pyvista.DataObjectFilters.resize

pyvista.DataObjectFilters.resize#

DataObjectFilters.resize(
*,
bounds: VectorLike[float] | None = None,
bounds_size: float | VectorLike[float] | None = None,
center: VectorLike[float] | None = None,
transform_all_input_vectors: bool = False,
inplace: bool = False,
) → _MeshType_co[source]#

Resize the dataset’s bounds.

This filter rescales and translates the mesh to fit specified bounds. This is useful for normalizing datasets, changing units, or fitting datasets into specific coordinate ranges.

Use bounds to set the mesh’s bounds directly or use bounds_size and center to implicitly set the new bounds.

Added in version 0.46.

Parameters:
boundsVectorLike[float], optional

Target bounds for the resized dataset in the format [xmin, xmax, ymin, ymax, zmin, zmax]. If provided, the dataset is scaled and translated to fit exactly within these bounds. Cannot be used together with bounds_size or center.

bounds_sizefloat | VectorLike[float], optional

Target size of the bounds for the resized dataset. Use a single float to specify the size of all three axes, or a 3-element vector to set the size of each axis independently. Cannot be used together with bounds.

centerVectorLike[float], optional

Center of the resized dataset in [x, y, z]. By default, the mesh’s center is used. Only used when bounds_size is specified.

transform_all_input_vectorsbool, default: False

When True, all input vectors are transformed as part of the resize. Otherwise, only the points, normals and active vectors are transformed.

inplacebool, default: False

If True, the dataset is modified in place. If False, a new dataset is returned.

Returns:
DataSet | MultiBlock

Resized dataset. Return type matches input.

See also

scale(), translate()

Scale and/or translate a mesh. Used internally by resize().

Examples

Load a mesh with asymmetric bounds and show them.

>>> import pyvista as pv
>>> mesh = pv.Cube(
...     x_length=1.0, y_length=2.0, z_length=3.0, center=(1.0, 2.0, 3.0)
... )
>>> mesh.bounds
BoundsTuple(x_min = 0.5,
            x_max = 1.5,
            y_min = 1.0,
            y_max = 3.0,
            z_min = 1.5,
            z_max = 4.5)

Resize it to fit specific bounds.

>>> resized = mesh.resize(bounds=[-1, 2, -3, 4, -5, 6])
>>> resized.bounds
BoundsTuple(x_min = -1.0,
            x_max =  2.0,
            y_min = -3.0,
            y_max =  4.0,
            z_min = -5.0,
            z_max =  6.0)

Resize the mesh so its size is 4.0.

>>> resized = mesh.resize(bounds_size=4.0)
>>> resized.bounds_size
(4.0, 4.0, 4.0)
>>> resized.bounds
BoundsTuple(x_min = -1.0,
            x_max =  3.0,
            y_min =  0.0,
            y_max =  4.0,
            z_min =  1.0,
            z_max =  5.0)

Specify a different size for each axis and set the desired center.

>>> resized = mesh.resize(bounds_size=(2.0, 1.0, 0.5), center=(1.0, 0.5, 0.25))
>>> resized.bounds_size
(2.0, 1.0, 0.5)
>>> resized.center
(1.0, 0.5, 0.25)

Center the mesh at the origin and normalize its bounds to 1.0.

>>> resized = mesh.resize(bounds_size=1.0, center=(0.0, 0.0, 0.0))
>>> resized.bounds
BoundsTuple(x_min = -0.5,
            x_max =  0.5,
            y_min = -0.5,
            y_max =  0.5,
            z_min = -0.5,
            z_max =  0.5)
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