229 lines
9.4 KiB
Python
229 lines
9.4 KiB
Python
import logging
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import urllib
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from lxml import etree
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import courseware.content_parser as content_parser
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from models import StudentModule
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from django.conf import settings
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import courseware.modules
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from student.models import UserProfile
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log = logging.getLogger("mitx.courseware")
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def get_grade(user, problem, cache):
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## HACK: assumes max score is fixed per problem
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id = problem.get('id')
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correct = 0
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# If the ID is not in the cache, add the item
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if id not in cache:
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module = StudentModule(module_type = 'problem', # TODO: Move into StudentModule.__init__?
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module_id = id,
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student = user,
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state = None,
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grade = 0,
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max_grade = None,
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done = 'i')
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cache[id] = module
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# Grab the # correct from cache
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if id in cache:
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response = cache[id]
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if response.grade!=None:
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correct=response.grade
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# Grab max grade from cache, or if it doesn't exist, compute and save to DB
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if id in cache and response.max_grade != None:
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total = response.max_grade
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else:
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total=courseware.modules.capa_module.Module(etree.tostring(problem), "id").max_score()
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response.max_grade = total
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response.save()
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return (correct, total)
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def grade_sheet(student):
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dom=content_parser.course_file(student)
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course = dom.xpath('//course/@name')[0]
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xmlChapters = dom.xpath('//course[@name=$course]/chapter', course=course)
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responses=StudentModule.objects.filter(student=student)
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response_by_id = {}
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for response in responses:
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response_by_id[response.module_id] = response
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total_scores = {}
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chapters=[]
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for c in xmlChapters:
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sections = []
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chname=c.get('name')
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for s in dom.xpath('//course[@name=$course]/chapter[@name=$chname]/section',
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course=course, chname=chname):
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problems=dom.xpath('//course[@name=$course]/chapter[@name=$chname]/section[@name=$section]//problem',
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course=course, chname=chname, section=s.get('name'))
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graded = True if s.get('graded') == "true" else False
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scores=[]
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if len(problems)>0:
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for p in problems:
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(correct,total) = get_grade(student, p, response_by_id)
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# id = p.get('id')
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# correct = 0
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# if id in response_by_id:
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# response = response_by_id[id]
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# if response.grade!=None:
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# correct=response.grade
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# total=courseware.modules.capa_module.Module(etree.tostring(p), "id").max_score() # TODO: Add state. Not useful now, but maybe someday problems will have randomized max scores?
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# print correct, total
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if settings.GENERATE_PROFILE_SCORES:
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if total > 1:
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correct = random.randrange( max(total-2, 1) , total + 1 )
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else:
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correct = total
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scores.append((int(correct),total, graded ))
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section_total = (sum([score[0] for score in scores]),
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sum([score[1] for score in scores]))
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graded_total = (sum([score[0] for score in scores if score[2]]),
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sum([score[1] for score in scores if score[2]]))
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#Add the graded total to total_scores
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format = s.get('format') if s.get('format') else ""
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subtitle = s.get('subtitle') if s.get('subtitle') else format
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if format and graded_total[1] > 0:
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format_scores = total_scores[ format ] if format in total_scores else []
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format_scores.append( graded_total + (s.get("name"),) )
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total_scores[ format ] = format_scores
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score={'section':s.get("name"),
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'scores':scores,
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'section_total' : section_total,
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'format' : format,
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'subtitle' : subtitle,
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'due' : s.get("due") or "",
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'graded' : graded,
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}
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sections.append(score)
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chapters.append({'course':course,
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'chapter' : c.get("name"),
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'sections' : sections,})
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def totalWithDrops(scores, drop_count):
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#Note that this key will sort the list descending
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sorted_scores = sorted( enumerate(scores), key=lambda x: -x[1]['percentage'] )
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# A list of the indices of the dropped scores
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dropped_indices = [score[0] for score in sorted_scores[-drop_count:]]
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aggregate_score = 0
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for index, score in enumerate(scores):
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if index not in dropped_indices:
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aggregate_score += score['percentage']
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aggregate_score /= len(scores) - drop_count
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return aggregate_score, dropped_indices
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#Figure the homework scores
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homework_scores = total_scores['Homework'] if 'Homework' in total_scores else []
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homework_percentages = []
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for i in range(12):
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if i < len(homework_scores):
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percentage = homework_scores[i][0] / float(homework_scores[i][1])
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summary = "Homework {0} - {1} - {2:.0%} ({3:g}/{4:g})".format( i + 1, homework_scores[i][2] , percentage, homework_scores[i][0], homework_scores[i][1] )
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else:
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percentage = 0
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summary = "Unreleased Homework {0} - 0% (?/?)".format(i + 1)
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if settings.GENERATE_PROFILE_SCORES:
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points_possible = random.randrange(10, 50)
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points_earned = random.randrange(5, points_possible)
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percentage = points_earned / float(points_possible)
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summary = "Random Homework - {0:.0%} ({1:g}/{2:g})".format( percentage, points_earned, points_possible )
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label = "HW {0:02d}".format(i + 1)
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homework_percentages.append( {'percentage': percentage, 'summary': summary, 'label' : label} )
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homework_total, homework_dropped_indices = totalWithDrops(homework_percentages, 2)
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#Figure the lab scores
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lab_scores = total_scores['Lab'] if 'Lab' in total_scores else []
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lab_percentages = []
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log.debug("lab_scores: {0}".format(lab_scores))
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for i in range(12):
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if i < len(lab_scores):
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percentage = lab_scores[i][0] / float(lab_scores[i][1])
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summary = "Lab {0} - {1} - {2:.0%} ({3:g}/{4:g})".format( i + 1, lab_scores[i][2] , percentage, lab_scores[i][0], lab_scores[i][1] )
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else:
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percentage = 0
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summary = "Unreleased Lab {0} - 0% (?/?)".format(i + 1)
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if settings.GENERATE_PROFILE_SCORES:
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points_possible = random.randrange(10, 50)
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points_earned = random.randrange(5, points_possible)
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percentage = points_earned / float(points_possible)
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summary = "Random Lab - {0:.0%} ({1:g}/{2:g})".format( percentage, points_earned, points_possible )
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label = "Lab {0:02d}".format(i + 1)
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lab_percentages.append( {'percentage': percentage, 'summary': summary, 'label' : label} )
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lab_total, lab_dropped_indices = totalWithDrops(lab_percentages, 2)
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#TODO: Pull this data about the midterm and final from the databse. It should be exactly similar to above, but we aren't sure how exams will be done yet.
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midterm_score = ('?', '?')
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midterm_percentage = 0
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final_score = ('?', '?')
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final_percentage = 0
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if settings.GENERATE_PROFILE_SCORES:
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midterm_score = (random.randrange(50, 150), 150)
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midterm_percentage = midterm_score[0] / float(midterm_score[1])
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final_score = (random.randrange(100, 300), 300)
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final_percentage = final_score[0] / float(final_score[1])
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grade_summary = [
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{
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'category': 'Homework',
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'subscores' : homework_percentages,
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'dropped_indices' : homework_dropped_indices,
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'totalscore' : {'score' : homework_total, 'summary' : "Homework Average - {0:.0%}".format(homework_total)},
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'totallabel' : 'HW Avg',
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'weight' : 0.15,
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},
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{
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'category': 'Labs',
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'subscores' : lab_percentages,
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'dropped_indices' : lab_dropped_indices,
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'totalscore' : {'score' : lab_total, 'summary' : "Lab Average - {0:.0%}".format(lab_total)},
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'totallabel' : 'Lab Avg',
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'weight' : 0.15,
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},
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{
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'category': 'Midterm',
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'totalscore' : {'score' : midterm_percentage, 'summary' : "Midterm - {0:.0%} ({1}/{2})".format(midterm_percentage, midterm_score[0], midterm_score[1])},
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'totallabel' : 'Midterm',
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'weight' : 0.30,
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},
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{
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'category': 'Final',
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'totalscore' : {'score' : final_percentage, 'summary' : "Final - {0:.0%} ({1}/{2})".format(final_percentage, final_score[0], final_score[1])},
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'totallabel' : 'Final',
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'weight' : 0.40,
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}
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]
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return {'grade_summary' : grade_summary,
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'chapters':chapters}
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