dicube.validation

validation

Validation utilities for DiCube library.

This module provides reusable validation functions that automatically raise appropriate DicomCubeError exceptions with consistent error messages and context. These utilities help minimize code duplication and provide clean error handling throughout the DiCube library.

Functions

Name	Description
validate_array_shape	Validate numpy array shape and dimensions.
validate_file_exists	Validate that a file exists.
validate_folder_exists	Validate that a folder exists.
validate_not_none	Validate that a value is not None.
validate_numeric_range	Validate that a numeric value is within a specified range.
validate_parameter_type	Validate parameter type.
validate_shape_consistency	Validate that two arrays have consistent shapes.
validate_string_not_empty	Validate that a string is not None or empty.

validate_array_shape

validation.validate_array_shape(
    array,
    expected_dims=None,
    min_dims=None,
    max_dims=None,
    name='array',
    context='',
    exception_class=DataConsistencyError,
)

Validate numpy array shape and dimensions.

Args: array (np.ndarray): The array to validate. expected_dims (int, optional): Expected number of dimensions. min_dims (int, optional): Minimum number of dimensions. max_dims (int, optional): Maximum number of dimensions. name (str): Name of the array parameter. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: np.ndarray: The validated array.

Raises: DataConsistencyError: If array shape validation fails.

validate_file_exists

validation.validate_file_exists(
    file_path,
    context='',
    exception_class=InvalidCubeFileError,
)

Validate that a file exists.

Args: file_path (str): Path to the file to validate. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: str: The validated file path.

Raises: InvalidCubeFileError: If the file does not exist.

validate_folder_exists

validation.validate_folder_exists(
    folder_path,
    context='',
    exception_class=InvalidCubeFileError,
)

Validate that a folder exists.

Args: folder_path (str): Path to the folder to validate. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: str: The validated folder path.

Raises: InvalidCubeFileError: If the folder does not exist or is not a directory.

validate_not_none

validation.validate_not_none(
    value,
    name,
    context='',
    exception_class=DicomCubeError,
)

Validate that a value is not None.

Args: value: The value to validate. name (str): Name of the parameter being validated. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: Any: The validated value if not None.

Raises: DicomCubeError: If the value is None.

validate_numeric_range

validation.validate_numeric_range(
    value,
    name,
    min_value=None,
    max_value=None,
    context='',
    exception_class=DataConsistencyError,
)

Validate that a numeric value is within a specified range.

Args: value (int or float): The numeric value to validate. name (str): Name of the parameter being validated. min_value (int or float, optional): Minimum allowed value. max_value (int or float, optional): Maximum allowed value. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: int or float: The validated value.

Raises: DataConsistencyError: If the value is outside the specified range.

validate_parameter_type

validation.validate_parameter_type(
    value,
    expected_type,
    name,
    context='',
    exception_class=DataConsistencyError,
)

Validate parameter type.

Args: value: The value to validate. expected_type (Type or tuple): Expected type or tuple of types. name (str): Name of the parameter being validated. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: Any: The validated value.

Raises: DataConsistencyError: If the value is not of the expected type.

validate_shape_consistency

validation.validate_shape_consistency(
    array1,
    array2,
    name1='array1',
    name2='array2',
    context='',
    exception_class=DataConsistencyError,
)

Validate that two arrays have consistent shapes.

Args: array1 (np.ndarray): First array to compare. array2 (np.ndarray): Second array to compare. name1 (str): Name of the first array parameter. name2 (str): Name of the second array parameter. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: tuple: Both validated arrays.

Raises: DataConsistencyError: If array shapes are inconsistent.

validate_string_not_empty

validation.validate_string_not_empty(
    value,
    name,
    context='',
    exception_class=DicomCubeError,
)

Validate that a string is not None or empty.

Args: value (str): The string value to validate. name (str): Name of the parameter being validated. context (str): Context about the operation being performed. exception_class (Type[DicomCubeError]): Exception class to raise on failure.

Returns: str: The validated string.

Raises: DicomCubeError: If the string is None or empty.