def access(self, exam_id, **kw):
exam_collection = Exam()
metric_preprocessor = MetricPreProcessor()
client_preprocessor = ClientPreProcessor()
db_pre_processor = DBPreProcessor()
exam_doc = exam_collection.query_doc(exam_id)
#print exam_data
exam_dict = db_pre_processor.unicode_dict_from_DB_to_string(exam_doc)
packet_to_metric = metric_preprocessor.produce_packet(exam_dict)
#print "TEST\n", packet_to_metric
""" Calculate the statistics information """
statistics_info = after(packet_to_metric)
print statistics_info
""" Prepare information that will send to client """
exam_doc['practices'] = client_preprocessor.dict_to_list(exam_doc['practices'])
exam_doc['practices'] = client_preprocessor.sort_by_id(exam_doc['practices'])
exam_doc['practices'] = client_preprocessor.add_key_from_list(exam_doc['practices'],'correct_rate',statistics_info['correct_rate'])
exam_doc['practices'] = client_preprocessor.add_key_from_list(exam_doc['practices'],'real_difficulty',statistics_info['real_difficulty'])
exam = {
'exam_doc':exam_doc,
'exam_statics':statistics_info
}
return exam
def access_by_owner(self, user_id, **kw):
exam_collection = Exam()
db_pre_processor = DBPreProcessor()
exam_list = exam_collection.query_by_owner(user_id)
result = []
for exam_doc in exam_list:
exam_doc = db_pre_processor.unicode_dict_from_DB_to_string(exam_doc)
result.append(exam_doc)
return result
def access_fake_data(self, exam_id, **kw):
global packet_to_client
exam_collection = Exam()
metric_preprocessor = MetricPreProcessor()
db_pre_processor = DBPreProcessor()
exam_doc = exam_collection.query_doc(exam_id)
# print exam_data
exam_doc = db_pre_processor.unicode_dict_from_DB_to_string(exam_doc)
exam = {"exam_doc": exam_doc, "exam_statics": packet_to_client}
return exam
def access(self, exam_id, **kw):
exam_collection = Exam()
metric_preprocessor = MetricPreProcessor()
db_pre_processor = DBPreProcessor()
exam_doc = exam_collection.query_doc(exam_id)
#print exam_data
exam_dict = db_pre_processor.unicode_dict_from_DB_to_string(exam_doc)
packet_to_metric = metric_preprocessor.produce_packet(exam_dict)
#for key, val in packet_to_metric.iteritems():
#print '\n{} : {}\n'.format(key,val)
return packet_to_metric
def upload_excel(self,**kw):
global exam_info
"""Decode the json string"""
excel_info = json.loads(kw['excel_info'])
"""Preprocess the data from client"""
client_pre_processor = ClientPreProcessor()
""""Transform the format"""
raw_ans_info = client_pre_processor.transform_fromat(excel_info,u'學生答題狀況',u'學號')
raw_prac_info = client_pre_processor.transform_fromat(excel_info,u'試題資訊',u'試題編號')
#print raw_ans_info
#print raw_prac_info['1']
"""Delete unwanted column data"""
raw_ans_info = client_pre_processor.drop_unwanted_column(raw_ans_info)
#print raw_ans_info['1010102']
""" Transform key from Chinese to Eng"""
ans_info = client_pre_processor.chinese_to_eng(raw_ans_info)
prac_info = client_pre_processor.chinese_to_eng(raw_prac_info)
#print raw_ans_info['1010102']
#print raw_prac_info['1']
""" Transform meaning data to the format for DB """
db_pre_processor = DBPreProcessor()
exam_doc = db_pre_processor.init_exam_doc()
#print "Init Doc:\n", exam_doc
exam_doc = db_pre_processor.update_exam_mata_data(exam_doc,exam_info)
#print "With Metadata:\n",exam_doc
exam_doc = db_pre_processor.update_exam_ans_data(exam_doc,ans_info)
#print "With Ans:\n",exam_doc['stu_ans']['1010102']
exam_doc = db_pre_processor.update_exam_prac_data(exam_doc,prac_info)
#print "With Prac:\n",exam_doc['practices']['3']
"""Store in DB"""
exam_collection = Exam()
exam_collection.insert_doc(exam_doc)
redirect('/upload',params=dict(page='upload',message="Upload Success!"))
def access(self, exam_id, **kw):
exam_collection = Exam()
metric_preprocessor = MetricPreProcessor()
client_preprocessor = ClientPreProcessor()
db_pre_processor = DBPreProcessor()
exam_doc = exam_collection.query_doc(exam_id)
# print exam_data
exam_dict = db_pre_processor.unicode_dict_from_DB_to_string(exam_doc)
packet_to_metric = metric_preprocessor.produce_packet(exam_dict)
""" Calculate the statistics information """
statistics_info = all(packet_to_metric)
""" Prepare information that will send to client """
exam_doc["practices"] = client_preprocessor.dict_to_list(exam_doc["practices"])
exam_doc["practices"] = client_preprocessor.sort_by_id(exam_doc["practices"])
exam = {"exam_doc": exam_doc, "exam_statics": statistics_info}
return exam