better abstraction and error handling/logging

app/lib/irt/models/rasch.py

@@ -15,9 +15,18 @@ class Rasch:
 
   @classmethod
   def ability_estimate(self, items) -> float:
+    # responses are mapped into a matrix, where each row and item
+    # and each column is an exam form result
     # we'll likely have to change this to something more robust
     # when we get into more complex response types
     responses = np.array([[int(item.response)] for item in items])
+    # the difficulty (b param) for each item is in an ordered list
     difficulty = np.array([item.b_param for item in items])
+    # the package currently utilizes a fixed a param (discrimination)
     discrimination = np.linspace(1, 1, len(difficulty))
-    return ability_mle(responses, difficulty, discrimination)
+    # there are many methodologies to calculate ability from a data set of responses
+    # this is what our client currently uses but we should expand this to allow for
+    # switching between methodologies when needed
+    # it also currrently only does a single ability estimation
+    # at some point we can also accommodate batch ability estimates if need be
+    return ability_mle(responses, difficulty, discrimination).tolist()[0]