Merge pull request #11 from CYXDBY/main

新加了表面张力和杨氏模量的计算器

Exp02/计算器/README.md

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+新加了杨氏模量的计算器，需要自己改里面的数据，deltax的不确定度计算用了近似

Exp02/计算器/杨氏模量.py

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+import numpy as np
+import math
+
+'''
+1.常数及函数
+'''
+g = 9.7803
+pi = 3.1415926
+g = 9.78
+
+uL = 0.8 * 10**-3 / math.sqrt(3) # L的不确定度（只有卷尺误差所以是常数）
+uH = uL # 同理只有卷尺误差
+uD = 0.02 * 10**-3 / math.sqrt(3) # 同理只有游标卡尺误差
+
+
+# 求均值和不确定度
+def calculate_uncertainty(data, UL):
+    # 计算均值
+    mean_value = np.mean(data)
+
+    # 计算标准差作为测量误差的估计
+    std_dev = np.std(data, ddof=1)
+
+    # 计算标准误差，表示均值的不确定度SL
+    std_err = std_dev / np.sqrt(len(data))
+
+    # 计算不确定度的总值（包括测量误差和统计误差）
+    total_uncertainty = np.sqrt(std_err**2 + UL**2)
+
+    return mean_value, total_uncertainty
+
+'''
+2.计算杨氏模量（单位均为公制单位）
+'''
+m = 5 # 以5kg为间隔
+L = 0.7375
+H = 0.671
+D = 0.04628
+avgd = 0.000598
+avgDeltaX = 0.01881 # 自己算
+
+E = 8 * m * g * L * H / (pi * avgd * avgd * D * avgDeltaX)
+print("杨氏模量：", E)
+
+'''
+3.计算杨氏模量的不确定度
+'''
+deltaX = np.array([18.9, 18.8, 18.75, 18.95, 18.65]) * 10**-3
+d = np.array([0.599, 0.595, 0.595, 0.594, 0.605, 0.600]) * 10**-3
+_, uncertaintydeltaX = calculate_uncertainty(deltaX, 0.02 * 10**-3) # deltaX的不确定度用近似了（后面是游标卡尺精度）
+_, uncertaintyd = calculate_uncertainty(d, 0.01 * 10 ** -3) # 螺旋测微器精度
+
+ue = E * math.sqrt((uL/L) ** 2 + (uH/H) ** 2 + (2 * uncertaintyd/avgd) ** 2 + (uD/D) ** 2 + (uncertaintydeltaX/avgDeltaX) ** 2)
+
+print(uL, L, uH, H, uncertaintyd, avgd, uD, D, uncertaintydeltaX, avgDeltaX)
+print("△x的不确定度：", uncertaintydeltaX)
+print("d的不确定度：", uncertaintyd)
+print("E的不确定度：", ue)

Exp12/计算器/README.md

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+看到没有计算器就写了一个，毕竟算不确定度还是很烦人的。EK的计算用了近似，只算了deltaV的不确定度没有计入m的，同时deltaV的不确定度计算也用了近似。

Exp12/计算器/表面张力.py

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+'''
+--------------------------------------------------------------------------
+注：没有确认过是否正确，只是写出来带入数据算了一遍得到的数据和理论值差不多就直接懒得验证了
+--------------------------------------------------------------------------
+'''
+
+import numpy as np
+import math
+
+'''
+0.函数和常数定义
+'''
+g = 9.7803
+pi = 3.1415926
+
+# 求均值和不确定度
+def calculate_uncertainty(data, UL):
+    # 计算均值
+    mean_value = np.mean(data)
+
+    # 计算标准差作为测量误差的估计
+    std_dev = np.std(data, ddof=1)
+
+    # 计算标准误差，表示均值的不确定度SL
+    std_err = std_dev / np.sqrt(len(data))
+
+    # 计算不确定度的总值（包括测量误差和统计误差）
+    total_uncertainty = np.sqrt(std_err**2 + UL**2)
+
+    return mean_value, total_uncertainty
+
+'''
+1.计算L
+'''
+D1 = input("输入D内：").split(" ")
+D2 = input("输入D外：").split(" ")
+ul1 = 5 * 10**-5 / math.sqrt(3) # 游标卡尺误差
+
+data1 = np.array(D1, dtype=float) / 1000
+data2 = np.array(D2, dtype=float) / 1000
+print(data1)
+data3 = (data1 + data2) * pi
+meanD1, uncertaintyD1 = calculate_uncertainty(data1, ul1)
+meanD2, uncertaintyD2 = calculate_uncertainty(data2, ul1)
+meanL, _ = calculate_uncertainty(data3, ul1)
+EL = math.sqrt(uncertaintyD1**2 + uncertaintyD2**2) / (meanD1 + meanD2)
+
+print(f"D内均值: {meanD1}")
+print(f"D外均值: {meanD2}")
+print(f"L均值: {meanL}")
+print(f"△L/L（EL）: {EL}")
+
+'''
+2.计算K
+'''
+V = input("输入所有V平均值：")
+Vs = V.split(" ")
+deltaV = []
+for i in range(1, len(Vs)):
+    for j in range(i, len(Vs)):
+        deltaV.append((float(Vs[j]) - float(Vs[j - i])) / i)
+dataDeltaV = np.array(deltaV, dtype=float)
+meanDeltaV, uncertaintyDeltaV = calculate_uncertainty(dataDeltaV, 0)
+
+avgK = 500 * 10**-6 * g / meanDeltaV
+EK = uncertaintyDeltaV / meanDeltaV
+
+print("deltaV：", meanDeltaV)
+print("K值：", avgK)
+print("△K/K：", EK)
+
+
+'''
+3.计算室温a
+'''
+V1 = input("输入V1：").split(" ")
+V2 = input("输入V2：").split(" ")
+
+data1 = np.array(V1, dtype=float)
+data2 = np.array(V2, dtype=float)
+dataV = data1 - data2
+
+mean1, uncertainty1 = calculate_uncertainty(data1, 0)
+mean2, uncertainty2 = calculate_uncertainty(data2, 0)
+meanV, _ = calculate_uncertainty(dataV, 0)
+EV = math.sqrt(uncertainty1**2 + uncertainty2**2) / abs(mean1 - mean2)
+
+a = avgK * meanV / meanL
+uncertaintyA = a * math.sqrt(EK ** 2 + EV ** 2 + EL ** 2)
+
+# 打印结果
+print(f"V1均值: {mean1}")
+print(f"V2均值: {mean2}")
+print(f"V: {dataV}")
+print(f"V均值: {meanV}")
+print(f"△V/V: {EV}")
+print(f"a: {a}")
+print(f"a不确定度: {uncertaintyA}")
+
+
+'''
+3.计算不同温度a
+'''
+V1 = input("输入V1：").split(" ")
+V2 = input("输入V2：").split(" ")
+
+data1 = np.array(V1, dtype=float)
+data2 = np.array(V2, dtype=float)
+dataV = data1 - data2
+
+mean1, uncertainty1 = calculate_uncertainty(data1, 0)
+mean2, uncertainty2 = calculate_uncertainty(data2, 0)
+meanV, _ = calculate_uncertainty(dataV, 0)
+EV = math.sqrt(uncertainty1**2 + uncertainty2**2) / abs(mean1 - mean2)
+
+a = avgK * meanV / meanL
+uncertaintyA = a * math.sqrt(EK ** 2 + EV ** 2 + EL ** 2)
+
+# 打印结果
+print(f"V1均值: {mean1}")
+print(f"V2均值: {mean2}")
+print(f"V: {dataV}")
+print(f"V均值: {meanV}")
+print(f"△V/V: {EV}")
+print(f"a: {a}")
+print(f"a不确定度: {uncertaintyA}")