Introduce rating functions

These hotness and confidence calculation algorithms come from Reddit
and have been tweaked based on our experience on the Dillo project.

tests/test_api/test_utils.py

@@ -184,3 +184,31 @@ class NodeSetattrTest(unittest.TestCase):
 
         node_setattr(node, 'b.complex', {None: 5})
         self.assertEqual({'b': {'complex': {None: 5}}}, node)
+
+
+class TestRating(unittest.TestCase):
+    def test_hotness(self):
+        """We expect the sorted values to reflect the original order in the
+        list.
+        """
+        from datetime import datetime, timezone
+        from pillar.api.utils.rating import hot
+        t = datetime(2017, 2, 11, 0, 0, 0, 0, timezone.utc)
+        y = datetime(2017, 2, 10, 0, 0, 0, 0, timezone.utc)
+        w = datetime(2017, 2, 5, 0, 0, 0, 0, timezone.utc)
+        cases = [
+            (hot(1, 8, t), 'today super bad'),
+            (hot(0, 3, t), 'today slightly worse'),
+            (hot(0, 2, y), 'yesterday bad'),
+            (hot(0, 2, t), 'today bad'),
+            (hot(4, 4, w), 'last week controversial'),
+            (hot(7, 1, w), 'last week very good'),
+            (hot(5, 1, y), 'yesterday medium'),
+            (hot(5, 0, y), 'yesterday good'),
+            (hot(7, 1, y), 'yesterday very good'),
+            (hot(4, 4, t), 'today controversial'),
+            (hot(7, 1, t), 'today very good'),
+        ]
+        sorted_by_hot = sorted(cases, key=lambda tup: tup[0])
+        for idx, t in enumerate(sorted_by_hot):
+            self.assertEqual(cases[idx][0], t[0])