Overwrite this dataset inplace with the new dataset's geometries and data.
|
| Parameters
| ----------
| mesh : vtk.vtkDataSet
| The overwriting mesh.
|
| deep : bool, default: True
| Whether to perform a deep or shallow copy.
|
| Examples
| --------
| Create two meshes and overwrite ``mesh_a`` with ``mesh_b``.
| Show that ``mesh_a`` is equal to ``mesh_b``.
|
| >>> import pyvista as pv
| >>> mesh_a = pv.Sphere()
| >>> mesh_b = pv.Cube()
| >>> mesh_a.copy_from(mesh_b)
| >>> mesh_a == mesh_b
| True
|
| copy_meta_from(self, ido: 'DataSet', deep: 'bool' = True) -> 'None'
| Copy pyvista meta data onto this object from another object.
|
| Parameters
| ----------
| ido : pyvista.DataSet
| Dataset to copy the metadata from.
|
| deep : bool, default: True
| Deep or shallow copy.
|
| find_cells_along_line(self, pointa: 'VectorLike[float]', pointb: 'VectorLike[float]', tolerance: 'float' = 0.0) -> 'NumpyArray[int]'
| Find the index of cells whose bounds intersect a line.
|
| Line is defined from ``pointa`` to ``pointb``.
|
| Parameters
| ----------
| pointa : Vector
| Length 3 coordinate of the start of the line.
|
| pointb : Vector
| Length 3 coordinate of the end of the line.
|
| tolerance : float, default: 0.0
| The absolute tolerance to use to find cells along line.
|
| Returns
| -------
| numpy.ndarray
| Index or indices of the cell(s) whose bounds intersect
| the line.
|
| Warnings
| --------
| This method returns cells whose bounds intersect the line.
| This means that the line may not intersect the cell itself.
| To obtain cells that intersect the line, use
| :func:`pyvista.DataSet.find_cells_intersecting_line`.
|
| See Also
| --------
| DataSet.find_closest_point
| DataSet.find_closest_cell
| DataSet.find_containing_cell
| DataSet.find_cells_within_bounds
| DataSet.find_cells_intersecting_line
|
| Examples
| --------
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh.find_cells_along_line([0.0, 0, 0], [1.0, 0, 0])
| array([ 86, 87, 1652, 1653])
|
| find_cells_intersecting_line(self, pointa: 'VectorLike[float]', pointb: 'VectorLike[float]', tolerance: 'float' = 0.0) -> 'NumpyArray[int]'
| Find the index of cells that intersect a line.
|
| Line is defined from ``pointa`` to ``pointb``. This
| method requires vtk version >=9.2.0.
|
| Parameters
| ----------
| pointa : sequence[float]
| Length 3 coordinate of the start of the line.
|
| pointb : sequence[float]
| Length 3 coordinate of the end of the line.
|
| tolerance : float, default: 0.0
| The absolute tolerance to use to find cells along line.
|
| Returns
| -------
| numpy.ndarray
| Index or indices of the cell(s) that intersect
| the line.
|
| See Also
| --------
| DataSet.find_closest_point
| DataSet.find_closest_cell
| DataSet.find_containing_cell
| DataSet.find_cells_within_bounds
| DataSet.find_cells_along_line
|
| Examples
| --------
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh.find_cells_intersecting_line([0.0, 0, 0], [1.0, 0, 0])
| array([ 86, 1653])
|
| find_cells_within_bounds(self, bounds: 'BoundsLike') -> 'NumpyArray[int]'
| Find the index of cells in this mesh within bounds.
|
| Parameters
| ----------
| bounds : sequence[float]
| Bounding box. The form is: ``[xmin, xmax, ymin, ymax, zmin, zmax]``.
|
| Returns
| -------
| numpy.ndarray
| Index or indices of the cell in this mesh that are closest
| to the given point.
|
| See Also
| --------
| DataSet.find_closest_point
| DataSet.find_closest_cell
| DataSet.find_containing_cell
| DataSet.find_cells_along_line
|
| Examples
| --------
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> index = mesh.find_cells_within_bounds(
| ... [-2.0, 2.0, -2.0, 2.0, -2.0, 2.0]
| ... )
|
| find_closest_cell(self, point: 'VectorLike[float] | MatrixLike[float]', return_closest_point: 'bool' = False) -> 'int | NumpyArray[int] | tuple[int | NumpyArray[int], NumpyArray[int]]'
| Find index of closest cell in this mesh to the given point.
|
| Parameters
| ----------
| point : Vector | Matrix
| Coordinates of point to query (length 3) or a
| :class:`numpy.ndarray` of ``n`` points with shape ``(n, 3)``.
|
| return_closest_point : bool, default: False
| If ``True``, the closest point within a mesh cell to that point is
| returned. This is not necessarily the closest nodal point on the
| mesh. Default is ``False``.
|
| Returns
| -------
| int or numpy.ndarray
| Index or indices of the cell in this mesh that is/are closest
| to the given point(s).
|
| .. versionchanged:: 0.35.0
| Inputs of shape ``(1, 3)`` now return a :class:`numpy.ndarray`
| of shape ``(1,)``.
|
| numpy.ndarray
| Point or points inside a cell of the mesh that is/are closest
| to the given point(s). Only returned if
| ``return_closest_point=True``.
|
| .. versionchanged:: 0.35.0
| Inputs of shape ``(1, 3)`` now return a :class:`numpy.ndarray`
| of the same shape.
|
| Warnings
| --------
| This method may still return a valid cell index even if the point
| contains a value like ``numpy.inf`` or ``numpy.nan``.
|
| See Also
| --------
| DataSet.find_closest_point
| DataSet.find_containing_cell
| DataSet.find_cells_along_line
| DataSet.find_cells_within_bounds
|
| Examples
| --------
| Find nearest cell on a sphere centered on the
| origin to the point ``[0.1, 0.2, 0.3]``.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> point = [0.1, 0.2, 0.3]
| >>> index = mesh.find_closest_cell(point)
| >>> index
| 338
|
| Make sure that this cell indeed is the closest to
| ``[0.1, 0.2, 0.3]``.
|
| >>> import numpy as np
| >>> cell_centers = mesh.cell_centers()
| >>> relative_position = cell_centers.points - point
| >>> distance = np.linalg.norm(relative_position, axis=1)
| >>> np.argmin(distance)
| np.int64(338)
|
| Find the nearest cells to several random points that
| are centered on the origin.
|
| >>> points = 2 * np.random.default_rng().random((5000, 3)) - 1
| >>> indices = mesh.find_closest_cell(points)
| >>> indices.shape
| (5000,)
|
| For the closest cell, find the point inside the cell that is
| closest to the supplied point. The rectangle is a unit square
| with 1 cell and 4 nodal points at the corners in the plane with
| ``z`` normal and ``z=0``. The closest point inside the cell is
| not usually at a nodal point.
|
| >>> unit_square = pv.Rectangle()
| >>> index, closest_point = unit_square.find_closest_cell(
| ... [0.25, 0.25, 0.5], return_closest_point=True
| ... )
| >>> closest_point
| array([0.25, 0.25, 0. ])
|
| But, the closest point can be a nodal point, although the index of
| that point is not returned. If the closest nodal point by index is
| desired, see :func:`DataSet.find_closest_point`.
|
| >>> index, closest_point = unit_square.find_closest_cell(
| ... [1.0, 1.0, 0.5], return_closest_point=True
| ... )
| >>> closest_point
| array([1., 1., 0.])
|
| find_closest_point(self, point: 'Iterable[float]', n=1) -> 'int'
| Find index of closest point in this mesh to the given point.
|
| If wanting to query many points, use a KDTree with scipy or another
| library as those implementations will be easier to work with.
|
| See: https://github.com/pyvista/pyvista-support/issues/107
|
| Parameters
| ----------
| point : sequence[float]
| Length 3 coordinate of the point to query.
|
| n : int, optional
| If greater than ``1``, returns the indices of the ``n`` closest
| points.
|
| Returns
| -------
| int
| The index of the point in this mesh that is closest to the given point.
|
| See Also
| --------
| DataSet.find_closest_cell
| DataSet.find_containing_cell
| DataSet.find_cells_along_line
| DataSet.find_cells_within_bounds
|
| Examples
| --------
| Find the index of the closest point to ``(0, 1, 0)``.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> index = mesh.find_closest_point((0, 1, 0))
| >>> index
| 239
|
| Get the coordinate of that point.
|
| >>> mesh.points[index]
| pyvista_ndarray([-0.05218758, 0.49653167, 0.02706946], dtype=float32)
|
| find_containing_cell(self, point: 'VectorLike[float] | MatrixLike[float]') -> 'int | NumpyArray[int]'
| Find index of a cell that contains the given point.
|
| Parameters
| ----------
| point : Vector, Matrix
| Coordinates of point to query (length 3) or a
| :class:`numpy.ndarray` of ``n`` points with shape ``(n, 3)``.
|
| Returns
| -------
| int or numpy.ndarray
| Index or indices of the cell in this mesh that contains
| the given point.
|
| .. versionchanged:: 0.35.0
| Inputs of shape ``(1, 3)`` now return a :class:`numpy.ndarray`
| of shape ``(1,)``.
|
| See Also
| --------
| DataSet.find_closest_point
| DataSet.find_closest_cell
| DataSet.find_cells_along_line
| DataSet.find_cells_within_bounds
|
| Examples
| --------
| A unit square with 16 equal sized cells is created and a cell
| containing the point ``[0.3, 0.3, 0.0]`` is found.
|
| >>> import pyvista as pv
| >>> mesh = pv.ImageData(
| ... dimensions=[5, 5, 1], spacing=[1 / 4, 1 / 4, 0]
| ... )
| >>> mesh
| ImageData...
| >>> mesh.find_containing_cell([0.3, 0.3, 0.0])
| 5
|
| A point outside the mesh domain will return ``-1``.
|
| >>> mesh.find_containing_cell([0.3, 0.3, 1.0])
| -1
|
| Find the cells that contain 1000 random points inside the mesh.
|
| >>> import numpy as np
| >>> points = np.random.default_rng().random((1000, 3))
| >>> indices = mesh.find_containing_cell(points)
| >>> indices.shape
| (1000,)
|
| flip_normal(self, normal: 'VectorLike[float]', point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Flip mesh about the normal.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| normal : sequence[float]
| Normal vector to flip about.
|
| point : sequence[float]
| Point to rotate about. Defaults to center of mesh at
| :attr:`center <pyvista.DataSet.center>`.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Dataset flipped about its normal.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> pl.subplot(0, 0)
| >>> pl.show_axes()
| >>> mesh1 = examples.download_teapot()
| >>> _ = pl.add_mesh(mesh1)
| >>> pl.subplot(0, 1)
| >>> pl.show_axes()
| >>> mesh2 = mesh1.flip_normal([1.0, 1.0, 1.0], inplace=False)
| >>> _ = pl.add_mesh(mesh2)
| >>> pl.show(cpos="xy")
|
| flip_x(self, point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Flip mesh about the x-axis.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| point : sequence[float], optional
| Point to rotate about. Defaults to center of mesh at
| :attr:`center <pyvista.DataSet.center>`.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Flipped dataset.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> pl.subplot(0, 0)
| >>> pl.show_axes()
| >>> mesh1 = examples.download_teapot()
| >>> _ = pl.add_mesh(mesh1)
| >>> pl.subplot(0, 1)
| >>> pl.show_axes()
| >>> mesh2 = mesh1.flip_x(inplace=False)
| >>> _ = pl.add_mesh(mesh2)
| >>> pl.show(cpos="xy")
|
| flip_y(self, point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Flip mesh about the y-axis.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| point : sequence[float], optional
| Point to rotate about. Defaults to center of mesh at
| :attr:`center <pyvista.DataSet.center>`.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Flipped dataset.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> pl.subplot(0, 0)
| >>> pl.show_axes()
| >>> mesh1 = examples.download_teapot()
| >>> _ = pl.add_mesh(mesh1)
| >>> pl.subplot(0, 1)
| >>> pl.show_axes()
| >>> mesh2 = mesh1.flip_y(inplace=False)
| >>> _ = pl.add_mesh(mesh2)
| >>> pl.show(cpos="xy")
|
| flip_z(self, point: 'VectorLike[float] | None' = None, transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Flip mesh about the z-axis.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| point : Vector, optional
| Point to rotate about. Defaults to center of mesh at
| :attr:`center <pyvista.DataSet.center>`.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Flipped dataset.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> pl.subplot(0, 0)
| >>> pl.show_axes()
| >>> mesh1 = examples.download_teapot().rotate_x(90, inplace=False)
| >>> _ = pl.add_mesh(mesh1)
| >>> pl.subplot(0, 1)
| >>> pl.show_axes()
| >>> mesh2 = mesh1.flip_z(inplace=False)
| >>> _ = pl.add_mesh(mesh2)
| >>> pl.show(cpos="xz")
|
| get_array(self, name: 'str', preference: "Literal['cell', 'point', 'field']" = 'cell') -> 'pyvista.pyvista_ndarray'
| Search both point, cell and field data for an array.
|
| Parameters
| ----------
| name : str
| Name of the array.
|
| preference : str, default: "cell"
| When scalars is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'``, ``'cell'``, or ``'field'``.
|
| Returns
| -------
| pyvista.pyvista_ndarray
| Requested array.
|
| Examples
| --------
| Create a DataSet with a variety of arrays.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> mesh.clear_data()
| >>> mesh.point_data['point-data'] = range(mesh.n_points)
| >>> mesh.cell_data['cell-data'] = range(mesh.n_cells)
| >>> mesh.field_data['field-data'] = ['a', 'b', 'c']
| >>> mesh.array_names
| ['point-data', 'field-data', 'cell-data']
|
| Get the point data array.
|
| >>> mesh.get_array('point-data')
| pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7])
|
| Get the cell data array.
|
| >>> mesh.get_array('cell-data')
| pyvista_ndarray([0, 1, 2, 3, 4, 5])
|
| Get the field data array.
|
| >>> mesh.get_array('field-data')
| pyvista_ndarray(['a', 'b', 'c'], dtype='<U1')
|
| get_array_association(self, name: 'str', preference: "Literal['cell', 'point', 'field']" = 'cell') -> 'FieldAssociation'
| Get the association of an array.
|
| Parameters
| ----------
| name : str
| Name of the array.
|
| preference : str, default: "cell"
| When ``name`` is specified, this is the preferred array
| association to search for in the dataset. Must be either
| ``'point'``, ``'cell'``, or ``'field'``.
|
| Returns
| -------
| pyvista.core.utilities.arrays.FieldAssociation
| Field association of the array.
|
| Examples
| --------
| Create a DataSet with a variety of arrays.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> mesh.clear_data()
| >>> mesh.point_data['point-data'] = range(mesh.n_points)
| >>> mesh.cell_data['cell-data'] = range(mesh.n_cells)
| >>> mesh.field_data['field-data'] = ['a', 'b', 'c']
| >>> mesh.array_names
| ['point-data', 'field-data', 'cell-data']
|
| Get the point data array association.
|
| >>> mesh.get_array_association('point-data')
| <FieldAssociation.POINT: 0>
|
| Get the cell data array association.
|
| >>> mesh.get_array_association('cell-data')
| <FieldAssociation.CELL: 1>
|
| Get the field data array association.
|
| >>> mesh.get_array_association('field-data')
| <FieldAssociation.NONE: 2>
|
| get_cell(self, index: 'int') -> 'pyvista.Cell'
| Return a :class:`pyvista.Cell` object.
|
| Parameters
| ----------
| index : int
| Cell ID.
|
| Returns
| -------
| pyvista.Cell
| The i-th pyvista.Cell.
|
| Notes
| -----
| Cells returned from this method are deep copies of the original
| cells. Changing properties (for example, ``points``) will not affect
| the dataset they originated from.
|
| Examples
| --------
| Get the 0-th cell.
|
| >>> from pyvista import examples
| >>> mesh = examples.load_airplane()
| >>> cell = mesh.get_cell(0)
| >>> cell
| Cell ...
|
| Get the point ids of the first cell
|
| >>> cell.point_ids
| [0, 1, 2]
|
| Get the point coordinates of the first cell
|
| >>> cell.points
| array([[897.0, 48.8, 82.3],
| [906.6, 48.8, 80.7],
| [907.5, 55.5, 83.7]])
|
| For the first cell, get the points associated with the first edge
|
| >>> cell.edges[0].point_ids
| [0, 1]
|
| For a Tetrahedron, get the point ids of the last face
|
| >>> mesh = examples.cells.Tetrahedron()
| >>> cell = mesh.get_cell(0)
| >>> cell.faces[-1].point_ids
| [0, 2, 1]
|
| get_data_range(self, arr_var: 'str | NumpyArray[float] | None' = None, preference='cell') -> 'tuple[float, float]'
| Get the min and max of a named array.
|
| Parameters
| ----------
| arr_var : str, np.ndarray, optional
| The name of the array to get the range. If ``None``, the
| active scalars is used.
|
| preference : str, default: "cell"
| When scalars is specified, this is the preferred array type
| to search for in the dataset. Must be either ``'point'``,
| ``'cell'``, or ``'field'``.
|
| Returns
| -------
| tuple
| ``(min, max)`` of the named array.
|
| plot(var_item, off_screen=None, full_screen=None, screenshot=None, interactive=True, cpos=None, window_size=None, show_bounds=False, show_axes=None, notebook=None, background=None, text='', return_img=False, eye_dome_lighting=False, volume=False, parallel_projection=False, jupyter_backend=None, return_viewer=False, return_cpos=False, jupyter_kwargs=None, theme=None, anti_aliasing=None, zoom=None, border=False, border_color='k', border_width=2.0, ssao=False, **kwargs) from pyvista._plot
| Plot a PyVista, numpy, or vtk object.
|
| Parameters
| ----------
| var_item : pyvista.DataSet
| See :func:`Plotter.add_mesh <pyvista.Plotter.add_mesh>` for all
| supported types.
|
| off_screen : bool, optional
| Plots off screen when ``True``. Helpful for saving
| screenshots without a window popping up. Defaults to the
| global setting ``pyvista.OFF_SCREEN``.
|
| full_screen : bool, default: :attr:`pyvista.plotting.themes.Theme.full_screen`
| Opens window in full screen. When enabled, ignores
| ``window_size``.
|
| screenshot : str or bool, optional
| Saves screenshot to file when enabled. See:
| :func:`Plotter.screenshot() <pyvista.Plotter.screenshot>`.
| Default ``False``.
|
| When ``True``, takes screenshot and returns ``numpy`` array of
| image.
|
| interactive : bool, default: :attr:`pyvista.plotting.themes.Theme.interactive`
| Allows user to pan and move figure.
|
| cpos : list, optional
| List of camera position, focal point, and view up.
|
| window_size : sequence, default: :attr:`pyvista.plotting.themes.Theme.window_size`
| Window size in pixels.
|
| show_bounds : bool, default: False
| Shows mesh bounds when ``True``.
|
| show_axes : bool, default: :attr:`pyvista.plotting.themes._AxesConfig.show`
| Shows a vtk axes widget.
|
| notebook : bool, default: :attr:`pyvista.plotting.themes.Theme.notebook`
| When ``True``, the resulting plot is placed inline a jupyter
| notebook. Assumes a jupyter console is active.
|
| background : ColorLike, default: :attr:`pyvista.plotting.themes.Theme.background`
| Color of the background.
|
| text : str, optional
| Adds text at the bottom of the plot.
|
| return_img : bool, default: False
| Returns numpy array of the last image rendered.
|
| eye_dome_lighting : bool, optional
| Enables eye dome lighting.
|
| volume : bool, default: False
| Use the :func:`Plotter.add_volume()
| <pyvista.Plotter.add_volume>` method for volume rendering.
|
| parallel_projection : bool, default: False
| Enable parallel projection.
|
| jupyter_backend : str, default: :attr:`pyvista.plotting.themes.Theme.jupyter_backend`
| Jupyter notebook plotting backend to use. One of the
| following:
|
| * ``'none'`` : Do not display in the notebook.
| * ``'static'`` : Display a static figure.
| * ``'trame'`` : Display using ``trame``.
|
| This can also be set globally with
| :func:`pyvista.set_jupyter_backend`.
|
| return_viewer : bool, default: False
| Return the jupyterlab viewer, scene, or display object
| when plotting with jupyter notebook.
|
| return_cpos : bool, default: False
| Return the last camera position from the render window
| when enabled. Defaults to value in theme settings.
|
| jupyter_kwargs : dict, optional
| Keyword arguments for the Jupyter notebook plotting backend.
|
| theme : pyvista.plotting.themes.Theme, optional
| Plot-specific theme.
|
| anti_aliasing : str | bool, default: :attr:`pyvista.plotting.themes.Theme.anti_aliasing`
| Enable or disable anti-aliasing. If ``True``, uses ``"msaa"``. If False,
| disables anti_aliasing. If a string, should be either ``"fxaa"`` or
| ``"ssaa"``.
|
| zoom : float, str, optional
| Camera zoom. Either ``'tight'`` or a float. A value greater than 1
| is a zoom-in, a value less than 1 is a zoom-out. Must be greater
| than 0.
|
| border : bool, default: False
| Draw a border around each render window.
|
| border_color : ColorLike, default: "k"
| Either a string, rgb list, or hex color string. For example:
|
| * ``color='white'``
| * ``color='w'``
| * ``color=[1.0, 1.0, 1.0]``
| * ``color='#FFFFFF'``
|
| border_width : float, default: 2.0
| Width of the border in pixels when enabled.
|
| ssao : bool, optional
| Enable surface space ambient occlusion (SSAO). See
| :func:`Plotter.enable_ssao` for more details.
|
| **kwargs : dict, optional
| See :func:`pyvista.Plotter.add_mesh` for additional options.
|
| Returns
| -------
| cpos : list
| List of camera position, focal point, and view up.
| Returned only when ``return_cpos=True`` or set in the
| default global or plot theme. Not returned when in a
| jupyter notebook and ``return_viewer=True``.
|
| image : np.ndarray
| Numpy array of the last image when either ``return_img=True``
| or ``screenshot=True`` is set. Not returned when in a
| jupyter notebook with ``return_viewer=True``. Optionally
| contains alpha values. Sized:
|
| * [Window height x Window width x 3] if the theme sets
| ``transparent_background=False``.
| * [Window height x Window width x 4] if the theme sets
| ``transparent_background=True``.
|
| widget : ipywidgets.Widget
| IPython widget when ``return_viewer=True``.
|
| Examples
| --------
| Plot a simple sphere while showing its edges.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh.plot(show_edges=True)
|
| Plot a volume mesh. Color by distance from the center of the
| ImageData. Note ``volume=True`` is passed.
|
| >>> import numpy as np
| >>> grid = pv.ImageData(
| ... dimensions=(32, 32, 32), spacing=(0.5, 0.5, 0.5)
| ... )
| >>> grid['data'] = np.linalg.norm(grid.center - grid.points, axis=1)
| >>> grid['data'] = np.abs(grid['data'] - grid['data'].max()) ** 3
| >>> grid.plot(volume=True)
|
| point_cell_ids(self, ind: 'int') -> 'list[int]'
| Get the cell IDs that use the ind-th point.
|
| Implements vtkDataSet's `GetPointCells <https://vtk.org/doc/nightly/html/classvtkDataSet.html#a36d1d8f67ad67adf4d1a9cfb30dade49>`_.
|
| Parameters
| ----------
| ind : int
| Point ID.
|
| Returns
| -------
| listint]
| List of cell IDs using the ind-th point.
|
| Examples
| --------
| Get the cell ids using the 0-th point.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere(theta_resolution=10)
| >>> mesh.point_cell_ids(0)
| [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
|
| Plot them.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(mesh, show_edges=True)
| >>>
| >>> # Label the 0-th point
| >>> _ = pl.add_point_labels(
| ... mesh.points[0], ["0"], text_color="blue", font_size=20
| ... )
| >>>
| >>> # Get the cells ids using the 0-th point
| >>> ids = mesh.point_cell_ids(0)
| >>> cells = mesh.extract_cells(ids)
| >>> _ = pl.add_mesh(cells, color="red", show_edges=True)
| >>> centers = cells.cell_centers().points
| >>> _ = pl.add_point_labels(
| ... centers,
| ... labels=[f"{i}" for i in ids],
| ... text_color="white",
| ... font_size=20,
| ... shape=None,
| ... show_points=False,
| ... )
| >>>
| >>> # Plot the other cells
| >>> others = mesh.extract_cells(
| ... [i for i in range(mesh.n_cells) if i not in ids]
| ... )
| >>> _ = pl.add_mesh(others, show_edges=True)
| >>>
| >>> pl.camera_position = "yx"
| >>> pl.camera.zoom(7.0)
| >>> pl.show()
|
| point_is_inside_cell(self, ind: 'int', point: 'VectorLike[float] | MatrixLike[float]') -> 'bool | NumpyArray[np.bool_]'
| Return whether one or more points are inside a cell.
|
| .. versionadded:: 0.35.0
|
| Parameters
| ----------
| ind : int
| Cell ID.
|
| point : Matrix
| Point or points to query if are inside a cell.
|
| Returns
| -------
| bool or numpy.ndarray
| Whether point(s) is/are inside cell. A single bool is only returned if
| the input point has shape ``(3,)``.
|
| Examples
| --------
| >>> from pyvista import examples
| >>> mesh = examples.load_hexbeam()
| >>> mesh.get_cell(0).bounds
| (0.0, 0.5, 0.0, 0.5, 0.0, 0.5)
| >>> mesh.point_is_inside_cell(0, [0.2, 0.2, 0.2])
| True
|
| point_neighbors(self, ind: 'int') -> 'list[int]'
| Get the point neighbors of the ind-th point.
|
| Parameters
| ----------
| ind : int
| Point ID.
|
| Returns
| -------
| list[int]
| List of neighbor points IDs for the ind-th point.
|
| See Also
| --------
| pyvista.DataSet.point_neighbors_levels
|
| Examples
| --------
| Get the point neighbors of the 0-th point.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere(theta_resolution=10)
| >>> mesh.point_neighbors(0)
| [2, 226, 198, 170, 142, 114, 86, 254, 58, 30]
|
| Plot them.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(mesh, show_edges=True)
| >>>
| >>> # Label the 0-th point
| >>> _ = pl.add_point_labels(
| ... mesh.points[0], ["0"], text_color="blue", font_size=40
| ... )
| >>>
| >>> # Get the point neighbors and plot them
| >>> neighbors = mesh.point_neighbors(0)
| >>> _ = pl.add_point_labels(
| ... mesh.points[neighbors],
| ... labels=[f"{i}" for i in neighbors],
| ... text_color="red",
| ... font_size=40,
| ... )
| >>> pl.camera_position = "xy"
| >>> pl.camera.zoom(7.0)
| >>> pl.show()
|
| point_neighbors_levels(self, ind: 'int', n_levels: 'int' = 1) -> 'Generator[list[int], None, None]'
| Get consecutive levels of point neighbors.
|
| Parameters
| ----------
| ind : int
| Point ID.
|
| n_levels : int, default: 1
| Number of levels to search for point neighbors.
| When equal to 1, it is equivalent to :func:`pyvista.DataSet.point_neighbors`.
|
| Returns
| -------
| generator[list[[int]]
| A generator of list of neighbor points IDs for the ind-th point.
|
| See Also
| --------
| pyvista.DataSet.point_neighbors
|
| Examples
| --------
| Get the point neighbors IDs starting from the 0-th point
| up until the third level.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere(theta_resolution=10)
| >>> pt_nbr_levels = mesh.point_neighbors_levels(0, 3)
| >>> pt_nbr_levels = list(pt_nbr_levels)
| >>> pt_nbr_levels[0]
| [2, 226, 198, 170, 142, 114, 86, 30, 58, 254]
| >>> pt_nbr_levels[1]
| [3, 227, 255, 199, 171, 143, 115, 87, 59, 31]
| >>> pt_nbr_levels[2]
| [256, 32, 4, 228, 200, 172, 144, 116, 88, 60]
|
| Visualize these points IDs.
|
| >>> from functools import partial
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(mesh, show_edges=True)
| >>>
| >>> # Define partial function to add point labels
| >>> add_point_labels = partial(
| ... pl.add_point_labels,
| ... text_color="white",
| ... font_size=40,
| ... point_size=10,
| ... )
| >>>
| >>> # Add the first point label
| >>> _ = add_point_labels(
| ... mesh.points[0], labels=["0"], text_color="blue"
| ... )
| >>>
| >>> # Add the neighbors to the plot
| >>> neighbors = mesh.point_neighbors_levels(0, n_levels=3)
| >>> for i, ids in enumerate(neighbors, start=1):
| ... _ = add_point_labels(
| ... mesh.points[ids],
| ... labels=[f"{i}"] * len(ids),
| ... text_color="red",
| ... )
| ...
| >>>
| >>> pl.view_xy()
| >>> pl.camera.zoom(4.0)
| >>> pl.show()
|
| rename_array(self, old_name: 'str', new_name: 'str', preference='cell') -> 'None'
| Change array name by searching for the array then renaming it.
|
| Parameters
| ----------
| old_name : str
| Name of the array to rename.
|
| new_name : str
| Name to rename the array to.
|
| preference : str, default: "cell"
| If there are two arrays of the same name associated with
| points, cells, or field data, it will prioritize an array
| matching this type. Can be either ``'cell'``,
| ``'field'``, or ``'point'``.
|
| Examples
| --------
| Create a cube, assign a point array to the mesh named
| ``'my_array'``, and rename it to ``'my_renamed_array'``.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> cube = pv.Cube()
| >>> cube['my_array'] = range(cube.n_points)
| >>> cube.rename_array('my_array', 'my_renamed_array')
| >>> cube['my_renamed_array']
| pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7])
|
| rotate_vector(self, vector: 'VectorLike[float]', angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Rotate mesh about a vector.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| vector : Vector
| Vector to rotate about.
|
| angle : float
| Angle to rotate.
|
| point : Vector, default: (0.0, 0.0, 0.0)
| Point to rotate about. Defaults to origin.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Rotated dataset.
|
| Examples
| --------
| Rotate a mesh 30 degrees about the ``(1, 1, 1)`` axis.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> rot = mesh.rotate_vector((1, 1, 1), 30, inplace=False)
|
| Plot the rotated mesh.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(rot)
| >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
| >>> _ = pl.add_axes_at_origin()
| >>> pl.show()
|
| rotate_x(self, angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Rotate mesh about the x-axis.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| angle : float
| Angle in degrees to rotate about the x-axis.
|
| point : Vector, default: (0.0, 0.0, 0.0)
| Point to rotate about. Defaults to origin.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Rotated dataset.
|
| Examples
| --------
| Rotate a mesh 30 degrees about the x-axis.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> rot = mesh.rotate_x(30, inplace=False)
|
| Plot the rotated mesh.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(rot)
| >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
| >>> _ = pl.add_axes_at_origin()
| >>> pl.show()
|
| rotate_y(self, angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Rotate mesh about the y-axis.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| angle : float
| Angle in degrees to rotate about the y-axis.
|
| point : Vector, default: (0.0, 0.0, 0.0)
| Point to rotate about.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are transformed. Otherwise, only
| the points, normals and active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Rotated dataset.
|
| Examples
| --------
| Rotate a cube 30 degrees about the y-axis.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> rot = mesh.rotate_y(30, inplace=False)
|
| Plot the rotated mesh.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(rot)
| >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
| >>> _ = pl.add_axes_at_origin()
| >>> pl.show()
|
| rotate_z(self, angle: 'float', point: 'VectorLike[float]' = (0.0, 0.0, 0.0), transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Rotate mesh about the z-axis.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| angle : float
| Angle in degrees to rotate about the z-axis.
|
| point : Vector, default: (0.0, 0.0, 0.0)
| Point to rotate about. Defaults to origin.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Rotated dataset.
|
| Examples
| --------
| Rotate a mesh 30 degrees about the z-axis.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> rot = mesh.rotate_z(30, inplace=False)
|
| Plot the rotated mesh.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(rot)
| >>> _ = pl.add_mesh(mesh, style='wireframe', line_width=3)
| >>> _ = pl.add_axes_at_origin()
| >>> pl.show()
|
| scale(self, xyz: 'Number | VectorLike[float]', transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Scale the mesh.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| xyz : Number | Vector
| A vector sequence defining the scale factors along x, y, and z. If
| a scalar, the same uniform scale is used along all three axes.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are transformed. Otherwise, only
| the points, normals and active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Scaled dataset.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> pl.subplot(0, 0)
| >>> pl.show_axes()
| >>> _ = pl.show_grid()
| >>> mesh1 = examples.download_teapot()
| >>> _ = pl.add_mesh(mesh1)
| >>> pl.subplot(0, 1)
| >>> pl.show_axes()
| >>> _ = pl.show_grid()
| >>> mesh2 = mesh1.scale([10.0, 10.0, 10.0], inplace=False)
| >>> _ = pl.add_mesh(mesh2)
| >>> pl.show(cpos="xy")
|
| set_active_scalars(self, name: 'str | None', preference='cell') -> 'tuple[FieldAssociation, NumpyArray[float] | None]'
| Find the scalars by name and appropriately sets it as active.
|
| To deactivate any active scalars, pass ``None`` as the ``name``.
|
| Parameters
| ----------
| name : str, optional
| Name of the scalars array to assign as active. If
| ``None``, deactivates active scalars for both point and
| cell data.
|
| preference : str, default: "cell"
| If there are two arrays of the same name associated with
| points or cells, it will prioritize an array matching this
| type. Can be either ``'cell'`` or ``'point'``.
|
| Returns
| -------
| pyvista.core.utilities.arrays.FieldAssociation
| Association of the scalars matching ``name``.
|
| pyvista_ndarray
| An array from the dataset matching ``name``.
|
| set_active_tensors(self, name: 'str | None', preference: 'str' = 'point') -> 'None'
| Find the tensors by name and appropriately sets it as active.
|
| To deactivate any active tensors, pass ``None`` as the ``name``.
|
| Parameters
| ----------
| name : str, optional
| Name of the tensors array to assign as active.
|
| preference : str, default: "point"
| If there are two arrays of the same name associated with
| points, cells, or field data, it will prioritize an array
| matching this type. Can be either ``'cell'``,
| ``'field'``, or ``'point'``.
|
| set_active_vectors(self, name: 'str | None', preference: 'str' = 'point') -> 'None'
| Find the vectors by name and appropriately sets it as active.
|
| To deactivate any active vectors, pass ``None`` as the ``name``.
|
| Parameters
| ----------
| name : str, optional
| Name of the vectors array to assign as active.
|
| preference : str, default: "point"
| If there are two arrays of the same name associated with
| points, cells, or field data, it will prioritize an array
| matching this type. Can be either ``'cell'``,
| ``'field'``, or ``'point'``.
|
| translate(self, xyz: 'VectorLike[float]', transform_all_input_vectors: 'bool' = False, inplace: 'bool' = False)
| Translate the mesh.
|
| .. note::
| See also the notes at :func:`transform()
| <DataSetFilters.transform>` which is used by this filter
| under the hood.
|
| Parameters
| ----------
| xyz : Vector
| A vector of three floats.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are
| transformed. Otherwise, only the points, normals and
| active vectors are transformed.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| Returns
| -------
| pyvista.DataSet
| Translated dataset.
|
| Examples
| --------
| Create a sphere and translate it by ``(2, 1, 2)``.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh.center
| [0.0, 0.0, 0.0]
| >>> trans = mesh.translate((2, 1, 2), inplace=False)
| >>> trans.center
| [2.0, 1.0, 2.0]
|
| ----------------------------------------------------------------------
| Readonly properties inherited from pyvista.core.dataset.DataSet:
|
| active_normals
| Return the active normals as an array.
|
| Returns
| -------
| pyvista_ndarray
| Active normals of this dataset.
|
| Notes
| -----
| If both point and cell normals exist, this returns point
| normals by default.
|
| Examples
| --------
| Compute normals on an example sphere mesh and return the
| active normals for the dataset. Show that this is the same size
| as the number of points.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh = mesh.compute_normals()
| >>> normals = mesh.active_normals
| >>> normals.shape
| (842, 3)
| >>> mesh.n_points
| 842
|
| active_scalars
| Return the active scalars as an array.
|
| Returns
| -------
| Optional[pyvista_ndarray]
| Active scalars as an array.
|
| active_scalars_info
| Return the active scalar's association and name.
|
| Association refers to the data association (e.g. point, cell, or
| field) of the active scalars.
|
| Returns
| -------
| ActiveArrayInfo
| The scalars info in an object with namedtuple semantics,
| with attributes ``association`` and ``name``.
|
| Notes
| -----
| If both cell and point scalars are present and neither have
| been set active within at the dataset level, point scalars
| will be made active.
|
| Examples
| --------
| Create a mesh, add scalars to the mesh, and return the active
| scalars info. Note how when the scalars are added, they
| automatically become the active scalars.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh['Z Height'] = mesh.points[:, 2]
| >>> mesh.active_scalars_info
| ActiveArrayInfoTuple(association=<FieldAssociation.POINT: 0>, name='Z Height')
|
| active_tensors
| Return the active tensors array.
|
| Returns
| -------
| Optional[np.ndarray]
| Active tensors array.
|
| active_tensors_info
| Return the active tensor's field and name: [field, name].
|
| Returns
| -------
| ActiveArrayInfo
| Active tensor's field and name: [field, name].
|
| active_vectors
| Return the active vectors array.
|
| Returns
| -------
| Optional[pyvista_ndarray]
| Active vectors array.
|
| Examples
| --------
| Create a mesh, compute the normals inplace, and return the
| normals vector array.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> _ = mesh.compute_normals(inplace=True)
| >>> mesh.active_vectors # doctest:+SKIP
| pyvista_ndarray([[-2.48721432e-10, -1.08815623e-09, -1.00000000e+00],
| [-2.48721432e-10, -1.08815623e-09, 1.00000000e+00],
| [-1.18888125e-01, 3.40539310e-03, -9.92901802e-01],
| ...,
| [-3.11940581e-01, -6.81432486e-02, 9.47654784e-01],
| [-2.09880397e-01, -4.65070531e-02, 9.76620376e-01],
| [-1.15582108e-01, -2.80492082e-02, 9.92901802e-01]],
| dtype=float32)
|
| active_vectors_info
| Return the active vector's association and name.
|
| Association refers to the data association (e.g. point, cell, or
| field) of the active vectors.
|
| Returns
| -------
| ActiveArrayInfo
| The vectors info in an object with namedtuple semantics,
| with attributes ``association`` and ``name``.
|
| Notes
| -----
| If both cell and point vectors are present and neither have
| been set active within at the dataset level, point vectors
| will be made active.
|
| Examples
| --------
| Create a mesh, compute the normals inplace, set the active
| vectors to the normals, and show that the active vectors are
| the ``'Normals'`` array associated with points.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> _ = mesh.compute_normals(inplace=True)
| >>> mesh.active_vectors_name = 'Normals'
| >>> mesh.active_vectors_info
| ActiveArrayInfoTuple(association=<FieldAssociation.POINT: 0>, name='Normals')
|
| area
| Return the mesh area if 2D.
|
| This will return 0 for meshes with 3D cells.
|
| Returns
| -------
| float
| Total area of the mesh.
|
| Examples
| --------
| Get the area of a square of size 2x2.
| Note 5 points in each direction.
|
| >>> import pyvista as pv
| >>> mesh = pv.ImageData(dimensions=(5, 5, 1))
| >>> mesh.area
| 16.0
|
| A mesh with 3D cells does not have an area. To get
| the outer surface area, first extract the surface using
| :func:`pyvista.DataSetFilters.extract_surface`.
|
| >>> mesh = pv.ImageData(dimensions=(5, 5, 5))
| >>> mesh.area
| 0.0
|
| Get the area of a sphere. Discretization error results
| in slight difference from ``pi``.
|
| >>> mesh = pv.Sphere()
| >>> mesh.area
| 3.13
|
| array_names
| Return a list of array names for the dataset.
|
| This makes sure to put the active scalars' name first in the list.
|
| Returns
| -------
| list[str]
| List of array names for the dataset.
|
| Examples
| --------
| Return the array names for a mesh.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh.point_data['my_array'] = range(mesh.n_points)
| >>> mesh.array_names
| ['my_array', 'Normals']
|
| arrows
| Return a glyph representation of the active vector data as arrows.
|
| Arrows will be located at the points of the mesh and
| their size will be dependent on the norm of the vector.
| Their direction will be the "direction" of the vector
|
| Returns
| -------
| pyvista.PolyData
| Active vectors represented as arrows.
|
| Examples
| --------
| Create a mesh, compute the normals and set them active, and
| plot the active vectors.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> mesh_w_normals = mesh.compute_normals()
| >>> mesh_w_normals.active_vectors_name = 'Normals'
| >>> arrows = mesh_w_normals.arrows
| >>> arrows.plot(show_scalar_bar=False)
|
| bounds
| Return the bounding box of this dataset.
|
| Returns
| -------
| BoundsLike
| Bounding box of this dataset.
| The form is: ``(xmin, xmax, ymin, ymax, zmin, zmax)``.
|
| Examples
| --------
| Create a cube and return the bounds of the mesh.
|
| >>> import pyvista as pv
| >>> cube = pv.Cube()
| >>> cube.bounds
| (-0.5, 0.5, -0.5, 0.5, -0.5, 0.5)
|
| cell
| A generator that provides an easy way to loop over all cells.
|
| To access a single cell, use :func:`pyvista.DataSet.get_cell`.
|
| .. versionchanged:: 0.39.0
| Now returns a generator instead of a list.
| Use ``get_cell(i)`` instead of ``cell[i]``.
|
| Yields
| ------
| pyvista.Cell
|
| See Also
| --------
| pyvista.DataSet.get_cell
|
| Examples
| --------
| Loop over the cells
|
| >>> import pyvista as pv
| >>> # Create a grid with 9 points and 4 cells
| >>> mesh = pv.ImageData(dimensions=(3, 3, 1))
| >>> for cell in mesh.cell: # doctest: +SKIP
| ... cell
| ...
|
| cell_data
| Return cell data as DataSetAttributes.
|
| Returns
| -------
| DataSetAttributes
| Cell data as DataSetAttributes.
|
| Examples
| --------
| Add cell arrays to a mesh and list the available ``cell_data``.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> mesh = pv.Cube()
| >>> mesh.clear_data()
| >>> mesh.cell_data['my_array'] = np.random.default_rng().random(
| ... mesh.n_cells
| ... )
| >>> mesh.cell_data['my_other_array'] = np.arange(mesh.n_cells)
| >>> mesh.cell_data
| pyvista DataSetAttributes
| Association : CELL
| Active Scalars : my_array
| Active Vectors : None
| Active Texture : None
| Active Normals : None
| Contains arrays :
| my_array float64 (6,) SCALARS
| my_other_array int64 (6,)
|
| Access an array from ``cell_data``.
|
| >>> mesh.cell_data['my_other_array']
| pyvista_ndarray([0, 1, 2, 3, 4, 5])
|
| Or access it directly from the mesh.
|
| >>> mesh['my_array'].shape
| (6,)
|
| center
| Return the center of the bounding box.
|
| Returns
| -------
| Vector
| Center of the bounding box.
|
| Examples
| --------
| Get the center of a mesh.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere(center=(1, 2, 0))
| >>> mesh.center
| [1.0, 2.0, 0.0]
|
| length
| Return the length of the diagonal of the bounding box.
|
| Returns
| -------
| float
| Length of the diagonal of the bounding box.
|
| Examples
| --------
| Get the length of the bounding box of a cube. This should
| match ``3**(1/2)`` since it is the diagonal of a cube that is
| ``1 x 1 x 1``.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> mesh.length
| 1.7320508075688772
|
| n_arrays
| Return the number of arrays present in the dataset.
|
| Returns
| -------
| int
| Number of arrays present in the dataset.
|
| n_cells
| Return the number of cells in the entire dataset.
|
| Returns
| -------
| int :
| Number of cells in the entire dataset.
|
| Notes
| -----
| This returns the total number of cells -- for :class:`pyvista.PolyData`
| this includes vertices, lines, triangle strips and polygonal faces.
|
| Examples
| --------
| Create a mesh and return the number of cells in the
| mesh.
|
| >>> import pyvista as pv
| >>> cube = pv.Cube()
| >>> cube.n_cells
| 6
|
| n_points
| Return the number of points in the entire dataset.
|
| Returns
| -------
| int
| Number of points in the entire dataset.
|
| Examples
| --------
| Create a mesh and return the number of points in the
| mesh.
|
| >>> import pyvista as pv
| >>> cube = pv.Cube()
| >>> cube.n_points
| 8
|
| number_of_cells
| Return the number of cells.
|
| Returns
| -------
| int :
| Number of cells.
|
| number_of_points
| Return the number of points.
|
| Returns
| -------
| int :
| Number of points.
|
| point_data
| Return point data as DataSetAttributes.
|
| Returns
| -------
| DataSetAttributes
| Point data as DataSetAttributes.
|
| Examples
| --------
| Add point arrays to a mesh and list the available ``point_data``.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> mesh = pv.Cube()
| >>> mesh.clear_data()
| >>> mesh.point_data['my_array'] = np.random.default_rng().random(
| ... mesh.n_points
| ... )
| >>> mesh.point_data['my_other_array'] = np.arange(mesh.n_points)
| >>> mesh.point_data
| pyvista DataSetAttributes
| Association : POINT
| Active Scalars : my_array
| Active Vectors : None
| Active Texture : None
| Active Normals : None
| Contains arrays :
| my_array float64 (8,) SCALARS
| my_other_array int64 (8,)
|
| Access an array from ``point_data``.
|
| >>> mesh.point_data['my_other_array']
| pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7])
|
| Or access it directly from the mesh.
|
| >>> mesh['my_array'].shape
| (8,)
|
| volume
| Return the mesh volume.
|
| This will return 0 for meshes with 2D cells.
|
| Returns
| -------
| float
| Total volume of the mesh.
|
| Examples
| --------
| Get the volume of a cube of size 4x4x4.
| Note that there are 5 points in each direction.
|
| >>> import pyvista as pv
| >>> mesh = pv.ImageData(dimensions=(5, 5, 5))
| >>> mesh.volume
| 64.0
|
| A mesh with 2D cells has no volume.
|
| >>> mesh = pv.ImageData(dimensions=(5, 5, 1))
| >>> mesh.volume
| 0.0
|
| :class:`pyvista.PolyData` is special as a 2D surface can
| enclose a 3D volume. This case uses a different methodology,
| see :func:`pyvista.PolyData.volume`.
|
| >>> mesh = pv.Sphere()
| >>> mesh.volume
| 0.51825
|
| ----------------------------------------------------------------------
| Data descriptors inherited from pyvista.core.dataset.DataSet:
|
| active_scalars_name
| Return the name of the active scalars.
|
| Returns
| -------
| str
| Name of the active scalars.
|
| Examples
| --------
| Create a mesh, add scalars to the mesh, and return the name of
| the active scalars.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh['Z Height'] = mesh.points[:, 2]
| >>> mesh.active_scalars_name
| 'Z Height'
|
| active_t_coords
| Return the active texture coordinates on the points.
|
| Returns
| -------
| Optional[pyvista_ndarray]
| Active texture coordinates on the points.
|
| active_tensors_name
| Return the name of the active tensor array.
|
| Returns
| -------
| str
| Name of the active tensor array.
|
| active_texture_coordinates
| Return the active texture coordinates on the points.
|
| Returns
| -------
| Optional[pyvista_ndarray]
| Active texture coordinates on the points.
|
| Examples
| --------
| Return the active texture coordinates from the globe example.
|
| >>> from pyvista import examples
| >>> globe = examples.load_globe()
| >>> globe.active_texture_coordinates
| pyvista_ndarray([[0. , 0. ],
| [0. , 0.07142857],
| [0. , 0.14285714],
| ...,
| [1. , 0.85714286],
| [1. , 0.92857143],
| [1. , 1. ]])
|
| active_vectors_name
| Return the name of the active vectors array.
|
| Returns
| -------
| str
| Name of the active vectors array.
|
| Examples
| --------
| Create a mesh, compute the normals, set them as active, and
| return the name of the active vectors.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh_w_normals = mesh.compute_normals()
| >>> mesh_w_normals.active_vectors_name = 'Normals'
| >>> mesh_w_normals.active_vectors_name
| 'Normals'
|
| ----------------------------------------------------------------------
| Methods inherited from pyvista.core.filters.image_data.ImageDataFilters:
|
| cells_to_points(self, scalars: 'str | None' = None, *, copy: 'bool' = True)
| Re-mesh image data from a cell-based to a point-based representation.
|
| This filter changes how image data is represented. Data represented as cells
| at the input is re-meshed into an alternative representation as points at the
| output. Only the :class:`~pyvista.ImageData` container is modified so that
| the number of input cells equals the number of output points. The re-meshing is
| otherwise lossless in the sense that cell data at the input is passed through
| unmodified and stored as point data at the output. Any point data at the input is
| ignored and is not used by this filter.
|
| To change the image data's representation, the input cell centers are used to
| represent the output points. This has the effect of "shrinking" the
| input image dimensions by one along each axis (i.e. half the cell width on each
| side). For example, an image with 101 points and 100 cells along an axis at the
| input will have 100 points and 99 cells at the output. If the input has 1mm
| spacing, the axis size will also decrease from 100mm to 99mm.
|
| Since filters may be inherently cell-based (e.g. some :class:`~pyvista.DataSetFilters`)
| or may operate on point data exclusively (e.g. most :class:`~pyvista.ImageDataFilters`),
| re-meshing enables the same data to be used with either kind of filter while
| ensuring the input data to those filters has the appropriate representation.
| This filter is also useful when plotting image data to achieve a desired visual
| effect, such as plotting images as points instead of as voxel cells.
|
| .. versionadded:: 0.44.0
|
| See Also
| --------
| points_to_cells
| Inverse of this filter to represent points as cells.
| :meth:`~pyvista.DataSetFilters.cell_data_to_point_data`
| Resample cell data as point data without modifying the container.
| :meth:`~pyvista.DataSetFilters.point_data_to_cell_data`
| Resample point data as cell data without modifying the container.
|
| Parameters
| ----------
| scalars : str, optional
| Name of cell data scalars to pass through to the output as point data. Use
| this parameter to restrict the output to only include the specified array.
| By default, all cell data arrays at the input are passed through as point
| data at the output.
|
| copy : bool, default: True
| Copy the input cell data before associating it with the output point data.
| If ``False``, the input and output will both refer to the same data array(s).
|
| Returns
| -------
| pyvista.ImageData
| Image with a point-based representation.
|
| Examples
| --------
| Load an image with cell data.
|
| >>> from pyvista import examples
| >>> image = examples.load_uniform()
|
| Show the current properties and cell arrays of the image.
|
| >>> image
| ImageData (...)
| N Cells: 729
| N Points: 1000
| X Bounds: 0.000e+00, 9.000e+00
| Y Bounds: 0.000e+00, 9.000e+00
| Z Bounds: 0.000e+00, 9.000e+00
| Dimensions: 10, 10, 10
| Spacing: 1.000e+00, 1.000e+00, 1.000e+00
| N Arrays: 2
|
| >>> image.cell_data.keys()
| ['Spatial Cell Data']
|
| Re-mesh the cells and cell data as points and point data.
|
| >>> points_image = image.cells_to_points()
|
| Show the properties and point arrays of the re-meshed image.
|
| >>> points_image
| ImageData (...)
| N Cells: 512
| N Points: 729
| X Bounds: 5.000e-01, 8.500e+00
| Y Bounds: 5.000e-01, 8.500e+00
| Z Bounds: 5.000e-01, 8.500e+00
| Dimensions: 9, 9, 9
| Spacing: 1.000e+00, 1.000e+00, 1.000e+00
| N Arrays: 1
|
| >>> points_image.point_data.keys()
| ['Spatial Cell Data']
|
| Observe that:
|
| - The input cell array is now a point array
| - The output has one less array (the input point data is ignored)
| - The dimensions have decreased by one
| - The bounds have decreased by half the spacing
| - The output N Points equals the input N Cells
|
| See :ref:`image_representations_example` for more examples using this filter.
|
| contour_labeled(self, n_labels: 'int | None' = None, smoothing: 'bool' = False, smoothing_num_iterations: 'int' = 50, smoothing_relaxation_factor: 'float' = 0.5, smoothing_constraint_distance: 'float' = 1, output_mesh_type: "Literal['quads', 'triangles']" = 'quads', output_style: "Literal['default', 'boundary']" = 'default', scalars: 'str | None' = None, progress_bar: 'bool' = False) -> 'pyvista.PolyData'
| Generate labeled contours from 3D label maps.
|
| SurfaceNets algorithm is used to extract contours preserving sharp
| boundaries for the selected labels from the label maps.
| Optionally, the boundaries can be smoothened to reduce the staircase
| appearance in case of low resolution input label maps.
|
| This filter requires that the :class:`ImageData` has integer point
| scalars, such as multi-label maps generated from image segmentation.
|
| .. note::
| Requires ``vtk>=9.3.0``.
|
| Parameters
| ----------
| n_labels : int, optional
| Number of labels to be extracted (all are extracted if None is given).
|
| smoothing : bool, default: False
| Apply smoothing to the meshes.
|
| smoothing_num_iterations : int, default: 50
| Number of smoothing iterations.
|
| smoothing_relaxation_factor : float, default: 0.5
| Relaxation factor of the smoothing.
|
| smoothing_constraint_distance : float, default: 1
| Constraint distance of the smoothing.
|
| output_mesh_type : str, default: 'quads'
| Type of the output mesh. Must be either ``'quads'``, or ``'triangles'``.
|
| output_style : str, default: 'default'
| Style of the output mesh. Must be either ``'default'`` or ``'boundary'``.
| When ``'default'`` is specified, the filter produces a mesh with both
| interior and exterior polygons. When ``'boundary'`` is selected, only
| polygons on the border with the background are produced (without interior
| polygons). Note that style ``'selected'`` is currently not implemented.
|
| scalars : str, optional
| Name of scalars to process. Defaults to currently active scalars.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| :class:`pyvista.PolyData` Labeled mesh with the segments labeled.
|
| References
| ----------
| Sarah F. Frisken, SurfaceNets for Multi-Label Segmentations with Preservation
| of Sharp Boundaries, Journal of Computer Graphics Techniques (JCGT), vol. 11,
| no. 1, 34-54, 2022. Available online http://jcgt.org/published/0011/01/03/
|
| https://www.kitware.com/really-fast-isocontouring/
|
| Examples
| --------
| See :ref:`contouring_example` for a full example using this filter.
|
| See Also
| --------
| pyvista.DataSetFilters.contour
| Generalized contouring method which uses MarchingCubes or FlyingEdges.
|
| pyvista.DataSetFilters.pack_labels
| Function used internally by SurfaceNets to generate contiguous label data.
|
| extract_subset(self, voi, rate=(1, 1, 1), boundary=False, progress_bar=False)
| Select piece (e.g., volume of interest).
|
| To use this filter set the VOI ivar which are i-j-k min/max indices
| that specify a rectangular region in the data. (Note that these are
| 0-offset.) You can also specify a sampling rate to subsample the
| data.
|
| Typical applications of this filter are to extract a slice from a
| volume for image processing, subsampling large volumes to reduce data
| size, or extracting regions of a volume with interesting data.
|
| Parameters
| ----------
| voi : sequence[int]
| Length 6 iterable of ints: ``(xmin, xmax, ymin, ymax, zmin, zmax)``.
| These bounds specify the volume of interest in i-j-k min/max
| indices.
|
| rate : sequence[int], default: (1, 1, 1)
| Length 3 iterable of ints: ``(xrate, yrate, zrate)``.
|
| boundary : bool, default: False
| Control whether to enforce that the "boundary" of the grid
| is output in the subsampling process. This only has effect
| when the rate in any direction is not equal to 1. When
| this is enabled, the subsampling will always include the
| boundary of the grid even though the sample rate is not an
| even multiple of the grid dimensions. By default this is
| disabled.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| ImageData subset.
|
| fft(self, output_scalars_name=None, progress_bar=False)
| Apply a fast Fourier transform (FFT) to the active scalars.
|
| The input can be real or complex data, but the output is always
| :attr:`numpy.complex128`. The filter is fastest for images that have
| power of two sizes.
|
| The filter uses a butterfly diagram for each prime factor of the
| dimension. This makes images with prime number dimensions (i.e. 17x17)
| much slower to compute. FFTs of multidimensional meshes (i.e volumes)
| are decomposed so that each axis executes serially.
|
| The frequencies of the output assume standard order: along each axis
| first positive frequencies are assumed from 0 to the maximum, then
| negative frequencies are listed from the largest absolute value to
| smallest. This implies that the corners of the grid correspond to low
| frequencies, while the center of the grid corresponds to high
| frequencies.
|
| Parameters
| ----------
| output_scalars_name : str, optional
| The name of the output scalars. By default, this is the same as the
| active scalars of the dataset.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| :class:`pyvista.ImageData` with applied FFT.
|
| See Also
| --------
| rfft : The reverse transform.
| low_pass : Low-pass filtering of FFT output.
| high_pass : High-pass filtering of FFT output.
|
| Examples
| --------
| Apply FFT to an example image.
|
| >>> from pyvista import examples
| >>> image = examples.download_moonlanding_image()
| >>> fft_image = image.fft()
| >>> fft_image.point_data # doctest:+SKIP
| pyvista DataSetAttributes
| Association : POINT
| Active Scalars : PNGImage
| Active Vectors : None
| Active Texture : None
| Active Normals : None
| Contains arrays :
| PNGImage complex128 (298620,) SCALARS
|
| See :ref:`image_fft_example` for a full example using this filter.
|
| gaussian_smooth(self, radius_factor=1.5, std_dev=2.0, scalars=None, progress_bar=False)
| Smooth the data with a Gaussian kernel.
|
| Parameters
| ----------
| radius_factor : float | sequence[float], default: 1.5
| Unitless factor to limit the extent of the kernel.
|
| std_dev : float | sequence[float], default: 2.0
| Standard deviation of the kernel in pixel units.
|
| scalars : str, optional
| Name of scalars to process. Defaults to currently active scalars.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| Uniform grid with smoothed scalars.
|
| Notes
| -----
| This filter only supports point data. For inputs with cell data, consider
| re-meshing the cell data as point data with :meth:`~pyvista.ImageDataFilters.cells_to_points`
| or resampling the cell data to point data with :func:`~pyvista.DataSetFilters.cell_data_to_point_data`.
|
| Examples
| --------
| First, create sample data to smooth. Here, we use
| :func:`pyvista.perlin_noise() <pyvista.core.utilities.features.perlin_noise>`
| to create meaningful data.
|
| >>> import numpy as np
| >>> import pyvista as pv
| >>> noise = pv.perlin_noise(0.1, (2, 5, 8), (0, 0, 0))
| >>> grid = pv.sample_function(
| ... noise, [0, 1, 0, 1, 0, 1], dim=(20, 20, 20)
| ... )
| >>> grid.plot(show_scalar_bar=False)
|
| Next, smooth the sample data.
|
| >>> smoothed = grid.gaussian_smooth()
| >>> smoothed.plot(show_scalar_bar=False)
|
| See :ref:`gaussian_smoothing_example` for a full example using this filter.
|
| high_pass(self, x_cutoff, y_cutoff, z_cutoff, order=1, output_scalars_name=None, progress_bar=False)
| Perform a Butterworth high pass filter in the frequency domain.
|
| This filter requires that the :class:`ImageData` have a complex point
| scalars, usually generated after the :class:`ImageData` has been
| converted to the frequency domain by a :func:`ImageDataFilters.fft`
| filter.
|
| A :func:`ImageDataFilters.rfft` filter can be used to convert the
| output back into the spatial domain. This filter attenuates low
| frequency components. Input and output are complex arrays with
| datatype :attr:`numpy.complex128`.
|
| The frequencies of the input assume standard order: along each axis
| first positive frequencies are assumed from 0 to the maximum, then
| negative frequencies are listed from the largest absolute value to
| smallest. This implies that the corners of the grid correspond to low
| frequencies, while the center of the grid corresponds to high
| frequencies.
|
| Parameters
| ----------
| x_cutoff : float
| The cutoff frequency for the x-axis.
|
| y_cutoff : float
| The cutoff frequency for the y-axis.
|
| z_cutoff : float
| The cutoff frequency for the z-axis.
|
| order : int, default: 1
| The order of the cutoff curve. Given from the equation
| ``1/(1 + (cutoff/freq(i, j))**(2*order))``.
|
| output_scalars_name : str, optional
| The name of the output scalars. By default, this is the same as the
| active scalars of the dataset.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| :class:`pyvista.ImageData` with the applied high pass filter.
|
| See Also
| --------
| fft : Direct fast Fourier transform.
| rfft : Reverse fast Fourier transform.
| low_pass : Low-pass filtering of FFT output.
|
| Examples
| --------
| See :ref:`image_fft_perlin_example` for a full example using this filter.
|
| image_dilate_erode(self, dilate_value=1.0, erode_value=0.0, kernel_size=(3, 3, 3), scalars=None, progress_bar=False)
| Dilates one value and erodes another.
|
| ``image_dilate_erode`` will dilate one value and erode another. It uses
| an elliptical footprint, and only erodes/dilates on the boundary of the
| two values. The filter is restricted to the X, Y, and Z axes for now.
| It can degenerate to a 2 or 1-dimensional filter by setting the kernel
| size to 1 for a specific axis.
|
| Parameters
| ----------
| dilate_value : float, default: 1.0
| Dilate value in the dataset.
|
| erode_value : float, default: 0.0
| Erode value in the dataset.
|
| kernel_size : sequence[int], default: (3, 3, 3)
| Determines the size of the kernel along the three axes.
|
| scalars : str, optional
| Name of scalars to process. Defaults to currently active scalars.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| Dataset that has been dilated/eroded on the boundary of the specified scalars.
|
| Notes
| -----
| This filter only supports point data. For inputs with cell data, consider
| re-meshing the cell data as point data with :meth:`~pyvista.ImageDataFilters.cells_to_points`
| or resampling the cell data to point data with :func:`~pyvista.DataSetFilters.cell_data_to_point_data`.
|
| Examples
| --------
| Demonstrate image dilate/erode on an example dataset. First, plot
| the example dataset with the active scalars.
|
| >>> from pyvista import examples
| >>> uni = examples.load_uniform()
| >>> uni.plot()
|
| Now, plot the image threshold with ``threshold=[400, 600]``. Note how
| values within the threshold are 1 and outside are 0.
|
| >>> ithresh = uni.image_threshold([400, 600])
| >>> ithresh.plot()
|
| Note how there is a hole in the thresholded image. Apply a dilation/
| erosion filter with a large kernel to fill that hole in.
|
| >>> idilate = ithresh.image_dilate_erode(kernel_size=[5, 5, 5])
| >>> idilate.plot()
|
| image_threshold(self, threshold, in_value=1.0, out_value=0.0, scalars=None, preference='point', progress_bar=False)
| Apply a threshold to scalar values in a uniform grid.
|
| If a single value is given for threshold, scalar values above or equal
| to the threshold are ``'in'`` and scalar values below the threshold are ``'out'``.
| If two values are given for threshold (sequence) then values equal to
| or between the two values are ``'in'`` and values outside the range are ``'out'``.
|
| If ``None`` is given for ``in_value``, scalars that are ``'in'`` will not be replaced.
| If ``None`` is given for ``out_value``, scalars that are ``'out'`` will not be replaced.
|
| Warning: applying this filter to cell data will send the output to a
| new point array with the same name, overwriting any existing point data
| array with the same name.
|
| Parameters
| ----------
| threshold : float or sequence[float]
| Single value or (min, max) to be used for the data threshold. If
| a sequence, then length must be 2. Threshold(s) for deciding which
| cells/points are ``'in'`` or ``'out'`` based on scalar data.
|
| in_value : float, default: 1.0
| Scalars that match the threshold criteria for ``'in'`` will be replaced with this.
|
| out_value : float, default: 0.0
| Scalars that match the threshold criteria for ``'out'`` will be replaced with this.
|
| scalars : str, optional
| Name of scalars to process. Defaults to currently active scalars.
|
| preference : str, default: "point"
| When scalars is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'`` or ``'cell'``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| Dataset with the specified scalars thresholded.
|
| See Also
| --------
| :meth:`~pyvista.DataSetFilters.threshold`
|
| Examples
| --------
| Demonstrate image threshold on an example dataset. First, plot
| the example dataset with the active scalars.
|
| >>> from pyvista import examples
| >>> uni = examples.load_uniform()
| >>> uni.plot()
|
| Now, plot the image threshold with ``threshold=100``. Note how
| values above the threshold are 1 and below are 0.
|
| >>> ithresh = uni.image_threshold(100)
| >>> ithresh.plot()
|
| See :ref:`image_representations_example` for more examples using this filter.
|
| low_pass(self, x_cutoff, y_cutoff, z_cutoff, order=1, output_scalars_name=None, progress_bar=False)
| Perform a Butterworth low pass filter in the frequency domain.
|
| This filter requires that the :class:`ImageData` have a complex point
| scalars, usually generated after the :class:`ImageData` has been
| converted to the frequency domain by a :func:`ImageDataFilters.fft`
| filter.
|
| A :func:`ImageDataFilters.rfft` filter can be used to convert the
| output back into the spatial domain. This filter attenuates high
| frequency components. Input and output are complex arrays with
| datatype :attr:`numpy.complex128`.
|
| The frequencies of the input assume standard order: along each axis
| first positive frequencies are assumed from 0 to the maximum, then
| negative frequencies are listed from the largest absolute value to
| smallest. This implies that the corners of the grid correspond to low
| frequencies, while the center of the grid corresponds to high
| frequencies.
|
| Parameters
| ----------
| x_cutoff : float
| The cutoff frequency for the x-axis.
|
| y_cutoff : float
| The cutoff frequency for the y-axis.
|
| z_cutoff : float
| The cutoff frequency for the z-axis.
|
| order : int, default: 1
| The order of the cutoff curve. Given from the equation
| ``1 + (cutoff/freq(i, j))**(2*order)``.
|
| output_scalars_name : str, optional
| The name of the output scalars. By default, this is the same as the
| active scalars of the dataset.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| :class:`pyvista.ImageData` with the applied low pass filter.
|
| See Also
| --------
| fft : Direct fast Fourier transform.
| rfft : Reverse fast Fourier transform.
| high_pass : High-pass filtering of FFT output.
|
| Examples
| --------
| See :ref:`image_fft_perlin_example` for a full example using this filter.
|
| median_smooth(self, kernel_size=(3, 3, 3), scalars=None, preference='point', progress_bar=False)
| Smooth data using a median filter.
|
| The Median filter that replaces each pixel with the median value from a
| rectangular neighborhood around that pixel. Neighborhoods can be no
| more than 3 dimensional. Setting one axis of the neighborhood
| kernelSize to 1 changes the filter into a 2D median.
|
| See `vtkImageMedian3D
| <https://vtk.org/doc/nightly/html/classvtkImageMedian3D.html#details>`_
| for more details.
|
| Parameters
| ----------
| kernel_size : sequence[int], default: (3, 3, 3)
| Size of the kernel in each dimension (units of voxels), for example
| ``(x_size, y_size, z_size)``. Default is a 3D median filter. If you
| want to do a 2D median filter, set the size to 1 in the dimension
| you don't want to filter over.
|
| scalars : str, optional
| Name of scalars to process. Defaults to currently active scalars.
|
| preference : str, default: "point"
| When scalars is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'`` or ``'cell'``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| Uniform grid with smoothed scalars.
|
| Warnings
| --------
| Applying this filter to cell data will send the output to a new point
| array with the same name, overwriting any existing point data array
| with the same name.
|
| Examples
| --------
| First, create sample data to smooth. Here, we use
| :func:`pyvista.perlin_noise() <pyvista.core.utilities.features.perlin_noise>`
| to create meaningful data.
|
| >>> import numpy as np
| >>> import pyvista as pv
| >>> noise = pv.perlin_noise(0.1, (2, 5, 8), (0, 0, 0))
| >>> grid = pv.sample_function(
| ... noise, [0, 1, 0, 1, 0, 1], dim=(20, 20, 20)
| ... )
| >>> grid.plot(show_scalar_bar=False)
|
| Next, smooth the sample data.
|
| >>> smoothed = grid.median_smooth(kernel_size=(10, 10, 10))
| >>> smoothed.plot(show_scalar_bar=False)
|
| pad_image(self, pad_value: "float | VectorLike[float] | Literal['wrap', 'mirror']" = 0.0, *, pad_size: 'int | VectorLike[int]' = 1, pad_singleton_dims: 'bool' = False, scalars: 'str | None' = None, pad_all_scalars: 'bool' = False, progress_bar=False) -> 'pyvista.ImageData'
| Enlarge an image by padding its boundaries with new points.
|
| .. versionadded:: 0.44.0
|
| Padded points may be mirrored, wrapped, or filled with a constant value. By
| default, all boundaries of the image are padded with a single constant value.
|
| This filter is designed to work with 1D, 2D, or 3D image data and will only pad
| non-singleton dimensions unless otherwise specified.
|
| Parameters
| ----------
| pad_value : float | sequence[float] | 'mirror' | 'wrap', default : 0.0
| Padding value(s) given to new points outside the original image extent.
| Specify:
|
| - a number: New points are filled with the specified constant value.
| - a vector: New points are filled with the specified multi-component vector.
| - ``'wrap'``: New points are filled by wrapping around the padding axis.
| - ``'mirror'``: New points are filled by mirroring the padding axis.
|
| pad_size : int | sequence[int], default : 1
| Number of points to add to the image boundaries. Specify:
|
| - A single value to pad all boundaries equally.
| - Two values, one for each ``(X, Y)`` axis, to apply symmetric padding to
| each axis independently.
| - Three values, one for each ``(X, Y, Z)`` axis, to apply symmetric padding
| to each axis independently.
| - Four values, one for each ``(-X, +X, -Y, +Y)`` boundary, to apply
| padding to each boundary independently.
| - Six values, one for each ``(-X, +X, -Y, +Y, -Z, +Z)`` boundary, to apply
| padding to each boundary independently.
|
| .. note::
| The pad size for singleton dimensions is set to ``0`` by default, even
| if non-zero pad sizes are specified for these axes with this parameter.
| Set ``pad_singleton_dims`` to ``True`` to override this behavior and
| enable padding any or all dimensions.
|
| pad_singleton_dims : bool, default : False
| Control whether to pad singleton dimensions. By default, only non-singleton
| dimensions are padded, which means that 1D or 2D inputs will remain 1D or
| 2D after padding. Set this to ``True`` to enable padding any or all
| dimensions.
|
| scalars : str, optional
| Name of scalars to pad. Defaults to currently active scalars. Unless
| ``pad_all_scalars`` is ``True``, only the specified ``scalars`` are included
| in the output.
|
| pad_all_scalars : bool, default: False
| Pad all point data scalars and include them in the output. This is useful
| for padding images with multiple scalars. If ``False``, only the specified
| ``scalars`` are padded.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| Padded image.
|
| Examples
| --------
| Pad a grayscale image with a 100-pixel wide border. The padding is black
| (i.e. has a value of ``0``) by default.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>>
| >>> gray_image = examples.download_moonlanding_image()
| >>> gray_image.dimensions
| (630, 474, 1)
| >>> padded = gray_image.pad_image(pad_size=100)
| >>> padded.dimensions
| (830, 674, 1)
|
| Plot the image. To show grayscale images correctly, we define a custom plotting
| method.
|
| >>> def grayscale_image_plotter(image):
| ... import vtk
| ...
| ... actor = vtk.vtkImageActor()
| ... actor.GetMapper().SetInputData(image)
| ... actor.GetProperty().SetInterpolationTypeToNearest()
| ... plot = pv.Plotter()
| ... plot.add_actor(actor)
| ... plot.view_xy()
| ... plot.camera.tight()
| ... return plot
| ...
| >>>
| >>> plot = grayscale_image_plotter(padded)
| >>> plot.show()
|
| Pad only the x-axis with a white border.
|
| >>> padded = gray_image.pad_image(pad_value=255, pad_size=(200, 0))
| >>> plot = grayscale_image_plotter(padded)
| >>> plot.show()
|
| Pad with wrapping.
|
| >>> padded = gray_image.pad_image('wrap', pad_size=100)
| >>> plot = grayscale_image_plotter(padded)
| >>> plot.show()
|
| Pad with mirroring.
|
| >>> padded = gray_image.pad_image('mirror', pad_size=100)
| >>> plot = grayscale_image_plotter(padded)
| >>> plot.show()
|
| Pad a color image using multi-component color vectors. Here, RGBA values are
| used.
|
| >>> color_image = examples.load_logo()
| >>> red = (255, 0, 0, 255) # RGBA
| >>> padded = color_image.pad_image(pad_value=red, pad_size=200)
| >>>
| >>> plot_kwargs = dict(
| ... cpos='xy', zoom='tight', rgb=True, show_axes=False
| ... )
| >>> padded.plot(**plot_kwargs)
|
| Pad each edge of the image separately with a different color.
|
| >>> orange = pv.Color('orange').int_rgba
| >>> purple = pv.Color('purple').int_rgba
| >>> blue = pv.Color('blue').int_rgba
| >>> green = pv.Color('green').int_rgba
| >>>
| >>> padded = color_image.pad_image(orange, pad_size=(100, 0, 0, 0))
| >>> padded = padded.pad_image(purple, pad_size=(0, 100, 0, 0))
| >>> padded = padded.pad_image(blue, pad_size=(0, 0, 100, 0))
| >>> padded = padded.pad_image(green, pad_size=(0, 0, 0, 100))
| >>>
| >>> padded.plot(**plot_kwargs)
|
| points_to_cells(self, scalars: 'str | None' = None, *, copy: 'bool' = True)
| Re-mesh image data from a point-based to a cell-based representation.
|
| This filter changes how image data is represented. Data represented as points
| at the input is re-meshed into an alternative representation as cells at the
| output. Only the :class:`~pyvista.ImageData` container is modified so that
| the number of input points equals the number of output cells. The re-meshing is
| otherwise lossless in the sense that point data at the input is passed through
| unmodified and stored as cell data at the output. Any cell data at the input is
| ignored and is not used by this filter.
|
| To change the image data's representation, the input points are used to
| represent the centers of the output cells. This has the effect of "growing" the
| input image dimensions by one along each axis (i.e. half the cell width on each
| side). For example, an image with 100 points and 99 cells along an axis at the
| input will have 101 points and 100 cells at the output. If the input has 1mm
| spacing, the axis size will also increase from 99mm to 100mm.
|
| Since filters may be inherently cell-based (e.g. some :class:`~pyvista.DataSetFilters`)
| or may operate on point data exclusively (e.g. most :class:`~pyvista.ImageDataFilters`),
| re-meshing enables the same data to be used with either kind of filter while
| ensuring the input data to those filters has the appropriate representation.
| This filter is also useful when plotting image data to achieve a desired visual
| effect, such as plotting images as voxel cells instead of as points.
|
| .. versionadded:: 0.44.0
|
| See Also
| --------
| cells_to_points
| Inverse of this filter to represent cells as points.
| :meth:`~pyvista.DataSetFilters.point_data_to_cell_data`
| Resample point data as cell data without modifying the container.
| :meth:`~pyvista.DataSetFilters.cell_data_to_point_data`
| Resample cell data as point data without modifying the container.
|
| Parameters
| ----------
| scalars : str, optional
| Name of point data scalars to pass through to the output as cell data. Use
| this parameter to restrict the output to only include the specified array.
| By default, all point data arrays at the input are passed through as cell
| data at the output.
|
| copy : bool, default: True
| Copy the input point data before associating it with the output cell data.
| If ``False``, the input and output will both refer to the same data array(s).
|
| Returns
| -------
| pyvista.ImageData
| Image with a cell-based representation.
|
| Examples
| --------
| Load an image with point data.
|
| >>> from pyvista import examples
| >>> image = examples.load_uniform()
|
| Show the current properties and point arrays of the image.
|
| >>> image
| ImageData (...)
| N Cells: 729
| N Points: 1000
| X Bounds: 0.000e+00, 9.000e+00
| Y Bounds: 0.000e+00, 9.000e+00
| Z Bounds: 0.000e+00, 9.000e+00
| Dimensions: 10, 10, 10
| Spacing: 1.000e+00, 1.000e+00, 1.000e+00
| N Arrays: 2
|
| >>> image.point_data.keys()
| ['Spatial Point Data']
|
| Re-mesh the points and point data as cells and cell data.
|
| >>> cells_image = image.points_to_cells()
|
| Show the properties and cell arrays of the re-meshed image.
|
| >>> cells_image
| ImageData (...)
| N Cells: 1000
| N Points: 1331
| X Bounds: -5.000e-01, 9.500e+00
| Y Bounds: -5.000e-01, 9.500e+00
| Z Bounds: -5.000e-01, 9.500e+00
| Dimensions: 11, 11, 11
| Spacing: 1.000e+00, 1.000e+00, 1.000e+00
| N Arrays: 1
|
| >>> cells_image.cell_data.keys()
| ['Spatial Point Data']
|
| Observe that:
|
| - The input point array is now a cell array
| - The output has one less array (the input cell data is ignored)
| - The dimensions have increased by one
| - The bounds have increased by half the spacing
| - The output N Cells equals the input N Points
|
| See :ref:`image_representations_example` for more examples using this filter.
|
| rfft(self, output_scalars_name=None, progress_bar=False)
| Apply a reverse fast Fourier transform (RFFT) to the active scalars.
|
| The input can be real or complex data, but the output is always
| :attr:`numpy.complex128`. The filter is fastest for images that have power
| of two sizes.
|
| The filter uses a butterfly diagram for each prime factor of the
| dimension. This makes images with prime number dimensions (i.e. 17x17)
| much slower to compute. FFTs of multidimensional meshes (i.e volumes)
| are decomposed so that each axis executes serially.
|
| The frequencies of the input assume standard order: along each axis
| first positive frequencies are assumed from 0 to the maximum, then
| negative frequencies are listed from the largest absolute value to
| smallest. This implies that the corners of the grid correspond to low
| frequencies, while the center of the grid corresponds to high
| frequencies.
|
| Parameters
| ----------
| output_scalars_name : str, optional
| The name of the output scalars. By default, this is the same as the
| active scalars of the dataset.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.ImageData
| :class:`pyvista.ImageData` with the applied reverse FFT.
|
| See Also
| --------
| fft : The direct transform.
| low_pass : Low-pass filtering of FFT output.
| high_pass : High-pass filtering of FFT output.
|
| Examples
| --------
| Apply reverse FFT to an example image.
|
| >>> from pyvista import examples
| >>> image = examples.download_moonlanding_image()
| >>> fft_image = image.fft()
| >>> image_again = fft_image.rfft()
| >>> image_again.point_data # doctest:+SKIP
| pyvista DataSetAttributes
| Association : POINT
| Active Scalars : PNGImage
| Active Vectors : None
| Active Texture : None
| Active Normals : None
| Contains arrays :
| PNGImage complex128 (298620,) SCALARS
|
| See :ref:`image_fft_example` for a full example using this filter.
|
| ----------------------------------------------------------------------
| Methods inherited from pyvista.core.filters.data_set.DataSetFilters:
|
| __add__(self, dataset)
| Combine this mesh with another into a :class:`pyvista.UnstructuredGrid`.
|
| __iadd__(self, dataset)
| Merge another mesh into this one if possible.
|
| "If possible" means that ``self`` is a :class:`pyvista.UnstructuredGrid`.
| Otherwise we have to return a new object, and the attempted in-place
| merge will raise.
|
| align(self, target, max_landmarks=100, max_mean_distance=1e-05, max_iterations=500, check_mean_distance=True, start_by_matching_centroids=True, return_matrix=False)
| Align a dataset to another.
|
| Uses the iterative closest point algorithm to align the points of the
| two meshes. See the VTK class `vtkIterativeClosestPointTransform
| <https://vtk.org/doc/nightly/html/classvtkIterativeClosestPointTransform.html>`_
|
| Parameters
| ----------
| target : pyvista.DataSet
| The target dataset to align to.
|
| max_landmarks : int, default: 100
| The maximum number of landmarks.
|
| max_mean_distance : float, default: 1e-5
| The maximum mean distance for convergence.
|
| max_iterations : int, default: 500
| The maximum number of iterations.
|
| check_mean_distance : bool, default: True
| Whether to check the mean distance for convergence.
|
| start_by_matching_centroids : bool, default: True
| Whether to start the alignment by matching centroids. Default is True.
|
| return_matrix : bool, default: False
| Return the transform matrix as well as the aligned mesh.
|
| Returns
| -------
| aligned : pyvista.DataSet
| The dataset aligned to the target mesh.
|
| matrix : numpy.ndarray
| Transform matrix to transform the input dataset to the target dataset.
|
| Examples
| --------
| Create a cylinder, translate it, and use iterative closest point to
| align mesh to its original position.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> source = pv.Cylinder(resolution=30).triangulate().subdivide(1)
| >>> transformed = source.rotate_y(20).translate([-0.75, -0.5, 0.5])
| >>> aligned = transformed.align(source)
| >>> _, closest_points = aligned.find_closest_cell(
| ... source.points, return_closest_point=True
| ... )
| >>> dist = np.linalg.norm(source.points - closest_points, axis=1)
|
| Visualize the source, transformed, and aligned meshes.
|
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> _ = pl.add_text('Before Alignment')
| >>> _ = pl.add_mesh(
| ... source, style='wireframe', opacity=0.5, line_width=2
| ... )
| >>> _ = pl.add_mesh(transformed)
| >>> pl.subplot(0, 1)
| >>> _ = pl.add_text('After Alignment')
| >>> _ = pl.add_mesh(
| ... source, style='wireframe', opacity=0.5, line_width=2
| ... )
| >>> _ = pl.add_mesh(
| ... aligned,
| ... scalars=dist,
| ... scalar_bar_args={
| ... 'title': 'Distance to Source',
| ... 'fmt': '%.1E',
| ... },
| ... )
| >>> pl.show()
|
| Show that the mean distance between the source and the target is
| nearly zero.
|
| >>> np.abs(dist).mean() # doctest:+SKIP
| 9.997635192915073e-05
|
| cell_centers(self, vertex=True, progress_bar=False)
| Generate points at the center of the cells in this dataset.
|
| These points can be used for placing glyphs or vectors.
|
| Parameters
| ----------
| vertex : bool, default: True
| Enable or disable the generation of vertex cells.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Polydata where the points are the cell centers of the
| original dataset.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> mesh = pv.Plane()
| >>> mesh.point_data.clear()
| >>> centers = mesh.cell_centers()
| >>> pl = pv.Plotter()
| >>> actor = pl.add_mesh(mesh, show_edges=True)
| >>> actor = pl.add_points(
| ... centers,
| ... render_points_as_spheres=True,
| ... color='red',
| ... point_size=20,
| ... )
| >>> pl.show()
|
| See :ref:`cell_centers_example` for more examples using this filter.
|
| cell_data_to_point_data(self, pass_cell_data=False, progress_bar=False)
| Transform cell data into point data.
|
| Point data are specified per node and cell data specified
| within cells. Optionally, the input point data can be passed
| through to the output.
|
| The method of transformation is based on averaging the data
| values of all cells using a particular point. Optionally, the
| input cell data can be passed through to the output as well.
|
| Parameters
| ----------
| pass_cell_data : bool, default: False
| If enabled, pass the input cell data through to the output.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with the point data transformed into cell data.
| Return type matches input.
|
| See Also
| --------
| point_data_to_cell_data
| Similar transformation applied to point data.
| :meth:`~pyvista.ImageDataFilters.cells_to_points`
| Re-mesh :class:`~pyvista.ImageData` to a points-based representation.
|
| Examples
| --------
| First compute the face area of the example airplane mesh and
| show the cell values. This is to show discrete cell data.
|
| >>> from pyvista import examples
| >>> surf = examples.load_airplane()
| >>> surf = surf.compute_cell_sizes(length=False, volume=False)
| >>> surf.plot(scalars='Area')
|
| These cell scalars can be applied to individual points to
| effectively smooth out the cell data onto the points.
|
| >>> from pyvista import examples
| >>> surf = examples.load_airplane()
| >>> surf = surf.compute_cell_sizes(length=False, volume=False)
| >>> surf = surf.cell_data_to_point_data()
| >>> surf.plot(scalars='Area')
|
| clip(self, normal='x', origin=None, invert=True, value=0.0, inplace=False, return_clipped=False, progress_bar=False, crinkle=False)
| Clip a dataset by a plane by specifying the origin and normal.
|
| If no parameters are given the clip will occur in the center
| of that dataset.
|
| Parameters
| ----------
| normal : tuple(float) or str, default: 'x'
| Length 3 tuple for the normal vector direction. Can also
| be specified as a string conventional direction such as
| ``'x'`` for ``(1, 0, 0)`` or ``'-x'`` for ``(-1, 0, 0)``, etc.
|
| origin : sequence[float], optional
| The center ``(x, y, z)`` coordinate of the plane on which the clip
| occurs. The default is the center of the dataset.
|
| invert : bool, default: True
| Flag on whether to flip/invert the clip.
|
| value : float, default: 0.0
| Set the clipping value along the normal direction.
|
| inplace : bool, default: False
| Updates mesh in-place.
|
| return_clipped : bool, default: False
| Return both unclipped and clipped parts of the dataset.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| crinkle : bool, default: False
| Crinkle the clip by extracting the entire cells along the
| clip. This adds the ``"cell_ids"`` array to the ``cell_data``
| attribute that tracks the original cell IDs of the original
| dataset.
|
| Returns
| -------
| pyvista.PolyData or tuple[pyvista.PolyData]
| Clipped mesh when ``return_clipped=False``,
| otherwise a tuple containing the unclipped and clipped datasets.
|
| Examples
| --------
| Clip a cube along the +X direction. ``triangulate`` is used as
| the cube is initially composed of quadrilateral faces and
| subdivide only works on triangles.
|
| >>> import pyvista as pv
| >>> cube = pv.Cube().triangulate().subdivide(3)
| >>> clipped_cube = cube.clip()
| >>> clipped_cube.plot()
|
| Clip a cube in the +Z direction. This leaves half a cube
| below the XY plane.
|
| >>> import pyvista as pv
| >>> cube = pv.Cube().triangulate().subdivide(3)
| >>> clipped_cube = cube.clip('z')
| >>> clipped_cube.plot()
|
| See :ref:`clip_with_surface_example` for more examples using this filter.
|
| clip_box(self, bounds=None, invert=True, factor=0.35, progress_bar=False, merge_points=True, crinkle=False)
| Clip a dataset by a bounding box defined by the bounds.
|
| If no bounds are given, a corner of the dataset bounds will be removed.
|
| Parameters
| ----------
| bounds : sequence[float], optional
| Length 6 sequence of floats: ``(xmin, xmax, ymin, ymax, zmin, zmax)``.
| Length 3 sequence of floats: distances from the min coordinate of
| of the input mesh. Single float value: uniform distance from the
| min coordinate. Length 12 sequence of length 3 sequence of floats:
| a plane collection (normal, center, ...).
| :class:`pyvista.PolyData`: if a poly mesh is passed that represents
| a box with 6 faces that all form a standard box, then planes will
| be extracted from the box to define the clipping region.
|
| invert : bool, default: True
| Flag on whether to flip/invert the clip.
|
| factor : float, default: 0.35
| If bounds are not given this is the factor along each axis to
| extract the default box.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| merge_points : bool, default: True
| If ``True``, coinciding points of independently defined mesh
| elements will be merged.
|
| crinkle : bool, default: False
| Crinkle the clip by extracting the entire cells along the
| clip. This adds the ``"cell_ids"`` array to the ``cell_data``
| attribute that tracks the original cell IDs of the original
| dataset.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Clipped dataset.
|
| Examples
| --------
| Clip a corner of a cube. The bounds of a cube are normally
| ``[-0.5, 0.5, -0.5, 0.5, -0.5, 0.5]``, and this removes 1/8 of
| the cube's surface.
|
| >>> import pyvista as pv
| >>> cube = pv.Cube().triangulate().subdivide(3)
| >>> clipped_cube = cube.clip_box([0, 1, 0, 1, 0, 1])
| >>> clipped_cube.plot()
|
| See :ref:`clip_with_plane_box_example` for more examples using this filter.
|
| clip_scalar(self, scalars=None, invert=True, value=0.0, inplace=False, progress_bar=False, both=False)
| Clip a dataset by a scalar.
|
| Parameters
| ----------
| scalars : str, optional
| Name of scalars to clip on. Defaults to currently active scalars.
|
| invert : bool, default: True
| Flag on whether to flip/invert the clip. When ``True``,
| only the mesh below ``value`` will be kept. When
| ``False``, only values above ``value`` will be kept.
|
| value : float, default: 0.0
| Set the clipping value.
|
| inplace : bool, default: False
| Update mesh in-place.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| both : bool, default: False
| If ``True``, also returns the complementary clipped mesh.
|
| Returns
| -------
| pyvista.PolyData or tuple
| Clipped dataset if ``both=False``. If ``both=True`` then
| returns a tuple of both clipped datasets.
|
| Examples
| --------
| Remove the part of the mesh with "sample_point_scalars" above 100.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> dataset = examples.load_hexbeam()
| >>> clipped = dataset.clip_scalar(
| ... scalars="sample_point_scalars", value=100
| ... )
| >>> clipped.plot()
|
| Get clipped meshes corresponding to the portions of the mesh above and below 100.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> dataset = examples.load_hexbeam()
| >>> _below, _above = dataset.clip_scalar(
| ... scalars="sample_point_scalars", value=100, both=True
| ... )
|
| Remove the part of the mesh with "sample_point_scalars" below 100.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> dataset = examples.load_hexbeam()
| >>> clipped = dataset.clip_scalar(
| ... scalars="sample_point_scalars", value=100, invert=False
| ... )
| >>> clipped.plot()
|
| clip_surface(self, surface, invert=True, value=0.0, compute_distance=False, progress_bar=False, crinkle=False)
| Clip any mesh type using a :class:`pyvista.PolyData` surface mesh.
|
| This will return a :class:`pyvista.UnstructuredGrid` of the clipped
| mesh. Geometry of the input dataset will be preserved where possible.
| Geometries near the clip intersection will be triangulated/tessellated.
|
| Parameters
| ----------
| surface : pyvista.PolyData
| The ``PolyData`` surface mesh to use as a clipping
| function. If this input mesh is not a :class`pyvista.PolyData`,
| the external surface will be extracted.
|
| invert : bool, default: True
| Flag on whether to flip/invert the clip.
|
| value : float, default: 0.0
| Set the clipping value of the implicit function (if
| clipping with implicit function) or scalar value (if
| clipping with scalars).
|
| compute_distance : bool, default: False
| Compute the implicit distance from the mesh onto the input
| dataset. A new array called ``'implicit_distance'`` will
| be added to the output clipped mesh.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| crinkle : bool, default: False
| Crinkle the clip by extracting the entire cells along the
| clip. This adds the ``"cell_ids"`` array to the ``cell_data``
| attribute that tracks the original cell IDs of the original
| dataset.
|
| Returns
| -------
| pyvista.PolyData
| Clipped surface.
|
| Examples
| --------
| Clip a cube with a sphere.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere(center=(-0.4, -0.4, -0.4))
| >>> cube = pv.Cube().triangulate().subdivide(3)
| >>> clipped = cube.clip_surface(sphere)
| >>> clipped.plot(show_edges=True, cpos='xy', line_width=3)
|
| See :ref:`clip_with_surface_example` for more examples using
| this filter.
|
| compute_boundary_mesh_quality(self, *, progress_bar=False)
| Compute metrics on the boundary faces of a mesh.
|
| The metrics that can be computed on the boundary faces of the mesh and are:
|
| - Distance from cell center to face center
| - Distance from cell center to face plane
| - Angle of faces plane normal and cell center to face center vector
|
| Parameters
| ----------
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with the computed metrics on the boundary faces of a mesh.
| ``cell_data`` as the ``"CellQuality"`` array.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> mesh = examples.download_can_crushed_vtu()
| >>> cqual = mesh.compute_boundary_mesh_quality()
| >>> plotter = pv.Plotter(shape=(2, 2))
| >>> _ = plotter.add_mesh(mesh, show_edges=True)
| >>> plotter.subplot(1, 0)
| >>> _ = plotter.add_mesh(
| ... cqual, scalars="DistanceFromCellCenterToFaceCenter"
| ... )
| >>> plotter.subplot(0, 1)
| >>> _ = plotter.add_mesh(
| ... cqual, scalars="DistanceFromCellCenterToFacePlane"
| ... )
| >>> plotter.subplot(1, 1)
| >>> _ = plotter.add_mesh(
| ... cqual,
| ... scalars="AngleFaceNormalAndCellCenterToFaceCenterVector",
| ... )
| >>> plotter.show()
|
| compute_cell_quality(self, quality_measure='scaled_jacobian', null_value=-1.0, progress_bar=False)
| Compute a function of (geometric) quality for each cell of a mesh.
|
| The per-cell quality is added to the mesh's cell data, in an
| array named ``"CellQuality"``. Cell types not supported by this
| filter or undefined quality of supported cell types will have an
| entry of -1.
|
| Defaults to computing the scaled Jacobian.
|
| Options for cell quality measure:
|
| - ``'area'``
| - ``'aspect_beta'``
| - ``'aspect_frobenius'``
| - ``'aspect_gamma'``
| - ``'aspect_ratio'``
| - ``'collapse_ratio'``
| - ``'condition'``
| - ``'diagonal'``
| - ``'dimension'``
| - ``'distortion'``
| - ``'jacobian'``
| - ``'max_angle'``
| - ``'max_aspect_frobenius'``
| - ``'max_edge_ratio'``
| - ``'med_aspect_frobenius'``
| - ``'min_angle'``
| - ``'oddy'``
| - ``'radius_ratio'``
| - ``'relative_size_squared'``
| - ``'scaled_jacobian'``
| - ``'shape'``
| - ``'shape_and_size'``
| - ``'shear'``
| - ``'shear_and_size'``
| - ``'skew'``
| - ``'stretch'``
| - ``'taper'``
| - ``'volume'``
| - ``'warpage'``
|
| Notes
| -----
| There is a `discussion about shape option <https://github.com/pyvista/pyvista/discussions/6143>`_.
|
| Parameters
| ----------
| quality_measure : str, default: 'scaled_jacobian'
| The cell quality measure to use.
|
| null_value : float, default: -1.0
| Float value for undefined quality. Undefined quality are qualities
| that could be addressed by this filter but is not well defined for
| the particular geometry of cell in question, e.g. a volume query
| for a triangle. Undefined quality will always be undefined.
| The default value is -1.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with the computed mesh quality in the
| ``cell_data`` as the ``"CellQuality"`` array.
|
| Examples
| --------
| Compute and plot the minimum angle of a sample sphere mesh.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere(theta_resolution=20, phi_resolution=20)
| >>> cqual = sphere.compute_cell_quality('min_angle')
| >>> cqual.plot(show_edges=True)
|
| See the :ref:`mesh_quality_example` for more examples using this filter.
|
| compute_cell_sizes(self, length=True, area=True, volume=True, progress_bar=False, vertex_count=False)
| Compute sizes for 0D (vertex count), 1D (length), 2D (area) and 3D (volume) cells.
|
| Parameters
| ----------
| length : bool, default: True
| Specify whether or not to compute the length of 1D cells.
|
| area : bool, default: True
| Specify whether or not to compute the area of 2D cells.
|
| volume : bool, default: True
| Specify whether or not to compute the volume of 3D cells.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| vertex_count : bool, default: False
| Specify whether or not to compute sizes for vertex and polyvertex cells (0D cells).
| The computed value is the number of points in the cell.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with `cell_data` containing the ``"VertexCount"``,
| ``"Length"``, ``"Area"``, and ``"Volume"`` arrays if set
| in the parameters. Return type matches input.
|
| Notes
| -----
| If cells do not have a dimension (for example, the length of
| hexahedral cells), the corresponding array will be all zeros.
|
| Examples
| --------
| Compute the face area of the example airplane mesh.
|
| >>> from pyvista import examples
| >>> surf = examples.load_airplane()
| >>> surf = surf.compute_cell_sizes(length=False, volume=False)
| >>> surf.plot(show_edges=True, scalars='Area')
|
| compute_derivative(self, scalars=None, gradient=True, divergence=None, vorticity=None, qcriterion=None, faster=False, preference='point', progress_bar=False)
| Compute derivative-based quantities of point/cell scalar field.
|
| Utilize ``vtkGradientFilter`` to compute derivative-based quantities,
| such as gradient, divergence, vorticity, and Q-criterion, of the
| selected point or cell scalar field.
|
| Parameters
| ----------
| scalars : str, optional
| String name of the scalars array to use when computing the
| derivative quantities. Defaults to the active scalars in
| the dataset.
|
| gradient : bool | str, default: True
| Calculate gradient. If a string is passed, the string will be used
| for the resulting array name. Otherwise, array name will be
| ``'gradient'``. Default ``True``.
|
| divergence : bool | str, optional
| Calculate divergence. If a string is passed, the string will be
| used for the resulting array name. Otherwise, default array name
| will be ``'divergence'``.
|
| vorticity : bool | str, optional
| Calculate vorticity. If a string is passed, the string will be used
| for the resulting array name. Otherwise, default array name will be
| ``'vorticity'``.
|
| qcriterion : bool | str, optional
| Calculate qcriterion. If a string is passed, the string will be
| used for the resulting array name. Otherwise, default array name
| will be ``'qcriterion'``.
|
| faster : bool, default: False
| Use faster algorithm for computing derivative quantities. Result is
| less accurate and performs fewer derivative calculations,
| increasing computation speed. The error will feature smoothing of
| the output and possibly errors at boundaries. Option has no effect
| if DataSet is not :class:`pyvista.UnstructuredGrid`.
|
| preference : str, default: "point"
| Data type preference. Either ``'point'`` or ``'cell'``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with calculated derivative.
|
| Examples
| --------
| First, plot the random hills dataset with the active elevation
| scalars. These scalars will be used for the derivative
| calculations.
|
| >>> from pyvista import examples
| >>> hills = examples.load_random_hills()
| >>> hills.plot(smooth_shading=True)
|
| Compute and plot the gradient of the active scalars.
|
| >>> from pyvista import examples
| >>> hills = examples.load_random_hills()
| >>> deriv = hills.compute_derivative()
| >>> deriv.plot(scalars='gradient')
|
| See the :ref:`gradients_example` for more examples using this filter.
|
| compute_implicit_distance(self, surface, inplace=False)
| Compute the implicit distance from the points to a surface.
|
| This filter will compute the implicit distance from all of the
| nodes of this mesh to a given surface. This distance will be
| added as a point array called ``'implicit_distance'``.
|
| Nodes of this mesh which are interior to the input surface
| geometry have a negative distance, and nodes on the exterior
| have a positive distance. Nodes which intersect the input
| surface has a distance of zero.
|
| Parameters
| ----------
| surface : pyvista.DataSet
| The surface used to compute the distance.
|
| inplace : bool, default: False
| If ``True``, a new scalar array will be added to the
| ``point_data`` of this mesh and the modified mesh will
| be returned. Otherwise a copy of this mesh is returned
| with that scalar field added.
|
| Returns
| -------
| pyvista.DataSet
| Dataset containing the ``'implicit_distance'`` array in
| ``point_data``.
|
| Examples
| --------
| Compute the distance between all the points on a sphere and a
| plane.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere(radius=0.35)
| >>> plane = pv.Plane()
| >>> _ = sphere.compute_implicit_distance(plane, inplace=True)
| >>> dist = sphere['implicit_distance']
| >>> type(dist)
| <class 'pyvista.core.pyvista_ndarray.pyvista_ndarray'>
|
| Plot these distances as a heatmap. Note how distances above the
| plane are positive, and distances below the plane are negative.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(
| ... sphere, scalars='implicit_distance', cmap='bwr'
| ... )
| >>> _ = pl.add_mesh(plane, color='w', style='wireframe')
| >>> pl.show()
|
| We can also compute the distance from all the points on the
| plane to the sphere.
|
| >>> _ = plane.compute_implicit_distance(sphere, inplace=True)
|
| Again, we can plot these distances as a heatmap. Note how
| distances inside the sphere are negative and distances outside
| the sphere are positive.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(
| ... plane,
| ... scalars='implicit_distance',
| ... cmap='bwr',
| ... clim=[-0.35, 0.35],
| ... )
| >>> _ = pl.add_mesh(sphere, color='w', style='wireframe')
| >>> pl.show()
|
| See :ref:`clip_with_surface_example` and
| :ref:`voxelize_surface_mesh_example` for more examples using
| this filter.
|
| connectivity(self, extraction_mode: "Literal['all', 'largest', 'specified', 'cell_seed', 'point_seed', 'closest']" = 'all', variable_input=None, scalar_range=None, scalars=None, label_regions=True, region_ids=None, point_ids=None, cell_ids=None, closest_point=None, inplace=False, progress_bar=False, **kwargs)
| Find and label connected regions.
|
| This filter extracts cell regions based on a specified connectivity
| criterion. The extraction criterion can be controlled with
| ``extraction_mode`` to extract the largest region or the closest
| region to a seed point, for example.
|
| In general, cells are considered to be connected if they
| share a point. However, if a ``scalar_range`` is provided, cells
| must also have at least one point with scalar values in the
| specified range to be considered connected.
|
| See :ref:`connectivity_example` and :ref:`volumetric_example` for
| more examples using this filter.
|
| .. versionadded:: 0.43.0
|
| * New extraction modes: ``'specified'``, ``'cell_seed'``, ``'point_seed'``,
| and ``'closest'``.
| * Extracted regions are now sorted in descending order by
| cell count.
| * Region connectivity can be controlled using ``scalar_range``.
|
| .. deprecated:: 0.43.0
| Parameter ``largest`` is deprecated. Use ``'largest'`` or
| ``extraction_mode='largest'`` instead.
|
| Parameters
| ----------
| extraction_mode : str, default: "all"
| * ``'all'``: Extract all connected regions.
| * ``'largest'`` : Extract the largest connected region (by cell
| count).
| * ``'specified'``: Extract specific region IDs. Use ``region_ids``
| to specify the region IDs to extract.
| * ``'cell_seed'``: Extract all regions sharing the specified cell
| ids. Use ``cell_ids`` to specify the cell ids.
| * ``'point_seed'`` : Extract all regions sharing the specified
| point ids. Use ``point_ids`` to specify the point ids.
| * ``'closest'`` : Extract the region closest to the specified
| point. Use ``closest_point`` to specify the point.
|
| variable_input : float | sequence[float], optional
| The convenience parameter used for specifying any required input
| values for some values of ``extraction_mode``. Setting
| ``variable_input`` is equivalent to setting:
|
| * ``'region_ids'`` if mode is ``'specified'``.
| * ``'cell_ids'`` if mode is ``'cell_seed'``.
| * ``'point_ids'`` if mode is ``'point_seed'``.
| * ``'closest_point'`` if mode is ``'closest'``.
|
| It has no effect if the mode is ``'all'`` or ``'largest'``.
|
| scalar_range : sequence[float], optional
| Scalar range in the form ``[min, max]``. If set, the connectivity is
| restricted to cells with at least one point with scalar values in
| the specified range.
|
| scalars : str, optional
| Name of scalars to use if ``scalar_range`` is specified. Defaults
| to currently active scalars.
|
| .. note::
| This filter requires point scalars to determine region
| connectivity. If cell scalars are provided, they are first
| converted to point scalars with :func:`cell_data_to_point_data`
| before applying the filter. The converted point scalars are
| removed from the output after applying the filter.
|
| label_regions : bool, default: True
| If ``True``, ``'RegionId'`` point and cell scalar arrays are stored.
| Each region is assigned a unique ID. IDs are zero-indexed and are
| assigned by region cell count in descending order (i.e. the largest
| region has ID ``0``).
|
| region_ids : sequence[int], optional
| Region ids to extract. Only used if ``extraction_mode`` is
| ``specified``.
|
| point_ids : sequence[int], optional
| Point ids to use as seeds. Only used if ``extraction_mode`` is
| ``point_seed``.
|
| cell_ids : sequence[int], optional
| Cell ids to use as seeds. Only used if ``extraction_mode`` is
| ``cell_seed``.
|
| closest_point : sequence[int], optional
| Point coordinates in ``(x, y, z)``. Only used if
| ``extraction_mode`` is ``closest``.
|
| inplace : bool, default: False
| If ``True`` the mesh is updated in-place, otherwise a copy
| is returned. A copy is always returned if the input type is
| not ``pyvista.PolyData`` or ``pyvista.UnstructuredGrid``.
|
| progress_bar : bool, default: False
| Display a progress bar.
|
| **kwargs : dict, optional
| Used for handling deprecated parameters.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with labeled connected regions. Return type is
| ``pyvista.PolyData`` if input type is ``pyvista.PolyData`` and
| ``pyvista.UnstructuredGrid`` otherwise.
|
| See Also
| --------
| extract_largest, split_bodies, threshold, extract_values
|
| Examples
| --------
| Create a single mesh with three disconnected regions where each
| region has a different cell count.
|
| >>> import pyvista as pv
| >>> large = pv.Sphere(
| ... center=(-4, 0, 0), phi_resolution=40, theta_resolution=40
| ... )
| >>> medium = pv.Sphere(
| ... center=(-2, 0, 0), phi_resolution=15, theta_resolution=15
| ... )
| >>> small = pv.Sphere(
| ... center=(0, 0, 0), phi_resolution=7, theta_resolution=7
| ... )
| >>> mesh = large + medium + small
|
| Plot their connectivity.
|
| >>> conn = mesh.connectivity('all')
| >>> conn.plot(cmap=['red', 'green', 'blue'], show_edges=True)
|
| Restrict connectivity to a scalar range.
|
| >>> mesh['y_coordinates'] = mesh.points[:, 1]
| >>> conn = mesh.connectivity('all', scalar_range=[-1, 0])
| >>> conn.plot(cmap=['red', 'green', 'blue'], show_edges=True)
|
| Extract the region closest to the origin.
|
| >>> conn = mesh.connectivity('closest', (0, 0, 0))
| >>> conn.plot(color='blue', show_edges=True)
|
| Extract a region using a cell ID ``100`` as a seed.
|
| >>> conn = mesh.connectivity('cell_seed', 100)
| >>> conn.plot(color='green', show_edges=True)
|
| Extract the largest region.
|
| >>> conn = mesh.connectivity('largest')
| >>> conn.plot(color='red', show_edges=True)
|
| Extract the largest and smallest regions by specifying their
| region IDs. Note that the region IDs of the output differ from
| the specified IDs since the input has three regions but the output
| only has two.
|
| >>> large_id = 0 # largest always has ID '0'
| >>> small_id = 2 # smallest has ID 'N-1' with N=3 regions
| >>> conn = mesh.connectivity('specified', (small_id, large_id))
| >>> conn.plot(cmap=['red', 'blue'], show_edges=True)
|
| contour(self, isosurfaces=10, scalars=None, compute_normals=False, compute_gradients=False, compute_scalars=True, rng=None, preference='point', method='contour', progress_bar=False)
| Contour an input self by an array.
|
| ``isosurfaces`` can be an integer specifying the number of
| isosurfaces in the data range or a sequence of values for
| explicitly setting the isosurfaces.
|
| Parameters
| ----------
| isosurfaces : int | sequence[float], optional
| Number of isosurfaces to compute across valid data range or a
| sequence of float values to explicitly use as the isosurfaces.
|
| scalars : str | array_like[float], optional
| Name or array of scalars to threshold on. If this is an array, the
| output of this filter will save them as ``"Contour Data"``.
| Defaults to currently active scalars.
|
| compute_normals : bool, default: False
| Compute normals for the dataset.
|
| compute_gradients : bool, default: False
| Compute gradients for the dataset.
|
| compute_scalars : bool, default: True
| Preserves the scalar values that are being contoured.
|
| rng : sequence[float], optional
| If an integer number of isosurfaces is specified, this is
| the range over which to generate contours. Default is the
| scalars array's full data range.
|
| preference : str, default: "point"
| When ``scalars`` is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'`` or ``'cell'``.
|
| method : str, default: "contour"
| Specify to choose which vtk filter is used to create the contour.
| Must be one of ``'contour'``, ``'marching_cubes'`` and
| ``'flying_edges'``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Contoured surface.
|
| Examples
| --------
| Generate contours for the random hills dataset.
|
| >>> from pyvista import examples
| >>> hills = examples.load_random_hills()
| >>> contours = hills.contour()
| >>> contours.plot(line_width=5)
|
| Generate the surface of a mobius strip using flying edges.
|
| >>> import pyvista as pv
| >>> a = 0.4
| >>> b = 0.1
| >>> def f(x, y, z):
| ... xx = x * x
| ... yy = y * y
| ... zz = z * z
| ... xyz = x * y * z
| ... xx_yy = xx + yy
| ... a_xx = a * xx
| ... b_yy = b * yy
| ... return (
| ... (xx_yy + 1) * (a_xx + b_yy)
| ... + zz * (b * xx + a * yy)
| ... - 2 * (a - b) * xyz
| ... - a * b * xx_yy
| ... ) ** 2 - 4 * (xx + yy) * (a_xx + b_yy - xyz * (a - b)) ** 2
| ...
| >>> n = 100
| >>> x_min, y_min, z_min = -1.35, -1.7, -0.65
| >>> grid = pv.ImageData(
| ... dimensions=(n, n, n),
| ... spacing=(
| ... abs(x_min) / n * 2,
| ... abs(y_min) / n * 2,
| ... abs(z_min) / n * 2,
| ... ),
| ... origin=(x_min, y_min, z_min),
| ... )
| >>> x, y, z = grid.points.T
| >>> values = f(x, y, z)
| >>> out = grid.contour(
| ... 1,
| ... scalars=values,
| ... rng=[0, 0],
| ... method='flying_edges',
| ... )
| >>> out.plot(color='lightblue', smooth_shading=True)
|
| See :ref:`common_filter_example` or
| :ref:`marching_cubes_example` for more examples using this
| filter.
|
| ctp(self, pass_cell_data=False, progress_bar=False, **kwargs)
| Transform cell data into point data.
|
| Point data are specified per node and cell data specified
| within cells. Optionally, the input point data can be passed
| through to the output.
|
| This method is an alias for
| :func:`pyvista.DataSetFilters.cell_data_to_point_data`.
|
| Parameters
| ----------
| pass_cell_data : bool, default: False
| If enabled, pass the input cell data through to the output.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| **kwargs : dict, optional
| Deprecated keyword argument ``pass_cell_arrays``.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with the cell data transformed into point data.
| Return type matches input.
|
| decimate_boundary(self, target_reduction=0.5, progress_bar=False)
| Return a decimated version of a triangulation of the boundary.
|
| Only the outer surface of the input dataset will be considered.
|
| Parameters
| ----------
| target_reduction : float, default: 0.5
| Fraction of the original mesh to remove.
| TargetReduction is set to ``0.9``, this filter will try to reduce
| the data set to 10% of its original size and will remove 90%
| of the input triangles.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Decimated boundary.
|
| Examples
| --------
| See the :ref:`linked_views_example` example.
|
| delaunay_3d(self, alpha=0.0, tol=0.001, offset=2.5, progress_bar=False)
| Construct a 3D Delaunay triangulation of the mesh.
|
| This filter can be used to generate a 3D tetrahedral mesh from
| a surface or scattered points. If you want to create a
| surface from a point cloud, see
| :func:`pyvista.PolyDataFilters.reconstruct_surface`.
|
| Parameters
| ----------
| alpha : float, default: 0.0
| Distance value to control output of this filter. For a
| non-zero alpha value, only vertices, edges, faces, or
| tetrahedra contained within the circumsphere (of radius
| alpha) will be output. Otherwise, only tetrahedra will be
| output.
|
| tol : float, default: 0.001
| Tolerance to control discarding of closely spaced points.
| This tolerance is specified as a fraction of the diagonal
| length of the bounding box of the points.
|
| offset : float, default: 2.5
| Multiplier to control the size of the initial, bounding
| Delaunay triangulation.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| UnstructuredGrid containing the Delaunay triangulation.
|
| Examples
| --------
| Generate a 3D Delaunay triangulation of a surface mesh of a
| sphere and plot the interior edges generated.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere(theta_resolution=5, phi_resolution=5)
| >>> grid = sphere.delaunay_3d()
| >>> edges = grid.extract_all_edges()
| >>> edges.plot(line_width=5, color='k')
|
| elevation(self, low_point=None, high_point=None, scalar_range=None, preference='point', set_active=True, progress_bar=False)
| Generate scalar values on a dataset.
|
| The scalar values lie within a user specified range, and are
| generated by computing a projection of each dataset point onto
| a line. The line can be oriented arbitrarily. A typical
| example is to generate scalars based on elevation or height
| above a plane.
|
| .. warning::
| This will create a scalars array named ``'Elevation'`` on the
| point data of the input dataset and overwrite the array
| named ``'Elevation'`` if present.
|
| Parameters
| ----------
| low_point : sequence[float], optional
| The low point of the projection line in 3D space. Default is bottom
| center of the dataset. Otherwise pass a length 3 sequence.
|
| high_point : sequence[float], optional
| The high point of the projection line in 3D space. Default is top
| center of the dataset. Otherwise pass a length 3 sequence.
|
| scalar_range : str | sequence[float], optional
| The scalar range to project to the low and high points on the line
| that will be mapped to the dataset. If None given, the values will
| be computed from the elevation (Z component) range between the
| high and low points. Min and max of a range can be given as a length
| 2 sequence. If ``str``, name of scalar array present in the
| dataset given, the valid range of that array will be used.
|
| preference : str, default: "point"
| When an array name is specified for ``scalar_range``, this is the
| preferred array type to search for in the dataset.
| Must be either ``'point'`` or ``'cell'``.
|
| set_active : bool, default: True
| A boolean flag on whether or not to set the new
| ``'Elevation'`` scalar as the active scalars array on the
| output dataset.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset containing elevation scalars in the
| ``"Elevation"`` array in ``point_data``.
|
| Examples
| --------
| Generate the "elevation" scalars for a sphere mesh. This is
| simply the height in Z from the XY plane.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere()
| >>> sphere_elv = sphere.elevation()
| >>> sphere_elv.plot(smooth_shading=True)
|
| Access the first 4 elevation scalars. This is a point-wise
| array containing the "elevation" of each point.
|
| >>> sphere_elv['Elevation'][:4] # doctest:+SKIP
| array([-0.5 , 0.5 , -0.49706897, -0.48831028], dtype=float32)
|
| See :ref:`common_filter_example` for more examples using this filter.
|
| explode(self, factor=0.1)
| Push each individual cell away from the center of the dataset.
|
| Parameters
| ----------
| factor : float, default: 0.1
| How much each cell will move from the center of the dataset
| relative to its distance from it. Increase this number to push the
| cells farther away.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| UnstructuredGrid containing the exploded cells.
|
| Notes
| -----
| This is similar to :func:`shrink <pyvista.DataSetFilters.shrink>`
| except that it does not change the size of the cells.
|
| Examples
| --------
| >>> import numpy as np
| >>> import pyvista as pv
| >>> xrng = np.linspace(0, 1, 3)
| >>> yrng = np.linspace(0, 2, 4)
| >>> zrng = np.linspace(0, 3, 5)
| >>> grid = pv.RectilinearGrid(xrng, yrng, zrng)
| >>> exploded = grid.explode()
| >>> exploded.plot(show_edges=True)
|
| extract_all_edges(self, use_all_points=False, clear_data=False, progress_bar=False)
| Extract all the internal/external edges of the dataset as PolyData.
|
| This produces a full wireframe representation of the input dataset.
|
| Parameters
| ----------
| use_all_points : bool, default: False
| Indicates whether all of the points of the input mesh should exist
| in the output. When ``True``, point numbering does not change and
| a threaded approach is used, which avoids the use of a point locator
| and is quicker.
|
| By default this is set to ``False``, and unused points are omitted
| from the output.
|
| This parameter can only be set to ``True`` with ``vtk==9.1.0`` or newer.
|
| clear_data : bool, default: False
| Clear any point, cell, or field data. This is useful
| if wanting to strictly extract the edges.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Edges extracted from the dataset.
|
| Examples
| --------
| Extract the edges of a sample unstructured grid and plot the edges.
| Note how it plots interior edges.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> hex_beam = pv.read(examples.hexbeamfile)
| >>> edges = hex_beam.extract_all_edges()
| >>> edges.plot(line_width=5, color='k')
|
| See :ref:`cell_centers_example` for more examples using this filter.
|
| extract_cells(self, ind, invert=False, progress_bar=False)
| Return a subset of the grid.
|
| Parameters
| ----------
| ind : sequence[int]
| Numpy array of cell indices to be extracted.
|
| invert : bool, default: False
| Invert the selection.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| See Also
| --------
| extract_points, extract_values
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Subselected grid.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> grid = pv.read(examples.hexbeamfile)
| >>> subset = grid.extract_cells(range(20))
| >>> subset.n_cells
| 20
| >>> pl = pv.Plotter()
| >>> actor = pl.add_mesh(
| ... grid, style='wireframe', line_width=5, color='black'
| ... )
| >>> actor = pl.add_mesh(subset, color='grey')
| >>> pl.show()
|
| extract_cells_by_type(self, cell_types, progress_bar=False)
| Extract cells of a specified type.
|
| Given an input dataset and a list of cell types, produce an output
| dataset containing only cells of the specified type(s). Note that if
| the input dataset is homogeneous (e.g., all cells are of the same type)
| and the cell type is one of the cells specified, then the input dataset
| is shallow copied to the output.
|
| The type of output dataset is always the same as the input type. Since
| structured types of data (i.e., :class:`pyvista.ImageData`,
| :class:`pyvista.StructuredGrid`, :class`pyvista.RectilnearGrid`)
| are all composed of a cell of the same
| type, the output is either empty, or a shallow copy of the input.
| Unstructured data (:class:`pyvista.UnstructuredGrid`,
| :class:`pyvista.PolyData`) input may produce a subset of the input data
| (depending on the selected cell types).
|
| Parameters
| ----------
| cell_types : int | sequence[int]
| The cell types to extract. Must be a single or list of integer cell
| types. See :class:`pyvista.CellType`.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with the extracted cells. Type is the same as the input.
|
| Notes
| -----
| Unlike :func:`pyvista.DataSetFilters.extract_cells` which always
| produces a :class:`pyvista.UnstructuredGrid` output, this filter
| produces the same output type as input type.
|
| Examples
| --------
| Create an unstructured grid with both hexahedral and tetrahedral
| cells and then extract each individual cell type.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> beam = examples.load_hexbeam()
| >>> beam = beam.translate([1, 0, 0])
| >>> ugrid = beam + examples.load_tetbeam()
| >>> hex_cells = ugrid.extract_cells_by_type(pv.CellType.HEXAHEDRON)
| >>> tet_cells = ugrid.extract_cells_by_type(pv.CellType.TETRA)
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> _ = pl.add_text('Extracted Hexahedron cells')
| >>> _ = pl.add_mesh(hex_cells, show_edges=True)
| >>> pl.subplot(0, 1)
| >>> _ = pl.add_text('Extracted Tetrahedron cells')
| >>> _ = pl.add_mesh(tet_cells, show_edges=True)
| >>> pl.show()
|
| extract_feature_edges(self, feature_angle=30.0, boundary_edges=True, non_manifold_edges=True, feature_edges=True, manifold_edges=True, clear_data=False, progress_bar=False)
| Extract edges from the surface of the mesh.
|
| If the given mesh is not PolyData, the external surface of the given
| mesh is extracted and used.
|
| From vtk documentation, the edges are one of the following:
|
| 1) Boundary (used by one polygon) or a line cell.
| 2) Non-manifold (used by three or more polygons).
| 3) Feature edges (edges used by two triangles and whose
| dihedral angle > feature_angle).
| 4) Manifold edges (edges used by exactly two polygons).
|
| Parameters
| ----------
| feature_angle : float, default: 30.0
| Feature angle (in degrees) used to detect sharp edges on
| the mesh. Used only when ``feature_edges=True``.
|
| boundary_edges : bool, default: True
| Extract the boundary edges.
|
| non_manifold_edges : bool, default: True
| Extract non-manifold edges.
|
| feature_edges : bool, default: True
| Extract edges exceeding ``feature_angle``.
|
| manifold_edges : bool, default: True
| Extract manifold edges.
|
| clear_data : bool, default: False
| Clear any point, cell, or field data. This is useful
| if wanting to strictly extract the edges.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Extracted edges.
|
| Examples
| --------
| Extract the edges from an unstructured grid.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> hex_beam = pv.read(examples.hexbeamfile)
| >>> feat_edges = hex_beam.extract_feature_edges()
| >>> feat_edges.clear_data() # clear array data for plotting
| >>> feat_edges.plot(line_width=10)
|
| See the :ref:`extract_edges_example` for more examples using this filter.
|
| extract_geometry(self, extent: 'Sequence[float] | None' = None, progress_bar=False)
| Extract the outer surface of a volume or structured grid dataset.
|
| This will extract all 0D, 1D, and 2D cells producing the
| boundary faces of the dataset.
|
| .. note::
| This tends to be less efficient than :func:`extract_surface`.
|
| Parameters
| ----------
| extent : sequence[float], optional
| Specify a ``(xmin, xmax, ymin, ymax, zmin, zmax)`` bounding box to
| clip data.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Surface of the dataset.
|
| Examples
| --------
| Extract the surface of a sample unstructured grid.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> hex_beam = pv.read(examples.hexbeamfile)
| >>> hex_beam.extract_geometry()
| PolyData (...)
| N Cells: 88
| N Points: 90
| N Strips: 0
| X Bounds: 0.000e+00, 1.000e+00
| Y Bounds: 0.000e+00, 1.000e+00
| Z Bounds: 0.000e+00, 5.000e+00
| N Arrays: 3
|
| See :ref:`surface_smoothing_example` for more examples using this filter.
|
| extract_largest(self, inplace=False, progress_bar=False)
| Extract largest connected set in mesh.
|
| Can be used to reduce residues obtained when generating an
| isosurface. Works only if residues are not connected (share
| at least one point with) the main component of the image.
|
| Parameters
| ----------
| inplace : bool, default: False
| Updates mesh in-place.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Largest connected set in the dataset. Return type matches input.
|
| Examples
| --------
| Join two meshes together, extract the largest, and plot it.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere() + pv.Cube()
| >>> largest = mesh.extract_largest()
| >>> largest.plot()
|
| See :ref:`connectivity_example` and :ref:`volumetric_example` for
| more examples using this filter.
|
| .. seealso::
| :func:`pyvista.DataSetFilters.connectivity`
|
| extract_points(self, ind, adjacent_cells=True, include_cells=True, progress_bar=False)
| Return a subset of the grid (with cells) that contains any of the given point indices.
|
| Parameters
| ----------
| ind : sequence[int]
| Sequence of point indices to be extracted.
|
| adjacent_cells : bool, default: True
| If ``True``, extract the cells that contain at least one of
| the extracted points. If ``False``, extract the cells that
| contain exclusively points from the extracted points list.
| Has no effect if ``include_cells`` is ``False``.
|
| include_cells : bool, default: True
| Specifies if the cells shall be returned or not.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| See Also
| --------
| extract_cells, extract_values
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Subselected grid.
|
| Examples
| --------
| Extract all the points of a sphere with a Z coordinate greater than 0
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere()
| >>> extracted = sphere.extract_points(
| ... sphere.points[:, 2] > 0, include_cells=False
| ... )
| >>> extracted.clear_data() # clear for plotting
| >>> extracted.plot()
|
| extract_surface(self, pass_pointid=True, pass_cellid=True, nonlinear_subdivision=1, progress_bar=False)
| Extract surface mesh of the grid.
|
| Parameters
| ----------
| pass_pointid : bool, default: True
| Adds a point array ``"vtkOriginalPointIds"`` that
| identifies which original points these surface points
| correspond to.
|
| pass_cellid : bool, default: True
| Adds a cell array ``"vtkOriginalCellIds"`` that
| identifies which original cells these surface cells
| correspond to.
|
| nonlinear_subdivision : int, default: 1
| If the input is an unstructured grid with nonlinear faces,
| this parameter determines how many times the face is
| subdivided into linear faces.
|
| If 0, the output is the equivalent of its linear
| counterpart (and the midpoints determining the nonlinear
| interpolation are discarded). If 1 (the default), the
| nonlinear face is triangulated based on the midpoints. If
| greater than 1, the triangulated pieces are recursively
| subdivided to reach the desired subdivision. Setting the
| value to greater than 1 may cause some point data to not
| be passed even if no nonlinear faces exist. This option
| has no effect if the input is not an unstructured grid.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Surface mesh of the grid.
|
| Warnings
| --------
| Both ``"vtkOriginalPointIds"`` and ``"vtkOriginalCellIds"`` may be
| affected by other VTK operations. See `issue 1164
| <https://github.com/pyvista/pyvista/issues/1164>`_ for
| recommendations on tracking indices across operations.
|
| Examples
| --------
| Extract the surface of an UnstructuredGrid.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> grid = examples.load_hexbeam()
| >>> surf = grid.extract_surface()
| >>> type(surf)
| <class 'pyvista.core.pointset.PolyData'>
| >>> surf["vtkOriginalPointIds"]
| pyvista_ndarray([ 0, 2, 36, 27, 7, 8, 81, 1, 18, 4, 54, 3, 6, 45,
| 72, 5, 63, 9, 35, 44, 11, 16, 89, 17, 10, 26, 62, 13,
| 12, 53, 80, 15, 14, 71, 19, 37, 55, 20, 38, 56, 21, 39,
| 57, 22, 40, 58, 23, 41, 59, 24, 42, 60, 25, 43, 61, 28,
| 82, 29, 83, 30, 84, 31, 85, 32, 86, 33, 87, 34, 88, 46,
| 73, 47, 74, 48, 75, 49, 76, 50, 77, 51, 78, 52, 79, 64,
| 65, 66, 67, 68, 69, 70])
| >>> surf["vtkOriginalCellIds"]
| pyvista_ndarray([ 0, 0, 0, 1, 1, 1, 3, 3, 3, 2, 2, 2, 36, 36,
| 36, 37, 37, 37, 39, 39, 39, 38, 38, 38, 5, 5, 9, 9,
| 13, 13, 17, 17, 21, 21, 25, 25, 29, 29, 33, 33, 4, 4,
| 8, 8, 12, 12, 16, 16, 20, 20, 24, 24, 28, 28, 32, 32,
| 7, 7, 11, 11, 15, 15, 19, 19, 23, 23, 27, 27, 31, 31,
| 35, 35, 6, 6, 10, 10, 14, 14, 18, 18, 22, 22, 26, 26,
| 30, 30, 34, 34])
|
| Note that in the "vtkOriginalCellIds" array, the same original cells
| appears multiple times since this array represents the original cell of
| each surface cell extracted.
|
| See the :ref:`extract_surface_example` for more examples using this filter.
|
| extract_values(self, values: 'None | (float | VectorLike[float] | MatrixLike[float] | dict[str, float] | dict[float, str])' = None, *, ranges: 'None | (VectorLike[float] | MatrixLike[float] | dict[str, VectorLike[float]] | dict[tuple[float, float], str])' = None, scalars: 'str | None' = None, preference: "Literal['point', 'cell']" = 'point', component_mode: "Literal['any', 'all', 'multi'] | int" = 'all', invert: 'bool' = False, adjacent_cells: 'bool' = True, include_cells: 'bool | None' = None, split: 'bool' = False, pass_point_ids: 'bool' = True, pass_cell_ids: 'bool' = True, progress_bar: 'bool' = False)
| Return a subset of the mesh based on the value(s) of point or cell data.
|
| Points and cells may be extracted with a single value, multiple values, a range
| of values, or any mix of values and ranges. This enables threshold-like
| filtering of data in a discontinuous manner to extract a single label or groups
| of labels from categorical data, or to extract multiple regions from continuous
| data. Extracted values may optionally be split into separate meshes.
|
| This filter operates on point data and cell data distinctly:
|
| **Point data**
|
| All cells with at least one point with the specified value(s) are returned.
| Optionally, set ``adjacent_cells`` to ``False`` to only extract points from
| cells where all points in the cell strictly have the specified value(s).
| In these cases, a point is only included in the output if that point is part
| of an extracted cell.
|
| Alternatively, set ``include_cells`` to ``False`` to exclude cells from
| the operation completely and extract all points with a specified value.
|
| **Cell Data**
|
| Only the cells (and their points) with the specified values(s) are included
| in the output.
|
| Internally, :meth:`~pyvista.DataSetFilters.extract_points` is called to extract
| points for point data, and :meth:`~pyvista.DataSetFilters.extract_cells` is
| called to extract cells for cell data.
|
| By default, two arrays are included with the output: ``'vtkOriginalPointIds'``
| and ``'vtkOriginalCellIds'``. These arrays can be used to link the filtered
| points or cells directly to the input.
|
| .. versionadded:: 0.44
|
| Parameters
| ----------
| values : number | array_like | dict, optional
| Value(s) to extract. Can be a number, an iterable of numbers, or a dictionary
| with numeric entries. For ``dict`` inputs, either its keys or values may be
| numeric, and the other field must be strings. The numeric field is used as
| the input for this parameter, and if ``split`` is ``True``, the string field
| is used to set the block names of the returned :class:`~pyvista.MultiBlock`.
|
| .. note::
| When extracting multi-component values with ``component_mode=multi``,
| each value is specified as a multi-component scalar. In this case,
| ``values`` can be a single vector or an array of row vectors.
|
| ranges : array_like | dict, optional
| Range(s) of values to extract. Can be a single range (i.e. a sequence of
| two numbers in the form ``[lower, upper]``), a sequence of ranges, or a
| dictionary with range entries. Any combination of ``values`` and ``ranges``
| may be specified together. The endpoints of the ranges are included in the
| extraction. Ranges cannot be set when ``component_mode=multi``.
|
| For ``dict`` inputs, either its keys or values may be numeric, and the other
| field must be strings. The numeric field is used as the input for this
| parameter, and if ``split`` is ``True``, the string field is used to set the
| block names of the returned :class:`~pyvista.MultiBlock`.
|
| .. note::
| Use ``+/-`` infinity to specify an unlimited bound, e.g.:
|
| - ``[0, float('inf')]`` to extract values greater than or equal to zero.
| - ``[float('-inf'), 0]`` to extract values less than or equal to zero.
|
| scalars : str, optional
| Name of scalars to extract with. Defaults to currently active scalars.
|
| preference : str, default: 'point'
| When ``scalars`` is specified, this is the preferred array type to search
| for in the dataset. Must be either ``'point'`` or ``'cell'``.
|
| component_mode : int | 'any' | 'all' | 'multi', default: 'all'
| Specify the component(s) to use when ``scalars`` is a multi-component array.
| Has no effect when the scalars have a single component. Must be one of:
|
| - number: specify the component number as a 0-indexed integer. The selected
| component must have the specified value(s).
| - ``'any'``: any single component can have the specified value(s).
| - ``'all'``: all individual components must have the specified values(s).
| - ``'multi'``: the entire multi-component item must have the specified value.
|
| invert : bool, default: False
| Invert the extraction values. If ``True`` extract the points (with cells)
| which do *not* have the specified values.
|
| adjacent_cells : bool, default: True
| If ``True``, include cells (and their points) that contain at least one of
| the extracted points. If ``False``, only include cells that contain
| exclusively points from the extracted points list. Has no effect if
| ``include_cells`` is ``False``. Has no effect when extracting values from
| cell data.
|
| include_cells : bool, default: None
| Specify if cells shall be used for extraction or not. If ``False``, points
| with the specified values are extracted regardless of their cell
| connectivity, and all cells at the output will be vertex cells (one for each
| point.) Has no effect when extracting values from cell data.
|
| By default, this value is ``True`` if the input has at least one cell and
| ``False`` otherwise.
|
| split : bool, default: False
| If ``True``, each value in ``values`` and each range in ``range`` is
| extracted independently and returned as a :class:`~pyvista.MultiBlock`.
| The number of blocks returned equals the number of input values and ranges.
| The blocks may be named if a dictionary is used as input. See ``values``
| and ``ranges`` for details.
|
| .. note::
| Output blocks may contain empty meshes if no values meet the extraction
| criteria. This can impact plotting since empty meshes cannot be plotted
| by default. Use :meth:`pyvista.MultiBlock.clean` on the output to remove
| empty meshes, or set ``pv.global_theme.allow_empty_mesh = True`` to
| enable plotting empty meshes.
|
| pass_point_ids : bool, default: True
| Add a point array ``'vtkOriginalPointIds'`` that identifies the original
| points the extracted points correspond to.
|
| pass_cell_ids : bool, default: True
| Add a cell array ``'vtkOriginalCellIds'`` that identifies the original cells
| the extracted cells correspond to.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| See Also
| --------
| split_values, extract_points, extract_cells, threshold, partition
|
| Returns
| -------
| pyvista.UnstructuredGrid or pyvista.MultiBlock
| An extracted mesh or a composite of extracted meshes, depending on ``split``.
|
| Examples
| --------
| Extract a single value from a grid's point data.
|
| >>> import numpy as np
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> mesh = examples.load_uniform()
| >>> extracted = mesh.extract_values(0)
|
| Plot extracted values. Since adjacent cells are included by default, points with
| values other than ``0`` are included in the output.
|
| >>> extracted.get_data_range()
| (np.float64(0.0), np.float64(81.0))
| >>> extracted.plot()
|
| Set ``include_cells=False`` to only extract points. The output scalars now
| strictly contain zeros.
|
| >>> extracted = mesh.extract_values(0, include_cells=False)
| >>> extracted.get_data_range()
| (np.float64(0.0), np.float64(0.0))
| >>> extracted.plot(render_points_as_spheres=True, point_size=100)
|
| Use ``ranges`` to extract values from a grid's point data in range.
|
| Here, we use ``+/-`` infinity to extract all values of ``100`` or less.
|
| >>> extracted = mesh.extract_values(ranges=[-np.inf, 100])
| >>> extracted.plot()
|
| Extract every third cell value from cell data.
|
| >>> mesh = examples.load_hexbeam()
| >>> lower, upper = mesh.get_data_range()
| >>> step = 3
| >>> extracted = mesh.extract_values(
| ... range(lower, upper, step) # values 0, 3, 6, ...
| ... )
|
| Plot result and show an outline of the input for context.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(extracted)
| >>> _ = pl.add_mesh(mesh.extract_all_edges())
| >>> pl.show()
|
| Any combination of values and ranges may be specified.
|
| E.g. extract a single value and two ranges, and split the result into separate
| blocks of a MultiBlock.
|
| >>> extracted = mesh.extract_values(
| ... values=18, ranges=[[0, 8], [29, 40]], split=True
| ... )
| >>> extracted
| MultiBlock (...)
| N Blocks 3
| X Bounds 0.000, 1.000
| Y Bounds 0.000, 1.000
| Z Bounds 0.000, 5.000
| >>> extracted.plot(multi_colors=True)
|
| Extract values from multi-component scalars.
|
| First, create a point cloud with a 3-component RGB color array.
|
| >>> rng = np.random.default_rng(seed=1)
| >>> points = rng.random((30, 3))
| >>> colors = rng.random((30, 3))
| >>> point_cloud = pv.PointSet(points)
| >>> point_cloud['colors'] = colors
| >>> plot_kwargs = dict(
| ... render_points_as_spheres=True, point_size=50, rgb=True
| ... )
| >>> point_cloud.plot(**plot_kwargs)
|
| Extract values from a single component.
|
| E.g. extract points with a strong red component (i.e. > 0.8).
|
| >>> extracted = point_cloud.extract_values(
| ... ranges=[0.8, 1.0], component_mode=0
| ... )
| >>> extracted.plot(**plot_kwargs)
|
| Extract values from all components.
|
| E.g. extract points where all RGB components are dark (i.e. < 0.5).
|
| >>> extracted = point_cloud.extract_values(
| ... ranges=[0.0, 0.5], component_mode='all'
| ... )
| >>> extracted.plot(**plot_kwargs)
|
| Extract specific multi-component values.
|
| E.g. round the scalars to create binary RGB components, and extract only green
| and blue components.
|
| >>> point_cloud['colors'] = np.round(point_cloud['colors'])
| >>> green = [0, 1, 0]
| >>> blue = [0, 0, 1]
| >>>
| >>> extracted = point_cloud.extract_values(
| ... values=[blue, green],
| ... component_mode='multi',
| ... )
| >>> extracted.plot(**plot_kwargs)
|
| Use the original IDs returned by the extraction to modify the original point
| cloud.
|
| For example, change the color of the blue and green points to yellow.
|
| >>> point_ids = extracted['vtkOriginalPointIds']
| >>> yellow = [1, 1, 0]
| >>> point_cloud['colors'][point_ids] = yellow
| >>> point_cloud.plot(**plot_kwargs)
|
| glyph(self, orient=True, scale=True, factor=1.0, geom=None, indices=None, tolerance=None, absolute=False, clamping=False, rng=None, color_mode='scale', progress_bar=False)
| Copy a geometric representation (called a glyph) to the input dataset.
|
| The glyph may be oriented along the input vectors, and it may
| be scaled according to scalar data or vector
| magnitude. Passing a table of glyphs to choose from based on
| scalars or vector magnitudes is also supported. The arrays
| used for ``orient`` and ``scale`` must be either both point data
| or both cell data.
|
| Parameters
| ----------
| orient : bool | str, default: True
| If ``True``, use the active vectors array to orient the glyphs.
| If string, the vector array to use to orient the glyphs.
| If ``False``, the glyphs will not be orientated.
|
| scale : bool | str | sequence[float], default: True
| If ``True``, use the active scalars to scale the glyphs.
| If string, the scalar array to use to scale the glyphs.
| If ``False``, the glyphs will not be scaled.
|
| factor : float, default: 1.0
| Scale factor applied to scaling array.
|
| geom : vtk.vtkDataSet or tuple(vtk.vtkDataSet), optional
| The geometry to use for the glyph. If missing, an arrow glyph
| is used. If a sequence, the datasets inside define a table of
| geometries to choose from based on scalars or vectors. In this
| case a sequence of numbers of the same length must be passed as
| ``indices``. The values of the range (see ``rng``) affect lookup
| in the table.
|
| indices : sequence[float], optional
| Specifies the index of each glyph in the table for lookup in case
| ``geom`` is a sequence. If given, must be the same length as
| ``geom``. If missing, a default value of ``range(len(geom))`` is
| used. Indices are interpreted in terms of the scalar range
| (see ``rng``). Ignored if ``geom`` has length 1.
|
| tolerance : float, optional
| Specify tolerance in terms of fraction of bounding box length.
| Float value is between 0 and 1. Default is None. If ``absolute``
| is ``True`` then the tolerance can be an absolute distance.
| If ``None``, points merging as a preprocessing step is disabled.
|
| absolute : bool, default: False
| Control if ``tolerance`` is an absolute distance or a fraction.
|
| clamping : bool, default: False
| Turn on/off clamping of "scalar" values to range.
|
| rng : sequence[float], optional
| Set the range of values to be considered by the filter
| when scalars values are provided.
|
| color_mode : str, optional, default: ``'scale'``
| If ``'scale'`` , color the glyphs by scale.
| If ``'scalar'`` , color the glyphs by scalar.
| If ``'vector'`` , color the glyphs by vector.
|
| .. versionadded:: 0.44
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Glyphs at either the cell centers or points.
|
| Examples
| --------
| Create arrow glyphs oriented by vectors and scaled by scalars.
| Factor parameter is used to reduce the size of the arrows.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> mesh = examples.load_random_hills()
| >>> arrows = mesh.glyph(
| ... scale="Normals", orient="Normals", tolerance=0.05
| ... )
| >>> pl = pv.Plotter()
| >>> actor = pl.add_mesh(arrows, color="black")
| >>> actor = pl.add_mesh(
| ... mesh,
| ... scalars="Elevation",
| ... cmap="terrain",
| ... show_scalar_bar=False,
| ... )
| >>> pl.show()
|
| See :ref:`glyph_example` and :ref:`glyph_table_example` for more
| examples using this filter.
|
| integrate_data(self, progress_bar=False)
| Integrate point and cell data.
|
| Area or volume is also provided in point data.
|
| This filter uses the VTK `vtkIntegrateAttributes
| <https://vtk.org/doc/nightly/html/classvtkIntegrateAttributes.html>`_
| and requires VTK v9.1.0 or newer.
|
| Parameters
| ----------
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Mesh with 1 point and 1 vertex cell with integrated data in point
| and cell data.
|
| Examples
| --------
| Integrate data on a sphere mesh.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> sphere = pv.Sphere(theta_resolution=100, phi_resolution=100)
| >>> sphere.point_data["data"] = 2 * np.ones(sphere.n_points)
| >>> integrated = sphere.integrate_data()
|
| There is only 1 point and cell, so access the only value.
|
| >>> integrated["Area"][0]
| np.float64(3.14)
| >>> integrated["data"][0]
| np.float64(6.28)
|
| See the :ref:`integrate_example` for more examples using this filter.
|
| interpolate(self, target, sharpness=2.0, radius=1.0, strategy='null_value', null_value=0.0, n_points=None, pass_cell_data=True, pass_point_data=True, progress_bar=False)
| Interpolate values onto this mesh from a given dataset.
|
| The ``target`` dataset is typically a point cloud. Only point data from
| the ``target`` mesh will be interpolated onto points of this mesh. Whether
| preexisting point and cell data of this mesh are preserved in the
| output can be customized with the ``pass_point_data`` and
| ``pass_cell_data`` parameters.
|
| This uses a Gaussian interpolation kernel. Use the ``sharpness`` and
| ``radius`` parameters to adjust this kernel. You can also switch this
| kernel to use an N closest points approach.
|
| If the cell topology is more useful for interpolating, e.g. from a
| discretized FEM or CFD simulation, use
| :func:`pyvista.DataSetFilters.sample` instead.
|
| Parameters
| ----------
| target : pyvista.DataSet
| The vtk data object to sample from. Point and cell arrays from
| this object are interpolated onto this mesh.
|
| sharpness : float, default: 2.0
| Set the sharpness (i.e., falloff) of the Gaussian kernel. As the
| sharpness increases the effects of distant points are reduced.
|
| radius : float, optional
| Specify the radius within which the basis points must lie.
|
| strategy : str, default: "null_value"
| Specify a strategy to use when encountering a "null" point during
| the interpolation process. Null points occur when the local
| neighborhood (of nearby points to interpolate from) is empty. If
| the strategy is set to ``'mask_points'``, then an output array is
| created that marks points as being valid (=1) or null (invalid =0)
| (and the NullValue is set as well). If the strategy is set to
| ``'null_value'``, then the output data value(s) are set to the
| ``null_value`` (specified in the output point data). Finally, the
| strategy ``'closest_point'`` is to simply use the closest point to
| perform the interpolation.
|
| null_value : float, default: 0.0
| Specify the null point value. When a null point is encountered
| then all components of each null tuple are set to this value.
|
| n_points : int, optional
| If given, specifies the number of the closest points used to form
| the interpolation basis. This will invalidate the radius argument
| in favor of an N closest points approach. This typically has poorer
| results.
|
| pass_cell_data : bool, default: True
| Preserve input mesh's original cell data arrays.
|
| pass_point_data : bool, default: True
| Preserve input mesh's original point data arrays.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Interpolated dataset. Return type matches input.
|
| See Also
| --------
| pyvista.DataSetFilters.sample
|
| Examples
| --------
| Interpolate the values of 5 points onto a sample plane.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> rng = np.random.default_rng(7)
| >>> point_cloud = rng.random((5, 3))
| >>> point_cloud[:, 2] = 0
| >>> point_cloud -= point_cloud.mean(0)
| >>> pdata = pv.PolyData(point_cloud)
| >>> pdata['values'] = rng.random(5)
| >>> plane = pv.Plane()
| >>> plane.clear_data()
| >>> plane = plane.interpolate(pdata, sharpness=3)
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(
| ... pdata, render_points_as_spheres=True, point_size=50
| ... )
| >>> _ = pl.add_mesh(plane, style='wireframe', line_width=5)
| >>> pl.show()
|
| See :ref:`interpolate_example` for more examples using this filter.
|
| merge(self, grid=None, merge_points=True, tolerance=0.0, inplace=False, main_has_priority=True, progress_bar=False)
| Join one or many other grids to this grid.
|
| Grid is updated in-place by default.
|
| Can be used to merge points of adjacent cells when no grids
| are input.
|
| .. note::
| The ``+`` operator between two meshes uses this filter with
| the default parameters. When the target mesh is already a
| :class:`pyvista.UnstructuredGrid`, in-place merging via
| ``+=`` is similarly possible.
|
| Parameters
| ----------
| grid : vtk.UnstructuredGrid or list of vtk.UnstructuredGrids, optional
| Grids to merge to this grid.
|
| merge_points : bool, default: True
| Points in exactly the same location will be merged between
| the two meshes. Warning: this can leave degenerate point data.
|
| tolerance : float, default: 0.0
| The absolute tolerance to use to find coincident points when
| ``merge_points=True``.
|
| inplace : bool, default: False
| Updates grid inplace when True if the input type is an
| :class:`pyvista.UnstructuredGrid`.
|
| main_has_priority : bool, default: True
| When this parameter is true and merge_points is true,
| the arrays of the merging grids will be overwritten
| by the original main mesh.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Merged grid.
|
| Notes
| -----
| When two or more grids are joined, the type and name of each
| array must match or the arrays will be ignored and not
| included in the final merged mesh.
|
| Examples
| --------
| Merge three separate spheres into a single mesh.
|
| >>> import pyvista as pv
| >>> sphere_a = pv.Sphere(center=(1, 0, 0))
| >>> sphere_b = pv.Sphere(center=(0, 1, 0))
| >>> sphere_c = pv.Sphere(center=(0, 0, 1))
| >>> merged = sphere_a.merge([sphere_b, sphere_c])
| >>> merged.plot()
|
| outline(self, generate_faces=False, progress_bar=False)
| Produce an outline of the full extent for the input dataset.
|
| Parameters
| ----------
| generate_faces : bool, default: False
| Generate solid faces for the box. This is disabled by default.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Mesh containing an outline of the original dataset.
|
| Examples
| --------
| Generate and plot the outline of a sphere. This is
| effectively the ``(x, y, z)`` bounds of the mesh.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere()
| >>> outline = sphere.outline()
| >>> pv.plot([sphere, outline], line_width=5)
|
| See :ref:`common_filter_example` for more examples using this filter.
|
| outline_corners(self, factor=0.2, progress_bar=False)
| Produce an outline of the corners for the input dataset.
|
| Parameters
| ----------
| factor : float, default: 0.2
| Controls the relative size of the corners to the length of
| the corresponding bounds.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Mesh containing outlined corners.
|
| Examples
| --------
| Generate and plot the corners of a sphere. This is
| effectively the ``(x, y, z)`` bounds of the mesh.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere()
| >>> corners = sphere.outline_corners(factor=0.1)
| >>> pv.plot([sphere, corners], line_width=5)
|
| pack_labels(self, sort=False, scalars=None, preference='point', output_scalars=None, progress_bar=False, inplace=False)
| Renumber labeled data such that labels are contiguous.
|
| This filter renumbers scalar label data of any type with ``N`` labels
| such that the output labels are contiguous from ``[0, N)``. The
| output may optionally be sorted by label count.
|
| The output array ``'packed_labels'`` is added to the output by default,
| and is automatically set as the active scalars.
|
| See Also
| --------
| sort_labels
| Similar function with ``sort=True`` by default.
|
| Notes
| -----
| This filter uses ``vtkPackLabels`` as the underlying method which
| requires VTK version 9.3 or higher. If ``vtkPackLabels`` is not
| available, packing is done with ``NumPy`` instead which may be
| slower. For best performance, consider upgrading VTK.
|
| .. versionadded:: 0.43
|
| Parameters
| ----------
| sort : bool, default: False
| Whether to sort the output by label count in descending order
| (i.e. from largest to smallest).
|
| scalars : str, optional
| Name of scalars to pack. Defaults to currently active scalars.
|
| preference : str, default: "point"
| When ``scalars`` is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'`` or ``'cell'``.
|
| output_scalars : str, None
| Name of the packed output scalars. By default, the output is
| saved to ``'packed_labels'``.
|
| progress_bar : bool, default: False
| If ``True``, display a progress bar. Has no effect if VTK
| version is lower than 9.3.
|
| inplace : bool, default: False
| If ``True``, the mesh is updated in-place.
|
| Returns
| -------
| pyvista.Dataset
| Dataset with packed labels.
|
| Examples
| --------
| Pack segmented image labels.
|
| Load non-contiguous image labels
|
| >>> from pyvista import examples
| >>> import numpy as np
| >>> image_labels = examples.load_frog_tissues()
|
| Show range of labels
|
| >>> image_labels.get_data_range()
| (np.uint8(0), np.uint8(29))
|
| Find 'gaps' in the labels
|
| >>> label_numbers = np.unique(image_labels.active_scalars)
| >>> label_max = np.max(label_numbers)
| >>> missing_labels = set(range(label_max)) - set(label_numbers)
| >>> len(missing_labels)
| 4
|
| Pack labels to remove gaps
|
| >>> packed_labels = image_labels.pack_labels()
|
| Show range of packed labels
|
| >>> packed_labels.get_data_range()
| (np.uint8(0), np.uint8(25))
|
| partition(self, n_partitions, generate_global_id=False, as_composite=True)
| Break down input dataset into a requested number of partitions.
|
| Cells on boundaries are uniquely assigned to each partition without duplication.
|
| It uses a kdtree implementation that builds balances the cell
| centers among a requested number of partitions. The current implementation
| only supports power-of-2 target partition. If a non-power of two value
| is specified for ``n_partitions``, then the load balancing simply
| uses the power-of-two greater than the requested value
|
| For more details, see `vtkRedistributeDataSetFilter
| <https://vtk.org/doc/nightly/html/classvtkRedistributeDataSetFilter.html>`_.
|
| Parameters
| ----------
| n_partitions : int
| Specify the number of partitions to split the input dataset
| into. Current implementation results in a number of partitions equal
| to the power of 2 greater than or equal to the chosen value.
|
| generate_global_id : bool, default: False
| Generate global cell ids if ``None`` are present in the input. If
| global cell ids are present in the input then this flag is
| ignored.
|
| This is stored as ``"vtkGlobalCellIds"`` within the ``cell_data``
| of the output dataset(s).
|
| as_composite : bool, default: False
| Return the partitioned dataset as a :class:`pyvista.MultiBlock`.
|
| See Also
| --------
| split_bodies, extract_values
|
| Returns
| -------
| pyvista.MultiBlock or pyvista.UnstructuredGrid
| UnStructuredGrid if ``as_composite=False`` and MultiBlock when ``True``.
|
| Examples
| --------
| Partition a simple ImageData into a :class:`pyvista.MultiBlock`
| containing each partition.
|
| >>> import pyvista as pv
| >>> grid = pv.ImageData(dimensions=(5, 5, 5))
| >>> out = grid.partition(4, as_composite=True)
| >>> out.plot(multi_colors=True, show_edges=True)
|
| Partition of the Stanford bunny.
|
| >>> from pyvista import examples
| >>> mesh = examples.download_bunny()
| >>> out = mesh.partition(4, as_composite=True)
| >>> out.plot(multi_colors=True, cpos='xy')
|
| plot_over_circular_arc(self, pointa, pointb, center, resolution=None, scalars=None, title=None, ylabel=None, figsize=None, figure=True, show=True, tolerance=None, fname=None, progress_bar=False)
| Sample a dataset along a circular arc and plot it.
|
| Plot the variables of interest in 2D where the X-axis is
| distance from Point A and the Y-axis is the variable of
| interest. Note that this filter returns ``None``.
|
| Parameters
| ----------
| pointa : sequence[float]
| Location in ``[x, y, z]``.
|
| pointb : sequence[float]
| Location in ``[x, y, z]``.
|
| center : sequence[float]
| Location in ``[x, y, z]``.
|
| resolution : int, optional
| Number of pieces to divide the circular arc into. Defaults
| to number of cells in the input mesh. Must be a positive
| integer.
|
| scalars : str, optional
| The string name of the variable in the input dataset to
| probe. The active scalar is used by default.
|
| title : str, optional
| The string title of the ``matplotlib`` figure.
|
| ylabel : str, optional
| The string label of the Y-axis. Defaults to the variable name.
|
| figsize : tuple(int), optional
| The size of the new figure.
|
| figure : bool, default: True
| Flag on whether or not to create a new figure.
|
| show : bool, default: True
| Shows the ``matplotlib`` figure when ``True``.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is
| in a cell of the input. If not given, tolerance is
| automatically generated.
|
| fname : str, optional
| Save the figure this file name when set.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Examples
| --------
| Sample a dataset along a high resolution circular arc and plot.
|
| >>> from pyvista import examples
| >>> mesh = examples.load_uniform()
| >>> a = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[5]]
| >>> b = [mesh.bounds[1], mesh.bounds[2], mesh.bounds[4]]
| >>> center = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[4]]
| >>> mesh.plot_over_circular_arc(
| ... a, b, center, resolution=1000, show=False
| ... ) # doctest:+SKIP
|
| plot_over_circular_arc_normal(self, center, resolution=None, normal=None, polar=None, angle=None, scalars=None, title=None, ylabel=None, figsize=None, figure=True, show=True, tolerance=None, fname=None, progress_bar=False)
| Sample a dataset along a resolution circular arc defined by a normal and polar vector and plot it.
|
| Plot the variables of interest in 2D where the X-axis is
| distance from Point A and the Y-axis is the variable of
| interest. Note that this filter returns ``None``.
|
| Parameters
| ----------
| center : sequence[int]
| Location in ``[x, y, z]``.
|
| resolution : int, optional
| Number of pieces to divide circular arc into. Defaults to
| number of cells in the input mesh. Must be a positive
| integer.
|
| normal : sequence[float], optional
| The normal vector to the plane of the arc. By default it
| points in the positive Z direction.
|
| polar : sequence[float], optional
| Starting point of the arc in polar coordinates. By
| default it is the unit vector in the positive x direction.
|
| angle : float, optional
| Arc length (in degrees), beginning at the polar vector. The
| direction is counterclockwise. By default it is 360.
|
| scalars : str, optional
| The string name of the variable in the input dataset to
| probe. The active scalar is used by default.
|
| title : str, optional
| The string title of the `matplotlib` figure.
|
| ylabel : str, optional
| The string label of the Y-axis. Defaults to variable name.
|
| figsize : tuple(int), optional
| The size of the new figure.
|
| figure : bool, optional
| Flag on whether or not to create a new figure.
|
| show : bool, default: True
| Shows the matplotlib figure.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is
| in a cell of the input. If not given, tolerance is
| automatically generated.
|
| fname : str, optional
| Save the figure this file name when set.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Examples
| --------
| Sample a dataset along a high resolution circular arc and plot.
|
| >>> from pyvista import examples
| >>> mesh = examples.load_uniform()
| >>> normal = normal = [0, 0, 1]
| >>> polar = [0, 9, 0]
| >>> angle = 90
| >>> center = [mesh.bounds[0], mesh.bounds[2], mesh.bounds[4]]
| >>> mesh.plot_over_circular_arc_normal(
| ... center, polar=polar, angle=angle
| ... ) # doctest:+SKIP
|
| plot_over_line(self, pointa, pointb, resolution=None, scalars=None, title=None, ylabel=None, figsize=None, figure=True, show=True, tolerance=None, fname=None, progress_bar=False)
| Sample a dataset along a high resolution line and plot.
|
| Plot the variables of interest in 2D using matplotlib where the
| X-axis is distance from Point A and the Y-axis is the variable
| of interest. Note that this filter returns ``None``.
|
| Parameters
| ----------
| pointa : sequence[float]
| Location in ``[x, y, z]``.
|
| pointb : sequence[float]
| Location in ``[x, y, z]``.
|
| resolution : int, optional
| Number of pieces to divide line into. Defaults to number of cells
| in the input mesh. Must be a positive integer.
|
| scalars : str, optional
| The string name of the variable in the input dataset to probe. The
| active scalar is used by default.
|
| title : str, optional
| The string title of the matplotlib figure.
|
| ylabel : str, optional
| The string label of the Y-axis. Defaults to variable name.
|
| figsize : tuple(int), optional
| The size of the new figure.
|
| figure : bool, default: True
| Flag on whether or not to create a new figure.
|
| show : bool, default: True
| Shows the matplotlib figure.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is in a
| cell of the input. If not given, tolerance is automatically generated.
|
| fname : str, optional
| Save the figure this file name when set.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Examples
| --------
| See the :ref:`plot_over_line_example` example.
|
| point_data_to_cell_data(self, pass_point_data=False, progress_bar=False)
| Transform point data into cell data.
|
| Point data are specified per node and cell data specified within cells.
| Optionally, the input point data can be passed through to the output.
|
| Parameters
| ----------
| pass_point_data : bool, default: False
| If enabled, pass the input point data through to the output.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with the point data transformed into cell data.
| Return type matches input.
|
| See Also
| --------
| cell_data_to_point_data
| Similar transformation applied to cell data.
| :meth:`~pyvista.ImageDataFilters.points_to_cells`
| Re-mesh :class:`~pyvista.ImageData` to a cells-based representation.
|
| Examples
| --------
| Color cells by their z coordinates. First, create point
| scalars based on z-coordinates of a sample sphere mesh. Then
| convert this point data to cell data. Use a low resolution
| sphere for emphasis of cell valued data.
|
| First, plot these values as point values to show the
| difference between point and cell data.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere(theta_resolution=10, phi_resolution=10)
| >>> sphere['Z Coordinates'] = sphere.points[:, 2]
| >>> sphere.plot()
|
| Now, convert these values to cell data and then plot it.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere(theta_resolution=10, phi_resolution=10)
| >>> sphere['Z Coordinates'] = sphere.points[:, 2]
| >>> sphere = sphere.point_data_to_cell_data()
| >>> sphere.plot()
|
| ptc(self, pass_point_data=False, progress_bar=False, **kwargs)
| Transform point data into cell data.
|
| Point data are specified per node and cell data specified
| within cells. Optionally, the input point data can be passed
| through to the output.
|
| This method is an alias for
| :func:`pyvista.DataSetFilters.point_data_to_cell_data`.
|
| Parameters
| ----------
| pass_point_data : bool, default: False
| If enabled, pass the input point data through to the output.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| **kwargs : dict, optional
| Deprecated keyword argument ``pass_point_arrays``.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with the point data transformed into cell data.
| Return type matches input.
|
| reflect(self, normal, point=None, inplace=False, transform_all_input_vectors=False, progress_bar=False)
| Reflect a dataset across a plane.
|
| Parameters
| ----------
| normal : array_like[float]
| Normal direction for reflection.
|
| point : array_like[float]
| Point which, along with ``normal``, defines the reflection
| plane. If not specified, this is the origin.
|
| inplace : bool, default: False
| When ``True``, modifies the dataset inplace.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all input vectors are transformed. Otherwise,
| only the points, normals and active vectors are transformed.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Reflected dataset. Return type matches input.
|
| Examples
| --------
| >>> from pyvista import examples
| >>> mesh = examples.load_airplane()
| >>> mesh = mesh.reflect((0, 0, 1), point=(0, 0, -100))
| >>> mesh.plot(show_edges=True)
|
| See the :ref:`reflect_example` for more examples using this filter.
|
| sample(self, target, tolerance=None, pass_cell_data=True, pass_point_data=True, categorical=False, progress_bar=False, locator=None, pass_field_data=True, mark_blank=True, snap_to_closest_point=False)
| Resample array data from a passed mesh onto this mesh.
|
| For `mesh1.sample(mesh2)`, the arrays from `mesh2` are sampled onto
| the points of `mesh1`. This function interpolates within an
| enclosing cell. This contrasts with
| :function`pyvista.DataSetFilters.interpolate` that uses a distance
| weighting for nearby points. If there is cell topology, `sample` is
| usually preferred.
|
| The point data 'vtkValidPointMask' stores whether the point could be sampled
| with a value of 1 meaning successful sampling. And a value of 0 means
| unsuccessful.
|
| This uses :class:`vtk.vtkResampleWithDataSet`.
|
| Parameters
| ----------
| target : pyvista.DataSet
| The vtk data object to sample from - point and cell arrays from
| this object are sampled onto the nodes of the ``dataset`` mesh.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is
| in a cell of the input. If not given, tolerance is
| automatically generated.
|
| pass_cell_data : bool, default: True
| Preserve source mesh's original cell data arrays.
|
| pass_point_data : bool, default: True
| Preserve source mesh's original point data arrays.
|
| categorical : bool, default: False
| Control whether the source point data is to be treated as
| categorical. If the data is categorical, then the resultant data
| will be determined by a nearest neighbor interpolation scheme.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| locator : vtkAbstractCellLocator or str, optional
| Prototype cell locator to perform the ``FindCell()``
| operation. Default uses the DataSet ``FindCell`` method.
| Valid strings with mapping to vtk cell locators are
|
| * 'cell' - vtkCellLocator
| * 'cell_tree' - vtkCellTreeLocator
| * 'obb_tree' - vtkOBBTree
| * 'static_cell' - vtkStaticCellLocator
|
| pass_field_data : bool, default: True
| Preserve source mesh's original field data arrays.
|
| mark_blank : bool, default: True
| Whether to mark blank points and cells in "vtkGhostType".
|
| snap_to_closest_point : bool, default: False
| Whether to snap to cell with closest point if no cell is found. Useful
| when sampling from data with vertex cells. Requires vtk >=9.3.0.
|
| .. versionadded:: 0.43
|
| Returns
| -------
| pyvista.DataSet
| Dataset containing resampled data.
|
| See Also
| --------
| pyvista.DataSetFilters.interpolate
|
| Examples
| --------
| Resample data from another dataset onto a sphere.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> mesh = pv.Sphere(center=(4.5, 4.5, 4.5), radius=4.5)
| >>> data_to_probe = examples.load_uniform()
| >>> result = mesh.sample(data_to_probe)
| >>> result.plot(scalars="Spatial Point Data")
|
| If sampling from a set of points represented by a ``(n, 3)``
| shaped ``numpy.ndarray``, they need to be converted to a
| PyVista DataSet, e.g. :class:`pyvista.PolyData`, first.
|
| >>> import numpy as np
| >>> points = np.array([[1.5, 5.0, 6.2], [6.7, 4.2, 8.0]])
| >>> mesh = pv.PolyData(points)
| >>> result = mesh.sample(data_to_probe)
| >>> result["Spatial Point Data"]
| pyvista_ndarray([ 46.5 , 225.12])
|
| See :ref:`resampling_example` for more examples using this filter.
|
| sample_over_circular_arc(self, pointa, pointb, center, resolution=None, tolerance=None, progress_bar=False)
| Sample a dataset over a circular arc.
|
| Parameters
| ----------
| pointa : sequence[float]
| Location in ``[x, y, z]``.
|
| pointb : sequence[float]
| Location in ``[x, y, z]``.
|
| center : sequence[float]
| Location in ``[x, y, z]``.
|
| resolution : int, optional
| Number of pieces to divide circular arc into. Defaults to
| number of cells in the input mesh. Must be a positive
| integer.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is
| in a cell of the input. If not given, tolerance is
| automatically generated.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Arc containing the sampled data.
|
| Examples
| --------
| Sample a dataset over a circular arc and plot it.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> uniform = examples.load_uniform()
| >>> uniform["height"] = uniform.points[:, 2]
| >>> pointa = [
| ... uniform.bounds[1],
| ... uniform.bounds[2],
| ... uniform.bounds[5],
| ... ]
| >>> pointb = [
| ... uniform.bounds[1],
| ... uniform.bounds[3],
| ... uniform.bounds[4],
| ... ]
| >>> center = [
| ... uniform.bounds[1],
| ... uniform.bounds[2],
| ... uniform.bounds[4],
| ... ]
| >>> sampled_arc = uniform.sample_over_circular_arc(
| ... pointa, pointb, center
| ... )
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(uniform, style='wireframe')
| >>> _ = pl.add_mesh(sampled_arc, line_width=10)
| >>> pl.show_axes()
| >>> pl.show()
|
| sample_over_circular_arc_normal(self, center, resolution=None, normal=None, polar=None, angle=None, tolerance=None, progress_bar=False)
| Sample a dataset over a circular arc defined by a normal and polar vector and plot it.
|
| The number of segments composing the polyline is controlled by
| setting the object resolution.
|
| Parameters
| ----------
| center : sequence[float]
| Location in ``[x, y, z]``.
|
| resolution : int, optional
| Number of pieces to divide circular arc into. Defaults to
| number of cells in the input mesh. Must be a positive
| integer.
|
| normal : sequence[float], optional
| The normal vector to the plane of the arc. By default it
| points in the positive Z direction.
|
| polar : sequence[float], optional
| Starting point of the arc in polar coordinates. By
| default it is the unit vector in the positive x direction.
|
| angle : float, optional
| Arc length (in degrees), beginning at the polar vector. The
| direction is counterclockwise. By default it is 360.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is
| in a cell of the input. If not given, tolerance is
| automatically generated.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Sampled Dataset.
|
| Examples
| --------
| Sample a dataset over a circular arc.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> uniform = examples.load_uniform()
| >>> uniform["height"] = uniform.points[:, 2]
| >>> normal = [0, 0, 1]
| >>> polar = [0, 9, 0]
| >>> center = [
| ... uniform.bounds[1],
| ... uniform.bounds[2],
| ... uniform.bounds[5],
| ... ]
| >>> arc = uniform.sample_over_circular_arc_normal(
| ... center, normal=normal, polar=polar
| ... )
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(uniform, style='wireframe')
| >>> _ = pl.add_mesh(arc, line_width=10)
| >>> pl.show_axes()
| >>> pl.show()
|
| sample_over_line(self, pointa, pointb, resolution=None, tolerance=None, progress_bar=False)
| Sample a dataset onto a line.
|
| Parameters
| ----------
| pointa : sequence[float]
| Location in ``[x, y, z]``.
|
| pointb : sequence[float]
| Location in ``[x, y, z]``.
|
| resolution : int, optional
| Number of pieces to divide line into. Defaults to number of cells
| in the input mesh. Must be a positive integer.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is in a
| cell of the input. If not given, tolerance is automatically generated.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Line object with sampled data from dataset.
|
| Examples
| --------
| Sample over a plane that is interpolating a point cloud.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> rng = np.random.default_rng(12)
| >>> point_cloud = rng.random((5, 3))
| >>> point_cloud[:, 2] = 0
| >>> point_cloud -= point_cloud.mean(0)
| >>> pdata = pv.PolyData(point_cloud)
| >>> pdata['values'] = rng.random(5)
| >>> plane = pv.Plane()
| >>> plane.clear_data()
| >>> plane = plane.interpolate(pdata, sharpness=3.5)
| >>> sample = plane.sample_over_line((-0.5, -0.5, 0), (0.5, 0.5, 0))
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(
| ... pdata, render_points_as_spheres=True, point_size=50
| ... )
| >>> _ = pl.add_mesh(sample, scalars='values', line_width=10)
| >>> _ = pl.add_mesh(plane, scalars='values', style='wireframe')
| >>> pl.show()
|
| sample_over_multiple_lines(self, points, tolerance=None, progress_bar=False)
| Sample a dataset onto a multiple lines.
|
| Parameters
| ----------
| points : array_like[float]
| List of points defining multiple lines.
|
| tolerance : float, optional
| Tolerance used to compute whether a point in the source is in a
| cell of the input. If not given, tolerance is automatically generated.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Line object with sampled data from dataset.
|
| Examples
| --------
| Sample over a plane that is interpolating a point cloud.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> rng = np.random.default_rng(12)
| >>> point_cloud = rng.random((5, 3))
| >>> point_cloud[:, 2] = 0
| >>> point_cloud -= point_cloud.mean(0)
| >>> pdata = pv.PolyData(point_cloud)
| >>> pdata['values'] = rng.random(5)
| >>> plane = pv.Plane()
| >>> plane.clear_data()
| >>> plane = plane.interpolate(pdata, sharpness=3.5)
| >>> sample = plane.sample_over_multiple_lines(
| ... [[-0.5, -0.5, 0], [0.5, -0.5, 0], [0.5, 0.5, 0]]
| ... )
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(
| ... pdata, render_points_as_spheres=True, point_size=50
| ... )
| >>> _ = pl.add_mesh(sample, scalars='values', line_width=10)
| >>> _ = pl.add_mesh(plane, scalars='values', style='wireframe')
| >>> pl.show()
|
| select_enclosed_points(self, surface, tolerance=0.001, inside_out=False, check_surface=True, progress_bar=False)
| Mark points as to whether they are inside a closed surface.
|
| This evaluates all the input points to determine whether they are in an
| enclosed surface. The filter produces a (0,1) mask
| (in the form of a vtkDataArray) that indicates whether points are
| outside (mask value=0) or inside (mask value=1) a provided surface.
| (The name of the output vtkDataArray is ``"SelectedPoints"``.)
|
| This filter produces and output data array, but does not modify the
| input dataset. If you wish to extract cells or poinrs, various
| threshold filters are available (i.e., threshold the output array).
|
| .. warning::
| The filter assumes that the surface is closed and
| manifold. A boolean flag can be set to force the filter to
| first check whether this is true. If ``False`` and not manifold,
| an error will be raised.
|
| Parameters
| ----------
| surface : pyvista.PolyData
| Set the surface to be used to test for containment. This must be a
| :class:`pyvista.PolyData` object.
|
| tolerance : float, default: 0.001
| The tolerance on the intersection. The tolerance is expressed as a
| fraction of the bounding box of the enclosing surface.
|
| inside_out : bool, default: False
| By default, points inside the surface are marked inside or sent
| to the output. If ``inside_out`` is ``True``, then the points
| outside the surface are marked inside.
|
| check_surface : bool, default: True
| Specify whether to check the surface for closure. When ``True``, the
| algorithm first checks to see if the surface is closed and
| manifold. If the surface is not closed and manifold, a runtime
| error is raised.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Mesh containing the ``point_data['SelectedPoints']`` array.
|
| Examples
| --------
| Determine which points on a plane are inside a manifold sphere
| surface mesh. Extract these points using the
| :func:`DataSetFilters.extract_points` filter and then plot them.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere()
| >>> plane = pv.Plane()
| >>> selected = plane.select_enclosed_points(sphere)
| >>> pts = plane.extract_points(
| ... selected['SelectedPoints'].view(bool),
| ... adjacent_cells=False,
| ... )
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(sphere, style='wireframe')
| >>> _ = pl.add_points(pts, color='r')
| >>> pl.show()
|
| separate_cells(self)
| Return a copy of the dataset with separated cells with no shared points.
|
| This method may be useful when datasets have scalars that need to be
| associated to each point of each cell rather than either each cell or
| just the points of the dataset.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| UnstructuredGrid with isolated cells.
|
| Examples
| --------
| Load the example hex beam and separate its cells. This increases the
| total number of points in the dataset since points are no longer
| shared.
|
| >>> from pyvista import examples
| >>> grid = examples.load_hexbeam()
| >>> grid.n_points
| 99
| >>> sep_grid = grid.separate_cells()
| >>> sep_grid.n_points
| 320
|
| See the :ref:`point_cell_scalars_example` for a more detailed example
| using this filter.
|
| shrink(self, shrink_factor=1.0, progress_bar=False)
| Shrink the individual faces of a mesh.
|
| This filter shrinks the individual faces of a mesh rather than
| scaling the entire mesh.
|
| Parameters
| ----------
| shrink_factor : float, default: 1.0
| Fraction of shrink for each cell. Default does not modify the
| faces.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with shrunk faces. Return type matches input.
|
| Examples
| --------
| First, plot the original cube.
|
| >>> import pyvista as pv
| >>> mesh = pv.Cube()
| >>> mesh.plot(show_edges=True, line_width=5)
|
| Now, plot the mesh with shrunk faces.
|
| >>> shrunk = mesh.shrink(0.5)
| >>> shrunk.clear_data() # cleans up plot
| >>> shrunk.plot(show_edges=True, line_width=5)
|
| slice(self, normal='x', origin=None, generate_triangles=False, contour=False, progress_bar=False)
| Slice a dataset by a plane at the specified origin and normal vector orientation.
|
| If no origin is specified, the center of the input dataset will be used.
|
| Parameters
| ----------
| normal : sequence[float] | str, default: 'x'
| Length 3 tuple for the normal vector direction. Can also be
| specified as a string conventional direction such as ``'x'`` for
| ``(1, 0, 0)`` or ``'-x'`` for ``(-1, 0, 0)``, etc.
|
| origin : sequence[float], optional
| The center ``(x, y, z)`` coordinate of the plane on which
| the slice occurs.
|
| generate_triangles : bool, default: False
| If this is enabled (``False`` by default), the output will
| be triangles. Otherwise the output will be the intersection
| polygons.
|
| contour : bool, default: False
| If ``True``, apply a ``contour`` filter after slicing.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Sliced dataset.
|
| Examples
| --------
| Slice the surface of a sphere.
|
| >>> import pyvista as pv
| >>> sphere = pv.Sphere()
| >>> slice_x = sphere.slice(normal='x')
| >>> slice_y = sphere.slice(normal='y')
| >>> slice_z = sphere.slice(normal='z')
| >>> slices = slice_x + slice_y + slice_z
| >>> slices.plot(line_width=5)
|
| See :ref:`slice_example` for more examples using this filter.
|
| slice_along_axis(self, n=5, axis='x', tolerance=None, generate_triangles=False, contour=False, bounds=None, center=None, progress_bar=False)
| Create many slices of the input dataset along a specified axis.
|
| Parameters
| ----------
| n : int, default: 5
| The number of slices to create.
|
| axis : str | int, default: 'x'
| The axis to generate the slices along. Perpendicular to the
| slices. Can be string name (``'x'``, ``'y'``, or ``'z'``) or
| axis index (``0``, ``1``, or ``2``).
|
| tolerance : float, optional
| The tolerance to the edge of the dataset bounds to create
| the slices. The ``n`` slices are placed equidistantly with
| an absolute padding of ``tolerance`` inside each side of the
| ``bounds`` along the specified axis. Defaults to 1% of the
| ``bounds`` along the specified axis.
|
| generate_triangles : bool, default: False
| When ``True``, the output will be triangles. Otherwise the output
| will be the intersection polygons.
|
| contour : bool, default: False
| If ``True``, apply a ``contour`` filter after slicing.
|
| bounds : sequence[float], optional
| A 6-length sequence overriding the bounds of the mesh.
| The bounds along the specified axis define the extent
| where slices are taken.
|
| center : sequence[float], optional
| A 3-length sequence specifying the position of the line
| along which slices are taken. Defaults to the center of
| the mesh.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Sliced dataset.
|
| Examples
| --------
| Slice the random hills dataset in the X direction.
|
| >>> from pyvista import examples
| >>> hills = examples.load_random_hills()
| >>> slices = hills.slice_along_axis(n=10)
| >>> slices.plot(line_width=5)
|
| Slice the random hills dataset in the Z direction.
|
| >>> from pyvista import examples
| >>> hills = examples.load_random_hills()
| >>> slices = hills.slice_along_axis(n=10, axis='z')
| >>> slices.plot(line_width=5)
|
| See :ref:`slice_example` for more examples using this filter.
|
| slice_along_line(self, line, generate_triangles=False, contour=False, progress_bar=False)
| Slice a dataset using a polyline/spline as the path.
|
| This also works for lines generated with :func:`pyvista.Line`.
|
| Parameters
| ----------
| line : pyvista.PolyData
| A PolyData object containing one single PolyLine cell.
|
| generate_triangles : bool, default: False
| When ``True``, the output will be triangles. Otherwise the output
| will be the intersection polygons.
|
| contour : bool, default: False
| If ``True``, apply a ``contour`` filter after slicing.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Sliced dataset.
|
| Examples
| --------
| Slice the random hills dataset along a circular arc.
|
| >>> import numpy as np
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> hills = examples.load_random_hills()
| >>> center = np.array(hills.center)
| >>> point_a = center + np.array([5, 0, 0])
| >>> point_b = center + np.array([-5, 0, 0])
| >>> arc = pv.CircularArc(point_a, point_b, center, resolution=100)
| >>> line_slice = hills.slice_along_line(arc)
|
| Plot the circular arc and the hills mesh.
|
| >>> pl = pv.Plotter()
| >>> _ = pl.add_mesh(hills, smooth_shading=True, style='wireframe')
| >>> _ = pl.add_mesh(
| ... line_slice,
| ... line_width=10,
| ... render_lines_as_tubes=True,
| ... color='k',
| ... )
| >>> _ = pl.add_mesh(arc, line_width=10, color='grey')
| >>> pl.show()
|
| See :ref:`slice_example` for more examples using this filter.
|
| slice_implicit(self, implicit_function, generate_triangles=False, contour=False, progress_bar=False)
| Slice a dataset by a VTK implicit function.
|
| Parameters
| ----------
| implicit_function : vtk.vtkImplicitFunction
| Specify the implicit function to perform the cutting.
|
| generate_triangles : bool, default: False
| If this is enabled (``False`` by default), the output will
| be triangles. Otherwise the output will be the intersection
| polygons. If the cutting function is not a plane, the
| output will be 3D polygons, which might be nice to look at
| but hard to compute with downstream.
|
| contour : bool, default: False
| If ``True``, apply a ``contour`` filter after slicing.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Sliced dataset.
|
| Examples
| --------
| Slice the surface of a sphere.
|
| >>> import pyvista as pv
| >>> import vtk
| >>> sphere = vtk.vtkSphere()
| >>> sphere.SetRadius(10)
| >>> mesh = pv.Wavelet()
| >>> slice = mesh.slice_implicit(sphere)
| >>> slice.plot(show_edges=True, line_width=5)
|
| >>> cylinder = vtk.vtkCylinder()
| >>> cylinder.SetRadius(10)
| >>> mesh = pv.Wavelet()
| >>> slice = mesh.slice_implicit(cylinder)
| >>> slice.plot(show_edges=True, line_width=5)
|
| slice_orthogonal(self, x=None, y=None, z=None, generate_triangles=False, contour=False, progress_bar=False)
| Create three orthogonal slices through the dataset on the three cartesian planes.
|
| Yields a MutliBlock dataset of the three slices.
|
| Parameters
| ----------
| x : float, optional
| The X location of the YZ slice.
|
| y : float, optional
| The Y location of the XZ slice.
|
| z : float, optional
| The Z location of the XY slice.
|
| generate_triangles : bool, default: False
| When ``True``, the output will be triangles. Otherwise the output
| will be the intersection polygons.
|
| contour : bool, default: False
| If ``True``, apply a ``contour`` filter after slicing.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Sliced dataset.
|
| Examples
| --------
| Slice the random hills dataset with three orthogonal planes.
|
| >>> from pyvista import examples
| >>> hills = examples.load_random_hills()
| >>> slices = hills.slice_orthogonal(contour=False)
| >>> slices.plot(line_width=5)
|
| See :ref:`slice_example` for more examples using this filter.
|
| sort_labels(self, scalars=None, preference='point', output_scalars=None, progress_bar=False, inplace=False)
| Sort labeled data by number of points or cells.
|
| This filter renumbers scalar label data of any type with ``N`` labels
| such that the output labels are contiguous from ``[0, N)`` and
| sorted in descending order from largest to smallest (by label count).
| I.e., the largest label will have a value of ``0`` and the smallest
| label will have a value of ``N-1``.
|
| The filter is a convenience method for :func:`pyvista.DataSetFilters.pack_labels`
| with ``sort=True``.
|
| Parameters
| ----------
| scalars : str, optional
| Name of scalars to sort. Defaults to currently active scalars.
|
| preference : str, default: "point"
| When ``scalars`` is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'`` or ``'cell'``.
|
| output_scalars : str, None
| Name of the sorted output scalars. By default, the output is
| saved to ``'packed_labels'``.
|
| progress_bar : bool, default: False
| If ``True``, display a progress bar. Has no effect if VTK
| version is lower than 9.3.
|
| inplace : bool, default: False
| If ``True``, the mesh is updated in-place.
|
| Returns
| -------
| pyvista.Dataset
| Dataset with sorted labels.
|
| Examples
| --------
| Sort segmented image labels.
|
| Load image labels
|
| >>> from pyvista import examples
| >>> import numpy as np
| >>> image_labels = examples.load_frog_tissues()
|
| Show label info for first four labels
|
| >>> label_number, label_size = np.unique(
| ... image_labels['MetaImage'], return_counts=True
| ... )
| >>> label_number[:4]
| pyvista_ndarray([0, 1, 2, 3], dtype=uint8)
| >>> label_size[:4]
| array([30805713, 35279, 19172, 38129])
|
| Sort labels
|
| >>> sorted_labels = image_labels.sort_labels()
|
| Show sorted label info for the four largest labels. Note
| the difference in label size after sorting.
|
| >>> sorted_label_number, sorted_label_size = np.unique(
| ... sorted_labels["packed_labels"], return_counts=True
| ... )
| >>> sorted_label_number[:4]
| pyvista_ndarray([0, 1, 2, 3], dtype=uint8)
| >>> sorted_label_size[:4]
| array([30805713, 438052, 204672, 133880])
|
| split_bodies(self, label=False, progress_bar=False)
| Find, label, and split connected bodies/volumes.
|
| This splits different connected bodies into blocks in a
| :class:`pyvista.MultiBlock` dataset.
|
| Parameters
| ----------
| label : bool, default: False
| A flag on whether to keep the ID arrays given by the
| ``connectivity`` filter.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| See Also
| --------
| extract_values, partition, connectivity
|
| Returns
| -------
| pyvista.MultiBlock
| MultiBlock with a split bodies.
|
| Examples
| --------
| Split a uniform grid thresholded to be non-connected.
|
| >>> from pyvista import examples
| >>> dataset = examples.load_uniform()
| >>> _ = dataset.set_active_scalars('Spatial Cell Data')
| >>> threshed = dataset.threshold_percent([0.15, 0.50], invert=True)
| >>> bodies = threshed.split_bodies()
| >>> len(bodies)
| 2
|
| See :ref:`split_vol` for more examples using this filter.
|
| split_values(self, values: 'None | (float | VectorLike[float] | MatrixLike[float] | dict[str, float] | dict[float, str])' = None, *, ranges: 'None | (VectorLike[float] | MatrixLike[float] | dict[str, VectorLike[float]] | dict[tuple[float, float], str])' = None, scalars: 'str | None' = None, preference: "Literal['point', 'cell']" = 'point', component_mode: "Literal['any', 'all', 'multi'] | int" = 'all', **kwargs)
| Split mesh into separate sub-meshes using point or cell data.
|
| By default, this filter generates a separate mesh for each unique value in the
| data array and combines them as blocks in a :class:`~pyvista.MultiBlock`
| dataset. Optionally, specific values and/or ranges of values may be specified to
| control which values to split from the input.
|
| This filter is a convenience method for :meth:`~pyvista.DataSetFilter.extract_values`
| with ``split`` set to ``True`` by default. Refer to that filter's documentation
| for more details.
|
| .. versionadded:: 0.44
|
| Parameters
| ----------
| values : number | array_like | dict, optional
| Value(s) to extract. Can be a number, an iterable of numbers, or a dictionary
| with numeric entries. For ``dict`` inputs, either its keys or values may be
| numeric, and the other field must be strings. The numeric field is used as
| the input for this parameter, and if ``split`` is ``True``, the string field
| is used to set the block names of the returned :class:`~pyvista.MultiBlock`.
|
| .. note::
| When extracting multi-component values with ``component_mode=multi``,
| each value is specified as a multi-component scalar. In this case,
| ``values`` can be a single vector or an array of row vectors.
|
| ranges : array_like | dict, optional
| Range(s) of values to extract. Can be a single range (i.e. a sequence of
| two numbers in the form ``[lower, upper]``), a sequence of ranges, or a
| dictionary with range entries. Any combination of ``values`` and ``ranges``
| may be specified together. The endpoints of the ranges are included in the
| extraction. Ranges cannot be set when ``component_mode=multi``.
|
| For ``dict`` inputs, either its keys or values may be numeric, and the other
| field must be strings. The numeric field is used as the input for this
| parameter, and if ``split`` is ``True``, the string field is used to set the
| block names of the returned :class:`~pyvista.MultiBlock`.
|
| .. note::
| Use ``+/-`` infinity to specify an unlimited bound, e.g.:
|
| - ``[0, float('inf')]`` to extract values greater than or equal to zero.
| - ``[float('-inf'), 0]`` to extract values less than or equal to zero.
|
| scalars : str, optional
| Name of scalars to extract with. Defaults to currently active scalars.
|
| preference : str, default: 'point'
| When ``scalars`` is specified, this is the preferred array type to search
| for in the dataset. Must be either ``'point'`` or ``'cell'``.
|
| component_mode : int | 'any' | 'all' | 'multi', default: 'all'
| Specify the component(s) to use when ``scalars`` is a multi-component array.
| Has no effect when the scalars have a single component. Must be one of:
|
| - number: specify the component number as a 0-indexed integer. The selected
| component must have the specified value(s).
| - ``'any'``: any single component can have the specified value(s).
| - ``'all'``: all individual components must have the specified values(s).
| - ``'multi'``: the entire multi-component item must have the specified value.
|
| **kwargs : dict, optional
| Additional keyword arguments passed to :meth:`~pyvista.DataSetFilter.extract_values`.
|
| See Also
| --------
| extract_values, split_bodies, partition
|
| Returns
| -------
| pyvista.MultiBlock
| Composite of split meshes with :class:`pyvista.UnstructuredGrid` blocks.
|
| Examples
| --------
| Load image with labeled regions.
|
| >>> import numpy as np
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> image = examples.load_channels()
| >>> np.unique(image.active_scalars)
| pyvista_ndarray([0, 1, 2, 3, 4])
|
| Split the image into its separate regions. Here, we also remove the first
| region for visualization.
|
| >>> multiblock = image.split_values()
| >>> _ = multiblock.pop(0) # Remove first region
|
| Plot the regions.
|
| >>> plot = pv.Plotter()
| >>> _ = plot.add_composite(multiblock, multi_colors=True)
| >>> _ = plot.show_grid()
| >>> plot.show()
|
| Note that the block names are generic by default.
|
| >>> multiblock.keys()
| ['Block-01', 'Block-02', 'Block-03', 'Block-04']
|
| To name the output blocks, use a dictionary as input instead.
|
| Here, we also explicitly omit the region with ``0`` values from the input
| instead of removing it from the output.
|
| >>> labels = dict(region1=1, region2=2, region3=3, region4=4)
| >>>
| >>> multiblock = image.split_values(labels)
| >>> multiblock.keys()
| ['region1', 'region2', 'region3', 'region4']
|
| Plot the regions as separate meshes using the labels instead of plotting
| the MultiBlock directly.
|
| Clear scalar data so we can color each mesh using a single color
| >>> _ = [block.clear_data() for block in multiblock]
| >>>
| >>> plot = pv.Plotter()
| >>> plot.set_color_cycler('default')
| >>> _ = [
| ... plot.add_mesh(block, label=label)
| ... for block, label in zip(multiblock, labels)
| ... ]
| >>> _ = plot.add_legend()
| >>> plot.show()
|
| streamlines(self, vectors=None, source_center=None, source_radius=None, n_points=100, start_position=None, return_source=False, pointa=None, pointb=None, progress_bar=False, **kwargs)
| Integrate a vector field to generate streamlines.
|
| The default behavior uses a sphere as the source - set its
| location and radius via the ``source_center`` and
| ``source_radius`` keyword arguments. ``n_points`` defines the
| number of starting points on the sphere surface.
| Alternatively, a line source can be used by specifying
| ``pointa`` and ``pointb``. ``n_points`` again defines the
| number of points on the line.
|
| You can retrieve the source by specifying
| ``return_source=True``.
|
| Optional keyword parameters from
| :func:`pyvista.DataSetFilters.streamlines_from_source` can be
| used here to control the generation of streamlines.
|
| Parameters
| ----------
| vectors : str, optional
| The string name of the active vector field to integrate across.
|
| source_center : sequence[float], optional
| Length 3 tuple of floats defining the center of the source
| particles. Defaults to the center of the dataset.
|
| source_radius : float, optional
| Float radius of the source particle cloud. Defaults to one-tenth of
| the diagonal of the dataset's spatial extent.
|
| n_points : int, default: 100
| Number of particles present in source sphere or line.
|
| start_position : sequence[float], optional
| A single point. This will override the sphere point source.
|
| return_source : bool, default: False
| Return the source particles as :class:`pyvista.PolyData` as well as the
| streamlines. This will be the second value returned if ``True``.
|
| pointa, pointb : sequence[float], optional
| The coordinates of a start and end point for a line source. This
| will override the sphere and start_position point source.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| **kwargs : dict, optional
| See :func:`pyvista.DataSetFilters.streamlines_from_source`.
|
| Returns
| -------
| streamlines : pyvista.PolyData
| This produces polylines as the output, with each cell
| (i.e., polyline) representing a streamline. The attribute values
| associated with each streamline are stored in the cell data, whereas
| those associated with streamline-points are stored in the point data.
|
| source : pyvista.PolyData
| The points of the source are the seed points for the streamlines.
| Only returned if ``return_source=True``.
|
| Examples
| --------
| See the :ref:`streamlines_example` example.
|
| streamlines_evenly_spaced_2D(self, vectors=None, start_position=None, integrator_type=2, step_length=0.5, step_unit='cl', max_steps=2000, terminal_speed=1e-12, interpolator_type='point', separating_distance=10, separating_distance_ratio=0.5, closed_loop_maximum_distance=0.5, loop_angle=20, minimum_number_of_loop_points=4, compute_vorticity=True, progress_bar=False)
| Generate evenly spaced streamlines on a 2D dataset.
|
| This filter only supports datasets that lie on the xy plane, i.e. ``z=0``.
| Particular care must be used to choose a `separating_distance`
| that do not result in too much memory being utilized. The
| default unit is cell length.
|
| Parameters
| ----------
| vectors : str, optional
| The string name of the active vector field to integrate across.
|
| start_position : sequence[float], optional
| The seed point for generating evenly spaced streamlines.
| If not supplied, a random position in the dataset is chosen.
|
| integrator_type : {2, 4}, default: 2
| The integrator type to be used for streamline generation.
| The default is Runge-Kutta2. The recognized solvers are:
| RUNGE_KUTTA2 (``2``) and RUNGE_KUTTA4 (``4``).
|
| step_length : float, default: 0.5
| Constant Step size used for line integration, expressed in length
| units or cell length units (see ``step_unit`` parameter).
|
| step_unit : {'cl', 'l'}, default: "cl"
| Uniform integration step unit. The valid unit is now limited to
| only LENGTH_UNIT (``'l'``) and CELL_LENGTH_UNIT (``'cl'``).
| Default is CELL_LENGTH_UNIT.
|
| max_steps : int, default: 2000
| Maximum number of steps for integrating a streamline.
|
| terminal_speed : float, default: 1e-12
| Terminal speed value, below which integration is terminated.
|
| interpolator_type : str, optional
| Set the type of the velocity field interpolator to locate cells
| during streamline integration either by points or cells.
| The cell locator is more robust then the point locator. Options
| are ``'point'`` or ``'cell'`` (abbreviations of ``'p'`` and ``'c'``
| are also supported).
|
| separating_distance : float, default: 10
| The distance between streamlines expressed in ``step_unit``.
|
| separating_distance_ratio : float, default: 0.5
| Streamline integration is stopped if streamlines are closer than
| ``SeparatingDistance*SeparatingDistanceRatio`` to other streamlines.
|
| closed_loop_maximum_distance : float, default: 0.5
| The distance between points on a streamline to determine a
| closed loop.
|
| loop_angle : float, default: 20
| The maximum angle in degrees between points to determine a closed loop.
|
| minimum_number_of_loop_points : int, default: 4
| The minimum number of points before which a closed loop will
| be determined.
|
| compute_vorticity : bool, default: True
| Vorticity computation at streamline points. Necessary for generating
| proper stream-ribbons using the ``vtkRibbonFilter``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| This produces polylines as the output, with each cell
| (i.e., polyline) representing a streamline. The attribute
| values associated with each streamline are stored in the
| cell data, whereas those associated with streamline-points
| are stored in the point data.
|
| Examples
| --------
| Plot evenly spaced streamlines for cylinder in a crossflow.
| This dataset is a multiblock dataset, and the fluid velocity is in the
| first block.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> mesh = examples.download_cylinder_crossflow()
| >>> streams = mesh[0].streamlines_evenly_spaced_2D(
| ... start_position=(4, 0.1, 0.0),
| ... separating_distance=3,
| ... separating_distance_ratio=0.2,
| ... )
| >>> plotter = pv.Plotter()
| >>> _ = plotter.add_mesh(
| ... streams.tube(radius=0.02), scalars="vorticity_mag"
| ... )
| >>> plotter.view_xy()
| >>> plotter.show()
|
| See :ref:`2d_streamlines_example` for more examples using this filter.
|
| streamlines_from_source(self, source, vectors=None, integrator_type=45, integration_direction='both', surface_streamlines=False, initial_step_length=0.5, step_unit='cl', min_step_length=0.01, max_step_length=1.0, max_steps=2000, terminal_speed=1e-12, max_error=1e-06, max_time=None, compute_vorticity=True, rotation_scale=1.0, interpolator_type='point', progress_bar=False)
| Generate streamlines of vectors from the points of a source mesh.
|
| The integration is performed using a specified integrator, by default
| Runge-Kutta2. This supports integration through any type of dataset.
| If the dataset contains 2D cells like polygons or triangles and the
| ``surface_streamlines`` parameter is used, the integration is constrained
| to lie on the surface defined by 2D cells.
|
| Parameters
| ----------
| source : pyvista.DataSet
| The points of the source provide the starting points of the
| streamlines. This will override both sphere and line sources.
|
| vectors : str, optional
| The string name of the active vector field to integrate across.
|
| integrator_type : {45, 2, 4}, default: 45
| The integrator type to be used for streamline generation.
| The default is Runge-Kutta45. The recognized solvers are:
| RUNGE_KUTTA2 (``2``), RUNGE_KUTTA4 (``4``), and RUNGE_KUTTA45
| (``45``). Options are ``2``, ``4``, or ``45``.
|
| integration_direction : str, default: "both"
| Specify whether the streamline is integrated in the upstream or
| downstream directions (or both). Options are ``'both'``,
| ``'backward'``, or ``'forward'``.
|
| surface_streamlines : bool, default: False
| Compute streamlines on a surface.
|
| initial_step_length : float, default: 0.5
| Initial step size used for line integration, expressed ib length
| unitsL or cell length units (see ``step_unit`` parameter).
| either the starting size for an adaptive integrator, e.g., RK45, or
| the constant / fixed size for non-adaptive ones, i.e., RK2 and RK4).
|
| step_unit : {'cl', 'l'}, default: "cl"
| Uniform integration step unit. The valid unit is now limited to
| only LENGTH_UNIT (``'l'``) and CELL_LENGTH_UNIT (``'cl'``).
| Default is CELL_LENGTH_UNIT.
|
| min_step_length : float, default: 0.01
| Minimum step size used for line integration, expressed in length or
| cell length units. Only valid for an adaptive integrator, e.g., RK45.
|
| max_step_length : float, default: 1.0
| Maximum step size used for line integration, expressed in length or
| cell length units. Only valid for an adaptive integrator, e.g., RK45.
|
| max_steps : int, default: 2000
| Maximum number of steps for integrating a streamline.
|
| terminal_speed : float, default: 1e-12
| Terminal speed value, below which integration is terminated.
|
| max_error : float, 1e-6
| Maximum error tolerated throughout streamline integration.
|
| max_time : float, optional
| Specify the maximum length of a streamline expressed in LENGTH_UNIT.
|
| compute_vorticity : bool, default: True
| Vorticity computation at streamline points. Necessary for generating
| proper stream-ribbons using the ``vtkRibbonFilter``.
|
| rotation_scale : float, default: 1.0
| This can be used to scale the rate with which the streamribbons
| twist.
|
| interpolator_type : str, default: "point"
| Set the type of the velocity field interpolator to locate cells
| during streamline integration either by points or cells.
| The cell locator is more robust then the point locator. Options
| are ``'point'`` or ``'cell'`` (abbreviations of ``'p'`` and ``'c'``
| are also supported).
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Streamlines. This produces polylines as the output, with
| each cell (i.e., polyline) representing a streamline. The
| attribute values associated with each streamline are
| stored in the cell data, whereas those associated with
| streamline-points are stored in the point data.
|
| Examples
| --------
| See the :ref:`streamlines_example` example.
|
| surface_indices(self, progress_bar=False)
| Return the surface indices of a grid.
|
| Parameters
| ----------
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| numpy.ndarray
| Indices of the surface points.
|
| Examples
| --------
| Return the first 10 surface indices of an UnstructuredGrid.
|
| >>> from pyvista import examples
| >>> grid = examples.load_hexbeam()
| >>> ind = grid.surface_indices()
| >>> ind[:10] # doctest:+SKIP
| pyvista_ndarray([ 0, 2, 36, 27, 7, 8, 81, 1, 18, 4])
|
| tessellate(self, max_n_subdivide=3, merge_points=True, progress_bar=False)
| Tessellate a mesh.
|
| This filter approximates nonlinear FEM-like elements with linear
| simplices. The output mesh will have geometry and any fields specified
| as attributes in the input mesh's point data. The attribute's copy
| flags are honored, except for normals.
|
| For more details see `vtkTessellatorFilter <https://vtk.org/doc/nightly/html/classvtkTessellatorFilter.html#details>`_.
|
| Parameters
| ----------
| max_n_subdivide : int, default: 3
| Maximum number of subdivisions.
|
| merge_points : bool, default: True
| The adaptive tessellation will output vertices that are not shared among cells,
| even where they should be. This can be corrected to some extent.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset with tessellated mesh. Return type matches input.
|
| Examples
| --------
| First, plot the high order FEM-like elements.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> points = np.array(
| ... [
| ... [0.0, 0.0, 0.0],
| ... [2.0, 0.0, 0.0],
| ... [1.0, 2.0, 0.0],
| ... [1.0, 0.5, 0.0],
| ... [1.5, 1.5, 0.0],
| ... [0.5, 1.5, 0.0],
| ... ]
| ... )
| >>> cells = np.array([6, 0, 1, 2, 3, 4, 5])
| >>> cell_types = np.array([69])
| >>> mesh = pv.UnstructuredGrid(cells, cell_types, points)
| >>> mesh.plot(show_edges=True, line_width=5)
|
| Now, plot the tessellated mesh.
|
| >>> tessellated = mesh.tessellate()
| >>> tessellated.clear_data() # cleans up plot
| >>> tessellated.plot(show_edges=True, line_width=5)
|
| texture_map_to_plane(self, origin=None, point_u=None, point_v=None, inplace=False, name='Texture Coordinates', use_bounds=False, progress_bar=False)
| Texture map this dataset to a user defined plane.
|
| This is often used to define a plane to texture map an image
| to this dataset. The plane defines the spatial reference and
| extent of that image.
|
| Parameters
| ----------
| origin : sequence[float], optional
| Length 3 iterable of floats defining the XYZ coordinates of the
| bottom left corner of the plane.
|
| point_u : sequence[float], optional
| Length 3 iterable of floats defining the XYZ coordinates of the
| bottom right corner of the plane.
|
| point_v : sequence[float], optional
| Length 3 iterable of floats defining the XYZ coordinates of the
| top left corner of the plane.
|
| inplace : bool, default: False
| If ``True``, the new texture coordinates will be added to this
| dataset. If ``False``, a new dataset is returned with the texture
| coordinates.
|
| name : str, default: "Texture Coordinates"
| The string name to give the new texture coordinates if applying
| the filter inplace.
|
| use_bounds : bool, default: False
| Use the bounds to set the mapping plane by default (bottom plane
| of the bounding box).
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Original dataset with texture coordinates if
| ``inplace=True``, otherwise a copied dataset.
|
| Examples
| --------
| See :ref:`topo_map_example`
|
| texture_map_to_sphere(self, center=None, prevent_seam=True, inplace=False, name='Texture Coordinates', progress_bar=False)
| Texture map this dataset to a user defined sphere.
|
| This is often used to define a sphere to texture map an image
| to this dataset. The sphere defines the spatial reference and
| extent of that image.
|
| Parameters
| ----------
| center : sequence[float], optional
| Length 3 iterable of floats defining the XYZ coordinates of the
| center of the sphere. If ``None``, this will be automatically
| calculated.
|
| prevent_seam : bool, default: True
| Control how the texture coordinates are generated. If
| set, the s-coordinate ranges from 0 to 1 and 1 to 0
| corresponding to the theta angle variation between 0 to
| 180 and 180 to 0 degrees. Otherwise, the s-coordinate
| ranges from 0 to 1 between 0 to 360 degrees.
|
| inplace : bool, default: False
| If ``True``, the new texture coordinates will be added to
| the dataset inplace. If ``False`` (default), a new dataset
| is returned with the texture coordinates.
|
| name : str, default: "Texture Coordinates"
| The string name to give the new texture coordinates if applying
| the filter inplace.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Dataset containing the texture mapped to a sphere. Return
| type matches input.
|
| Examples
| --------
| See :ref:`texture_example`.
|
| threshold(self, value=None, scalars=None, invert=False, continuous=False, preference='cell', all_scalars=False, component_mode='all', component=0, method='upper', progress_bar=False)
| Apply a ``vtkThreshold`` filter to the input dataset.
|
| This filter will apply a ``vtkThreshold`` filter to the input
| dataset and return the resulting object. This extracts cells
| where the scalar value in each cell satisfies the threshold
| criterion. If ``scalars`` is ``None``, the input's active
| scalars array is used.
|
| .. warning::
| Thresholding is inherently a cell operation, even though it can use
| associated point data for determining whether to keep a cell. In
| other words, whether or not a given point is included after
| thresholding depends on whether that point is part of a cell that
| is kept after thresholding.
|
| Please also note the default ``preference`` choice for CELL data
| over POINT data. This is contrary to most other places in PyVista's
| API where the preference typically defaults to POINT data. We chose
| to prefer CELL data here so that if thresholding by a named array
| that exists for both the POINT and CELL data, this filter will
| default to the CELL data array while performing the CELL-wise
| operation.
|
| Parameters
| ----------
| value : float | sequence[float], optional
| Single value or ``(min, max)`` to be used for the data threshold. If
| a sequence, then length must be 2. If no value is specified, the
| non-NaN data range will be used to remove any NaN values.
| Please reference the ``method`` parameter for how single values
| are handled.
|
| scalars : str, optional
| Name of scalars to threshold on. Defaults to currently active scalars.
|
| invert : bool, default: False
| Invert the threshold results. That is, cells that would have been
| in the output with this option off are excluded, while cells that
| would have been excluded from the output are included.
|
| continuous : bool, default: False
| When True, the continuous interval [minimum cell scalar,
| maximum cell scalar] will be used to intersect the threshold bound,
| rather than the set of discrete scalar values from the vertices.
|
| preference : str, default: 'cell'
| When ``scalars`` is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'`` or ``'cell'``. Throughout PyVista, the preference
| is typically ``'point'`` but since the threshold filter is a
| cell-wise operation, we prefer cell data for thresholding
| operations.
|
| all_scalars : bool, default: False
| If using scalars from point data, all
| points in a cell must satisfy the threshold when this
| value is ``True``. When ``False``, any point of the cell
| with a scalar value satisfying the threshold criterion
| will extract the cell. Has no effect when using cell data.
|
| component_mode : {'selected', 'all', 'any'}
| The method to satisfy the criteria for the threshold of
| multicomponent scalars. 'selected' (default)
| uses only the ``component``. 'all' requires all
| components to meet criteria. 'any' is when
| any component satisfies the criteria.
|
| component : int, default: 0
| When using ``component_mode='selected'``, this sets
| which component to threshold on.
|
| method : str, default: 'upper'
| Set the threshold method for single-values, defining which
| threshold bounds to use. If the ``value`` is a range, this
| parameter will be ignored, extracting data between the two
| values. For single values, ``'lower'`` will extract data
| lower than the ``value``. ``'upper'`` will extract data
| larger than the ``value``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| See Also
| --------
| threshold_percent, :meth:`~pyvista.ImageDataFilters.image_threshold`, extract_values
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Dataset containing geometry that meets the threshold requirements.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> import numpy as np
| >>> volume = np.zeros([10, 10, 10])
| >>> volume[:3] = 1
| >>> vol = pv.wrap(volume)
| >>> threshed = vol.threshold(0.1)
| >>> threshed
| UnstructuredGrid (...)
| N Cells: 243
| N Points: 400
| X Bounds: 0.000e+00, 3.000e+00
| Y Bounds: 0.000e+00, 9.000e+00
| Z Bounds: 0.000e+00, 9.000e+00
| N Arrays: 1
|
| Apply the threshold filter to Perlin noise. First generate
| the structured grid.
|
| >>> import pyvista as pv
| >>> noise = pv.perlin_noise(0.1, (1, 1, 1), (0, 0, 0))
| >>> grid = pv.sample_function(
| ... noise, [0, 1.0, -0, 1.0, 0, 1.0], dim=(20, 20, 20)
| ... )
| >>> grid.plot(
| ... cmap='gist_earth_r',
| ... show_scalar_bar=True,
| ... show_edges=False,
| ... )
|
| Next, apply the threshold.
|
| >>> import pyvista as pv
| >>> noise = pv.perlin_noise(0.1, (1, 1, 1), (0, 0, 0))
| >>> grid = pv.sample_function(
| ... noise, [0, 1.0, -0, 1.0, 0, 1.0], dim=(20, 20, 20)
| ... )
| >>> threshed = grid.threshold(value=0.02)
| >>> threshed.plot(
| ... cmap='gist_earth_r',
| ... show_scalar_bar=False,
| ... show_edges=True,
| ... )
|
| See :ref:`common_filter_example` for more examples using this filter.
|
| threshold_percent(self, percent=0.5, scalars=None, invert=False, continuous=False, preference='cell', method='upper', progress_bar=False)
| Threshold the dataset by a percentage of its range on the active scalars array.
|
| .. warning::
| Thresholding is inherently a cell operation, even though it can use
| associated point data for determining whether to keep a cell. In
| other words, whether or not a given point is included after
| thresholding depends on whether that point is part of a cell that
| is kept after thresholding.
|
| Parameters
| ----------
| percent : float | sequence[float], optional
| The percentage in the range ``(0, 1)`` to threshold. If value is
| out of 0 to 1 range, then it will be divided by 100 and checked to
| be in that range.
|
| scalars : str, optional
| Name of scalars to threshold on. Defaults to currently active scalars.
|
| invert : bool, default: False
| Invert the threshold results. That is, cells that would have been
| in the output with this option off are excluded, while cells that
| would have been excluded from the output are included.
|
| continuous : bool, default: False
| When True, the continuous interval [minimum cell scalar,
| maximum cell scalar] will be used to intersect the threshold bound,
| rather than the set of discrete scalar values from the vertices.
|
| preference : str, default: 'cell'
| When ``scalars`` is specified, this is the preferred array
| type to search for in the dataset. Must be either
| ``'point'`` or ``'cell'``. Throughout PyVista, the preference
| is typically ``'point'`` but since the threshold filter is a
| cell-wise operation, we prefer cell data for thresholding
| operations.
|
| method : str, default: 'upper'
| Set the threshold method for single-values, defining which
| threshold bounds to use. If the ``value`` is a range, this
| parameter will be ignored, extracting data between the two
| values. For single values, ``'lower'`` will extract data
| lower than the ``value``. ``'upper'`` will extract data
| larger than the ``value``.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.UnstructuredGrid
| Dataset containing geometry that meets the threshold requirements.
|
| Examples
| --------
| Apply a 50% threshold filter.
|
| >>> import pyvista as pv
| >>> noise = pv.perlin_noise(0.1, (2, 2, 2), (0, 0, 0))
| >>> grid = pv.sample_function(
| ... noise, [0, 1.0, -0, 1.0, 0, 1.0], dim=(30, 30, 30)
| ... )
| >>> threshed = grid.threshold_percent(0.5)
| >>> threshed.plot(
| ... cmap='gist_earth_r',
| ... show_scalar_bar=False,
| ... show_edges=True,
| ... )
|
| Apply a 80% threshold filter.
|
| >>> threshed = grid.threshold_percent(0.8)
| >>> threshed.plot(
| ... cmap='gist_earth_r',
| ... show_scalar_bar=False,
| ... show_edges=True,
| ... )
|
| See :ref:`common_filter_example` for more examples using a similar filter.
|
| transform(self: '_vtk.vtkDataSet', trans: '_vtk.vtkMatrix4x4 | _vtk.vtkTransform | NumpyArray[float]', transform_all_input_vectors=False, inplace=True, progress_bar=False)
| Transform this mesh with a 4x4 transform.
|
| .. warning::
| When using ``transform_all_input_vectors=True``, there is
| no distinction in VTK between vectors and arrays with
| three components. This may be an issue if you have scalar
| data with three components (e.g. RGB data). This will be
| improperly transformed as if it was vector data rather
| than scalar data. One possible (albeit ugly) workaround
| is to store the three components as separate scalar
| arrays.
|
| .. warning::
| In general, transformations give non-integer results. This
| method converts integer-typed vector data to float before
| performing the transformation. This applies to the points
| array, as well as any vector-valued data that is affected
| by the transformation. To prevent subtle bugs arising from
| in-place transformations truncating the result to integers,
| this conversion always applies to the input mesh.
|
| Parameters
| ----------
| trans : vtk.vtkMatrix4x4, vtk.vtkTransform, or numpy.ndarray
| Accepts a vtk transformation object or a 4x4
| transformation matrix.
|
| transform_all_input_vectors : bool, default: False
| When ``True``, all arrays with three components are
| transformed. Otherwise, only the normals and vectors are
| transformed. See the warning for more details.
|
| inplace : bool, default: False
| When ``True``, modifies the dataset inplace.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.DataSet
| Transformed dataset. Return type matches input unless
| input dataset is a :class:`pyvista.ImageData`, in which
| case the output datatype is a :class:`pyvista.StructuredGrid`.
|
| Examples
| --------
| Translate a mesh by ``(50, 100, 200)``.
|
| >>> import numpy as np
| >>> from pyvista import examples
| >>> mesh = examples.load_airplane()
|
| Here a 4x4 :class:`numpy.ndarray` is used, but
| ``vtk.vtkMatrix4x4`` and ``vtk.vtkTransform`` are also
| accepted.
|
| >>> transform_matrix = np.array(
| ... [
| ... [1, 0, 0, 50],
| ... [0, 1, 0, 100],
| ... [0, 0, 1, 200],
| ... [0, 0, 0, 1],
| ... ]
| ... )
| >>> transformed = mesh.transform(transform_matrix)
| >>> transformed.plot(show_edges=True)
|
| triangulate(self, inplace=False, progress_bar=False)
| Return an all triangle mesh.
|
| More complex polygons will be broken down into triangles.
|
| Parameters
| ----------
| inplace : bool, default: False
| Updates mesh in-place.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| Mesh containing only triangles.
|
| Examples
| --------
| Generate a mesh with quadrilateral faces.
|
| >>> import pyvista as pv
| >>> plane = pv.Plane()
| >>> plane.point_data.clear()
| >>> plane.plot(show_edges=True, line_width=5)
|
| Convert it to an all triangle mesh.
|
| >>> mesh = plane.triangulate()
| >>> mesh.plot(show_edges=True, line_width=5)
|
| warp_by_scalar(self, scalars=None, factor=1.0, normal=None, inplace=False, progress_bar=False, **kwargs)
| Warp the dataset's points by a point data scalars array's values.
|
| This modifies point coordinates by moving points along point
| normals by the scalar amount times the scale factor.
|
| Parameters
| ----------
| scalars : str, optional
| Name of scalars to warp by. Defaults to currently active scalars.
|
| factor : float, default: 1.0
| A scaling factor to increase the scaling effect. Alias
| ``scale_factor`` also accepted - if present, overrides ``factor``.
|
| normal : sequence, optional
| User specified normal. If given, data normals will be
| ignored and the given normal will be used to project the
| warp.
|
| inplace : bool, default: False
| If ``True``, the points of the given dataset will be updated.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| **kwargs : dict, optional
| Accepts ``scale_factor`` instead of ``factor``.
|
| Returns
| -------
| pyvista.DataSet
| Warped Dataset. Return type matches input.
|
| Examples
| --------
| First, plot the unwarped mesh.
|
| >>> from pyvista import examples
| >>> mesh = examples.download_st_helens()
| >>> mesh.plot(cmap='gist_earth', show_scalar_bar=False)
|
| Now, warp the mesh by the ``'Elevation'`` scalars.
|
| >>> warped = mesh.warp_by_scalar('Elevation')
| >>> warped.plot(cmap='gist_earth', show_scalar_bar=False)
|
| See :ref:`surface_normal_example` for more examples using this filter.
|
| warp_by_vector(self, vectors=None, factor=1.0, inplace=False, progress_bar=False)
| Warp the dataset's points by a point data vectors array's values.
|
| This modifies point coordinates by moving points along point
| vectors by the local vector times the scale factor.
|
| A classical application of this transform is to visualize
| eigenmodes in mechanics.
|
| Parameters
| ----------
| vectors : str, optional
| Name of vector to warp by. Defaults to currently active vector.
|
| factor : float, default: 1.0
| A scaling factor that multiplies the vectors to warp by. Can
| be used to enhance the warping effect.
|
| inplace : bool, default: False
| If ``True``, the function will update the mesh in-place.
|
| progress_bar : bool, default: False
| Display a progress bar to indicate progress.
|
| Returns
| -------
| pyvista.PolyData
| The warped mesh resulting from the operation.
|
| Examples
| --------
| Warp a sphere by vectors.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> sphere = examples.load_sphere_vectors()
| >>> warped = sphere.warp_by_vector()
| >>> pl = pv.Plotter(shape=(1, 2))
| >>> pl.subplot(0, 0)
| >>> actor = pl.add_text("Before warp")
| >>> actor = pl.add_mesh(sphere, color='white')
| >>> pl.subplot(0, 1)
| >>> actor = pl.add_text("After warp")
| >>> actor = pl.add_mesh(warped, color='white')
| >>> pl.show()
|
| See :ref:`warp_by_vectors_example` and :ref:`eigenmodes_example` for
| more examples using this filter.
|
| ----------------------------------------------------------------------
| Data descriptors inherited from pyvista.core.filters.data_set.DataSetFilters:
|
| __dict__
| dictionary for instance variables
|
| __weakref__
| list of weak references to the object
|
| ----------------------------------------------------------------------
| Methods inherited from pyvista.core.dataobject.DataObject:
|
| __eq__(self, other: 'object') -> 'bool'
| Test equivalency between data objects.
|
| __getstate__(self)
| Support pickle by serializing the VTK object data to something which can be pickled natively.
|
| The format of the serialized VTK object data depends on `pyvista.PICKLE_FORMAT` (case-insensitive).
| - If `pyvista.PICKLE_FORMAT == 'xml'`, the data is serialized as an XML-formatted string.
| - If `pyvista.PICKLE_FORMAT == 'legacy'`, the data is serialized to bytes in VTK's binary format.
|
| __setstate__(self, state)
| Support unpickle.
|
| add_field_data(self, array: 'NumpyArray[float]', name: 'str', deep: 'bool' = True)
| Add field data.
|
| Use field data when size of the data you wish to associate
| with the dataset does not match the number of points or cells
| of the dataset.
|
| Parameters
| ----------
| array : sequence
| Array of data to add to the dataset as a field array.
|
| name : str
| Name to assign the field array.
|
| deep : bool, default: True
| Perform a deep copy of the data when adding it to the
| dataset.
|
| Examples
| --------
| Add field data to a PolyData dataset.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> mesh = pv.Sphere()
| >>> mesh.add_field_data(np.arange(10), 'my-field-data')
| >>> mesh['my-field-data']
| pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
|
| Add field data to a ImageData dataset.
|
| >>> mesh = pv.ImageData(dimensions=(2, 2, 1))
| >>> mesh.add_field_data(
| ... ['I could', 'write', 'notes', 'here'], 'my-field-data'
| ... )
| >>> mesh['my-field-data']
| pyvista_ndarray(['I could', 'write', 'notes', 'here'], dtype='<U7')
|
| Add field data to a MultiBlock dataset.
|
| >>> blocks = pv.MultiBlock()
| >>> blocks.append(pv.Sphere())
| >>> blocks["cube"] = pv.Cube(center=(0, 0, -1))
| >>> blocks.add_field_data([1, 2, 3], 'my-field-data')
| >>> blocks.field_data['my-field-data']
| pyvista_ndarray([1, 2, 3])
|
| clear_field_data(self) -> 'None'
| Remove all field data.
|
| Examples
| --------
| Add field data to a PolyData dataset and then remove it.
|
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh.field_data['my-field-data'] = range(10)
| >>> len(mesh.field_data)
| 1
| >>> mesh.clear_field_data()
| >>> len(mesh.field_data)
| 0
|
| copy(self, deep=True)
| Return a copy of the object.
|
| Parameters
| ----------
| deep : bool, default: True
| When ``True`` makes a full copy of the object. When
| ``False``, performs a shallow copy where the points, cell,
| and data arrays are references to the original object.
|
| Returns
| -------
| pyvista.DataSet
| Deep or shallow copy of the input. Type is identical to
| the input.
|
| Examples
| --------
| Create and make a deep copy of a PolyData object.
|
| >>> import pyvista as pv
| >>> mesh_a = pv.Sphere()
| >>> mesh_b = mesh_a.copy()
| >>> mesh_a == mesh_b
| True
|
| copy_attributes(self, dataset: '_vtk.vtkDataSet') -> 'None'
| Copy the data attributes of the input dataset object.
|
| Parameters
| ----------
| dataset : pyvista.DataSet
| Dataset to copy the data attributes from.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> source = pv.ImageData(dimensions=(10, 10, 5))
| >>> source = source.compute_cell_sizes()
| >>> target = pv.ImageData(dimensions=(10, 10, 5))
| >>> target.copy_attributes(source)
| >>> target.plot(scalars='Volume', show_edges=True)
|
| copy_structure(self, dataset: '_vtk.vtkDataSet') -> 'None'
| Copy the structure (geometry and topology) of the input dataset object.
|
| Parameters
| ----------
| dataset : vtk.vtkDataSet
| Dataset to copy the geometry and topology from.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> source = pv.ImageData(dimensions=(10, 10, 5))
| >>> target = pv.ImageData()
| >>> target.copy_structure(source)
| >>> target.plot(show_edges=True)
|
| deep_copy(self, to_copy: '_vtk.vtkDataObject') -> 'None'
| Overwrite this data object with another data object as a deep copy.
|
| Parameters
| ----------
| to_copy : pyvista.DataObject or vtk.vtkDataObject
| Data object to perform a deep copy from.
|
| head(self, display=True, html=None)
| Return the header stats of this dataset.
|
| If in IPython, this will be formatted to HTML. Otherwise
| returns a console friendly string.
|
| Parameters
| ----------
| display : bool, default: True
| Display this header in iPython.
|
| html : bool, optional
| Generate the output as HTML.
|
| Returns
| -------
| str
| Header statistics.
|
| save(self, filename: 'Path | str', binary: 'bool' = True, texture: 'NumpyArray[np.uint8] | str | None' = None) -> 'None'
| Save this vtk object to file.
|
| Parameters
| ----------
| filename : str, pathlib.Path
| Filename of output file. Writer type is inferred from
| the extension of the filename.
|
| binary : bool, default: True
| If ``True``, write as binary. Otherwise, write as ASCII.
|
| texture : str, np.ndarray, optional
| Write a single texture array to file when using a PLY
| file. Texture array must be a 3 or 4 component array with
| the datatype ``np.uint8``. Array may be a cell array or a
| point array, and may also be a string if the array already
| exists in the PolyData.
|
| If a string is provided, the texture array will be saved
| to disk as that name. If an array is provided, the
| texture array will be saved as ``'RGBA'``
|
| .. note::
| This feature is only available when saving PLY files.
|
| Notes
| -----
| Binary files write much faster than ASCII and have a smaller
| file size.
|
| shallow_copy(self, to_copy: '_vtk.vtkDataObject') -> 'None'
| Shallow copy the given mesh to this mesh.
|
| Parameters
| ----------
| to_copy : pyvista.DataObject or vtk.vtkDataObject
| Data object to perform a shallow copy from.
|
| ----------------------------------------------------------------------
| Readonly properties inherited from pyvista.core.dataobject.DataObject:
|
| actual_memory_size
| Return the actual size of the dataset object.
|
| Returns
| -------
| int
| The actual size of the dataset object in kibibytes (1024
| bytes).
|
| Examples
| --------
| >>> from pyvista import examples
| >>> mesh = examples.load_airplane()
| >>> mesh.actual_memory_size # doctest:+SKIP
| 93
|
| field_data
| Return FieldData as DataSetAttributes.
|
| Use field data when size of the data you wish to associate
| with the dataset does not match the number of points or cells
| of the dataset.
|
| Returns
| -------
| DataSetAttributes
| FieldData as DataSetAttributes.
|
| Examples
| --------
| Add field data to a PolyData dataset and then return it.
|
| >>> import pyvista as pv
| >>> import numpy as np
| >>> mesh = pv.Sphere()
| >>> mesh.field_data['my-field-data'] = np.arange(10)
| >>> mesh.field_data['my-field-data']
| pyvista_ndarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
|
| memory_address
| Get address of the underlying VTK C++ object.
|
| Returns
| -------
| str
| Memory address formatted as ``'Addr=%p'``.
|
| Examples
| --------
| >>> import pyvista as pv
| >>> mesh = pv.Sphere()
| >>> mesh.memory_address
| 'Addr=...'
|
| ----------------------------------------------------------------------
| Data descriptors inherited from pyvista.core.dataobject.DataObject:
|
| user_dict
| Set or return a user-specified data dictionary.
|
| The dictionary is stored as a JSON-serialized string as part of the mesh's
| field data. Unlike regular field data, which requires values to be stored
| as an array, the user dict provides a mapping for scalar values.
|
| Since the user dict is stored as field data, it is automatically saved
| with the mesh when it is saved in a compatible file format (e.g. ``'.vtk'``).
| Any saved metadata is automatically de-serialized by PyVista whenever
| the user dict is accessed again. Since the data is stored as JSON, it
| may also be easily retrieved or read by other programs.
|
| Any JSON-serializable values are permitted by the user dict, i.e. values
| can have type ``dict``, ``list``, ``tuple``, ``str``, ``int``, ``float``,
| ``bool``, or ``None``. Storing NumPy arrays is not directly supported, but
| these may be cast beforehand to a supported type, e.g. by calling ``tolist()``
| on the array.
|
| To completely remove the user dict string from the dataset's field data,
| set its value to ``None``.
|
| .. note::
|
| The user dict is a convenience property and is intended for metadata storage.
| It has an inefficient dictionary implementation and should only be used to
| store a small number of infrequently-accessed keys with relatively small
| values. It should not be used to store frequently accessed array data
| with many entries (a regular field data array should be used instead).
|
| .. warning::
|
| Field data is typically passed-through by dataset filters, and therefore
| the user dict's items can generally be expected to persist and remain
| unchanged in the output of filtering methods. However, this behavior is
| not guaranteed, as it's possible that some filters may modify or clear
| field data. Use with caution.
|
| Returns
| -------
| UserDict
| JSON-serialized dict-like object which is subclassed from :py:class:`collections.UserDict`.
|
| Examples
| --------
| Load a mesh.
|
| >>> import pyvista as pv
| >>> from pyvista import examples
| >>> mesh = examples.load_ant()
|
| Add data to the user dict. The contents are serialized as JSON.
|
| >>> mesh.user_dict['name'] = 'ant'
| >>> mesh.user_dict
| {"name": "ant"}
|
| Alternatively, set the user dict from an existing dict.
|
| >>> mesh.user_dict = dict(name='ant')
|
| The user dict can be updated like a regular dict.
|
| >>> mesh.user_dict.update(
| ... {
| ... 'num_legs': 6,
| ... 'body_parts': ['head', 'thorax', 'abdomen'],
| ... }
| ... )
| >>> mesh.user_dict
| {"name": "ant", "num_legs": 6, "body_parts": ["head", "thorax", "abdomen"]}
|
| Data in the user dict is stored as field data.
|
| >>> mesh.field_data
| pyvista DataSetAttributes
| Association : NONE
| Contains arrays :
| _PYVISTA_USER_DICT str "{"name": "ant",..."
|
| Since it's field data, the user dict can be saved to file along with the
| mesh and retrieved later.
|
| >>> mesh.save('ant.vtk')
| >>> mesh_from_file = pv.read('ant.vtk')
| >>> mesh_from_file.user_dict
| {"name": "ant", "num_legs": 6, "body_parts": ["head", "thorax", "abdomen"]}
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from pyvista.core.dataobject.DataObject:
|
| __hash__ = None
|
| ----------------------------------------------------------------------
| Methods inherited from vtkmodules.vtkCommonDataModel.vtkImageData:
|
| AllocateScalars(...)
| AllocateScalars(self, dataType:int, numComponents:int) -> None
| C++: virtual void AllocateScalars(int dataType, int numComponents)
| AllocateScalars(self, pipeline_info:vtkInformation) -> None
| C++: virtual void AllocateScalars(vtkInformation *pipeline_info)
|
| Allocate the point scalars for this dataset. The data type
| determines the type of the array (VTK_FLOAT, VTK_INT etc.) where
| as numComponents determines its number of components.
|
| ComputeBounds(...)
| ComputeBounds(self) -> None
| C++: void ComputeBounds() override;
|
| Compute the data bounding box from data points. THIS METHOD IS
| NOT THREAD SAFE.
|
| ComputeCellId(...)
| ComputeCellId(self, ijk:[int, int, int]) -> int
| C++: virtual vtkIdType ComputeCellId(int ijk[3])
|
| Given a location in structured coordinates (i-j-k), return the
| cell id.
|
| ComputeIndexToPhysicalMatrix(...)
| ComputeIndexToPhysicalMatrix(origin:(float, float, float),
| spacing:(float, float, float), direction:(float, float, float,
| float, float, float, float, float, float), result:[float,
| float, float, float, float, float, float, float, float, float,
| float, float, float, float, float, float]) -> None
| C++: static void ComputeIndexToPhysicalMatrix(
| double const origin[3], double const spacing[3],
| double const direction[9], double result[16])
|
| ComputeInternalExtent(...)
| ComputeInternalExtent(self, intExt:[int, ...], tgtExt:[int, ...],
| bnds:[int, ...]) -> None
| C++: void ComputeInternalExtent(int *intExt, int *tgtExt,
| int *bnds)
|
| Given how many pixel are required on a side for boundary
| conditions (in bnds), the target extent to traverse, compute the
| internal extent (the extent for this ImageData that does not
| suffer from any boundary conditions) and place it in intExt
|
| ComputePointId(...)
| ComputePointId(self, ijk:[int, int, int]) -> int
| C++: virtual vtkIdType ComputePointId(int ijk[3])
|
| Given a location in structured coordinates (i-j-k), return the
| point id.
|
| ComputeStructuredCoordinates(...)
| ComputeStructuredCoordinates(self, x:(float, float, float),
| ijk:[int, int, int], pcoords:[float, float, float]) -> int
| C++: virtual int ComputeStructuredCoordinates(const double x[3],
| int ijk[3], double pcoords[3])
|
| Convenience function computes the structured coordinates for a
| point x[3]. The voxel is specified by the array ijk[3], and the
| parametric coordinates in the cell are specified with pcoords[3].
| The function returns a 0 if the point x is outside of the volume,
| and a 1 if inside the volume.
|
| CopyAndCastFrom(...)
| CopyAndCastFrom(self, inData:vtkImageData, extent:[int, int, int,
| int, int, int]) -> None
| C++: virtual void CopyAndCastFrom(vtkImageData *inData,
| int extent[6])
| CopyAndCastFrom(self, inData:vtkImageData, x0:int, x1:int, y0:int,
| y1:int, z0:int, z1:int) -> None
| C++: virtual void CopyAndCastFrom(vtkImageData *inData, int x0,
| int x1, int y0, int y1, int z0, int z1)
|
| This method is passed a input and output region, and executes the
| filter algorithm to fill the output from the input. It just
| executes a switch statement to call the correct function for the
| regions data types.
|
| CopyInformationFromPipeline(...)
| CopyInformationFromPipeline(self, information:vtkInformation)
| -> None
| C++: void CopyInformationFromPipeline(vtkInformation *information)
| override;
|
| Override these to handle origin, spacing, scalar type, and scalar
| number of components. See vtkDataObject for details.
|
| CopyInformationToPipeline(...)
| CopyInformationToPipeline(self, information:vtkInformation)
| -> None
| C++: void CopyInformationToPipeline(vtkInformation *information)
| override;
|
| Copy information from this data object to the pipeline
| information. This is used by the vtkTrivialProducer that is
| created when someone calls SetInputData() to connect the image to
| a pipeline.
|
| CopyStructure(...)
| CopyStructure(self, ds:vtkDataSet) -> None
| C++: void CopyStructure(vtkDataSet *ds) override;
|
| Copy the geometric and topological structure of an input image
| data object.
|
| Crop(...)
| Crop(self, updateExtent:(int, ...)) -> None
| C++: void Crop(const int *updateExtent) override;
|
| Reallocates and copies to set the Extent to updateExtent. This is
| used internally when the exact extent is requested, and the
| source generated more than the update extent.
|
| DeepCopy(...)
| DeepCopy(self, src:vtkDataObject) -> None
| C++: void DeepCopy(vtkDataObject *src) override;
|
| The goal of the method is to copy the complete data from src into
| this object. The implementation is delegated to the differenent
| subclasses. If you want to copy the data up to the array pointers
| only, @see ShallowCopy.
|
| This method deep copy the field data and copy the internal
| structure.
|
| ExtendedNew(...)
| ExtendedNew() -> vtkImageData
| C++: static vtkImageData *ExtendedNew()
|
| FindAndGetCell(...)
| FindAndGetCell(self, x:[float, float, float], cell:vtkCell,
| cellId:int, tol2:float, subId:int, pcoords:[float, float,
| float], weights:[float, ...]) -> vtkCell
| C++: vtkCell *FindAndGetCell(double x[3], vtkCell *cell,
| vtkIdType cellId, double tol2, int &subId, double pcoords[3],
| double *weights) override;
|
| Locate the cell that contains a point and return the cell. Also
| returns the subcell id, parametric coordinates and weights for
| subsequent interpolation. This method combines the derived class
| methods int FindCell and vtkCell *GetCell. Derived classes may
| provide a more efficient implementation. See for example
| vtkStructuredPoints. THIS METHOD IS NOT THREAD SAFE.
|
| FindCell(...)
| FindCell(self, x:[float, float, float], cell:vtkCell, cellId:int,
| tol2:float, subId:int, pcoords:[float, float, float],
| weights:[float, ...]) -> int
| C++: vtkIdType FindCell(double x[3], vtkCell *cell,
| vtkIdType cellId, double tol2, int &subId, double pcoords[3],
| double *weights) override;
| FindCell(self, x:[float, float, float], cell:vtkCell,
| gencell:vtkGenericCell, cellId:int, tol2:float, subId:int,
| pcoords:[float, float, float], weights:[float, ...]) -> int
| C++: vtkIdType FindCell(double x[3], vtkCell *cell,
| vtkGenericCell *gencell, vtkIdType cellId, double tol2,
| int &subId, double pcoords[3], double *weights) override;
|
| Locate cell based on global coordinate x and tolerance squared.
| If cell and cellId is non-nullptr, then search starts from this
| cell and looks at immediate neighbors. Returns cellId >= 0 if
| inside, < 0 otherwise. The parametric coordinates are provided
| in pcoords[3]. The interpolation weights are returned in
| weights[]. (The number of weights is equal to the number of
| points in the found cell). Tolerance is used to control how close
| the point is to be considered "in" the cell. THIS METHOD IS NOT
| THREAD SAFE.
|
| FindPoint(...)
| FindPoint(self, x:float, y:float, z:float) -> int
| C++: virtual vtkIdType FindPoint(double x, double y, double z)
| FindPoint(self, x:[float, float, float]) -> int
| C++: vtkIdType FindPoint(double x[3]) override;
|
| Locate the closest point to the global coordinate x. Return the
| point id. If point id < 0; then no point found. (This may arise
| when point is outside of dataset.) THIS METHOD IS THREAD SAFE IF
| FIRST CALLED FROM A SINGLE THREAD AND THE DATASET IS NOT MODIFIED
|
| GetActualMemorySize(...)
| GetActualMemorySize(self) -> int
| C++: unsigned long GetActualMemorySize() override;
|
| Return the actual size of the data in kibibytes (1024 bytes).
| This number is valid only after the pipeline has updated. The
| memory size returned is guaranteed to be greater than or equal to
| the memory required to represent the data (e.g., extra space in
| arrays, etc. are not included in the return value). THIS METHOD
| IS THREAD SAFE.
|
| GetArrayIncrements(...)
| GetArrayIncrements(self, array:vtkDataArray, increments:[int, int,
| int]) -> None
| C++: void GetArrayIncrements(vtkDataArray *array,
| vtkIdType increments[3])
|
| Since various arrays have different number of components, the
| will have different increments.
|
| GetArrayPointer(...)
| GetArrayPointer(self, array:vtkDataArray, coordinates:[int, int,
| int]) -> Pointer
| C++: void *GetArrayPointer(vtkDataArray *array,
| int coordinates[3])
|
| GetArrayPointerForExtent(...)
| GetArrayPointerForExtent(self, array:vtkDataArray, extent:[int,
| int, int, int, int, int]) -> Pointer
| C++: void *GetArrayPointerForExtent(vtkDataArray *array,
| int extent[6])
|
| These are convenience methods for getting a pointer from any
| filed array. It is a start at expanding image filters to process
| any array (not just scalars).
|
| GetAxisUpdateExtent(...)
| GetAxisUpdateExtent(self, axis:int, min:int, max:int,
| updateExtent:(int, ...)) -> None
| C++: virtual void GetAxisUpdateExtent(int axis, int &min,
| int &max, const int *updateExtent)
|
| GetCell(...)
| GetCell(self, cellId:int) -> vtkCell
| C++: vtkCell *GetCell(vtkIdType cellId) override;
| GetCell(self, i:int, j:int, k:int) -> vtkCell
| C++: vtkCell *GetCell(int i, int j, int k) override;
| GetCell(self, cellId:int, cell:vtkGenericCell) -> None
| C++: void GetCell(vtkIdType cellId, vtkGenericCell *cell)
| override;
|
| Get cell with cellId such that: 0 <= cellId < NumberOfCells. The
| returned vtkCell is an object owned by this instance, hence the
| return value must not be deleted by the caller.
|
| @warning Repeat calls to this function for different face ids
| will change
| the data stored in the internal member object whose pointer is
| returned by this function.
|
| @warning THIS METHOD IS NOT THREAD SAFE. For a thread-safe
| version, please use
| void GetCell(vtkIdType cellId, vtkGenericCell* cell).
|
| GetCellBounds(...)
| GetCellBounds(self, cellId:int, bounds:[float, float, float,
| float, float, float]) -> None
| C++: void GetCellBounds(vtkIdType cellId, double bounds[6])
| override;
|
| Get the bounds of the cell with cellId such that: 0 <= cellId <
| NumberOfCells. A subclass may be able to determine the bounds of
| cell without using an expensive GetCell() method. A default
| implementation is provided that actually uses a GetCell() call.
| This is to ensure the method is available to all datasets.
| Subclasses should override this method to provide an efficient
| implementation. THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A
| SINGLE THREAD AND THE DATASET IS NOT MODIFIED
|
| GetCellDims(...)
| GetCellDims(self, cellDims:[int, int, int]) -> None
| C++: void GetCellDims(int cellDims[3])
|
| Given the node dimensions of this grid instance, this method
| computes the node dimensions. The value in each dimension can
| will have a lowest value of "1" such that computing the total
| number of cells can be achieved by simply by
| cellDims[0]*cellDims[1]*cellDims[2].
|
| GetCellNeighbors(...)
| GetCellNeighbors(self, cellId:int, ptIds:vtkIdList,
| cellIds:vtkIdList) -> None
| C++: void GetCellNeighbors(vtkIdType cellId, vtkIdList *ptIds,
| vtkIdList *cellIds) override;
| GetCellNeighbors(self, cellId:int, ptIds:vtkIdList,
| cellIds:vtkIdList, seedLoc:[int, ...]) -> None
| C++: void GetCellNeighbors(vtkIdType cellId, vtkIdList *ptIds,
| vtkIdList *cellIds, int *seedLoc)
|
| Topological inquiry to get all cells using list of points
| exclusive of cell specified (e.g., cellId). Note that the list
| consists of only cells that use ALL the points provided. THIS
| METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND
| THE DATASET IS NOT MODIFIED
|
| GetCellPoints(...)
| GetCellPoints(self, cellId:int, ptIds:vtkIdList) -> None
| C++: void GetCellPoints(vtkIdType cellId, vtkIdList *ptIds)
| override;
| GetCellPoints(self, cellId:int, npts:int, pts:(int, ...),
| ptIds:vtkIdList) -> None
| C++: virtual void GetCellPoints(vtkIdType cellId, vtkIdType &npts,
| vtkIdType const *&pts, vtkIdList *ptIds)
|
| Topological inquiry to get points defining cell. THIS METHOD IS
| THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND THE DATASET
| IS NOT MODIFIED
|
| GetCellSize(...)
| GetCellSize(self, cellId:int) -> int
| C++: vtkIdType GetCellSize(vtkIdType cellId) override;
|
| Get the size of cell with cellId such that: 0 <= cellId <
| NumberOfCells. THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A
| SINGLE THREAD AND THE DATASET IS NOT MODIFIED
|
| @warning This method MUST be overridden for performance reasons.
| Default implementation is very unefficient.
|
| GetCellType(...)
| GetCellType(self, cellId:int) -> int
| C++: int GetCellType(vtkIdType cellId) override;
|
| Get type of cell with cellId such that: 0 <= cellId <
| NumberOfCells. THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A
| SINGLE THREAD AND THE DATASET IS NOT MODIFIED
|
| GetContinuousIncrements(...)
| GetContinuousIncrements(self, extent:[int, int, int, int, int,
| int], incX:int, incY:int, incZ:int) -> None
| C++: virtual void GetContinuousIncrements(int extent[6],
| vtkIdType &incX, vtkIdType &incY, vtkIdType &incZ)
| GetContinuousIncrements(self, scalars:vtkDataArray, extent:[int,
| int, int, int, int, int], incX:int, incY:int, incZ:int)
| -> None
| C++: virtual void GetContinuousIncrements(vtkDataArray *scalars,
| int extent[6], vtkIdType &incX, vtkIdType &incY,
| vtkIdType &incZ)
|
| Different ways to get the increments for moving around the data.
| incX is always returned with 0. incY is returned with the
| increment needed to move from the end of one X scanline of data
| to the start of the next line. incZ is filled in with the
| increment needed to move from the end of one image to the start
| of the next. The proper way to use these values is to for a loop
| over Z, Y, X, C, incrementing the pointer by 1 after each
| component. When the end of the component is reached, the pointer
| is set to the beginning of the next pixel, thus incX is properly
| set to 0. The first form of GetContinuousIncrements uses the
| active scalar field while the second form allows the scalar array
| to be passed in.
|
| GetData(...)
| GetData(info:vtkInformation) -> vtkImageData
| C++: static vtkImageData *GetData(vtkInformation *info)
| GetData(v:vtkInformationVector, i:int=0) -> vtkImageData
| C++: static vtkImageData *GetData(vtkInformationVector *v,
| int i=0)
|
| Retrieve an instance of this class from an information object.
|
| GetDataDimension(...)
| GetDataDimension(self) -> int
| C++: virtual int GetDataDimension()
|
| Return the dimensionality of the data.
|
| GetDataObjectType(...)
| GetDataObjectType(self) -> int
| C++: int GetDataObjectType() override;
|
| Return what type of dataset this is.
|
| GetDimensions(...)
| GetDimensions(self) -> (int, int, int)
| C++: virtual int *GetDimensions()
| GetDimensions(self, dims:[int, int, int]) -> None
| C++: virtual void GetDimensions(int dims[3])
| GetDimensions(self, dims:[int, int, int]) -> None
| C++: virtual void GetDimensions(vtkIdType dims[3])
|
| Get dimensions of this structured points dataset. It is the
| number of points on each axis. Dimensions are computed from
| Extents during this call.
| \warning Non thread-safe, use second signature if you want it to
| be.
|
| GetDirectionMatrix(...)
| GetDirectionMatrix(self) -> vtkMatrix3x3
| C++: virtual vtkMatrix3x3 *GetDirectionMatrix()
|
| Set/Get the direction transform of the dataset. The direction
| matrix is a 3x3 transformation matrix supporting scaling and
| rotation.
|
| GetExtent(...)
| GetExtent(self) -> (int, int, int, int, int, int)
| C++: virtual int *GetExtent()
|
| GetExtentType(...)
| GetExtentType(self) -> int
| C++: int GetExtentType() override;
|
| The extent type is a 3D extent
|
| GetIncrements(...)
| GetIncrements(self) -> (int, int, int)
| C++: virtual vtkIdType *GetIncrements()
| GetIncrements(self, incX:int, incY:int, incZ:int) -> None
| C++: virtual void GetIncrements(vtkIdType &incX, vtkIdType &incY,
| vtkIdType &incZ)
| GetIncrements(self, inc:[int, int, int]) -> None
| C++: virtual void GetIncrements(vtkIdType inc[3])
| GetIncrements(self, scalars:vtkDataArray) -> (int, int, int)
| C++: virtual vtkIdType *GetIncrements(vtkDataArray *scalars)
| GetIncrements(self, scalars:vtkDataArray, incX:int, incY:int,
| incZ:int) -> None
| C++: virtual void GetIncrements(vtkDataArray *scalars,
| vtkIdType &incX, vtkIdType &incY, vtkIdType &incZ)
| GetIncrements(self, scalars:vtkDataArray, inc:[int, int, int])
| -> None
| C++: virtual void GetIncrements(vtkDataArray *scalars,
| vtkIdType inc[3])
|
| Different ways to get the increments for moving around the data.
| GetIncrements() calls ComputeIncrements() to ensure the
| increments are up to date. The first three methods compute the
| increments based on the active scalar field while the next three,
| the scalar field is passed in.
|
| Note that all methods which do not have the increments passed in
| are not thread-safe. When working on a given `vtkImageData`
| instance on multiple threads, each thread should use the `inc*`
| overloads to compute the increments to avoid racing with other
| threads.
|
| GetIndexToPhysicalMatrix(...)
| GetIndexToPhysicalMatrix(self) -> vtkMatrix4x4
| C++: virtual vtkMatrix4x4 *GetIndexToPhysicalMatrix()
|
| Get the transformation matrix from the index space to the
| physical space coordinate system of the dataset. The transform is
| a 4 by 4 matrix.
|
| GetMaxCellSize(...)
| GetMaxCellSize(self) -> int
| C++: int GetMaxCellSize() override;
|
| Convenience method returns largest cell size in dataset. This is
| generally used to allocate memory for supporting data structures.
| THIS METHOD IS THREAD SAFE
|
| GetNumberOfCells(...)
| GetNumberOfCells(self) -> int
| C++: vtkIdType GetNumberOfCells() override;
|
| Standard vtkDataSet API methods. See vtkDataSet for more
| information.
| \warning If GetCell(int,int,int) gets overridden in a subclass,
| it is
| necessary to override GetCell(vtkIdType) in that class as well
| since vtkImageData::GetCell(vtkIdType) will always call
| vkImageData::GetCell(int,int,int)
|
| GetNumberOfGenerationsFromBase(...)
| GetNumberOfGenerationsFromBase(self, type:str) -> int
| C++: vtkIdType GetNumberOfGenerationsFromBase(const char *type)
| override;
|
| Given the name of a base class of this class type, return the
| distance of inheritance between this class type and the named
| class (how many generations of inheritance are there between this
| class and the named class). If the named class is not in this
| class's inheritance tree, return a negative value. Valid
| responses will always be nonnegative. This method works in
| combination with vtkTypeMacro found in vtkSetGet.h.
|
| GetNumberOfGenerationsFromBaseType(...)
| GetNumberOfGenerationsFromBaseType(type:str) -> int
| C++: static vtkIdType GetNumberOfGenerationsFromBaseType(
| const char *type)
|
| Given a the name of a base class of this class type, return the
| distance of inheritance between this class type and the named
| class (how many generations of inheritance are there between this
| class and the named class). If the named class is not in this
| class's inheritance tree, return a negative value. Valid
| responses will always be nonnegative. This method works in
| combination with vtkTypeMacro found in vtkSetGet.h.
|
| GetNumberOfPoints(...)
| GetNumberOfPoints(self) -> int
| C++: vtkIdType GetNumberOfPoints() override;
|
| Determine the number of points composing the dataset. THIS METHOD
| IS THREAD SAFE
|
| GetNumberOfScalarComponents(...)
| GetNumberOfScalarComponents(meta_data:vtkInformation) -> int
| C++: static int GetNumberOfScalarComponents(
| vtkInformation *meta_data)
| GetNumberOfScalarComponents(self) -> int
| C++: int GetNumberOfScalarComponents()
|
| GetOrigin(...)
| GetOrigin(self) -> (float, float, float)
| C++: virtual double *GetOrigin()
|
| Set/Get the origin of the dataset. The origin is the position in
| world coordinates of the point of extent (0,0,0). This point does
| not have to be part of the dataset, in other words, the dataset
| extent does not have to start at (0,0,0) and the origin can be
| outside of the dataset bounding box. The origin plus spacing
| determine the position in space of the points.
|
| GetPhysicalToIndexMatrix(...)
| GetPhysicalToIndexMatrix(self) -> vtkMatrix4x4
| C++: virtual vtkMatrix4x4 *GetPhysicalToIndexMatrix()
|
| Get the transformation matrix from the physical space to the
| index space coordinate system of the dataset. The transform is a
| 4 by 4 matrix.
|
| GetPoint(...)
| GetPoint(self, ptId:int) -> (float, float, float)
| C++: double *GetPoint(vtkIdType ptId) override;
| GetPoint(self, id:int, x:[float, float, float]) -> None
| C++: void GetPoint(vtkIdType id, double x[3]) override;
|
| Get point coordinates with ptId such that: 0 <= ptId <
| NumberOfPoints. THIS METHOD IS NOT THREAD SAFE.
|
| GetPointCells(...)
| GetPointCells(self, ptId:int, cellIds:vtkIdList) -> None
| C++: void GetPointCells(vtkIdType ptId, vtkIdList *cellIds)
| override;
|
| Topological inquiry to get cells using point. THIS METHOD IS
| THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND THE DATASET
| IS NOT MODIFIED
|
| GetPointGradient(...)
| GetPointGradient(self, i:int, j:int, k:int, s:vtkDataArray,
| g:[float, float, float]) -> None
| C++: virtual void GetPointGradient(int i, int j, int k,
| vtkDataArray *s, double g[3])
|
| Given structured coordinates (i,j,k) for a point in a structured
| point dataset, compute the gradient vector from the scalar data
| at that point. The scalars s are the scalars from which the
| gradient is to be computed. This method will treat structured
| point datasets of any dimension.
|
| GetScalarComponentAsDouble(...)
| GetScalarComponentAsDouble(self, x:int, y:int, z:int,
| component:int) -> float
| C++: virtual double GetScalarComponentAsDouble(int x, int y,
| int z, int component)
|
| GetScalarComponentAsFloat(...)
| GetScalarComponentAsFloat(self, x:int, y:int, z:int,
| component:int) -> float
| C++: virtual float GetScalarComponentAsFloat(int x, int y, int z,
| int component)
|
| For access to data from wrappers
|
| GetScalarIndex(...)
| GetScalarIndex(self, coordinates:[int, int, int]) -> int
| C++: virtual vtkIdType GetScalarIndex(int coordinates[3])
| GetScalarIndex(self, x:int, y:int, z:int) -> int
| C++: virtual vtkIdType GetScalarIndex(int x, int y, int z)
|
| GetScalarIndexForExtent(...)
| GetScalarIndexForExtent(self, extent:[int, int, int, int, int,
| int]) -> int
| C++: virtual vtkIdType GetScalarIndexForExtent(int extent[6])
|
| Access the index for the scalar data
|
| GetScalarPointer(...)
| GetScalarPointer(self, coordinates:[int, int, int]) -> Pointer
| C++: virtual void *GetScalarPointer(int coordinates[3])
| GetScalarPointer(self, x:int, y:int, z:int) -> Pointer
| C++: virtual void *GetScalarPointer(int x, int y, int z)
| GetScalarPointer(self) -> Pointer
| C++: virtual void *GetScalarPointer()
|
| GetScalarPointerForExtent(...)
| GetScalarPointerForExtent(self, extent:[int, int, int, int, int,
| int]) -> Pointer
| C++: virtual void *GetScalarPointerForExtent(int extent[6])
|
| Access the native pointer for the scalar data
|
| GetScalarSize(...)
| GetScalarSize(self, meta_data:vtkInformation) -> int
| C++: virtual int GetScalarSize(vtkInformation *meta_data)
| GetScalarSize(self) -> int
| C++: virtual int GetScalarSize()
|
| Get the size of the scalar type in bytes.
|
| GetScalarType(...)
| GetScalarType(meta_data:vtkInformation) -> int
| C++: static int GetScalarType(vtkInformation *meta_data)
| GetScalarType(self) -> int
| C++: int GetScalarType()
|
| GetScalarTypeAsString(...)
| GetScalarTypeAsString(self) -> str
| C++: const char *GetScalarTypeAsString()
|
| GetScalarTypeMax(...)
| GetScalarTypeMax(self, meta_data:vtkInformation) -> float
| C++: virtual double GetScalarTypeMax(vtkInformation *meta_data)
| GetScalarTypeMax(self) -> float
| C++: virtual double GetScalarTypeMax()
|
| GetScalarTypeMin(...)
| GetScalarTypeMin(self, meta_data:vtkInformation) -> float
| C++: virtual double GetScalarTypeMin(vtkInformation *meta_data)
| GetScalarTypeMin(self) -> float
| C++: virtual double GetScalarTypeMin()
|
| These returns the minimum and maximum values the ScalarType can
| hold without overflowing.
|
| GetSpacing(...)
| GetSpacing(self) -> (float, float, float)
| C++: virtual double *GetSpacing()
|
| Set the spacing (width,height,length) of the cubical cells that
| compose the data set.
|
| GetTupleIndex(...)
| GetTupleIndex(self, array:vtkDataArray, coordinates:[int, int,
| int]) -> int
| C++: vtkIdType GetTupleIndex(vtkDataArray *array,
| int coordinates[3])
|
| Given a data array and a coordinate, return the index of the
| tuple in the array corresponding to that coordinate.
|
| This method is analogous to GetArrayPointer(), but it conforms to
| the API of vtkGenericDataArray.
|
| GetVoxelGradient(...)
| GetVoxelGradient(self, i:int, j:int, k:int, s:vtkDataArray,
| g:vtkDataArray) -> None
| C++: virtual void GetVoxelGradient(int i, int j, int k,
| vtkDataArray *s, vtkDataArray *g)
|
| Given structured coordinates (i,j,k) for a voxel cell, compute
| the eight gradient values for the voxel corners. The order in
| which the gradient vectors are arranged corresponds to the
| ordering of the voxel points. Gradient vector is computed by
| central differences (except on edges of volume where forward
| difference is used). The scalars s are the scalars from which the
| gradient is to be computed. This method will treat only 3D
| structured point datasets (i.e., volumes).
|
| HasAnyBlankCells(...)
| HasAnyBlankCells(self) -> bool
| C++: bool HasAnyBlankCells() override;
|
| Returns 1 if there is any visibility constraint on the cells, 0
| otherwise.
|
| HasAnyBlankPoints(...)
| HasAnyBlankPoints(self) -> bool
| C++: bool HasAnyBlankPoints() override;
|
| Returns 1 if there is any visibility constraint on the points, 0
| otherwise.
|
| HasNumberOfScalarComponents(...)
| HasNumberOfScalarComponents(meta_data:vtkInformation) -> bool
| C++: static bool HasNumberOfScalarComponents(
| vtkInformation *meta_data)
|
| HasScalarType(...)
| HasScalarType(meta_data:vtkInformation) -> bool
| C++: static bool HasScalarType(vtkInformation *meta_data)
|
| Initialize(...)
| Initialize(self) -> None
| C++: void Initialize() override;
|
| Restore data object to initial state.
|
| IsA(...)
| IsA(self, type:str) -> int
| C++: vtkTypeBool IsA(const char *type) override;
|
| Return 1 if this class is the same type of (or a subclass of) the
| named class. Returns 0 otherwise. This method works in
| combination with vtkTypeMacro found in vtkSetGet.h.
|
| IsCellVisible(...)
| IsCellVisible(self, cellId:int) -> int
| C++: unsigned char IsCellVisible(vtkIdType cellId)
|
| Return non-zero value if specified point is visible. These
| methods should be called only after the dimensions of the grid
| are set.
|
| IsPointVisible(...)
| IsPointVisible(self, ptId:int) -> int
| C++: unsigned char IsPointVisible(vtkIdType ptId)
|
| Return non-zero value if specified point is visible. These
| methods should be called only after the dimensions of the grid
| are set.
|
| IsTypeOf(...)
| IsTypeOf(type:str) -> int
| C++: static vtkTypeBool IsTypeOf(const char *type)
|
| Return 1 if this class type is the same type of (or a subclass
| of) the named class. Returns 0 otherwise. This method works in
| combination with vtkTypeMacro found in vtkSetGet.h.
|
| NewInstance(...)
| NewInstance(self) -> vtkImageData
| C++: vtkImageData *NewInstance()
|
| PrepareForNewData(...)
| PrepareForNewData(self) -> None
| C++: void PrepareForNewData() override;
|
| make the output data ready for new data to be inserted. For most
| objects we just call Initialize. But for image data we leave the
| old data in case the memory can be reused.
|
| SafeDownCast(...)
| SafeDownCast(o:vtkObjectBase) -> vtkImageData
| C++: static vtkImageData *SafeDownCast(vtkObjectBase *o)
|
| SetAxisUpdateExtent(...)
| SetAxisUpdateExtent(self, axis:int, min:int, max:int,
| updateExtent:(int, ...), axisUpdateExtent:[int, ...]) -> None
| C++: virtual void SetAxisUpdateExtent(int axis, int min, int max,
| const int *updateExtent, int *axisUpdateExtent)
|
| Set / Get the extent on just one axis
|
| SetDimensions(...)
| SetDimensions(self, i:int, j:int, k:int) -> None
| C++: virtual void SetDimensions(int i, int j, int k)
| SetDimensions(self, dims:(int, int, int)) -> None
| C++: virtual void SetDimensions(const int dims[3])
|
| Same as SetExtent(0, i-1, 0, j-1, 0, k-1)
|
| SetDirectionMatrix(...)
| SetDirectionMatrix(self, m:vtkMatrix3x3) -> None
| C++: virtual void SetDirectionMatrix(vtkMatrix3x3 *m)
| SetDirectionMatrix(self, elements:(float, float, float, float,
| float, float, float, float, float)) -> None
| C++: virtual void SetDirectionMatrix(const double elements[9])
| SetDirectionMatrix(self, e00:float, e01:float, e02:float,
| e10:float, e11:float, e12:float, e20:float, e21:float,
| e22:float) -> None
| C++: virtual void SetDirectionMatrix(double e00, double e01,
| double e02, double e10, double e11, double e12, double e20,
| double e21, double e22)
|
| SetExtent(...)
| SetExtent(self, extent:[int, int, int, int, int, int]) -> None
| C++: virtual void SetExtent(int extent[6])
| SetExtent(self, x1:int, x2:int, y1:int, y2:int, z1:int, z2:int)
| -> None
| C++: virtual void SetExtent(int x1, int x2, int y1, int y2,
| int z1, int z2)
|
| Set/Get the extent. On each axis, the extent is defined by the
| index of the first point and the index of the last point. The
| extent should be set before the "Scalars" are set or allocated.
| The Extent is stored in the order (X, Y, Z). The dataset extent
| does not have to start at (0,0,0). (0,0,0) is just the extent of
| the origin. The first point (the one with Id=0) is at extent
| (Extent[0],Extent[2],Extent[4]). As for any dataset, a data array
| on point data starts at Id=0.
|
| SetNumberOfScalarComponents(...)
| SetNumberOfScalarComponents(n:int, meta_data:vtkInformation)
| -> None
| C++: static void SetNumberOfScalarComponents(int n,
| vtkInformation *meta_data)
|
| Set/Get the number of scalar components for points. As with the
| SetScalarType method this is setting pipeline info.
|
| SetOrigin(...)
| SetOrigin(self, i:float, j:float, k:float) -> None
| C++: virtual void SetOrigin(double i, double j, double k)
| SetOrigin(self, ijk:(float, float, float)) -> None
| C++: virtual void SetOrigin(const double ijk[3])
|
| SetScalarComponentFromDouble(...)
| SetScalarComponentFromDouble(self, x:int, y:int, z:int,
| component:int, v:float) -> None
| C++: virtual void SetScalarComponentFromDouble(int x, int y,
| int z, int component, double v)
|
| SetScalarComponentFromFloat(...)
| SetScalarComponentFromFloat(self, x:int, y:int, z:int,
| component:int, v:float) -> None
| C++: virtual void SetScalarComponentFromFloat(int x, int y, int z,
| int component, float v)
|
| SetScalarType(...)
| SetScalarType(__a:int, meta_data:vtkInformation) -> None
| C++: static void SetScalarType(int, vtkInformation *meta_data)
|
| SetSpacing(...)
| SetSpacing(self, i:float, j:float, k:float) -> None
| C++: virtual void SetSpacing(double i, double j, double k)
| SetSpacing(self, ijk:(float, float, float)) -> None
| C++: virtual void SetSpacing(const double ijk[3])
|
| ShallowCopy(...)
| ShallowCopy(self, src:vtkDataObject) -> None
| C++: void ShallowCopy(vtkDataObject *src) override;
|
| Shallow and Deep copy.
|
| TransformContinuousIndexToPhysicalPoint(...)
| TransformContinuousIndexToPhysicalPoint(self, i:float, j:float,
| k:float, xyz:[float, float, float]) -> None
| C++: virtual void TransformContinuousIndexToPhysicalPoint(
| double i, double j, double k, double xyz[3])
| TransformContinuousIndexToPhysicalPoint(self, ijk:(float, float,
| float), xyz:[float, float, float]) -> None
| C++: virtual void TransformContinuousIndexToPhysicalPoint(
| const double ijk[3], double xyz[3])
| TransformContinuousIndexToPhysicalPoint(i:float, j:float, k:float,
| origin:(float, float, float), spacing:(float, float, float),
| direction:(float, float, float, float, float, float, float,
| float, float), xyz:[float, float, float]) -> None
| C++: static void TransformContinuousIndexToPhysicalPoint(double i,
| double j, double k, double const origin[3],
| double const spacing[3], double const direction[9],
| double xyz[3])
|
| Convert coordinates from index space (ijk) to physical space
| (xyz).
|
| TransformIndexToPhysicalPoint(...)
| TransformIndexToPhysicalPoint(self, i:int, j:int, k:int,
| xyz:[float, float, float]) -> None
| C++: virtual void TransformIndexToPhysicalPoint(int i, int j,
| int k, double xyz[3])
| TransformIndexToPhysicalPoint(self, ijk:(int, int, int),
| xyz:[float, float, float]) -> None
| C++: virtual void TransformIndexToPhysicalPoint(const int ijk[3],
| double xyz[3])
|
| TransformPhysicalNormalToContinuousIndex(...)
| TransformPhysicalNormalToContinuousIndex(self, xyz:(float, float,
| float), ijk:[float, float, float]) -> None
| C++: virtual void TransformPhysicalNormalToContinuousIndex(
| const double xyz[3], double ijk[3])
|
| Convert normal from physical space (xyz) to index space (ijk).
|
| TransformPhysicalPlaneToContinuousIndex(...)
| TransformPhysicalPlaneToContinuousIndex(self, pplane:(float,
| float, float, float), iplane:[float, float, float, float])
| -> None
| C++: virtual void TransformPhysicalPlaneToContinuousIndex(
| double const pplane[4], double iplane[4])
|
| Convert a plane from physical to a continuous index. The plane is
| represented as n(x-xo)=0; or using a four component normal:
| pplane=( nx,ny,nz,-(n(x0)) ).
|
| TransformPhysicalPointToContinuousIndex(...)
| TransformPhysicalPointToContinuousIndex(self, x:float, y:float,
| z:float, ijk:[float, float, float]) -> None
| C++: virtual void TransformPhysicalPointToContinuousIndex(
| double x, double y, double z, double ijk[3])
| TransformPhysicalPointToContinuousIndex(self, xyz:(float, float,
| float), ijk:[float, float, float]) -> None
| C++: virtual void TransformPhysicalPointToContinuousIndex(
| const double xyz[3], double ijk[3])
|
| Convert coordinates from physical space (xyz) to index space
| (ijk).
|
| __delattr__(self, name, /)
| Implement delattr(self, name).
|
| __getattribute__(self, name, /)
| Return getattr(self, name).
|
| __setattr__(self, name, value, /)
| Implement setattr(self, name, value).
|
| ----------------------------------------------------------------------
| Data descriptors inherited from vtkmodules.vtkCommonDataModel.vtkImageData:
|
| __this__
| Pointer to the C++ object.
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from vtkmodules.vtkCommonDataModel.vtkImageData:
|
| __vtkname__ = 'vtkImageData'
|
| ----------------------------------------------------------------------
| Methods inherited from vtkmodules.vtkCommonDataModel.vtkDataSet:
|
| AllocateCellGhostArray(...)
| AllocateCellGhostArray(self) -> vtkUnsignedCharArray
| C++: vtkUnsignedCharArray *AllocateCellGhostArray()
|
| Allocate ghost array for cells.
|
| AllocatePointGhostArray(...)
| AllocatePointGhostArray(self) -> vtkUnsignedCharArray
| C++: vtkUnsignedCharArray *AllocatePointGhostArray()
|
| Allocate ghost array for points.
|
| CheckAttributes(...)
| CheckAttributes(self) -> int
| C++: int CheckAttributes()
|
| This method checks to see if the cell and point attributes match
| the geometry. Many filters will crash if the number of tuples in
| an array is less than the number of points/cells. This method
| returns 1 if there is a mismatch, and 0 if everything is ok. It
| prints an error if an array is too short, and a warning if an
| array is too long.
|
| CopyAttributes(...)
| CopyAttributes(self, ds:vtkDataSet) -> None
| C++: virtual void CopyAttributes(vtkDataSet *ds)
|
| Copy the attributes associated with the specified dataset to this
| instance of vtkDataSet. THIS METHOD IS NOT THREAD SAFE.
|
| GenerateGhostArray(...)
| GenerateGhostArray(self, zeroExt:[int, int, int, int, int, int])
| -> None
| C++: virtual void GenerateGhostArray(int zeroExt[6])
| GenerateGhostArray(self, zeroExt:[int, int, int, int, int, int],
| cellOnly:bool) -> None
| C++: virtual void GenerateGhostArray(int zeroExt[6],
| bool cellOnly)
|
| Normally called by pipeline executives or algorithms only. This
| method computes the ghost arrays for a given dataset. The zeroExt
| argument specifies the extent of the region which ghost type = 0.
|
| GetAttributesAsFieldData(...)
| GetAttributesAsFieldData(self, type:int) -> vtkFieldData
| C++: vtkFieldData *GetAttributesAsFieldData(int type) override;
|
| Returns the attributes of the data object as a vtkFieldData. This
| returns non-null values in all the same cases as GetAttributes,
| in addition to the case of FIELD, which will return the field
| data for any vtkDataObject subclass.
|
| GetBounds(...)
| GetBounds(self) -> (float, float, float, float, float, float)
| C++: double *GetBounds()
| GetBounds(self, bounds:[float, float, float, float, float, float])
| -> None
| C++: void GetBounds(double bounds[6])
|
| Return a pointer to the geometry bounding box in the form
| (xmin,xmax, ymin,ymax, zmin,zmax). THIS METHOD IS NOT THREAD
| SAFE.
|
| GetCellData(...)
| GetCellData(self) -> vtkCellData
| C++: vtkCellData *GetCellData()
|
| Return a pointer to this dataset's cell data. THIS METHOD IS
| THREAD SAFE
|
| GetCellGhostArray(...)
| GetCellGhostArray(self) -> vtkUnsignedCharArray
| C++: vtkUnsignedCharArray *GetCellGhostArray()
|
| Get the array that defines the ghost type of each cell. We cache
| the pointer to the array to save a lookup involving string
| comparisons
|
| GetCellNumberOfFaces(...)
| GetCellNumberOfFaces(self, cellId:int, cellType:int,
| cell:vtkGenericCell) -> int
| C++: int GetCellNumberOfFaces(vtkIdType cellId,
| unsigned char &cellType, vtkGenericCell *cell)
|
| Get the number of faces of a cell.
|
| Most of the times extracting the number of faces requires only
| extracting the cell type. However, for some cell types, the
| number of faces is not constant. For example, a vtkPolyhedron
| cell can have a different number of faces for each cell. That's
| why this method requires the cell id and the dataset.
|
| GetCellTypes(...)
| GetCellTypes(self, types:vtkCellTypes) -> None
| C++: virtual void GetCellTypes(vtkCellTypes *types)
|
| Get a list of types of cells in a dataset. The list consists of
| an array of types (not necessarily in any order), with a single
| entry per type. For example a dataset 5 triangles, 3 lines, and
| 100 hexahedra would result a list of three entries, corresponding
| to the types VTK_TRIANGLE, VTK_LINE, and VTK_HEXAHEDRON. THIS
| METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND
| THE DATASET IS NOT MODIFIED
|
| GetCenter(...)
| GetCenter(self) -> (float, float, float)
| C++: double *GetCenter()
| GetCenter(self, center:[float, float, float]) -> None
| C++: void GetCenter(double center[3])
|
| Get the center of the bounding box. THIS METHOD IS NOT THREAD
| SAFE.
|
| GetGhostArray(...)
| GetGhostArray(self, type:int) -> vtkUnsignedCharArray
| C++: vtkUnsignedCharArray *GetGhostArray(int type) override;
|
| Returns the ghost array for the given type (point or cell). Takes
| advantage of the cache with the pointer to the array to save a
| string comparison.
|
| GetLength(...)
| GetLength(self) -> float
| C++: double GetLength()
|
| Return the length of the diagonal of the bounding box. THIS
| METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD AND
| THE DATASET IS NOT MODIFIED
|
| GetLength2(...)
| GetLength2(self) -> float
| C++: double GetLength2()
|
| Return the squared length of the diagonal of the bounding box.
| THIS METHOD IS THREAD SAFE IF FIRST CALLED FROM A SINGLE THREAD
| AND THE DATASET IS NOT MODIFIED
|
| GetMTime(...)
| GetMTime(self) -> int
| C++: vtkMTimeType GetMTime() override;
|
| Datasets are composite objects and need to check each part for
| MTime THIS METHOD IS THREAD SAFE
|
| GetNumberOfElements(...)
| GetNumberOfElements(self, type:int) -> int
| C++: vtkIdType GetNumberOfElements(int type) override;
|
| Get the number of elements for a specific attribute type (POINT,
| CELL, etc.).
|
| GetPointData(...)
| GetPointData(self) -> vtkPointData
| C++: vtkPointData *GetPointData()
|
| Return a pointer to this dataset's point data. THIS METHOD IS
| THREAD SAFE
|
| GetPointGhostArray(...)
| GetPointGhostArray(self) -> vtkUnsignedCharArray
| C++: vtkUnsignedCharArray *GetPointGhostArray()
|
| Gets the array that defines the ghost type of each point. We
| cache the pointer to the array to save a lookup involving string
| comparisons
|
| GetScalarRange(...)
| GetScalarRange(self, range:[float, float]) -> None
| C++: virtual void GetScalarRange(double range[2])
| GetScalarRange(self) -> (float, float)
| C++: double *GetScalarRange()
|
| Convenience method to get the range of the first component (and
| only the first component) of any scalars in the data set. If the
| data has both point data and cell data, it returns the (min/max)
| range of combined point and cell data. If there are no point or
| cell scalars the method will return (0,1). Note: It might be
| necessary to call Update to create or refresh the scalars before
| calling this method. THIS METHOD IS THREAD SAFE IF FIRST CALLED
| FROM A SINGLE THREAD AND THE DATASET IS NOT MODIFIED
|
| HasAnyGhostCells(...)
| HasAnyGhostCells(self) -> bool
| C++: bool HasAnyGhostCells()
|
| Returns 1 if there are any ghost cells 0 otherwise.
|
| HasAnyGhostPoints(...)
| HasAnyGhostPoints(self) -> bool
| C++: bool HasAnyGhostPoints()
|
| Returns 1 if there are any ghost points 0 otherwise.
|
| NewCellIterator(...)
| NewCellIterator(self) -> vtkCellIterator
| C++: virtual vtkCellIterator *NewCellIterator()
|
| Return an iterator that traverses the cells in this data set.
|
| SetCellOrderAndRationalWeights(...)
| SetCellOrderAndRationalWeights(self, cellId:int,
| cell:vtkGenericCell) -> None
| C++: void SetCellOrderAndRationalWeights(vtkIdType cellId,
| vtkGenericCell *cell)
|
| Squeeze(...)
| Squeeze(self) -> None
| C++: virtual void Squeeze()
|
| Reclaim any extra memory used to store data. THIS METHOD IS NOT
| THREAD SAFE.
|
| UpdateCellGhostArrayCache(...)
| UpdateCellGhostArrayCache(self) -> None
| C++: void UpdateCellGhostArrayCache()
|
| Updates the pointer to the cell ghost array.
|
| UpdatePointGhostArrayCache(...)
| UpdatePointGhostArrayCache(self) -> None
| C++: void UpdatePointGhostArrayCache()
|
| Updates the pointer to the point ghost array.
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from vtkmodules.vtkCommonDataModel.vtkDataSet:
|
| CELL_DATA_FIELD = 2
|
| DATA_OBJECT_FIELD = 0
|
| FieldDataType = <class 'vtkmodules.vtkCommonDataModel.vtkDataSet.Field...
|
| POINT_DATA_FIELD = 1
|
| ----------------------------------------------------------------------
| Methods inherited from vtkmodules.vtkCommonDataModel.vtkDataObject:
|
| ALL_PIECES_EXTENT(...)
| ALL_PIECES_EXTENT() -> vtkInformationIntegerVectorKey
| C++: static vtkInformationIntegerVectorKey *ALL_PIECES_EXTENT()
|
| BOUNDING_BOX(...)
| BOUNDING_BOX() -> vtkInformationDoubleVectorKey
| C++: static vtkInformationDoubleVectorKey *BOUNDING_BOX()
|
| CELL_DATA_VECTOR(...)
| CELL_DATA_VECTOR() -> vtkInformationInformationVectorKey
| C++: static vtkInformationInformationVectorKey *CELL_DATA_VECTOR()
|
| DATA_EXTENT(...)
| DATA_EXTENT() -> vtkInformationIntegerPointerKey
| C++: static vtkInformationIntegerPointerKey *DATA_EXTENT()
|
| DATA_EXTENT_TYPE(...)
| DATA_EXTENT_TYPE() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *DATA_EXTENT_TYPE()
|
| DATA_NUMBER_OF_GHOST_LEVELS(...)
| DATA_NUMBER_OF_GHOST_LEVELS() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *DATA_NUMBER_OF_GHOST_LEVELS(
| )
|
| DATA_NUMBER_OF_PIECES(...)
| DATA_NUMBER_OF_PIECES() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *DATA_NUMBER_OF_PIECES()
|
| DATA_OBJECT(...)
| DATA_OBJECT() -> vtkInformationDataObjectKey
| C++: static vtkInformationDataObjectKey *DATA_OBJECT()
|
| DATA_PIECE_NUMBER(...)
| DATA_PIECE_NUMBER() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *DATA_PIECE_NUMBER()
|
| DATA_TIME_STEP(...)
| DATA_TIME_STEP() -> vtkInformationDoubleKey
| C++: static vtkInformationDoubleKey *DATA_TIME_STEP()
|
| DATA_TYPE_NAME(...)
| DATA_TYPE_NAME() -> vtkInformationStringKey
| C++: static vtkInformationStringKey *DATA_TYPE_NAME()
|
| DIRECTION(...)
| DIRECTION() -> vtkInformationDoubleVectorKey
| C++: static vtkInformationDoubleVectorKey *DIRECTION()
|
| DataHasBeenGenerated(...)
| DataHasBeenGenerated(self) -> None
| C++: void DataHasBeenGenerated()
|
| This method is called by the source when it executes to generate
| data. It is sort of the opposite of ReleaseData. It sets the
| DataReleased flag to 0, and sets a new UpdateTime.
|
| EDGE_DATA_VECTOR(...)
| EDGE_DATA_VECTOR() -> vtkInformationInformationVectorKey
| C++: static vtkInformationInformationVectorKey *EDGE_DATA_VECTOR()
|
| FIELD_ACTIVE_ATTRIBUTE(...)
| FIELD_ACTIVE_ATTRIBUTE() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *FIELD_ACTIVE_ATTRIBUTE()
|
| FIELD_ARRAY_TYPE(...)
| FIELD_ARRAY_TYPE() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *FIELD_ARRAY_TYPE()
|
| FIELD_ASSOCIATION(...)
| FIELD_ASSOCIATION() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *FIELD_ASSOCIATION()
|
| FIELD_ATTRIBUTE_TYPE(...)
| FIELD_ATTRIBUTE_TYPE() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *FIELD_ATTRIBUTE_TYPE()
|
| FIELD_NAME(...)
| FIELD_NAME() -> vtkInformationStringKey
| C++: static vtkInformationStringKey *FIELD_NAME()
|
| FIELD_NUMBER_OF_COMPONENTS(...)
| FIELD_NUMBER_OF_COMPONENTS() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *FIELD_NUMBER_OF_COMPONENTS()
|
| FIELD_NUMBER_OF_TUPLES(...)
| FIELD_NUMBER_OF_TUPLES() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *FIELD_NUMBER_OF_TUPLES()
|
| FIELD_OPERATION(...)
| FIELD_OPERATION() -> vtkInformationIntegerKey
| C++: static vtkInformationIntegerKey *FIELD_OPERATION()
|
| FIELD_RANGE(...)
| FIELD_RANGE() -> vtkInformationDoubleVectorKey
| C++: static vtkInformationDoubleVectorKey *FIELD_RANGE()
|
| GetActiveFieldInformation(...)
| GetActiveFieldInformation(info:vtkInformation,
| fieldAssociation:int, attributeType:int) -> vtkInformation
| C++: static vtkInformation *GetActiveFieldInformation(
| vtkInformation *info, int fieldAssociation, int attributeType)
|
| Return the information object within the input information
| object's field data corresponding to the specified association
| (FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS) and
| attribute (SCALARS, VECTORS, NORMALS, TCOORDS, or TENSORS)
|
| GetAssociationTypeAsString(...)
| GetAssociationTypeAsString(associationType:int) -> str
| C++: static const char *GetAssociationTypeAsString(
| int associationType)
|
| Given an integer association type, this static method returns a
| string type for the attribute (i.e. associationType = 0: returns
| "Points").
|
| GetAssociationTypeFromString(...)
| GetAssociationTypeFromString(associationName:str) -> int
| C++: static int GetAssociationTypeFromString(
| const char *associationName)
|
| Given a string association name, this static method returns an
| integer association type for the attribute (i.e. associationName
| = "Points": returns 0).
|
| GetAttributeTypeForArray(...)
| GetAttributeTypeForArray(self, arr:vtkAbstractArray) -> int
| C++: virtual int GetAttributeTypeForArray(vtkAbstractArray *arr)
|
| Retrieves the attribute type that an array came from. This is
| useful for obtaining which attribute type a input array to an
| algorithm came from (retrieved from
| GetInputAbstractArrayToProcesss).
|
| GetAttributes(...)
| GetAttributes(self, type:int) -> vtkDataSetAttributes
| C++: virtual vtkDataSetAttributes *GetAttributes(int type)
|
| Returns the attributes of the data object of the specified
| attribute type. The type may be: POINT - Defined in vtkDataSet
| subclasses. CELL - Defined in vtkDataSet subclasses. VERTEX -
| Defined in vtkGraph subclasses. EDGE - Defined in vtkGraph
| subclasses. ROW - Defined in vtkTable. The other attribute
| type, FIELD, will return nullptr since field data is stored as a
| vtkFieldData instance, not a vtkDataSetAttributes instance. To
| retrieve field data, use GetAttributesAsFieldData.
|
| @warning This method NEEDS to be
| overridden in subclasses to work as documented. If not, it
| returns nullptr for any type but FIELD.
|
| GetDataReleased(...)
| GetDataReleased(self) -> int
| C++: virtual vtkTypeBool GetDataReleased()
|
| Get the flag indicating the data has been released.
|
| GetFieldData(...)
| GetFieldData(self) -> vtkFieldData
| C++: virtual vtkFieldData *GetFieldData()
|
| GetGlobalReleaseDataFlag(...)
| GetGlobalReleaseDataFlag() -> int
| C++: static vtkTypeBool GetGlobalReleaseDataFlag()
|
| GetInformation(...)
| GetInformation(self) -> vtkInformation
| C++: virtual vtkInformation *GetInformation()
|
| Set/Get the information object associated with this data object.
|
| GetNamedFieldInformation(...)
| GetNamedFieldInformation(info:vtkInformation,
| fieldAssociation:int, name:str) -> vtkInformation
| C++: static vtkInformation *GetNamedFieldInformation(
| vtkInformation *info, int fieldAssociation, const char *name)
|
| Return the information object within the input information
| object's field data corresponding to the specified association
| (FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS) and name.
|
| GetUpdateTime(...)
| GetUpdateTime(self) -> int
| C++: vtkMTimeType GetUpdateTime()
|
| Used by Threaded ports to determine if they should initiate an
| asynchronous update (still in development).
|
| GlobalReleaseDataFlagOff(...)
| GlobalReleaseDataFlagOff(self) -> None
| C++: void GlobalReleaseDataFlagOff()
|
| GlobalReleaseDataFlagOn(...)
| GlobalReleaseDataFlagOn(self) -> None
| C++: void GlobalReleaseDataFlagOn()
|
| ORIGIN(...)
| ORIGIN() -> vtkInformationDoubleVectorKey
| C++: static vtkInformationDoubleVectorKey *ORIGIN()
|
| PIECE_EXTENT(...)
| PIECE_EXTENT() -> vtkInformationIntegerVectorKey
| C++: static vtkInformationIntegerVectorKey *PIECE_EXTENT()
|
| POINT_DATA_VECTOR(...)
| POINT_DATA_VECTOR() -> vtkInformationInformationVectorKey
| C++: static vtkInformationInformationVectorKey *POINT_DATA_VECTOR(
| )
|
| ReleaseData(...)
| ReleaseData(self) -> None
| C++: void ReleaseData()
|
| Release data back to system to conserve memory resource. Used
| during visualization network execution. Releasing this data does
| not make down-stream data invalid.
|
| RemoveNamedFieldInformation(...)
| RemoveNamedFieldInformation(info:vtkInformation,
| fieldAssociation:int, name:str) -> None
| C++: static void RemoveNamedFieldInformation(vtkInformation *info,
| int fieldAssociation, const char *name)
|
| Remove the info associated with an array
|
| SIL(...)
| SIL() -> vtkInformationDataObjectKey
| C++: static vtkInformationDataObjectKey *SIL()
|
| SPACING(...)
| SPACING() -> vtkInformationDoubleVectorKey
| C++: static vtkInformationDoubleVectorKey *SPACING()
|
| SetActiveAttribute(...)
| SetActiveAttribute(info:vtkInformation, fieldAssociation:int,
| attributeName:str, attributeType:int) -> vtkInformation
| C++: static vtkInformation *SetActiveAttribute(
| vtkInformation *info, int fieldAssociation,
| const char *attributeName, int attributeType)
|
| Set the named array to be the active field for the specified type
| (SCALARS, VECTORS, NORMALS, TCOORDS, or TENSORS) and association
| (FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS). Returns
| the active field information object and creates on entry if one
| not found.
|
| SetActiveAttributeInfo(...)
| SetActiveAttributeInfo(info:vtkInformation, fieldAssociation:int,
| attributeType:int, name:str, arrayType:int, numComponents:int,
| numTuples:int) -> None
| C++: static void SetActiveAttributeInfo(vtkInformation *info,
| int fieldAssociation, int attributeType, const char *name,
| int arrayType, int numComponents, int numTuples)
|
| Set the name, array type, number of components, and number of
| tuples within the passed information object for the active
| attribute of type attributeType (in specified association,
| FIELD_ASSOCIATION_POINTS or FIELD_ASSOCIATION_CELLS). If there
| is not an active attribute of the specified type, an entry in the
| information object is created. If arrayType, numComponents, or
| numTuples equal to -1, or name=nullptr the value is not changed.
|
| SetFieldData(...)
| SetFieldData(self, __a:vtkFieldData) -> None
| C++: virtual void SetFieldData(vtkFieldData *)
|
| Assign or retrieve a general field data to this data object.
|
| SetGlobalReleaseDataFlag(...)
| SetGlobalReleaseDataFlag(val:int) -> None
| C++: static void SetGlobalReleaseDataFlag(vtkTypeBool val)
|
| Turn on/off flag to control whether every object releases its
| data after being used by a filter.
|
| SetInformation(...)
| SetInformation(self, __a:vtkInformation) -> None
| C++: virtual void SetInformation(vtkInformation *)
|
| SetPointDataActiveScalarInfo(...)
| SetPointDataActiveScalarInfo(info:vtkInformation, arrayType:int,
| numComponents:int) -> None
| C++: static void SetPointDataActiveScalarInfo(
| vtkInformation *info, int arrayType, int numComponents)
|
| Convenience version of previous method for use (primarily) by the
| Imaging filters. If arrayType or numComponents == -1, the value
| is not changed.
|
| VERTEX_DATA_VECTOR(...)
| VERTEX_DATA_VECTOR() -> vtkInformationInformationVectorKey
| C++: static vtkInformationInformationVectorKey *VERTEX_DATA_VECTOR(
| )
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from vtkmodules.vtkCommonDataModel.vtkDataObject:
|
| AttributeTypes = <class 'vtkmodules.vtkCommonDataModel.vtkDataObject.A...
|
| CELL = 1
|
| EDGE = 5
|
| FIELD = 2
|
| FIELD_ASSOCIATION_CELLS = 1
|
| FIELD_ASSOCIATION_EDGES = 5
|
| FIELD_ASSOCIATION_NONE = 2
|
| FIELD_ASSOCIATION_POINTS = 0
|
| FIELD_ASSOCIATION_POINTS_THEN_CELLS = 3
|
| FIELD_ASSOCIATION_ROWS = 6
|
| FIELD_ASSOCIATION_VERTICES = 4
|
| FIELD_OPERATION_MODIFIED = 2
|
| FIELD_OPERATION_PRESERVED = 0
|
| FIELD_OPERATION_REINTERPOLATED = 1
|
| FIELD_OPERATION_REMOVED = 3
|
| FieldAssociations = <class 'vtkmodules.vtkCommonDataModel.vtkDataObjec...
|
| FieldOperations = <class 'vtkmodules.vtkCommonDataModel.vtkDataObject....
|
| NUMBER_OF_ASSOCIATIONS = 7
|
| NUMBER_OF_ATTRIBUTE_TYPES = 7
|
| POINT = 0
|
| POINT_THEN_CELL = 3
|
| ROW = 6
|
| VERTEX = 4
|
| ----------------------------------------------------------------------
| Methods inherited from vtkmodules.vtkCommonCore.vtkObject:
|
| AddObserver(...)
| AddObserver(self, event:int, command:Callback, priority:float=0.0) -> int
| C++: unsigned long AddObserver(const char* event,
| vtkCommand* command, float priority=0.0f)
|
| Add an event callback command(o:vtkObject, event:int) for an event type.
| Returns a handle that can be used with RemoveEvent(event:int).
|
| BreakOnError(...)
| BreakOnError() -> None
| C++: static void BreakOnError()
|
| This method is called when vtkErrorMacro executes. It allows the
| debugger to break on error.
|
| DebugOff(...)
| DebugOff(self) -> None
| C++: virtual void DebugOff()
|
| Turn debugging output off.
|
| DebugOn(...)
| DebugOn(self) -> None
| C++: virtual void DebugOn()
|
| Turn debugging output on.
|
| GetCommand(...)
| GetCommand(self, tag:int) -> vtkCommand
| C++: vtkCommand *GetCommand(unsigned long tag)
|
| GetDebug(...)
| GetDebug(self) -> bool
| C++: bool GetDebug()
|
| Get the value of the debug flag.
|
| GetGlobalWarningDisplay(...)
| GetGlobalWarningDisplay() -> int
| C++: static vtkTypeBool GetGlobalWarningDisplay()
|
| GetObjectDescription(...)
| GetObjectDescription(self) -> str
| C++: std::string GetObjectDescription() override;
|
| The object description printed in messages and PrintSelf output.
| To be used only for reporting purposes.
|
| GetObjectName(...)
| GetObjectName(self) -> str
| C++: virtual std::string GetObjectName()
|
| GlobalWarningDisplayOff(...)
| GlobalWarningDisplayOff() -> None
| C++: static void GlobalWarningDisplayOff()
|
| GlobalWarningDisplayOn(...)
| GlobalWarningDisplayOn() -> None
| C++: static void GlobalWarningDisplayOn()
|
| HasObserver(...)
| HasObserver(self, event:int, __b:vtkCommand) -> int
| C++: vtkTypeBool HasObserver(unsigned long event, vtkCommand *)
| HasObserver(self, event:str, __b:vtkCommand) -> int
| C++: vtkTypeBool HasObserver(const char *event, vtkCommand *)
| HasObserver(self, event:int) -> int
| C++: vtkTypeBool HasObserver(unsigned long event)
| HasObserver(self, event:str) -> int
| C++: vtkTypeBool HasObserver(const char *event)
|
| InvokeEvent(...)
| InvokeEvent(self, event:int, callData:Any) -> int
| C++: int InvokeEvent(unsigned long event, void* callData)
| InvokeEvent(self, event:str, callData:Any) -> int
| C++: int InvokeEvent(const char* event, void* callData)
| InvokeEvent(self, event:int) -> int
| C++: int InvokeEvent(unsigned long event)
| InvokeEvent(self, event:str) -> int
| C++: int InvokeEvent(const char* event)
|
| This method invokes an event and returns whether the event was
| aborted or not. If the event was aborted, the return value is 1,
| otherwise it is 0.
|
| Modified(...)
| Modified(self) -> None
| C++: virtual void Modified()
|
| Update the modification time for this object. Many filters rely
| on the modification time to determine if they need to recompute
| their data. The modification time is a unique monotonically
| increasing unsigned long integer.
|
| RemoveAllObservers(...)
| RemoveAllObservers(self) -> None
| C++: void RemoveAllObservers()
|
| RemoveObserver(...)
| RemoveObserver(self, __a:vtkCommand) -> None
| C++: void RemoveObserver(vtkCommand *)
| RemoveObserver(self, tag:int) -> None
| C++: void RemoveObserver(unsigned long tag)
|
| RemoveObservers(...)
| RemoveObservers(self, event:int, __b:vtkCommand) -> None
| C++: void RemoveObservers(unsigned long event, vtkCommand *)
| RemoveObservers(self, event:str, __b:vtkCommand) -> None
| C++: void RemoveObservers(const char *event, vtkCommand *)
| RemoveObservers(self, event:int) -> None
| C++: void RemoveObservers(unsigned long event)
| RemoveObservers(self, event:str) -> None
| C++: void RemoveObservers(const char *event)
|
| SetDebug(...)
| SetDebug(self, debugFlag:bool) -> None
| C++: void SetDebug(bool debugFlag)
|
| Set the value of the debug flag. A true value turns debugging on.
|
| SetGlobalWarningDisplay(...)
| SetGlobalWarningDisplay(val:int) -> None
| C++: static void SetGlobalWarningDisplay(vtkTypeBool val)
|
| This is a global flag that controls whether any debug, warning or
| error messages are displayed.
|
| SetObjectName(...)
| SetObjectName(self, objectName:str) -> None
| C++: virtual void SetObjectName(const std::string &objectName)
|
| Set/get the name of this object for reporting purposes. The name
| appears in warning and debug messages and in the Print output.
| Setting the object name does not change the MTime and does not
| invoke a ModifiedEvent. Derived classes implementing copying
| methods are expected not to copy the ObjectName.
|
| ----------------------------------------------------------------------
| Methods inherited from vtkmodules.vtkCommonCore.vtkObjectBase:
|
| FastDelete(...)
| FastDelete(self) -> None
| C++: virtual void FastDelete()
|
| Delete a reference to this object. This version will not invoke
| garbage collection and can potentially leak the object if it is
| part of a reference loop. Use this method only when it is known
| that the object has another reference and would not be collected
| if a full garbage collection check were done.
|
| GetAddressAsString(...)
| GetAddressAsString(self, classname:str) -> str
|
| Get address of C++ object in format 'Addr=%p' after casting to
| the specified type. This method is obsolete, you can get the
| same information from o.__this__.
|
| GetClassName(...)
| GetClassName(self) -> str
| C++: const char *GetClassName()
|
| Return the class name as a string.
|
| GetIsInMemkind(...)
| GetIsInMemkind(self) -> bool
| C++: bool GetIsInMemkind()
|
| A local state flag that remembers whether this object lives in
| the normal or extended memory space.
|
| GetReferenceCount(...)
| GetReferenceCount(self) -> int
| C++: int GetReferenceCount()
|
| Return the current reference count of this object.
|
| GetUsingMemkind(...)
| GetUsingMemkind() -> bool
| C++: static bool GetUsingMemkind()
|
| A global state flag that controls whether vtkObjects are
| constructed in the usual way (the default) or within the extended
| memory space.
|
| InitializeObjectBase(...)
| InitializeObjectBase(self) -> None
| C++: void InitializeObjectBase()
|
| Register(...)
| Register(self, o:vtkObjectBase)
| C++: virtual void Register(vtkObjectBase *o)
|
| Increase the reference count by 1.
|
| SetMemkindDirectory(...)
| SetMemkindDirectory(directoryname:str) -> None
| C++: static void SetMemkindDirectory(const char *directoryname)
|
| The name of a directory, ideally mounted -o dax, to memory map an
| extended memory space within. This must be called before any
| objects are constructed in the extended space. It can not be
| changed once setup.
|
| SetReferenceCount(...)
| SetReferenceCount(self, __a:int) -> None
| C++: void SetReferenceCount(int)
|
| Sets the reference count. (This is very dangerous, use with
| care.)
|
| UnRegister(...)
| UnRegister(self, o:vtkObjectBase)
| C++: virtual void UnRegister(vtkObjectBase* o)
|
| Decrease the reference count (release by another object). This
| has the same effect as invoking Delete() (i.e., it reduces the
| reference count by 1).
|
| UsesGarbageCollector(...)
| UsesGarbageCollector(self) -> bool
| C++: virtual bool UsesGarbageCollector()
|
| Indicate whether the class uses `vtkGarbageCollector` or not.
|
| Most classes will not need to do this, but if the class
| participates in a strongly-connected reference count cycle,
| participation can resolve these cycles.
|
| If overriding this method to return true, the `ReportReferences`
| method should be overridden to report references that may be in
| cycles.
|
| ----------------------------------------------------------------------
| Class methods inherited from vtkmodules.vtkCommonCore.vtkObjectBase:
|
| override(...)
| 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")
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 = examples.download_knee_full()
p = pv.Plotter()
p.add_volume(vol, cmap="bone", opacity="sigmoid")
p.show()
vol = pv.Wavelet()
vol.plot(volume=True)
Total running time of the script: (0 minutes 5.485 seconds)
