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我发现我的问题很奇怪。我把整个文件放在这里，现在替换以前的文件。

import pandas as pd  
import numpy as np  
from option_pricer import BS_Call, r_input  
from scipy.optimize import leastsq  

## this function returns the error vector

def res_bs(p):  

    sigma = p  
    err = np.zeros(len(k))  
    for i in range(len(k)):  
        err[i] = (callEu[i]-BS_Call(sigma, r[i], T[i]/360, s[i], k[i] ))/sqrt(len(k))  
    return err

## this function returns the root mean squared error

def rmse_bs(p):

    return sqrt(sum(res_bs(p)**2))  

## this function converts the raw data into variables to be used      
def params_in(options):

    k = options.strk_prc  
    k = pd.np.array(k)  
    T = options.tmy  
    T = pd.np.array(T)  
    s = options.undl_prc  
    s = pd.np.array(s)  
    callEu = options.eur_call_prc  
    callEu = pd.np.array(callEu)  
    callAm = options.ame_call_prc  
    callAm = pd.np.array(callAm)  
    r1 = unique(options.r1)  
    r1 = pd.np.array(r1)      
    r2 = unique(options.r2)  
    r2 = pd.np.array(r2)      
    r3 = unique(options.r3)  
    r3 = pd.np.array(r3)      
    r4 = unique(options.r4)  
    r4 = pd.np.array(r4)      
    r5 = unique(options.r5)  
    r5 = pd.np.array(r5)  
    r = r_input(T, r1, r2, r3, r4, r5)  
    return [k, T, s, callEu, callAm, r]  

## the calibration routine
def calib_bs(data):

    tdDays = sorted(set(data.d_td))  
    N = len(tdDays)  
    sigma_implied = np.zeros(N)  
    rmseBS = np.zeros(N)  
    for i in range(N):  
        options = data[data['d_td']==tdDays[i]]  
        global k, T, s, callEu, callAm, r  
        [k, T, s, callEu, callAm, r] = params_in(options)  
        p0 = 0.05    
        sigma_implied[i] = leastsq(res_bs, p0)[0]  
        rmseBS[i] = rmse_bs(sigma_implied[i])  
    return [sigma_implied, rmseBS]  

data = pd.read_csv("mydata.csv")

[sigma, rmseBS] = calib_bs(data)

如果我直接在 Spyder 中执行上述操作（按“F5”），那么结果就是p0！但是，如果我以交互方式执行——即加载数据、定义函数，然后调用校准函数——我会得到正确的答案！

我的直觉告诉我这个问题与 Python 执行命令的方式或顺序有关。但我对此一无所知。

谁能告诉我如何修改代码以允许直接执行而不是交互？

附录： 我尝试了以下测试，没有发现任何问题。也就是说，结果是正确的，它不依赖于我如何执行代码，例如，直接（“F5”）或交互地执行它。“data.csv”文件可以是任何东西，我用 np.arrange(1,41).reshape(20,2) 制作了一个矩阵并将其保存为 data.csv

import numpy as np
from scipy.optimize import leastsq
import pandas as pd
from testFuncs import f ## f is f(x, p) return x*p，一个线性函数

def ssr(p):

    beta = p  
    err = np.zeros(len(x))  
    for i in range(len(x)):  
        err[i] = ( y[i]-f(x[i],beta) )  
    return err  

def calibration(data):

    l = data.count()[0]
    a = data.a
    a = pd.np.array(a)
    b = data.b
    b = pd.np.array(b)
    par = np.zeros(l/5)
    for i in range(l/5):
        global x, y
        x = a[i*5:(i+1)*5]
        y = b[i*5:(i+1)*5]
        p0 = 1
        par[i] = leastsq(ssr, p0)[0]
    return par

result = calibration(data)
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