gemseo / uncertainty / distributions / scipy

distribution module¶

Class to create a probability distribution from the SciPy library.

The SPDistribution class is a concrete class inheriting from Distribution which is an abstract one. SP stands for scipy which is the library it relies on.

The SPDistribution of a given uncertain variable is built from mandatory arguments:

a variable name,
a distribution name recognized by SciPy,
a set of parameters provided as a dictionary of keyword arguments named as the arguments of the scipy constructor of this distribution.

Warning

The distribution parameters must be provided according to the signature of the scipy classes. Access the scipy documentation.

The constructor has also optional arguments:

a variable dimension (default: 1),
a standard representation of these parameters (default: use parameters).

class gemseo.uncertainty.distributions.scipy.distribution.SPDistribution(variable, interfaced_distribution, parameters, dimension=1, standard_parameters=None)[source]

Bases: Distribution

SciPy probability distribution.

Create a probability distribution for an uncertain variable from its dimension and distribution names and properties.

Example

>>> from gemseo.uncertainty.distributions.scipy.distribution import (
...    SPDistribution
... )
>>> distribution = SPDistribution('x', 'expon', {'loc': 3, 'scale': 1/2.})
>>> print(distribution)
expon(loc=3, scale=0.5)

Parameters:

variable (str) – The name of the random variable.
interfaced_distribution (str) – The name of the probability distribution, typically the name of a class wrapped from an external library, such as "norm" for SciPy.
parameters (ParametersType) – The parameters of the probability distribution.
dimension (int) –
The dimension of the random variable.

By default it is set to 1.
standard_parameters (StandardParametersType | None) – The standard representation of the parameters of the probability distribution.

compute_cdf(vector)[source]

Evaluate the cumulative density function (CDF).

Evaluate the CDF of the components of the random variable for a given realization of this random variable.

Parameters:: vector (Iterable[float]) – A realization of the random variable.
Returns:: The CDF values of the components of the random variable.
Return type:: ndarray

compute_inverse_cdf(vector)[source]

Evaluate the inverse of the cumulative density function (ICDF).

Parameters:: vector (Iterable[float]) – A vector of values comprised between 0 and 1 whose length is equal to the dimension of the random variable.
Returns:: The ICDF values of the components of the random variable.
Return type:: ndarray

compute_samples(n_samples=1)[source]

Sample the random variable.

Parameters:

n_samples (int) –

The number of samples.

By default it is set to 1.

Returns:

The samples of the random variable,

The number of columns is equal to the dimension of the variable and the number of lines is equal to the number of samples.

Return type:

ndarray

dimension: int: The number of dimensions of the random variable.

distribution: type: The probability distribution of the random variable.

distribution_name: str: The name of the probability distribution.

marginals: list[type]: The marginal distributions of the components of the random variable.

math_lower_bound: ndarray: The mathematical lower bound of the random variable.

math_upper_bound: ndarray: The mathematical upper bound of the random variable.

property mean: ndarray: The analytical mean of the random variable.

num_lower_bound: ndarray: The numerical lower bound of the random variable.

num_upper_bound: ndarray: The numerical upper bound of the random variable.

parameters: tuple[Any] | dict[str, Any]: The parameters of the probability distribution.

property standard_deviation: ndarray: The analytical standard deviation of the random variable.

standard_parameters: dict[str, str] | None: The standard representation of the parameters of the distribution, used for its string representation.

transformation: str: The transformation applied to the random variable, e.g. ‘sin(x)’.

variable_name: str: The name of the random variable.

Examples using SPDistribution¶

Probability distributions based on SciPy