making general-purpose metric function

src/metrics.py

@@ -0,0 +1,47 @@
+import torch
+import pickle
+import numpy as np
+
+import intersim.collisions
+
+def metrics(filestr: str, test_dataset, policy):
+    """
+    Calculate metrics using a) base filestring to a simulation, and b) the test dataset and learned policy
+    Args:
+        filestr (str): base string to outputs of a simulation
+        test_dataset: a dataset held for testing
+        policy: policy
+    """
+
+    # compute metrics using either
+    # a) simulation files that were saved under the trained policy with prefix 'policy'
+    # b) applying the policy to observations in the test dataset
+
+    # load trajectory
+
+    # count collisions (from function in intersim.collisions)
+
+    # calculate average velocity
+
+    # calculate divergence between velocity distributions
+    
+    # calcuate divergence between acceleration distributions
+     
+    pass
+
+def average_velocity(x):
+    """
+
+    """
+    pass
+
+def divergence(p, q, type='kl')
+    """
+    Calculate a divergence between p and q
+    Args:
+        p (torch.tensor): (n) samples from p
+        q (torch.tensor): (n) samples from q
+    Returns:
+        d (float): approximate divergence
+    """
+    pass