Alexandr Velikiy
6 years ago
committed by
Alexey Velikiy
1 changed files with 123 additions and 1 deletions
@ -1,11 +1,133 @@
|
||||
import utils |
||||
import unittest |
||||
import numpy as np |
||||
import pandas as pd |
||||
import math |
||||
|
||||
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) |
||||
result = 1.6 |
||||
relative_tolerance = 1e-2 |
||||
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) |
||||
|
||||
def test_confidence_with_nan_value(self): |
||||
data = [np.NaN, np.NaN, 0, 8] |
||||
utils_result = utils.find_confidence(data) |
||||
result = 1.6 |
||||
relative_tolerance = 1e-2 |
||||
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 |
||||
av_model = [] |
||||
result = [] |
||||
self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), 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_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_jump_parameters(segment, 0)[0], 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_jump_parameters(segment, 0)[1], jump_height[0]) |
||||
self.assertLessEqual(utils.find_jump_parameters(segment, 0)[1], 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_jump_parameters(segment, 0)[2], 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] |
||||
self.assertIn(utils.find_drop_parameters(segment, 0)[0], 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_drop_parameters(segment, 0)[1], drop_height[0]) |
||||
self.assertLessEqual(utils.find_drop_parameters(segment, 0)[1], 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_drop_parameters(segment, 0)[2], drop_length) |
||||
|
||||
if __name__ == '__main__': |
||||
unittest.main() |
||||
|
Loading…
Reference in new issue