#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright (c) 2016 Jérémie DECOCK (http://www.jdhp.org)

# This script is provided under the terms and conditions of the MIT license:
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.

"""
This module contains unit tests for the "benchmark.assess" module.
"""

from datapipe.benchmark.assess import normalize

import numpy as np

import unittest

class TestAssess(unittest.TestCase):
    """
    Contains unit tests for the "benchmark.assess" module.
    """

    # Test the "normalize" function ###########################################

    def test_normalize(self):
        """Check the output of the "normalize" function."""

        # Input image #################

        # [[128 128 128 128]
        #  [128  64 256 256]
        #  [128 128 128 128]]

        input_img = np.array([[100, 200, 200, 100],
                              [100, 300, 300, 100],
                              [100, 200, 200, 100]])

        # Output image ################

        output_img = normalize(input_img)

        # Expected output image #######

        expected_output_img = np.array([[0., 0.5, 0.5, 0.],
                                        [0., 1.0, 1.0, 0.],
                                        [0., 0.5, 0.5, 0.]])

        np.testing.assert_array_equal(output_img, expected_output_img)
    

if __name__ == '__main__':
    unittest.main()


Add a unit test.