copulas.utils module

Utils module.

copulas.utils.check_valid_values(function)

Raise an exception if the given values are not supported.

Parameters:

function (callable) – Method whose unique argument is a numpy.array-like object.

Returns:

Decorated function

Return type:

callable

Raises:

ValueError – If there are missing or invalid values or if the dataset is empty.

copulas.utils.get_instance(obj, **kwargs)

Create new instance of the obj argument.

Parameters:

obj (str, type, instance)

copulas.utils.get_qualified_name(_object)

Return the Fully Qualified Name from an instance or class.

copulas.utils.random_state(function)

Set the random state before calling the function.

Parameters:

function (Callable) – The function to wrap around.

copulas.utils.scalarize(function)

Allow methods that only accepts 1-d vectors to work with scalars.

Parameters:

function (callable) – Function that accepts and returns vectors.

Returns:

Decorated function that accepts and returns scalars.

Return type:

callable

copulas.utils.set_random_state(random_state, set_model_random_state)

Context manager for managing the random state.

Parameters:

• random_state (int or np.random.RandomState) – The random seed or RandomState.

• set_model_random_state (function) – Function to set the random state on the model.

copulas.utils.store_args(__init__)

Save *args and **kwargs used in the __init__ of a copula.

Parameters:

__init__ (callable) – __init__ function to store their arguments.

Returns:

Decorated __init__ function.

Return type:

callable

copulas.utils.validate_random_state(random_state)

Validate random state argument.

Parameters:

random_state (int, numpy.random.RandomState, tuple, or None) – Seed or RandomState for the random generator.

Output:

numpy.random.RandomState

copulas.utils.vectorize(function)

Allow a method that only accepts scalars to accept vectors too.

This decorator has two different behaviors depending on the dimensionality of the array passed as an argument:

1-d array

It will work under the assumption that the function argument is a callable with signature:

function(self, X, *args, **kwargs)

where X is an scalar magnitude.

In this case the arguments of the input array will be given one at a time, and both the input and output of the decorated function will have shape (n,).

2-d array

It will work under the assumption that the function argument is a callable with signature:

function(self, X0, ..., Xj, *args, **kwargs)

where Xi are scalar magnitudes.

It will pass the contents of each row unpacked on each call. The input is espected to have shape (n, j), the output a shape of (n,)

It will return a function that is guaranteed to return a numpy.array.

Parameters:

function (callable) – Function that only accept and return scalars.

Returns:

Decorated function that can accept and return numpy.array.

Return type:

callable