How to show residual in the bottom of a matplotlib plot

Question

I want to reproduce this plot. The errors are shown in the bottom of the plot. Can you please share how its done?

There is an example that I found here on stackoverflow, but it is in R. How to create a graph showing the predictive model, data and residuals in R

Answer 1

You can create such plot in Matplotlib only by using add_axes. Here is an example.

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()