如何将多项式拟合到带有误差线的数据 [英] How to fit polynomial to data with error bars

问题描述

我目前正在使用numpy.polyfit(x，y，deg)将多项式拟合到实验数据.但是，我想拟合一个基于点的误差使用加权的多项式.

I am currently using numpy.polyfit(x,y,deg) to fit a polynomial to experimental data. I would however like to fit a polynomial that uses weighting based on the errors of the points.

我找到了 scipy.curve_fit 利用权重，我想我可以将函数"f"设置为所需顺序的多项式形式，然后将权重放入"sigma"，这应该可以实现我的目标.

I have found scipy.curve_fit which makes use of weights and I suppose I could just set the function, 'f', to the form a polynomial of my desired order, and put my weights in 'sigma', which should achieve my goal.

我想知道还有另一种更好的方法吗?

I was wondering is there another, better way of doing this?

非常感谢.

推荐答案

看看 http://scipy-cookbook.readthedocs.io/items/FittingData.html ，尤其是.它显示了如何将scipy.optimize.leastsq与包含错误权重的函数一起使用.

Take a look at http://scipy-cookbook.readthedocs.io/items/FittingData.html in particular the section 'Fitting a power-law to data with errors'. It shows how to use scipy.optimize.leastsq with a function that includes error weighting.

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