refactor distributions (minor)

lms/djangoapps/analytics/distributions.py

@@ -5,7 +5,9 @@ Profile Distributions
 from django.db.models import Count
 from student.models import CourseEnrollment, UserProfile
 
-AVAILABLE_PROFILE_FEATURES = ['gender', 'level_of_education', 'year_of_birth']
+_EASY_CHOICE_FEATURES = ('gender', 'level_of_education')
+_OPEN_CHOICE_FEATURES = ('year_of_birth',)
+AVAILABLE_PROFILE_FEATURES = _EASY_CHOICE_FEATURES + _OPEN_CHOICE_FEATURES
 
 
 def profile_distribution(course_id, feature):
@@ -26,24 +28,16 @@ def profile_distribution(course_id, feature):
         OPEN_CHOICE - choices with a larger domain e.g. year_of_birth
     """
 
-    EASY_CHOICE_FEATURES = ['gender', 'level_of_education']
-    OPEN_CHOICE_FEATURES = ['year_of_birth']
-
-    def raise_not_implemented():
-        raise NotImplementedError("feature requested not implemented but is advertised in AVAILABLE_PROFILE_FEATURES {}" .format(feature))
-
     feature_results = {}
 
     if not feature in AVAILABLE_PROFILE_FEATURES:
         raise ValueError("unsupported feature requested for distribution '{}'".format(feature))
 
-    if feature in EASY_CHOICE_FEATURES:
+    if feature in _EASY_CHOICE_FEATURES:
         if feature == 'gender':
             raw_choices = UserProfile.GENDER_CHOICES
         elif feature == 'level_of_education':
             raw_choices = UserProfile.LEVEL_OF_EDUCATION_CHOICES
-        else:
-            raise raise_not_implemented()
 
         choices = [(short, full) for (short, full) in raw_choices] + [('no_data', 'No Data')]
 
@@ -53,14 +47,12 @@ def profile_distribution(course_id, feature):
                 count = CourseEnrollment.objects.filter(course_id=course_id, user__profile__gender=short).count()
             elif feature == 'level_of_education':
                 count = CourseEnrollment.objects.filter(course_id=course_id, user__profile__level_of_education=short).count()
-            else:
-                raise raise_not_implemented()
             data[short] = count
 
         feature_results['data'] = data
         feature_results['type'] = 'EASY_CHOICE'
         feature_results['display_names'] = dict(choices)
-    elif feature in OPEN_CHOICE_FEATURES:
+    elif feature in _OPEN_CHOICE_FEATURES:
         profiles = UserProfile.objects.filter(user__courseenrollment__course_id=course_id)
         query_distribution = profiles.values(feature).annotate(Count(feature)).order_by()
         # query_distribution is of the form [{'featureval': 'value1', 'featureval__count': 4}, {'featureval': 'value2', 'featureval__count': 2}, ...]
@@ -77,7 +69,5 @@ def profile_distribution(course_id, feature):
 
         feature_results['data'] = distribution
         feature_results['type'] = 'OPEN_CHOICE'
-    else:
-        raise raise_not_implemented()
 
     return feature_results

lms/djangoapps/analytics/tests/test_distributions.py

@@ -14,10 +14,10 @@ class TestAnalyticsDistributions(TestCase):
     def setUp(self):
         self.course_id = 'some/robot/course/id'
 
-        self.users = tuple(UserFactory(
+        self.users = [UserFactory(
             profile__gender=['m', 'f', 'o'][i % 3],
             profile__year_of_birth=i + 1930
-        ) for i in xrange(30))
+        ) for i in xrange(30)]
 
         self.ces = tuple(CourseEnrollment.objects.create(course_id=self.course_id, user=user) for user in self.users)
 
@@ -27,13 +27,6 @@ class TestAnalyticsDistributions(TestCase):
         self.assertNotIn(feature, AVAILABLE_PROFILE_FEATURES)
         profile_distribution(self.course_id, feature)
 
-    @raises(NotImplementedError)
-    def test_profile_distribution_not_implemented_feature(self):
-        feature = 'ROBOT_DO_NOT_USE_FEATURE'
-        AVAILABLE_PROFILE_FEATURES.append(feature)
-        self.assertIn(feature, AVAILABLE_PROFILE_FEATURES)
-        profile_distribution(self.course_id, feature)
-
     def test_profile_distribution_easy_choice(self):
         feature = 'gender'
         self.assertIn(feature, AVAILABLE_PROFILE_FEATURES)