pyvista.Transform.apply

Transform.apply(

obj: VectorLike[float] | MatrixLike[float] | DataSet | MultiBlock,

/,

*,

inverse: bool = False,

copy: bool = True,

transform_all_input_vectors: bool = False,

)

Apply the current transformation matrix to points or a dataset.

Note

Points with integer values are cast to a float type before the transformation is applied. A similar casting is also performed when transforming datasets. See also the notes at transform() which is used by this filter under the hood.

Parameters:

objVectorLike[float] | MatrixLike[float] | pyvista.DataSet

Object to apply the transformation to.

inversebool, default: False

Apply the transformation using the inverse_matrix instead of the matrix.

copybool, default: True

Return a copy of the input with the transformation applied. Set this to False to transform the input directly and return it. Only applies to NumPy arrays and datasets. A copy is always returned for tuple and list inputs or point arrays with integers.

transform_all_input_vectorsbool, default: False

When True, all input vectors are transformed. Otherwise, only the points, normals and active vectors are transformed. Has no effect if the input is not a dataset.

Returns:

np.ndarray or pyvista.DataSet

Transformed array or dataset.

See also

pyvista.DataSetFilters.transform

Transform a dataset.

Examples

Apply a transformation to a point.

>>> import numpy as np
>>> import pyvista as pv
>>> point = (1, 2, 3)
>>> transform = pv.Transform().scale(2)
>>> transformed_point = transform.apply(point)
>>> transformed_point
array([2., 4., 6.])

Apply a transformation to a points array.

>>> points = np.array([[1, 2, 3], [4, 5, 6]])
>>> transformed_points = transform.apply(points)
>>> transformed_points
array([[ 2.,  4.,  6.],
       [ 8., 10., 12.]])

Apply a transformation to a dataset.

>>> dataset = pv.PolyData(points)
>>> transformed_dataset = transform.apply(dataset)
>>> transformed_dataset.points
pyvista_ndarray([[ 2.,  4.,  6.],
                 [ 8., 10., 12.]], dtype=float32)

Apply the inverse.

>>> inverted_dataset = transform.apply(dataset, inverse=True)
>>> inverted_dataset.points
pyvista_ndarray([[0.5, 1. , 1.5],
                 [2. , 2.5, 3. ]], dtype=float32)