first commit

芳林公司项目/pytest/utils/qq.py

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+import math
+import numpy as np
+from scipy import integrate
+
+#历史数据处理
+def handle_data(all_marks=[569,
+570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,
+595,596,597,598,599
+]):#以列表形式传入省份所有考生的成绩[1,2,3]
+    a = np.mean(all_marks)#平均值
+    sigma = np.std(all_marks)#方差
+    print('a=',a,'sigma=',sigma)
+    return a,sigma
+
+#等效分计算
+def mark(a=584.0,sigma=8.94427190999916,per_mark=600):#a和sigma由上一个函数得出，per_mark为需要转化成等效成绩的高考分数
+    re_mark = 100 * ((per_mark - a) / sigma) + 500
+    if re_mark > 900:
+        re_mark = 900
+    elif re_mark < 100:
+        re_mark = 100
+    return re_mark
+
+#录取概率
+def P_get(uni_marks=[580,581,582,583,
+584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599],per_mark=589): #以列表形式学校录取的所有考生的成绩
+    per_mark = mark(per_mark=per_mark)
+    re_uni_mark = [] #学校录取学生的等效分
+    for i in uni_marks:
+        re_uni_mark.append(mark(per_mark=i))
+    E = np.mean(re_uni_mark) #学校录取考生的等效分平均值即期望
+    S = np.std(re_uni_mark)
+    D = S ** 2 #样本方差
+    def p(x): #录取分数的概率密度函数
+        return (1/(D*(2*math.pi)**0.5))*((math.e)**(-((x-E)**2)/(2*D)))
+    P,err = integrate.quad(p,100,per_mark)
+    P = P*100
+    print('录取概率为',P)
+    return P
+# num = 570
+# while num < 600:
+#     print('分数为',num,'时','录取概率为',P_get(per_mark=num))
+#     num += 1