我想重现这个情节。错误显示在图的底部。你能分享一下它是怎么做到的吗?
我在 stackoverflow 上找到了一个示例,但它是在 R 中。 How to create a graph showing the predictive model, data and residuals in R
最佳答案
您只能使用 add_axes 在 Matplotlib 中创建此类绘图.这是一个例子。
from scipy.optimize import curve_fit
#Data
x = arange(1,10,0.2)
ynoise = x*numpy.random.rand(len(x))
#Noise; noise is scaled by x, in order to it be noticable on a x-squared function
ydata = x**2 + ynoise #Noisy data
#Model
Fofx = lambda x,a,b,c: a*x**2+b*x+c
#Best fit parameters
p, cov = curve_fit(Fofx,x,ydata)
#PLOT
fig1 = figure(1)
#Plot Data-model
frame1=fig1.add_axes((.1,.3,.8,.6))
#xstart, ystart, xend, yend [units are fraction of the image frame, from bottom left corner]
plot(x,ydata,'.b') #Noisy data
plot(x,Fofx(x,*p),'-r') #Best fit model
frame1.set_xticklabels([]) #Remove x-tic labels for the first frame
grid()
#Residual plot
difference = Fofx(x,*p) - ydata
frame2=fig1.add_axes((.1,.1,.8,.2))
plot(x,difference,'or')
grid()
关于python - 如何在 matplotlib 图的底部显示残差,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/24116318/