# Source code for gemseo.uncertainty.statistics.tolerance_interval.lognormal

# 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
#
# 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
"""Computation of tolerance intervals from a data-fitted log-normal distribution."""
from __future__ import annotations

from numpy import exp
from numpy import ndarray

from gemseo.uncertainty.statistics.tolerance_interval.distribution import (
ToleranceIntervalSide,
)
from gemseo.uncertainty.statistics.tolerance_interval.normal import (
NormalToleranceInterval,
)

[docs]class LogNormalToleranceInterval(NormalToleranceInterval):
"""Computation of tolerance intervals from a data-fitted log-normal distribution.

The formulae come from the R library *tolerance* [1]_.

.. [1] Derek S. Young,
*tolerance: An R Package for Estimating Tolerance Intervals*,
Journal of Statistical Software, 36(5), 2010
"""

def __init__(
self,
size: int,
mean: float,
std: float,
location: float,
) -> None:
""".. # noqa: D205 D212 D415
Args:
mean: The estimation of the mean of the natural logarithm
of a log-normal distributed random variable.
std: The estimation of the standard deviation of the natural logarithm
of a log-normal distributed random variable.
location: The estimation of the location of the log-normal distributed.
"""
super().__init__(size, mean, std)
self.__location = location

[docs]    def compute(  # noqa: D102
self,
coverage: float,
confidence: float = 0.95,
side: ToleranceIntervalSide = ToleranceIntervalSide.BOTH,
) -> tuple[ndarray, ndarray]:
lower, upper = super().compute(coverage, confidence, side)
return exp(lower) + self.__location, exp(upper) + self.__location