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This commit is contained in:
Ross
+118
-118
@@ -475,124 +475,124 @@ def exam_scores_refresh(request, pk):
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},
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)
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@login_required
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def exam_scores_cid(request, pk):
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exam = get_object_or_404(Exam, pk=pk)
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if not exam.exam_mode:
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raise Http404("Packet not in exam mode")
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questions = exam.exam_questions.all()
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cids = (
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CidUserAnswer.objects.filter(question__in=questions, exam__id=pk)
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.order_by("cid")
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.values_list("cid", flat=True)
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.distinct()
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)
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user_answers_and_marks = defaultdict(list)
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user_answers_marks = defaultdict(list)
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user_answers = defaultdict(list)
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user_names = {}
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by_question = defaultdict(list)
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unmarked = set()
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# Loop through all candidates
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for cid in cids:
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# Convoluted (probably...)
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user_names[cid] = cid
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for q in questions:
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# Get user answer
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s = q.cid_user_answers.filter(cid=cid, exam__id=pk).first()
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if not s:
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# skip if no answer
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user_answers_marks[cid].append(0)
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user_answers[cid].append("")
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by_question[q].append(("", 0))
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continue
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ans = s.answer
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answer_score = s.get_answer_score()
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if answer_score == "unmarked":
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index = exam.get_question_index(q)
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unmarked.add(index)
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user_answers[cid].append(ans)
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user_answers_marks[cid].append(answer_score)
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user_answers_and_marks[cid].append((ans, answer_score))
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by_question[q].append((ans, answer_score))
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user_scores = {}
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for user in user_answers_marks:
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user_scores[user] = sum(
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[i for i in user_answers_marks[user] if i != "unmarked"]
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)
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user_scores_list = list(user_scores.values())
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if len(user_scores_list) < 1:
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mean = 0
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median = 0
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mode = 0
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fig_html = ""
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else:
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mean = statistics.mean(user_scores_list)
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median = statistics.median(user_scores_list)
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try:
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mode = statistics.mode(user_scores_list)
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except statistics.StatisticsError:
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mode = "No unique mode"
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df = user_scores_list
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fig = px.histogram(
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df,
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x=0,
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title="{}: distribution of scores".format(exam),
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labels={"0": "Score"},
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height=400,
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width=600,
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)
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fig_html = fig.to_html()
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max_score = len(questions) * 2
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exam.stats_mean = mean
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exam.stats_median = median
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exam.stats_mode = mode
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exam.stats_candidates = len(user_scores_list)
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exam.stats_max_possible = max_score
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exam.stats_min = min(user_scores_list)
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exam.stats_max = max(user_scores_list)
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exam.stats_graph = fig_html
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exam.save()
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return render(
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request,
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"anatomy/exam_scores.html",
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{
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"cids": cids,
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"exam": exam,
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"unmarked": unmarked,
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"questions": questions,
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"by_question": by_question,
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"user_answers": dict(user_answers),
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"user_answers_marks": dict(user_answers_marks),
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"user_scores": user_scores,
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"user_scores_list": user_scores_list,
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"user_names": user_names,
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"user_answers_and_marks": user_answers_and_marks,
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"max_score": max_score,
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"mean": mean,
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"median": median,
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"mode": mode,
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"plot": fig_html,
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},
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)
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#@login_required
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#def exam_scores_cid(request, pk):
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# exam = get_object_or_404(Exam, pk=pk)
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#
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# if not exam.exam_mode:
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# raise Http404("Packet not in exam mode")
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#
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# questions = exam.exam_questions.all()
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#
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# cids = (
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# CidUserAnswer.objects.filter(question__in=questions, exam__id=pk)
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# .order_by("cid")
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# .values_list("cid", flat=True)
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# .distinct()
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# )
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#
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# user_answers_and_marks = defaultdict(list)
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# user_answers_marks = defaultdict(list)
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# user_answers = defaultdict(list)
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# user_names = {}
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#
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# by_question = defaultdict(list)
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# unmarked = set()
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#
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# # Loop through all candidates
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# for cid in cids:
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# # Convoluted (probably...)
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# user_names[cid] = cid
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# for q in questions:
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# # Get user answer
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# s = q.cid_user_answers.filter(cid=cid, exam__id=pk).first()
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# if not s:
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# # skip if no answer
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# user_answers_marks[cid].append(0)
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# user_answers[cid].append("")
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# by_question[q].append(("", 0))
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# continue
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#
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# ans = s.answer
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# answer_score = s.get_answer_score()
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# if answer_score == "unmarked":
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# index = exam.get_question_index(q)
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# unmarked.add(index)
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# user_answers[cid].append(ans)
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# user_answers_marks[cid].append(answer_score)
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# user_answers_and_marks[cid].append((ans, answer_score))
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#
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# by_question[q].append((ans, answer_score))
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#
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# user_scores = {}
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# for user in user_answers_marks:
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# user_scores[user] = sum(
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# [i for i in user_answers_marks[user] if i != "unmarked"]
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# )
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#
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# user_scores_list = list(user_scores.values())
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#
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# if len(user_scores_list) < 1:
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# mean = 0
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# median = 0
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# mode = 0
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# fig_html = ""
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# else:
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# mean = statistics.mean(user_scores_list)
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# median = statistics.median(user_scores_list)
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# try:
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# mode = statistics.mode(user_scores_list)
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# except statistics.StatisticsError:
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# mode = "No unique mode"
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#
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# df = user_scores_list
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# fig = px.histogram(
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# df,
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# x=0,
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# title="{}: distribution of scores".format(exam),
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# labels={"0": "Score"},
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# height=400,
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# width=600,
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# )
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# fig_html = fig.to_html()
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#
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# max_score = len(questions) * 2
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#
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# exam.stats_mean = mean
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# exam.stats_median = median
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# exam.stats_mode = mode
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#
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# exam.stats_candidates = len(user_scores_list)
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# exam.stats_max_possible = max_score
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#
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# exam.stats_min = min(user_scores_list)
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# exam.stats_max = max(user_scores_list)
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#
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# exam.stats_graph = fig_html
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# exam.save()
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#
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# return render(
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# request,
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# "anatomy/exam_scores.html",
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# {
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# "cids": cids,
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# "exam": exam,
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# "unmarked": unmarked,
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# "questions": questions,
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# "by_question": by_question,
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# "user_answers": dict(user_answers),
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# "user_answers_marks": dict(user_answers_marks),
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# "user_scores": user_scores,
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# "user_scores_list": user_scores_list,
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# "user_names": user_names,
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# "user_answers_and_marks": user_answers_and_marks,
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# "max_score": max_score,
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# "mean": mean,
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# "median": median,
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# "mode": mode,
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# "plot": fig_html,
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# },
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# )
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def exam_scores_cid_user(request, pk, sk, passcode):
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