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162 lines
6.3 KiB
162 lines
6.3 KiB
import utils |
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import unittest |
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import numpy as np |
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import pandas as pd |
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import math |
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class TestUtils(unittest.TestCase): |
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#example test for test's workflow purposes |
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def test_segment_parsion(self): |
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self.assertTrue(True) |
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def test_confidence_all_normal_value(self): |
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segment = [1, 2, 0, 6, 8, 5, 3] |
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utils_result = utils.find_confidence(segment) |
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result = 1.6 |
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relative_tolerance = 1e-2 |
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self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance)) |
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def test_confidence_all_nan_value(self): |
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segment = [np.NaN, np.NaN, np.NaN, np.NaN] |
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self.assertEqual(utils.find_confidence(segment), 0) |
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def test_confidence_with_nan_value(self): |
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data = [np.NaN, np.NaN, 0, 8] |
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utils_result = utils.find_confidence(data) |
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result = 1.6 |
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relative_tolerance = 1e-2 |
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self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance)) |
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def test_interval_all_normal_value(self): |
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data = [1, 2, 1, 2, 4, 1, 2, 4, 5, 6] |
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data = pd.Series(data) |
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center = 4 |
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window_size = 2 |
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result = [1, 2, 4, 1, 2] |
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self.assertEqual(list(utils.get_interval(data, center, window_size)), result) |
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def test_interval_wrong_ws(self): |
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data = [1, 2, 4, 1, 2, 4] |
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data = pd.Series(data) |
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center = 3 |
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window_size = 6 |
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result = [1, 2, 4, 1, 2, 4] |
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self.assertEqual(list(utils.get_interval(data, center, window_size)), result) |
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def test_subtract_min_without_nan(self): |
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segment = [1, 2, 4, 1, 2, 4] |
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segment = pd.Series(segment) |
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result = [0, 1, 3, 0, 1, 3] |
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utils_result = list(utils.subtract_min_without_nan(segment)) |
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self.assertEqual(utils_result, result) |
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def test_subtract_min_with_nan(self): |
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segment = [np.NaN, 2, 4, 1, 2, 4] |
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segment = pd.Series(segment) |
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result = [2, 4, 1, 2, 4] |
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utils_result = list(utils.subtract_min_without_nan(segment)[1:]) |
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self.assertEqual(utils_result, result) |
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def test_get_convolve(self): |
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data = [1, 2, 3, 2, 2, 0, 2, 3, 4, 3, 2, 1, 1, 2, 3, 4, 3, 2, 0] |
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data = pd.Series(data) |
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pattern_index = [2, 8, 15] |
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window_size = 2 |
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av_model = [1, 2, 3, 2, 1] |
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result = [] |
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self.assertNotEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) |
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def test_get_convolve_with_nan(self): |
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data = [1, 2, 3, 2, np.NaN, 0, 2, 3, 4, np.NaN, 2, 1, 1, 2, 3, 4, 3, np.NaN, 0] |
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data = pd.Series(data) |
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pattern_index = [2, 8, 15] |
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window_size = 2 |
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av_model = [1, 2, 3, 2, 1] |
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result = utils.get_convolve(pattern_index, av_model, data, window_size) |
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for val in result: |
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self.assertFalse(np.isnan(val)) |
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def test_get_convolve_empty_data(self): |
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data = [] |
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pattern_index = [] |
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window_size = 2 |
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window_size_zero = 0 |
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av_model = [] |
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result = [] |
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self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result) |
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self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size_zero), result) |
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def test_get_distribution_density(self): |
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segment = [1, 1, 1, 3, 5, 5, 5] |
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segment = pd.Series(segment) |
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result = (3, 5, 1) |
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self.assertEqual(utils.get_distribution_density(segment), result) |
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def test_find_jump_parameters_center(self): |
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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] |
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segment = pd.Series(segment) |
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jump_center = [10, 11] |
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self.assertIn(utils.find_parameters(segment, 0, 'jump')[0], jump_center) |
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def test_find_jump_parameters_height(self): |
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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] |
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segment = pd.Series(segment) |
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jump_height = [3.5, 4] |
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self.assertGreaterEqual(utils.find_parameters(segment, 0, 'jump')[1], jump_height[0]) |
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self.assertLessEqual(utils.find_parameters(segment, 0, 'jump')[1], jump_height[1]) |
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def test_find_jump_parameters_length(self): |
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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] |
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segment = pd.Series(segment) |
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jump_length = 2 |
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self.assertEqual(utils.find_parameters(segment, 0, 'jump')[2], jump_length) |
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def test_find_drop_parameters_center(self): |
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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] |
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segment = pd.Series(segment) |
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drop_center = [14, 15, 16] |
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self.assertIn(utils.find_parameters(segment, 0, 'drop')[0], drop_center) |
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def test_find_drop_parameters_height(self): |
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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] |
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segment = pd.Series(segment) |
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drop_height = [3.5, 4] |
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self.assertGreaterEqual(utils.find_parameters(segment, 0, 'drop')[1], drop_height[0]) |
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self.assertLessEqual(utils.find_parameters(segment, 0, 'drop')[1], drop_height[1]) |
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def test_find_drop_parameters_length(self): |
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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] |
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segment = pd.Series(segment) |
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drop_length = 2 |
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self.assertEqual(utils.find_parameters(segment, 0, 'drop')[2], drop_length) |
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def test_get_av_model_empty_data(self): |
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patterns_list = [] |
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result = [] |
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self.assertEqual(utils.get_av_model(patterns_list), result) |
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def test_find_jump_nan_data(self): |
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data = [np.NaN, np.NaN, np.NaN, np.NaN] |
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data = pd.Series(data) |
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length = 2 |
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height = 3 |
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length_zero = 0 |
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height_zero = 0 |
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result = [] |
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self.assertEqual(utils.find_jump(data, height, length), result) |
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self.assertEqual(utils.find_jump(data, height_zero, length_zero), result) |
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def test_find_drop_nan_data(self): |
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data = [np.NaN, np.NaN, np.NaN, np.NaN] |
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data = pd.Series(data) |
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length = 2 |
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height = 3 |
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length_zero = 0 |
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height_zero = 0 |
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result = [] |
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self.assertEqual(utils.find_drop(data, height, length), result) |
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self.assertEqual(utils.find_drop(data, height_zero, length_zero), result) |
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if __name__ == '__main__': |
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unittest.main()
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