补充性能优化后的函数，注释掉耗时大的函数

myproject/app01/models.py

@@ -158,24 +158,24 @@ class Student(models.Model):
                 sts.update(probability = (1 - (F('credit') - min_credit)/fm))
 
 #耗时最大，性能改进前的代码
-    def updateinfor (self,point,class_name,sid,style): #更新积分和概率
-        if style == 1 :
-            point = point
-        if style == 2 :
-            point = point * 2
-        #先改变该学生的积分值
-        current_s = Student.objects.filter(class_name=class_name, sid=sid).first()
-        current_credit = current_s.credit
-        current_s.credit = current_credit + point
-        current_s.save()
-        #查找最新的最大或最小值，与原来写在表中的上次更新的最大值和最小值做对比
-        max_credit = Student.objects.filter(class_name=class_name).aggregate(max_credit=Max('credit'))['max_credit']
-        min_credit = Student.objects.filter(class_name=class_name).aggregate(min_credit=Min('credit'))['min_credit']
-        fm = max_credit - min_credit  #最新的分母
-        sts = Student.objects.filter(class_name=class_name)
-        sts.update(min=min_credit, max=max_credit)
-        # 更新所有学生的概率
-        if fm == 0:
-            sts.update(probability=1.0)
-        else:
-            sts.update(probability=(1 - (F('credit') - min_credit) / fm))
+#    def updateinfor (self,point,class_name,sid,style): #更新积分和概率
+#       if style == 1 :
+#            point = point
+#        if style == 2 :
+#            point = point * 2
+#        #先改变该学生的积分值
+#        current_s = Student.objects.filter(class_name=class_name, sid=sid).first()
+#        current_credit = current_s.credit
+#        current_s.credit = current_credit + point
+#        current_s.save()
+#        #查找最新的最大或最小值，与原来写在表中的上次更新的最大值和最小值做对比
+#        max_credit = Student.objects.filter(class_name=class_name).aggregate(max_credit=Max('credit'))['max_credit']
+#        min_credit = Student.objects.filter(class_name=class_name).aggregate(min_credit=Min('credit'))['min_credit']
+#        fm = max_credit - min_credit  #最新的分母
+#        sts = Student.objects.filter(class_name=class_name)
+#        sts.update(min=min_credit, max=max_credit)
+#        # 更新所有学生的概率
+#        if fm == 0:
+#            sts.update(probability=1.0)
+#        else:
+#            sts.update(probability=(1 - (F('credit') - min_credit) / fm))