Validations¶

The _Validator class is used internally for validating parameters of transformation classes.

This interface is only necessary to use if you are creating your own custom transformations.

class sigment.internals.validator._Validator[source]¶

Performs internal validations on various input types.

integer(self, item, desc)[source]¶

Validates an integer.

Parameters

item: int: The item to validate.
desc: str: A description of the item being validated.

Returns

item: int: The original input item if valid.

Raises

TypeError: If the value is not an int.

Examples

>>> validator = _Validator()
>>> n_crops = 3
>>> validator.integer(n_crops, 'n_crops (number of sections to crop)')

real(self, item, desc)[source]¶

Validates a real number.

Parameters

item: float: The item to validate.
desc: str: A description of the item being validated.

Returns

item: float: The original input item if valid.

Raises

TypeError: If the value cannot be converted into a float.

Examples

>>> validator = _Validator()
>>> var = 5.0
>>> validator.real(var, 'var (variance)')

string(self, item, desc)[source]¶

Validates a string.

Parameters

item: str: The item to validate.
desc: str: A description of the item being validated.

Returns

item: str: The original input item if valid.

Raises

TypeError: If the value is not a str.

Examples

>>> validator = _Validator()
>>> method = 'median'
>>> method = validator.string(method, 'method (filter type)')

boolean(self, item, desc)[source]¶

Validates a boolean.

Parameters

item: bool: The item to validate.
desc: str: A description of the item being validated.

Returns

item: bool: The original input item if valid.

Raises

TypeError: If the value is not True or False.

Examples

>>> validator = _Validator()
>>> indep = True
>>> indep = validator.boolean(indep, 'indep (whether to independently normalize channels)')

one_of(self, item, desc, items)[source]¶

Validates that an item is one of some permitted values.

Parameters

item: Any: The item to validate.
desc: str: A description of the item being validated.
items: Iterable[Any]: The collection of permitted values to check against.

Returns

item: Any: The original input item if valid.

Raises

ValueError: If the value is not one of the specified permitted values.

Examples

>>> validator = _Validator()
>>> method = 'median'
>>> method = validator.one_of(method, 'method (filter type)', ['median', 'mean'])

restricted_integer(self, item, desc, condition, expected)[source]¶

Validates an integer and checks that it satisfies some condition.

Parameters

item: int: The item to validate.
desc: str: A description of the item being validated.
condition: lambda: A condition to check the item against.
expected: str: A description of the condition, or expected value.

Returns

item: int: The original input item if valid.

Raises

ValueError: If the int value does not satisfy the provided condition.

Examples

>>> validator = _Validator()
>>> n_crops = 3
>>> n_crops = validator.restricted_integer(
>>>     n_crops, 'n_crops (number of sections to crop)',
>>>     lambda x: 0 <= x <= 5, 'between zero and five')

restricted_float(self, item, desc, condition, expected)[source]¶

Validates a float and checks that it satisfies some condition.

Parameters

item: float: The item to validate.
desc: str: A description of the item being validated.
condition: lambda: A condition to check the item against.
expected: str: A description of the condition, or expected value.

Returns

item: float: The original input item if valid.

Raises

ValueError: If the float value does not satisfy the provided condition.

Examples

>>> validator = _Validator()
>>> var = 5.0
>>> validator.restricted_float(var, 'var (variance)', lambda x: x > 0, 'positive')

integer_value(self, item, desc, condition, expected)[source]¶

Validates an integer value or value range and checks that it satisfies some condition.

Parameters

item: int: The item to validate.
desc: str: A description of the item being validated.
condition: lambda: A condition to check the item against.
expected: str: A description of the condition, or expected value.

Returns

item: tuple(int, int): The value range if the input item is valid.

Raises

TypeError: If the value is not a int or (int, int).
ValueError: If the value is a tuple with more than two elements (sanity check).

Examples

>>> validator = _Validator()
>>> validator.integer_value(5, 'window_size (window size)', lambda a, b: 0 < a <= b, 'positive')
>>> #=> (5, 5)
>>> validator.integer_value((5, 10), 'window_size (window size)', lambda a, b: 0 < a <= b, 'positive')
>>> #=> (5, 10)

float_value(self, item, desc, condition, expected)[source]¶

Validates a float value or value range and checks that it satisfies some condition.

Parameters

item: float: The item to validate.
desc: str: A description of the item being validated.
condition: lambda: A condition to check the item against.
expected: str: A description of the condition, or expected value.

Returns

item: tuple(float, float): The value range if the input item is valid.

Raises

TypeError: If the value is not a float or (float, float).
ValueError: If the value is a tuple with more than two elements (sanity check).

Examples

>>> validator = _Validator()
>>> validator.float_value(5.0, 'var (variance)', lambda a, b: 0 < a <= b, 'positive')
>>> #=> (5.0, 5.0)
>>> validator.float_value((5, 10), 'var (variance)', lambda a, b: 0 < a <= b, 'positive')
>>> #=> (5.0, 10.0)

signal(self, signal)[source]¶

Validates a WAV audio signal.

Parameters

signal: numpy.ndarray [shape (T,) or (1xT) for mono, (2xT) for stereo]: The input signal to validate.

Returns

signal: numpy.ndarray [shape (T,) for mono, (2xT) for stereo]: The original signal if it is valid.

Raises

TypeError

If the signal is of the wrong type.
If the signal is not a floating point numpy.ndarray.
If the signal is a floating point numpy.ndarray with samples outside \([-1, 1]\).

ValueError

If the signal is a numpy.ndarray with more than two dimensions.
If the signal has more than two channels.

random_state(self, state)[source]¶

Validates a random state object or seed.

Parameters

state: None, int, numpy.random.RandomState: A random state object or seed.

Returns

state: numpy.random.RandomState: A random state object.

Raises

TypeError: If the random state object is of the incorrect type.