def test_override_fixture(self, app):
mr = MockRows(
io.StringIO('sid,first_name,last_name\n20000000,Martin,Van Buren'))
with register_mock(data_loch.get_sis_section_enrollments, mr):
data = data_loch.get_sis_section_enrollments(2178, 12345)
assert len(data) == 1
assert {
'sid': 20000000,
'first_name': 'Martin',
'last_name': 'Van Buren'
} == data[0]
def test_fixture_not_found(self, app):
no_db = data_loch.get_sis_section_enrollments(0, 0)
# TODO Real data_loch queries will return an empty list if the course is not found.
assert no_db is None
def get_course_student_profiles(term_id, section_id, offset=None, limit=None, featured=None):
benchmark = get_benchmarker('get_course_student_profiles')
benchmark('begin')
enrollment_rows = data_loch.get_sis_section_enrollments(
term_id,
section_id,
offset=offset,
limit=limit,
)
sids = [str(r['sid']) for r in enrollment_rows]
if offset or len(sids) >= 50:
count_result = data_loch.get_sis_section_enrollments_count(term_id, section_id)
total_student_count = count_result[0]['count']
else:
total_student_count = len(sids)
# If we have a featured UID not already present in the result set, add the corresponding SID only if the
# student is enrolled.
if featured and not next((r for r in enrollment_rows if str(r['uid']) == featured), None):
featured_enrollment_rows = data_loch.get_sis_section_enrollment_for_uid(term_id, section_id, featured)
if featured_enrollment_rows:
sids = [str(featured_enrollment_rows[0]['sid'])] + sids
# TODO It's probably more efficient to store class profiles in the loch, rather than distilling them
# on the fly from full profiles.
students = get_full_student_profiles(sids)
benchmark('begin enrollments query')
enrollments_for_term = data_loch.get_enrollments_for_term(term_id, sids)
benchmark('end enrollments query')
enrollments_by_sid = {row['sid']: json.loads(row['enrollment_term']) for row in enrollments_for_term}
academic_standing = get_academic_standing_by_sid(sids, as_dicts=True)
term_gpas = get_term_gpas_by_sid(sids, as_dicts=True)
all_canvas_sites = {}
benchmark('begin profile transformation')
for student in students:
# Strip SIS details to lighten the API load.
sis_profile = student.pop('sisProfile', None)
if sis_profile:
student['academicCareerStatus'] = sis_profile.get('academicCareerStatus')
student['cumulativeGPA'] = sis_profile.get('cumulativeGPA')
student['cumulativeUnits'] = sis_profile.get('cumulativeUnits')
student['degree'] = sis_profile.get('degree')
student['level'] = _get_sis_level_description(sis_profile)
student['currentTerm'] = sis_profile.get('currentTerm')
student['majors'] = _get_active_plan_descriptions(sis_profile)
student['transfer'] = sis_profile.get('transfer')
term = enrollments_by_sid.get(student['sid'])
if term:
# Strip the enrollments list down to the section of interest.
enrollments = term.pop('enrollments', [])
for enrollment in enrollments:
_section = next((s for s in enrollment['sections'] if str(s['ccn']) == section_id), None)
if _section:
canvas_sites = enrollment.get('canvasSites', [])
student['enrollment'] = {
'canvasSites': canvas_sites,
'enrollmentStatus': _section.get('enrollmentStatus', None),
'grade': enrollment.get('grade', None),
'gradingBasis': enrollment.get('gradingBasis', None),
'midtermGrade': enrollment.get('midtermGrade', None),
}
student['analytics'] = analytics.mean_metrics_across_sites(canvas_sites, 'student')
# If more than one course site is associated with this section, derive mean metrics from as many sites as possible.
for site in canvas_sites:
if site['canvasCourseId'] not in all_canvas_sites:
all_canvas_sites[site['canvasCourseId']] = site
continue
student['academicStanding'] = academic_standing.get(student['sid'])
student['termGpa'] = term_gpas.get(student['sid'])
benchmark('end profile transformation')
mean_metrics = analytics.mean_metrics_across_sites(all_canvas_sites.values(), 'courseMean')
mean_metrics['gpa'] = {}
mean_gpas = data_loch.get_sis_section_mean_gpas(term_id, section_id)
for row in mean_gpas:
mean_metrics['gpa'][str(row['gpa_term_id'])] = row['avg_gpa']
benchmark('end')
return {
'students': students,
'totalStudentCount': total_student_count,
'meanMetrics': mean_metrics,
}
def get_course_student_profiles(term_id, section_id, offset=None, limit=None):
enrollment_rows = data_loch.get_sis_section_enrollments(
term_id,
section_id,
scope=get_student_query_scope(),
offset=offset,
limit=limit,
)
sids = [str(r['sid']) for r in enrollment_rows]
if offset or len(sids) >= 50:
count_result = data_loch.get_sis_section_enrollments_count(
term_id, section_id, scope=get_student_query_scope())
total_student_count = count_result[0]['count']
else:
total_student_count = len(sids)
# TODO It's probably more efficient to store class profiles in the loch, rather than distilling them
# on the fly from full profiles.
students = get_full_student_profiles(sids)
enrollments_for_term = data_loch.get_enrollments_for_term(term_id, sids)
enrollments_by_sid = {
row['sid']: json.loads(row['enrollment_term'])
for row in enrollments_for_term
}
all_canvas_sites = {}
for student in students:
# Strip SIS details to lighten the API load.
sis_profile = student.pop('sisProfile', None)
if sis_profile:
student['cumulativeGPA'] = sis_profile.get('cumulativeGPA')
student['cumulativeUnits'] = sis_profile.get('cumulativeUnits')
student['level'] = sis_profile.get('level', {}).get('description')
student['majors'] = sorted(
plan.get('description')
for plan in sis_profile.get('plans', []))
term = enrollments_by_sid.get(student['sid'])
student['hasCurrentTermEnrollments'] = False
if term:
# Strip the enrollments list down to the section of interest.
enrollments = term.pop('enrollments', [])
for enrollment in enrollments:
_section = next((s for s in enrollment['sections']
if str(s['ccn']) == section_id), None)
if _section:
canvas_sites = enrollment.get('canvasSites', [])
student['enrollment'] = {
'canvasSites': canvas_sites,
'enrollmentStatus':
_section.get('enrollmentStatus', None),
'grade': enrollment.get('grade', None),
'gradingBasis': enrollment.get('gradingBasis', None),
}
student['analytics'] = analytics.mean_metrics_across_sites(
canvas_sites, 'student')
# If more than one course site is associated with this section, derive mean metrics from as many sites as possible.
for site in canvas_sites:
if site['canvasCourseId'] not in all_canvas_sites:
all_canvas_sites[site['canvasCourseId']] = site
continue
mean_metrics = analytics.mean_metrics_across_sites(
all_canvas_sites.values(), 'courseMean')
return {
'students': students,
'totalStudentCount': total_student_count,
'meanMetrics': mean_metrics,
}
