# 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.
"""Compatibility between different versions of openturns."""
from __future__ import annotations
from typing import Final
import openturns
from numpy import ndarray
from packaging import version
OT_VERSION: Final[version.Version] = version.parse(openturns.__version__)
IS_OT_LOWER_THAN_1_20: Final[bool] = OT_VERSION < version.parse("1.20")
if OT_VERSION < version.parse("1.18"):
def get_eigenvalues( # noqa:D103
result: openturns.KarhunenLoeveResult,
) -> openturns.Point:
return result.getEigenValues()
else:
[docs] def get_eigenvalues( # noqa:D103
result: openturns.KarhunenLoeveResult,
) -> openturns.Point:
return result.getEigenvalues()
if version.parse(openturns.__version__) >= version.parse("1.20"):
def compute_pcc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis(x, y).computePCC()
def compute_prcc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis(x, y).computePRCC()
def compute_pearson_correlation( # noqa: D103
x: ndarray, y: ndarray
) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computePearsonCorrelation()
def compute_spearman_correlation( # noqa: D103
x: ndarray, y: ndarray
) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computeSpearmanCorrelation()
def compute_src(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis(x, y).computeSRC()
def compute_srrc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis(x, y).computeSRRC()
def compute_kendall_tau(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis(x, y).computeKendallTau()
def compute_squared_src(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis(x, y).computeSquaredSRC()
elif version.parse(openturns.__version__) >= version.parse("1.19"):
def compute_pcc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis.PCC(x, y)
def compute_prcc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis.PRCC(x, y)
def compute_pearson_correlation( # noqa: D103
x: ndarray, y: ndarray
) -> openturns.Point:
return openturns.CorrelationAnalysis.PearsonCorrelation(x, y)
def compute_spearman_correlation( # noqa: D103
x: ndarray, y: ndarray
) -> openturns.Point:
return openturns.CorrelationAnalysis.SpearmanCorrelation(x, y)
def compute_src(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis.SRC(x, y)
def compute_srrc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis.SRRC(x, y)
def compute_kendall_tau(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
raise NotImplementedError("Requires openturns>=1.20")
def compute_squared_src(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
raise NotImplementedError("Requires openturns>=1.20")
else:
[docs] def compute_pcc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis_PCC(x, y)
[docs] def compute_prcc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis_PRCC(x, y)
[docs] def compute_pearson_correlation( # noqa: D103
x: ndarray, y: ndarray
) -> openturns.Point:
return openturns.CorrelationAnalysis_PearsonCorrelation(x, y)
[docs] def compute_spearman_correlation( # noqa: D103
x: ndarray, y: ndarray
) -> openturns.Point:
return openturns.CorrelationAnalysis_SpearmanCorrelation(x, y)
[docs] def compute_src(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis_SRC(x, y)
[docs] def compute_srrc(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
return openturns.CorrelationAnalysis_SRRC(x, y)
[docs] def compute_kendall_tau(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
raise NotImplementedError("Requires openturns>=1.20")
[docs] def compute_squared_src(x: ndarray, y: ndarray) -> openturns.Point: # noqa: D103
raise NotImplementedError("Requires openturns>=1.20")
