def get_cohort(cohort_id):
benchmark = get_benchmarker(f'cohort {cohort_id} get_cohort')
benchmark('begin')
filter_keys = list(request.args.keys())
order_by = get_param(request.args, 'orderBy', None)
if is_unauthorized_search(filter_keys, order_by):
raise ForbiddenRequestError(
'You are unauthorized to access student data managed by other departments'
)
include_students = to_bool(get_param(request.args, 'includeStudents'))
include_students = True if include_students is None else include_students
offset = get_param(request.args, 'offset', 0)
limit = get_param(request.args, 'limit', 50)
benchmark('begin cohort filter query')
cohort = CohortFilter.find_by_id(
int(cohort_id),
order_by=order_by,
offset=int(offset),
limit=int(limit),
include_alerts_for_user_id=current_user.get_id(),
include_profiles=True,
include_students=include_students,
)
if cohort and _can_current_user_view_cohort(cohort):
_decorate_cohort(cohort)
benchmark('end')
return tolerant_jsonify(cohort)
else:
raise ResourceNotFoundError(
f'No cohort found with identifier: {cohort_id}')
def get_cohort_per_filters():
benchmark = get_benchmarker('cohort get_students_per_filters')
benchmark('begin')
params = request.get_json()
filters = get_param(params, 'filters', [])
if not filters:
raise BadRequestError('API requires \'filters\'')
include_students = to_bool(get_param(params, 'includeStudents'))
include_students = True if include_students is None else include_students
order_by = get_param(params, 'orderBy', None)
offset = get_param(params, 'offset', 0)
limit = get_param(params, 'limit', 50)
filter_keys = list(map(lambda f: f['key'], filters))
if is_unauthorized_search(filter_keys, order_by):
raise ForbiddenRequestError(
'You are unauthorized to access student data managed by other departments'
)
benchmark('begin phantom cohort query')
cohort = _construct_phantom_cohort(
filters=filters,
order_by=order_by,
offset=int(offset),
limit=int(limit),
include_alerts_for_user_id=current_user.get_id(),
include_profiles=True,
include_students=include_students,
)
_decorate_cohort(cohort)
benchmark('end')
return tolerant_jsonify(cohort)
def translate_filters_to_cohort_criteria(cls, filters):
criteria = {}
for filter_ in filters:
key = filter_['key']
type_ = filter_['type']
value = filter_['value']
if type_ == 'boolean':
criteria[key] = to_bool(value)
elif type_ == 'array':
criteria[key] = criteria.get(key) or []
criteria[key].append(value)
elif type_ == 'range':
criteria[key] = value
return criteria