pyvista_validation.validate.validate_arrayNx3

pyvista_validation.validate.validate_arrayNx3#

pyvista_validation.validate.validate_arrayNx3(arr: VectorLike[float] | MatrixLike[float], /, *, reshape: bool = True, **kwargs)#

Validate an array is numeric and has shape Nx3.

The array is checked to ensure its input values:

  • have shape (N, 3) or can be reshaped to (N, 3)

  • are numeric

The returned array is formatted so that its values:

  • have shape (N, 3).

Parameters:
arrVectorLike[float] | MatrixLike[float]

Array to validate.

reshapebool, default: True

If True, 1D arrays with 3 elements are considered valid input and are reshaped to (1, 3) to ensure the output is two-dimensional.

**kwargsdict, optional

Additional keyword arguments passed to validate_array().

Returns:
np.ndarray

Validated array with shape (N, 3).

See also

validate_arrayN

Similar function for one-dimensional arrays.

validate_array

Generic array validation function.

Examples

Validate an Nx3 array.

>>> from pyvista_validation import validate_arrayNx3
>>> validate_arrayNx3(((1, 2, 3), (4, 5, 6)))
array([[1, 2, 3],
       [4, 5, 6]])

One-dimensional 3-element arrays are automatically reshaped to 2D.

>>> validate_arrayNx3([1, 2, 3])
array([[1, 2, 3]])

Add additional constraints.

>>> validate_arrayNx3(((1, 2, 3), (4, 5, 6)), must_be_in_range=[0, 10])
array([[1, 2, 3],
       [4, 5, 6]])
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