import utils import unittest import numpy as np import pandas as pd import math RELATIVE_TOLERANCE = 1e-1 class TestUtils(unittest.TestCase): #example test for test's workflow purposes def test_segment_parsion(self): self.assertTrue(True) def test_confidence_all_normal_value(self): segment = [1, 2, 0, 6, 8, 5, 3] utils_result = utils.find_confidence(segment)[0] result = 1.6 self.assertTrue(math.isclose(utils_result, result, rel_tol = RELATIVE_TOLERANCE)) def test_confidence_all_nan_value(self): segment = [np.NaN, np.NaN, np.NaN, np.NaN] self.assertEqual(utils.find_confidence(segment)[0], 0) def test_confidence_with_nan_value(self): data = [np.NaN, np.NaN, 0, 8] utils_result = utils.find_confidence(data)[0] result = 1.6 self.assertTrue(math.isclose(utils_result, result, rel_tol = RELATIVE_TOLERANCE)) def test_interval_all_normal_value(self): data = [1, 2, 1, 2, 4, 1, 2, 4, 5, 6] data = pd.Series(data) center = 4 window_size = 2 result = [1, 2, 4, 1, 2] self.assertEqual(list(utils.get_interval(data, center, window_size)), result) def test_interval_wrong_ws(self): data = [1, 2, 4, 1, 2, 4] data = pd.Series(data) center = 3 window_size = 6 result = [1, 2, 4, 1, 2, 4] self.assertEqual(list(utils.get_interval(data, center, window_size)), result) def test_subtract_min_without_nan(self): segment = [1, 2, 4, 1, 2, 4] segment = pd.Series(segment) result = [0, 1, 3, 0, 1, 3] utils_result = list(utils.subtract_min_without_nan(segment)) self.assertEqual(utils_result, result) def test_subtract_min_with_nan(self): segment = [np.NaN, 2, 4, 1, 2, 4] segment = pd.Series(segment) result = [2, 4, 1, 2, 4] utils_result = list(utils.subtract_min_without_nan(segment)[1:]) self.assertEqual(utils_result, result) def test_get_convolve(self): data = [1, 2, 3, 2, 2, 0, 2, 3, 4, 3, 2, 1, 1, 2, 3, 4, 3, 2, 0] data = pd.Series(data) pattern_index = [2, 8, 15] window_size = 2 av_model = [1, 2, 3, 2, 1] result = [] self.assertNotEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) def test_get_convolve_with_nan(self): data = [1, 2, 3, 2, np.NaN, 0, 2, 3, 4, np.NaN, 2, 1, 1, 2, 3, 4, 3, np.NaN, 0] data = pd.Series(data) pattern_index = [2, 8, 15] window_size = 2 av_model = [1, 2, 3, 2, 1] result = utils.get_convolve(pattern_index, av_model, data, window_size) for val in result: self.assertFalse(np.isnan(val)) def test_get_convolve_empty_data(self): data = [] pattern_index = [] window_size = 2 window_size_zero = 0 av_model = [] result = [] self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size_zero), result) def test_find_jump_parameters_center(self): segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5] segment = pd.Series(segment) jump_center = [10, 11] self.assertIn(utils.find_pattern_center(segment, 0, 'jump'), jump_center) def test_find_jump_parameters_height(self): segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5] segment = pd.Series(segment) jump_height = [3.5, 4] self.assertGreaterEqual(utils.find_parameters(segment, 0, 'jump')[0], jump_height[0]) self.assertLessEqual(utils.find_parameters(segment, 0, 'jump')[0], jump_height[1]) def test_find_jump_parameters_length(self): segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5] segment = pd.Series(segment) jump_length = 2 self.assertEqual(utils.find_parameters(segment, 0, 'jump')[1], jump_length) def test_find_drop_parameters_center(self): segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] segment = pd.Series(segment) drop_center = [14, 15, 16] self.assertIn(utils.find_pattern_center(segment, 0, 'drop'), drop_center) def test_find_drop_parameters_height(self): segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] segment = pd.Series(segment) drop_height = [3.5, 4] self.assertGreaterEqual(utils.find_parameters(segment, 0, 'drop')[0], drop_height[0]) self.assertLessEqual(utils.find_parameters(segment, 0, 'drop')[0], drop_height[1]) def test_find_drop_parameters_length(self): segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] segment = pd.Series(segment) drop_length = 2 self.assertEqual(utils.find_parameters(segment, 0, 'drop')[1], drop_length) def test_get_av_model_empty_data(self): patterns_list = [] result = [] self.assertEqual(utils.get_av_model(patterns_list), result) def test_get_av_model_normal_data(self): patterns_list = [[1, 1, 1], [2, 2, 2],[3,3,3]] result = [2.0, 2.0, 2.0] self.assertEqual(utils.get_av_model(patterns_list), result) def test_find_jump_nan_data(self): data = [np.NaN, np.NaN, np.NaN, np.NaN] data = pd.Series(data) length = 2 height = 3 length_zero = 0 height_zero = 0 result = [] self.assertEqual(utils.find_jump(data, height, length), result) self.assertEqual(utils.find_jump(data, height_zero, length_zero), result) def test_find_drop_nan_data(self): data = [np.NaN, np.NaN, np.NaN, np.NaN] data = pd.Series(data) length = 2 height = 3 length_zero = 0 height_zero = 0 result = [] self.assertEqual(utils.find_drop(data, height, length), result) self.assertEqual(utils.find_drop(data, height_zero, length_zero), result) def test_get_distribution_density(self): segment = [1, 1, 1, 3, 5, 5, 5] segment = pd.Series(segment) result = (3, 5, 1) self.assertEqual(utils.get_distribution_density(segment), result) def test_get_distribution_density_right(self): data = [1.0, 5.0, 5.0, 4.0] data = pd.Series(data) median = 3.0 max_line = 5.0 min_line = 1.0 utils_result = utils.get_distribution_density(data) self.assertTrue(math.isclose(utils_result[0], median, rel_tol = RELATIVE_TOLERANCE)) self.assertTrue(math.isclose(utils_result[1], max_line, rel_tol = RELATIVE_TOLERANCE)) self.assertTrue(math.isclose(utils_result[2], min_line, rel_tol = RELATIVE_TOLERANCE)) def test_get_distribution_density_left(self): data = [1.0, 1.0, 2.0, 1.0, 5.0] data = pd.Series(data) median = 3.0 max_line = 5.0 min_line = 1.0 utils_result = utils.get_distribution_density(data) self.assertTrue(math.isclose(utils_result[0], median, rel_tol = RELATIVE_TOLERANCE)) self.assertTrue(math.isclose(utils_result[1], max_line, rel_tol = RELATIVE_TOLERANCE)) self.assertTrue(math.isclose(utils_result[2], min_line, rel_tol = RELATIVE_TOLERANCE)) def test_get_distribution_density_short_data(self): data = [1.0, 5.0] data = pd.Series(data) segment = [1.0] segment = pd.Series(segment) utils_result_data = utils.get_distribution_density(data) utils_result_segment = utils.get_distribution_density(segment) self.assertEqual(len(utils_result_data), 3) self.assertEqual(utils_result_segment, (0, 0, 0)) def test_find_pattern_jump_center(self): data = [1.0, 1.0, 1.0, 5.0, 5.0, 5.0] data = pd.Series(data) median = 3.0 result = 3 self.assertEqual(result, utils.find_pattern_center(data, 0, 'jump')) def test_get_convolve_wrong_index(self): data = [1.0, 5.0, 2.0, 1.0, 6.0, 2.0] data = pd.Series(data) segemnts = [1, 11] av_model = [0.0, 4.0, 0.0] window_size = 1 try: utils.get_convolve(segemnts, av_model, data, window_size) except ValueError: self.fail('Method get_convolve raised unexpectedly') def test_get_av_model_for_different_length(self): patterns_list = [[1.0, 1.0, 2.0], [4.0, 4.0], [2.0, 2.0, 2.0], [3.0, 3.0], []] try: utils.get_av_model(patterns_list) except ValueError: self.fail('Method get_convolve raised unexpectedly') if __name__ == '__main__': unittest.main()