对数对数图的多项式拟合 [英] Polynomial fitting with log log plot

问题描述

我有一个简单的问题来在对数对数刻度上拟合一条直线.我的代码是，

I have a simple problem to fit a straight line on log-log scale. My code is,

data=loadtxt(filename)
xdata=data[:,0]
ydata=data[:,1]
polycoeffs = scipy.polyfit(xdata, ydata, 1)
yfit = scipy.polyval(polycoeffs, xdata)
pylab.plot(xdata, ydata, 'k.')
pylab.plot(xdata, yfit, 'r-')

现在我需要在对数刻度上绘制拟合线，所以我只需更改 x 和 y 轴，

Now I need to plot fit line on log scale so I just change x and y axis,

ax.set_yscale('log')
ax.set_xscale('log')

然后它没有绘制正确的拟合线.那么如何更改拟合函数(以对数刻度)，以便它可以在对数刻度上绘制拟合线?

then its not plotting correct fit line. So how can I change fit function (in log scale) so that it can plot fit line on log-log scale?

推荐答案

from scipy import polyfit
data = loadtxt("data.txt")
xdata,ydata = data[:,0],data[:,1]
xdata,ydata = zip(*sorted(zip(xdata,ydata))) # sorts the two lists after the xdata    

xd,yd = log10(xdata),log10(ydata)
polycoef = polyfit(xd, yd, 1)
yfit = 10**( polycoef[0]*xd+polycoef[1] )

plt.subplot(211)
plt.plot(xdata,ydata,'.k',xdata,yfit,'-r')
plt.subplot(212)
plt.loglog(xdata,ydata,'.k',xdata,yfit,'-r')
plt.show()

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