gemseo / uncertainty / distributions / scipy

triangular module

Class to create a triangular distribution from the SciPy library.

This class inherits from SPDistribution.

Classes:

SPTriangularDistribution(variable[, …])

Create a triangular distribution.

class gemseo.uncertainty.distributions.scipy.triangular.SPTriangularDistribution(variable, minimum=0.0, mode=0.5, maximum=1.0, dimension=1)[source]

Bases: gemseo.uncertainty.distributions.scipy.distribution.SPDistribution

Create a triangular distribution.

Attributes
  • math_lower_bound (ndarray) – The mathematical lower bound of the random variable.

  • math_upper_bound (ndarray) – The mathematical upper bound 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.

  • distribution (InterfacedDistributionClass) – The probability distribution of the random variable.

  • marginals (list(InterfacedDistributionClass)) – The marginal distributions of the components of the random variable.

  • dimension (int) – The number of dimensions of the random variable.

  • variable_name (str) – The name of the random variable.

  • distribution_name (str) – The name of the probability distribution.

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

  • parameters (tuple or dict) – The parameters of the probability distribution.

  • standard_parameters (dict, optional) – The standard representation of the parameters of the distribution, used for its string representation.

  • math_lower_bound (ndarray) – The mathematical lower bound of the random variable.

  • math_upper_bound (ndarray) – The mathematical upper bound 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.

  • distribution (InterfacedDistributionClass) – The probability distribution of the random variable.

  • marginals (list(InterfacedDistributionClass)) – The marginal distributions of the components of the random variable.

  • dimension (int) – The number of dimensions of the random variable.

  • variable_name (str) – The name of the random variable.

  • distribution_name (str) – The name of the probability distribution.

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

  • parameters (tuple or dict) – The parameters of the probability distribution.

  • standard_parameters (dict, optional) – The standard representation of the parameters of the distribution, used for its string representation.

Parameters
  • variable (str) –

  • minimum (float) –

  • mode (float) –

  • maximum (float) –

  • dimension (int) –

Return type

None

Example

>>> from gemseo.uncertainty.distributions.scipy.triangular import (
...     SPTriangularDistribution
... )
>>> distribution = SPTriangularDistribution('x', -1, 0, 1)
>>> print(distribution)
triang(lower=-1, mode=0, upper=1)

Parameters: variable: The name of the random variable. interfaced_distribution: The name of the probability distribution,

typically the name of a class wrapped from an external library, such as ‘Normal’ for OpenTURNS or ‘norm’ for SciPy.

parameters: The parameters of the class

related to distribution.

dimension: The dimension of the random variable. standard_parameters: The standard representation

of the parameters of the probability distribution.

variable: The name of the random variable. interfaced_distribution: The name of the probability distribution,

typically the name of a class wrapped from an external library, such as ‘Normal’ for OpenTURNS or ‘norm’ for SciPy.

parameters: The parameters of the probability distribution. dimension: The dimension of the random variable. standard_parameters (dict, optional): The standard representation

of the parameters of the probability distribution. variable: The name of the triangular random variable. minimum: The minimum of the triangular random variable. mode: The mode of the triangular random variable. maximum: The maximum of the triangular random variable. dimension: The dimension of the triangular random variable.

Methods:

compute_cdf(vector)

Evaluate the cumulative density function (CDF).

compute_inverse_cdf(vector)

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

compute_samples([n_samples])

Sample the random variable.

plot([index, show, save, file_path, …])

Plot both probability and cumulative density functions for a given component.

plot_all([show, save, file_path, …])

Plot both probability and cumulative density functions for all components.

Attributes:

mean

The analytical mean of the random variable.

range

The numerical range.

standard_deviation

The analytical standard deviation of the random variable.

support

The mathematical support.

compute_cdf(vector)

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

numpy.ndarray

compute_inverse_cdf(vector)

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

numpy.ndarray

compute_samples(n_samples=1)

Sample the random variable.

Parameters

n_samples (int) – The number of samples.

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

numpy.ndarray

property mean

The analytical mean of the random variable.

plot(index=0, show=True, save=False, file_path=None, directory_path=None, file_name=None, file_extension=None)

Plot both probability and cumulative density functions for a given component.

Parameters
  • index (int) – The index of a component of the random variable.

  • save (bool) – If True, save the figure.

  • show (bool) – If True, display the figure.

  • file_path (Optional[Union[str, pathlib.Path]]) – The path of the file to save the figures. If the extension is missing, use file_extension. If None, create a file path from directory_path, file_name and file_extension.

  • directory_path (Optional[Union[str, pathlib.Path]]) – The path of the directory to save the figures. If None, use the current working directory.

  • file_name (Optional[str]) – The name of the file to save the figures. If None, use a default one generated by the post-processing.

  • file_extension (Optional[str]) – A file extension, e.g. ‘png’, ‘pdf’, ‘svg’, … If None, use a default file extension.

Returns

The figure.

Return type

matplotlib.figure.Figure

plot_all(show=True, save=False, file_path=None, directory_path=None, file_name=None, file_extension=None)

Plot both probability and cumulative density functions for all components.

Parameters
  • save (bool) – If True, save the figure.

  • show (bool) – If True, display the figure.

  • file_path (Optional[Union[str, pathlib.Path]]) – The path of the file to save the figures. If the extension is missing, use file_extension. If None, create a file path from directory_path, file_name and file_extension.

  • directory_path (Optional[Union[str, pathlib.Path]]) – The path of the directory to save the figures. If None, use the current working directory.

  • file_name (Optional[str]) – The name of the file to save the figures. If None, use a default one generated by the post-processing.

  • file_extension (Optional[str]) – A file extension, e.g. ‘png’, ‘pdf’, ‘svg’, … If None, use a default file extension.

Returns

The figures.

Return type

List[matplotlib.figure.Figure]

property range

The numerical range.

The numerical range is the interval defined by the lower and upper bounds numerically reachable by the random variable.

Here, the numerical range of the random variable is defined by one array for each component of the random variable, whose first element is the lower bound of this component while the second one is its upper bound.

property standard_deviation

The analytical standard deviation of the random variable.

property support

The mathematical support.

The mathematical support is the interval defined by the theoretical lower and upper bounds of the random variable.

Here, the mathematical range of the random variable is defined by one array for each component of the random variable, whose first element is the lower bound of this component while the second one is its upper bound.