def get_dataset(request, course=None, use_cgpa=True):
data = {}
students = None
active_students = [
student for student in Student.objects.all()
if student.user.studentsetup.allow_my_result_for_analysis
]
preferred_students = [
student for student in Student.objects.all()
if cgpaData.get_fcgpa(student.id) >= float(
0.75 * max(cp.get_grades(student.institution)))
and student.user.studentsetup.allow_my_result_for_analysis
]
if use_cgpa:
students = preferred_students
if request.user.student not in students:
students.append(request.user.student)
else:
students = active_students
for student in students:
tmp_data = {}
results = None
if course:
results = get_result_queryset(student).filter(course=course)
else:
results = get_result_queryset(student)
for result in results:
tmp_data[result.course] = float(result.total_score) / 10
data[student] = tmp_data
return data
def course_recommendation(request):
try:
own_student = request.user.student
except:
pass
courses = {}
data = {}
is_new = True
if request.user.is_authenticated() and hasattr(request.user, 'student'):
for result in Result.objects.filter(student=request.user.student):
if result.total_score < 62.5 and cgpaData.get_fcgpa(
own_student.id) < float(
max(cp.get_grades(own_student.institution)) * 0.8):
courses[result.course] = result.total_score
#get the recommendation for each poorly performed course
for course in courses:
dataset = get_dataset(request, course=course)
matches = top_matches(dataset,
request.user.student,
length=5,
algorithm=euclidean_distance_score)
data['match'] = matches
if hasattr(request.user, 'student'):
is_new = not Result.objects.filter(
student=request.user.student).exists()
return {'recommendations': data, 'student_is_new': is_new}
def cgpa_by_level(request,
student_slug,
chartID='container',
chart_type='area',
chart_height=300):
student = get_object_or_404(Student, slug=student_slug)
fcgpa = cgpaData.get_fcgpa(student.id)
grades = cp.get_grades(student.institution)
degree = cp.get_cgpa_comment(student.institution, fcgpa)
cgpa = []
level = []
pseudo_level = []
data = {'level': [], 'cgpa': []}
res = Result.objects.filter(student_id=student.id)
for r in res:
pseudo_level.append(r.level)
for i in range(100, 700, 100):
if i in pseudo_level:
data['level'].append(i)
data['cgpa'].append(cgpaData.get_level_cgpa(i, student.id))
chart = {
"renderTo": chartID,
"type": chart_type,
"height": chart_height,
"zoomType": 'xy'
}
title = {"text": "%s's CGPA Breakdown" % (student)}
xAxis = {"title": {"text": 'Level'}, "categories": data['level']}
yAxis = {
"title": {
"text": "CGPA values"
},
"min": 0,
"max": max(grades),
"tickInterval": 0.5
}
series = [{"name": "Student CGPA", "data": data['cgpa']}]
context = {
'fcgpa': fcgpa,
'max_grade': max(grades),
'degree': degree,
'chartID': chartID,
'chart': chart,
'title': title,
'xAxis': xAxis,
'student': student,
'yAxis': yAxis,
'series': series,
}
return render(request, "analyzer/cgpa_level.html", context)
def get_context_data(self, **kwargs):
context = super(StudentAnalyticsView, self).get_context_data(**kwargs)
if Result.objects.filter(student=self.request.user.student).exists():
context['fcgpa'] = cgpaData.get_fcgpa(self.request.user.student.id)
context['exam_no'] = Result.objects.filter(student_id=self.request.user.student.id).count()
context['avg_score'] = "%.2f" % (Result.objects.filter(student_id=self.request.user.student.id).values('total_score')\
.aggregate(avg=Avg('total_score'))['avg'])
context['high_score'] = Result.objects.filter(student_id=self.request.user.student.id).order_by('-total_score')[0]
else:
context['fcgpa'] = max(cp.get_grades(self.request.user.student.institution))
context['avg_score'] = float(0)
return context
def performances(request):
performance_list = {}
for student in Student.objects.all():
tmp_dict = {}
tmp_dict['name'] = student
tmp_dict['department'] = student.department
tmp_dict['reg_number'] = student.reg_number
tmp_dict['user'] = student.user
try:
tmp_dict['are_friends'] = Friend.objects.are_friends(
request.user, student.user)
except:
pass
if student.photo:
tmp_dict['img'] = student.photo
else:
tmp_dict['img'] = 'None'
tmp_dict['institution'] = student.institution
if student.result_set.count() > 0:
tmp_dict['cgpa'] = cgpaData.get_fcgpa(student.id)
else:
tmp_dict['cgpa'] = float(max(cp.get_grades(student.institution)))
performance_list[student] = tmp_dict
key_list = sorted(performance_list.keys(),
key=lambda x: performance_list[x]['cgpa'],
reverse=True)
trending_performances = []
current_student_rank = None
count = 0
for key in key_list:
count += 1
if not request.user.is_anonymous() and hasattr(request.user,
'student'):
if key == request.user.student:
current_student_rank = count
trending_performances.append(performance_list[key])
return {
'trending_performances': trending_performances[:5],
'student_rank': current_student_rank,
}