Merge pull request #393 from Limmen/plotting

unit test plotting_util

simulation-system/libs/csle-common/tests/test_plotting_util.py

@@ -0,0 +1,48 @@
+from csle_common.util.plotting_util import PlottingUtil
+from scipy import stats
+import numpy as np
+
+
+class TestPlottingUtilSuite:
+ """
+ Test suite for plotting util
+ """
+
+ def test_running_average(self) -> None:
+ """
+ Test the function used to compute the running average of the last N elements of a vector x
+
+ :return: None
+ """
+ x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+ N = 3
+ expected = np.array([1, 2, 3, 3, 4, 5, 6, 7, 8, 9])
+ result = PlottingUtil.running_average(x, N)
+ assert result.any() == expected.any()
+
+ def test_mean_confidence_interval(self) -> None:
+ """
+ Test function that computes confidence intervals
+
+ :return: None
+ """
+ data = np.array([1, 2, 3, 4, 5])
+ mean, h = PlottingUtil.mean_confidence_interval(data=data, confidence=0.95)
+ expected_mean = np.mean(data)
+ expected_se = stats.sem(data)
+ expected_h = expected_se * stats.t.ppf((1 + 0.95) / 2.0, len(data) - 1)
+ assert expected_mean == mean
+ assert expected_h == h
+
+ def test_min_max_norm(self) -> None:
+ """
+ Test function that computes min-max normalization of a vector
+
+ :return: None
+ """
+ vec = np.array([1, 2, 3, 4, 5])
+ min_val = 1
+ max_val = 5
+ expected = np.array([0.0, 0.25, 0.5, 0.75, 1.0])
+ result = PlottingUtil.min_max_norm(vec, max_val, min_val)
+ assert result.any() == expected.any()