Added unit tests

src/data-hub/proactive-community-detection-microservice/app/tests/test_ClusterMetricsCalculator.py

+import unittest
+import sys
+for path in ['../', './']:
+    sys.path.insert(1, path)
+
+# python -m unittest discover
+from processing import ClusterMetricsCalculator2D
+
+
+class TestClusterMetricsCalculator(unittest.TestCase):
+    
+    def test__get_standard_deviation__same_points_many_decimals__zero_and_not_nan(self):
+        nodes = [{'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567},
+                {'f1': -8.58564, 'f2': 41.148567}]
+        calc = ClusterMetricsCalculator2D(nodes, ['f1','f2'], len(nodes), 1)
+        
+        self.assertAlmostEqual(0, calc.get_standard_deviation())
+
+    def test__get_range__almost_linear_distribution_in_2d__euclidean_distance(self):
+        l = [(-8.657802, 41.160978), (-8.65782, 41.160969), (-8.657838, 41.16096)]
+        nodes = [{'f1': e[0], 'f2': e[1]} for e in l]
+        
+        calc = ClusterMetricsCalculator2D(nodes, ['f1','f2'], len(nodes), 1)
+        
+        # https://www.calculatorsoup.com/calculators/geometry-plane/distance-two-points.php
+        self.assertAlmostEqual(4.0E-5, calc.get_range(), 5)
+    
+
+if __name__ == '__main__':
+    unittest.main()