# 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
import openturns
from numpy import ndarray
from packaging import version
if version.parse(openturns.__version__) < version.parse("1.18"):
def get_eigenvalues(
result: openturns.KarhunenLoeveResult,
) -> openturns.Point:
return result.getEigenValues()
else:
[docs] def get_eigenvalues(
result: openturns.KarhunenLoeveResult,
) -> openturns.Point:
return result.getEigenvalues()
if version.parse(openturns.__version__) < version.parse("1.19"):
def compute_pcc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis_PCC(x, y)
def compute_prcc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis_PRCC(x, y)
def compute_pearson_correlation(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis_PearsonCorrelation(x, y)
def compute_signed_src(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis_SignedSRC(x, y)
def compute_spearman_correlation(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis_SpearmanCorrelation(x, y)
def compute_src(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis_SRC(x, y)
def compute_srrc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis_SRRC(x, y)
elif version.parse(openturns.__version__) < version.parse("1.20"):
def compute_pcc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis.PCC(x, y)
def compute_prcc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis.PRCC(x, y)
def compute_pearson_correlation(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis.PearsonCorrelation(x, y)
def compute_signed_src(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis.SignedSRC(x, y)
def compute_spearman_correlation(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis.SpearmanCorrelation(x, y)
def compute_src(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis.SRC(x, y)
def compute_srrc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis.SRRC(x, y)
else:
[docs] def compute_pcc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computePCC()
[docs] def compute_prcc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computePRCC()
[docs] def compute_pearson_correlation(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computePearsonCorrelation()
[docs] def compute_signed_src(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computeSRC()
[docs] def compute_spearman_correlation(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computeSpearmanCorrelation()
[docs] def compute_src(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computeSRC()
[docs] def compute_srrc(x: ndarray, y: ndarray) -> openturns.Point:
return openturns.CorrelationAnalysis(x, y).computeSRRC()
