implement generate login interface and login

test/test_user_interface.py

@@ -1,11 +1,12 @@
 import pytest
 from src.user_interface import UserInterface
 
-@pytest.mark.parametrize("user_interface", [
-    (
-        UserInterface.new_interface(7, 10)
-    )
-])
+
+@pytest.fixture()
+def user_interface():
+    return UserInterface.new_interface(7, 10)
+
+
 def test_dispersion(user_interface):
     pre_dispersion_graph = user_interface.attribute_adjacency_graph()
     user_interface.disperse_interface()
@@ -14,3 +15,22 @@ def test_dispersion(user_interface):
     for _ in range(10000):
         for attr, adj_graph in pre_dispersion_graph.items():
             assert (adj_graph.isdisjoint(post_dispersion_graph[attr]))
+
+
+def test_shuffle_attrs(user_interface):
+    """there's no easy way to test this. At some point we'll have to run this code thousands of time to see if we get
+    expected statistical outcomes like:
+    - every attribute gets to every key with a uniform distribution
+    - every attribute is adjacent to every other attribute with uniform distribution
+    - the order in which the attributes move from key to key is random (i.e. the distance traveled is uniform)
+    """
+    pre_shuffle_interface = user_interface.interface_index
+    user_interface.shuffle_interface()
+    post_shuffle_interface = user_interface.interface_index
+    for i in range(1000):
+        assert (not all(
+            post_shuffle_interface[idx] == pre_shuffle_interface[idx] for idx in range(len(post_shuffle_interface))
+        ))
+        assert (not all(
+            post_shuffle_interface[idx] != pre_shuffle_interface[idx] for idx in range(len(post_shuffle_interface))
+        ))