# Source code for gemseo.uncertainty.distributions.distribution

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                           documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
r"""Abstract class defining the concept of probability distribution.

Overview
--------

The abstract :class:.Distribution class implements the concept of
probability distribution <https://en.wikipedia.org/wiki/Probability_distribution>_,
which is a mathematical function giving the probabilities of occurrence
of different possible outcomes of a random variable for an experiment.
The normal distribution <https://en.wikipedia.org/wiki/Normal_distribution>_
with its famous *bell curve* is a well-known example of probability distribution.

.. seealso::

This abstract class is enriched by concrete ones,
such as :class:.OTDistribution interfacing the OpenTURNS probability distributions
and :class:.SPDistribution interfacing the SciPy probability distributions.

Construction
------------

The :class:.Distribution of a given uncertain variable is built
from a recognized distribution name (e.g. 'Normal' for OpenTURNS or 'norm' for SciPy),
a variable dimension, a set of parameters
and optionally a standard representation of these parameters.

Capabilities
------------

From a :class:.Distribution, we can easily get statistics,
such as :attr:.Distribution.mean,
:attr:.Distribution.standard_deviation. We can also get the
numerical :attr:.Distribution.range and
mathematical :attr:.Distribution.support.

.. note::

We call mathematical *support* the set of values that the random variable
can take in theory, e.g. :math:]-\infty,+\infty[ for a Gaussian variable,
and numerical *range* the set of values that it can can take in practice,
taking into account the values rounded to zero double precision.
Both support and range are described in terms of lower and upper bounds

We can also evaluate the cumulative density function
(:meth:.Distribution.compute_cdf)
for the different marginals of the random variable,
as well as the inverse cumulative density function
(:meth:.Distribution.compute_inverse_cdf). We can plot them,
either for a given marginal (:meth:.Distribution.plot)
or for all marginals (:meth:.Distribution.plot_all).

Lastly, we can compute realizations of the random variable
by means of the :meth:.Distribution.compute_samples method.
"""
from __future__ import annotations

import logging
from pathlib import Path
from typing import Any
from typing import Callable
from typing import Iterable
from typing import Mapping
from typing import Tuple
from typing import Union

import matplotlib.pyplot as plt
from docstring_inheritance import GoogleDocstringInheritanceMeta
from matplotlib.figure import Figure
from numpy import arange
from numpy import array
from numpy import ndarray

from gemseo.utils.file_path_manager import FilePathManager
from gemseo.utils.file_path_manager import FileType
from gemseo.utils.matplotlib_figure import save_show_figure
from gemseo.utils.string_tools import MultiLineString
from gemseo.utils.string_tools import pretty_str

LOGGER = logging.getLogger(__name__)

StandardParametersType = Mapping[str, Union[str, int, float]]
ParametersType = Union[Tuple[str, int, float], StandardParametersType]

"""Probability distribution related to a random variable.

The dimension of the random variable can be greater than 1. In this case,
the same distribution is applied to all components of the random variable
under the hypothesis that these components are stochastically independent.

The string representation of a distribution
interfacing a distribution called :code:'MyDistribution'
with parameters :code:(2,3) is 'MyDistribution(2, 3)
if no standard parameters are passed.
If the standard parameters are :code:{a: 2, b: 3}
(resp. :code:{a_inv: 2, b: 3}),
then the standard representation is: 'MyDistribution(a=2, b=3)
(resp. 'MyDistribution(a_inv=0.5, b=3))
Standard parameters are useful to redefine the name of the parameters.
For example, some exponential distributions consider the notion of rate
while other ones consider the notion of scale, which is the inverse of the rate...
even in the background, the distribution is the same!
"""

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

marginals: list[type]
"""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[Any] | dict[str, Any]
"""The parameters of the probability distribution."""

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

_MU = "mu"
_SIGMA = "sigma"
_LOWER = "lower"
_UPPER = "upper"
_MODE = "mode"
_RATE = "rate"
_LOC = "loc"

_COMPOSED_DISTRIBUTION = None

def __init__(
self,
variable: str,
interfaced_distribution: str,
parameters: ParametersType,
dimension: int = 1,
standard_parameters: StandardParametersType | None = None,
) -> None:
""".. # noqa: D205,D212,D415
Args:
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.
"""
self.math_lower_bound = None
self.math_upper_bound = None
self.num_lower_bound = None
self.num_upper_bound = None
self.distribution = None
self.marginals = None
self.dimension = dimension
self.variable_name = variable
self.distribution_name = interfaced_distribution
self.transformation = variable
self.parameters = parameters
if standard_parameters is None:
self.standard_parameters = self.parameters
else:
self.standard_parameters = standard_parameters
self.__file_path_manager = FilePathManager(
FileType.FIGURE, default_name=f"distribution_{self.variable_name}"
)
msg = MultiLineString()
msg.add("Define the random variable: {}", variable)
msg.indent()
LOGGER.debug("%s", msg)

def __str__(self) -> str:
return f"{self.distribution_name}({pretty_str(self.standard_parameters)})"

[docs]    def compute_samples(
self,
n_samples: int = 1,
) -> ndarray:
"""Sample the random variable.

Args:
n_samples: 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.
"""
raise NotImplementedError

[docs]    def compute_cdf(
self,
vector: Iterable[float],
) -> ndarray:
"""Evaluate the cumulative density function (CDF).

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

Args:
vector: A realization of the random variable.

Returns:
The CDF values of the components of the random variable.
"""
raise NotImplementedError

[docs]    def compute_inverse_cdf(
self,
vector: Iterable[float],
) -> ndarray:
"""Evaluate the inverse of the cumulative density function (ICDF).

Args:
vector: 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.
"""
raise NotImplementedError

@property
def mean(self) -> ndarray:
"""The analytical mean of the random variable."""
raise NotImplementedError

@property
def standard_deviation(self) -> ndarray:
"""The analytical standard deviation of the random variable."""
raise NotImplementedError

@property
def range(self) -> list[ndarray]:
"""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.
"""
value = [
array([l_b, u_b])
for l_b, u_b in zip(self.num_lower_bound, self.num_upper_bound)
]
return value

@property
def support(self) -> list[ndarray]:
"""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.
"""
value = [
array([l_b, u_b])
for l_b, u_b in zip(self.math_lower_bound, self.math_upper_bound)
]
return value

[docs]    def plot_all(
self,
show: bool = True,
save: bool = False,
file_path: str | Path | None = None,
directory_path: str | Path | None = None,
file_name: str | None = None,
file_extension: str | None = None,
) -> list[Figure]:
"""Plot both probability and cumulative density functions for all components.

Args:
save: If True, save the figure.
show: If True, display the figure.
file_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: The path of the directory to save the figures.
If None, use the current working directory.
file_name: The name of the file to save the figures.
If None, use a default one generated by the post-processing.
file_extension: A file extension, e.g. 'png', 'pdf', 'svg', ...
If None, use a default file extension.

Returns:
The figures.
"""
figures = []
for index in range(self.dimension):
figures.append(
self.plot(
index=index,
show=show,
save=save,
file_path=file_path,
file_name=file_name,
file_extension=file_extension,
directory_path=directory_path,
)
)
return figures

[docs]    def plot(
self,
index: int = 0,
show: bool = True,
save: bool = False,
file_path: str | Path | None = None,
directory_path: str | Path | None = None,
file_name: str | None = None,
file_extension: str | None = None,
) -> Figure:
"""Plot both probability and cumulative density functions for a given component.

Args:
index: The index of a component of the random variable.
save: If True, save the figure.
show: If True, display the figure.
file_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: The path of the directory to save the figures.
If None, use the current working directory.
file_name: The name of the file to save the figures.
If None, use a default one generated by the post-processing.
file_extension: A file extension, e.g. 'png', 'pdf', 'svg', ...
If None, use a default file extension.

Returns:
The figure.
"""
variable_name = self.variable_name
if self.dimension > 1:
variable_name = f"{variable_name}({index})"
l_b = self.num_lower_bound[index]
u_b = self.num_upper_bound[index]
x_values = arange(l_b, u_b, (u_b - l_b) / 100)
y1_values = [self._pdf(index)(x_value) for x_value in x_values]
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.suptitle(f"Probability distribution of {variable_name}")
ax1.plot(x_values, y1_values)
ax1.set_xlabel(variable_name)
ax1.set_title("PDF")
y2_values = [self._cdf(index)(x_value) for x_value in x_values]
ax2.plot(x_values, y2_values)
ax2.set_xlabel(variable_name)
ax2.set_title("Cumulative density function")
if save:
file_path = self.__file_path_manager.create_file_path(
file_path=file_path,
file_name=file_name,
directory_path=directory_path,
file_extension=file_extension,
)
if self.dimension > 1:
file_path = self.__file_path_manager.add_suffix(file_path, index)
else:
file_path = None
save_show_figure(fig, show, file_path)
return fig

def _pdf(
self,
index: int,
) -> Callable:
"""Get the probability density function of a marginal.

Args:
index: The index of a component of the random variable.

Return:
The probability density function
of the given component of the random variable.
"""

def pdf(
point: float,
) -> float:
"""Probability Density Function (PDF).

Args:
point: An evaluation point.

Returns:
The PDF value at the evaluation point.
"""
raise NotImplementedError

return pdf

def _cdf(
self,
index: int,
) -> Callable:
"""Get the cumulative density function of a marginal.

Args:
index: The index of a component of the random variable.

Return:
The cumulative density function
of the given component of the random variable.
"""

def cdf(
level: float,
) -> float:
"""Cumulative Density Function (CDF).

Args:
level: A probability level.

Returns:
The CDF value for the probability level.
"""
raise NotImplementedError

return cdf
`