test_plotting_util w/mypy checked

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()