Python估计数据拟合后的标准差

Question

我试图用ipython--pylab将数据集拟合到超极化方程中： y=ax/（b+x）

下面是我的python代码：

from scipy import optimize as opti
import numpy as np
from pandas import DataFrame

x = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8])
y = np.array([0.375, 0.466, 0.509, 0.520, 0.525, 0.536, 0.541])
y_stdev = np.array([0.025, 0.016, 0.009, 0.009, 0.025, 0.019])

def func(x, a, b):
   return a*x / (b + x)

popt, pcov = opti.curve_fit(func, x, y)
print(popt)
print("a = ", popt.ix[0])
print("b = ", popt.ix[1])

a和b的值应在popt参数内。我想问的是，将数据集拟合到func（x，a，b）时，会推断出a和b的值，那么，我们如何估计a和b的标准差？ 谢谢您。在

Answer 1

就在docs：

pcov : 2d array

The estimated covariance of popt. The diagonals provide the variance of the parameter estimate. To compute one standard deviation errors on the parameters use perr = np.sqrt(np.diag(pcov))...