pyvista.core._validation.validate

Functions that validate input and return a standard representation.

Added in version 0.43.0.

A validate function typically:

• Uses check functions to check the type and/or value of input arguments.

• Applies (optional) constraints, e.g. input or output must have a specific length, shape, type, data-type, etc.

• Accepts many different input types or values and standardizes the output as a single representation with known properties.

Functions

validate_array(arr, /, *[, must_have_shape, ...])	Check and validate a numeric array meets specific requirements.
validate_array3(arr, /, *[, reshape, broadcast])	Validate a numeric 1D array with 3 elements.
validate_arrayN(arr, /, *[, reshape])	Validate a numeric 1D array.
validate_arrayN_unsigned(arr, /, *[, reshape])	Validate a numeric 1D array of non-negative (unsigned) integers.
validate_arrayNx3(arr, /, *[, reshape])	Validate an array is numeric and has shape Nx3.
validate_axes(*axes[, normalize, ...])	Validate 3D axes vectors.
validate_data_range(rng, /, **kwargs)	Validate a data range.
validate_dimensionality(dimensionality, /, *)	Validate a dimensionality.
validate_number(num, /, *[, reshape])	Validate a real, finite number.
validate_transform3x3(transform, /, *[, ...])	Validate transform-like input as a 3x3 ndarray.
validate_transform4x4(transform, /, *[, ...])	Validate transform-like input as a 4x4 ndarray.