text: # Copyright (c) 2019-2021 - for information on the respective copyright owner
  # see the NOTICE file and/or the repository
  # https://github.com/boschresearch/pylife
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import pytest
  import pandas as pd
  import numpy as np
  
  import pylife.stress.stresssignal
  
  
  def test_voigt():
      df = pd.DataFrame({'S11': [1.0], 'S22': [1.0], 'S33': [1.0],
                         'S12': [1.0], 'S13': [1.0], 'S23': [1.0]})
      df.voigt
  
  
  def test_voigt_fail():
      df = pd.DataFrame({'S11': [1.0], 'S22': [1.0], 'S33': [1.0],
                         'S12': [1.0], 'S31': [1.0], 'S23': [1.0]})
      with pytest.raises(AttributeError, match=r'^StressTensorVoigt.*Missing S13'):
          df.voigt
  
  
  def test_cyclic_sigma_a_sigma_m():
      df = pd.DataFrame({'sigma_a': [1.0], 'sigma_m': [1.0]})
      df.cyclic_stress
  
  
  def test_cyclic_only_sigma_a():
      df = pd.DataFrame({'sigma_a': [1.0]})
      np.testing.assert_equal(df.cyclic_stress.to_pandas()['sigma_m'].to_numpy(), np.zeros_like(df['sigma_a'].to_numpy()))
  
  
  def test_cyclic_sigma_a_R():
      df = pd.DataFrame({'sigma_a': [1.0, 1.0], 'R': [0.0, -1.0]})
      np.testing.assert_equal(df.cyclic_stress.to_pandas()['sigma_m'].to_numpy(), [1.0, 0.0])
  
  
  @pytest.mark.parametrize('R, sigma_m_check', [
      (0.0, 1.0),
      (-1.0, 0.0)
  ])
  def test_cyclic_sigma_constant_R(R, sigma_m_check):
      df = pd.DataFrame({'sigma_a': [1.0], 'R': [R]})
      np.testing.assert_equal(df.cyclic_stress.to_pandas()['sigma_m'].to_numpy(), sigma_m_check)
  
  
  def test_cyclic_no_sigma_a():
      df = pd.DataFrame({'sigma_m': [0.0]})
      with pytest.raises(AttributeError):
          df.cyclic_stress
  