def recalculate_category_grade(category):
total_earned = 0
total_possible = 0
# We could more simply sum this value, but we're maintaining a list so we can use it later to calculate the
# linear regression
grade_series = []
for grade in Homework.objects.for_category(
category.pk).graded().values_list('current_grade', flat=True):
earned, possible = grade.split('/')
total_earned += float(earned)
total_possible += float(possible)
grade_series.append(total_earned / total_possible)
average_grade = (total_earned /
total_possible) * 100 if len(grade_series) > 0 else -1
trend = commonutils.calculate_trend(range(len(grade_series)), grade_series)
logger.debug(
'Category {} average grade recalculated to {} with {} grade_by_weight for {} homework'
.format(category.pk, category.average_grade, category.grade_by_weight,
len(grade_series)))
# Update the values in the datastore, circumventing signals
Category.objects.filter(pk=category.pk).update(average_grade=average_grade,
trend=trend)
def recalculate_course_group_grade(course_group):
course_grades = Course.objects.for_course_group(
course_group.pk).graded().values_list('current_grade', flat=True)
total = sum(course_grades)
average_grade = total / len(course_grades) if len(
course_grades) > 0 else -1
logger.debug(
'Course Group {} average grade recalculated to {} with {} courses'.
format(course_group.pk, course_group.average_grade,
len(course_grades)))
homework_grades = Homework.objects.for_course_group(
course_group.pk).graded().values_list('current_grade', flat=True)
total_earned = 0
total_possible = 0
# We could more simply sum this value, but we're maintaining a list so we can use it later to calculate the
# linear regression
grade_series = []
for grade in homework_grades:
earned, possible = grade.split('/')
total_earned += float(earned)
total_possible += float(possible)
grade_series.append(total_earned / total_possible)
trend = commonutils.calculate_trend(range(len(grade_series)), grade_series)
logger.debug('Course Group {} trend recalculated to {}'.format(
course_group.pk, course_group.trend))
CourseGroup.objects.filter(pk=course_group.pk).update(
average_grade=average_grade, trend=trend)
def recalculate_course_grade(course):
course_has_weighted_grading = Course.objects.has_weighted_grading(
course.pk)
total_earned = 0
total_possible = 0
# Maintain grades by category as we iterate over all homework
category_totals = {}
# We could more simply sum this value, but we're maintaining a list so we can use it later to calculate the
# linear regression; note that this grade series is just for raw grade trend analysis and does not take weight
# into account
grade_series = []
for category_id, grade in Homework.objects.for_course(
course.pk).graded().values_list('category_id', 'current_grade'):
# The category may no longer exist by the time a recalculation request is processed, in which case we can simply
# and safely skip it
try:
category = Category.objects.get(pk=category_id)
except Category.DoesNotExist:
continue
earned, possible = grade.split('/')
earned = float(earned)
possible = float(possible)
total_earned += earned
total_possible += possible
grade_series.append(total_earned / total_possible)
if category.pk not in category_totals:
category_totals[category.pk] = {
'instance': category,
'total_earned': 0,
'total_possible': 0
}
category_totals[category.pk]['total_earned'] += earned
category_totals[category.pk]['total_possible'] += possible
category_earned = 0
category_possible = 0
for category in category_totals.values():
if course_has_weighted_grading:
grade_by_weight = (
((category['total_earned'] / category['total_possible']) *
(float(category['instance'].weight) / 100)) * 100)
category_earned += grade_by_weight
category_possible += float(category['instance'].weight)
logger.debug(
'Course triggered category {} recalculation of grade_by_weight to {}'
.format(category['instance'].pk, grade_by_weight))
else:
category_earned += total_earned
category_possible += total_possible
grade_by_weight = 0
# Update the values in the datastore, circumventing signals
Category.objects.filter(pk=category['instance'].pk).update(
grade_by_weight=grade_by_weight)
if len(grade_series) > 0 and category_possible > 0:
current_grade = (category_earned / category_possible) * 100
else:
current_grade = -1
logger.debug(
'Course {} current grade recalculated to {} with {} homework'.format(
course.pk, course.current_grade, course.num_homework))
trend = commonutils.calculate_trend(range(len(grade_series)), grade_series)
logger.debug('Course {} trend recalculated to {}'.format(
course.pk, course.trend))
Course.objects.filter(pk=course.pk).update(current_grade=current_grade,
trend=trend)