pyvista_validation.validate.validate_array3

pyvista_validation.validate.validate_array3#

pyvista_validation.validate.validate_array3(arr: float | VectorLike[float] | MatrixLike[float], /, *, reshape: bool = True, broadcast: bool = False, **kwargs)#

Validate a numeric 1D array with 3 elements.

The array is checked to ensure its input values:

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

  • are numeric and real

The returned array is formatted so that it has shape (3,).

Parameters:
arrfloat | VectorLike[float] | MatrixLike[float]

Array to validate.

reshapebool, default: True

If True, 2D vectors with shape (1, 3) are considered valid input, and are reshaped to (3,) to ensure the output is consistently one-dimensional.

broadcastbool, default: False

If True, scalar values or 1D arrays with a single element are considered valid input and the single value is broadcast to a length 3 array.

**kwargsdict, optional

Additional keyword arguments passed to validate_array().

Returns:
np.ndarray

Validated 1D array with 3 elements.

See also

validate_number

Similar function for a single number.

validate_arrayN

Similar function for one-dimensional arrays.

validate_array

Generic array validation function.

Examples

Validate a 1D array with three elements.

>>> from pyvista_validation import validate_array3
>>> validate_array3((1, 2, 3))
array([1, 2, 3])

2D 3-element arrays are automatically reshaped to be 1D.

>>> validate_array3([[1, 2, 3]])
array([1, 2, 3])

Scalar 0-dimensional values can be automatically broadcast as a 3-element 1D array.

>>> validate_array3(42.0, broadcast=True)
array([42.0, 42.0, 42.0])

Add additional constraints if needed.

>>> validate_array3((1, 2, 3), must_be_nonnegative=True)
array([1, 2, 3])
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