Python matplotlib contourf plot

Question

I have one questions about matplotlib and contourf.

I am using the last version of matplotlib with python3.7. Basically I have to matrix I want to plot on the same contour plot but using different colormap. One important aspect is that, for instance, if we have zero matrixA and matrixB with shape=(10,10) then the positions in which matrixA is different of zero are the positions in which matrixB are non-zero, and viceversa.

In other words I want to plot in different colors two different mask.

Thanks for your time.

Edited:

I add an example here

import numpy
import matplotlib.pyplot as plt

matrixA=numpy.random.randn(10,10).reshape(100,)
matrixB=numpy.random.randn(10,10).reshape(100,)

mask=numpy.random.uniform(10,10)
mask=mask.reshape(100,)

indexA=numpy.where(mask[mask>0.5])[0]
indexB=numpy.where(mask[mask<=0.5])[0]

matrixA_masked=numpy.zeros(100,)
matrixB_masked=numpy.zeros(100,)
matrixA_masked[indexA]=matrixA[indexA]
matrixB_masked[indexB]=matrixB[indexB]

matrixA_masked=matrixA_masked.reshape(100,100)
matrixB_masked=matrixB_masked.reshape(100,100)

x=numpy.linspace(0,10,1)
X,Y = numpy.meshgrid(x,x)
plt.contourf(X,Y,matrixA_masked,colormap='gray')
plt.contourf(X,Y,matrixB_masked,colormap='winter')
plt.show()

What I want is to be able to use different colormaps that appear in the same plot. So for instance in the plot there will be a part assigned to matrixA with a contour color (and 0 where matrixB take place), and the same to matrixB with a different colormap.

In other works each part of the contourf plot correspond to one matrix. I am plotting decision surfaces of Machine Learning Models.

Answer 1

I stumbled into some errors in your code so I have created my own dataset. To have two colormaps on one plot you need to open a figure and define the axes:

import numpy
import matplotlib.pyplot as plt

matrixA=numpy.linspace(1,20,100)
matrixA[matrixA >= 10] = numpy.nan
matrixA_2 = numpy.reshape(matrixA,[50,2])

matrixB=numpy.linspace(1,20,100)
matrixB[matrixB <= 10] = numpy.nan
matrixB_2 = numpy.reshape(matrixB,[50,2])

fig,ax = plt.subplots()
a = ax.contourf(matrixA_2,cmap='copper',alpha=0.5,zorder=0)
fig.colorbar(a,ax=ax,orientation='vertical')
b=ax.contourf(matrixB_2,cmap='cool',alpha=0.5,zorder=1)
fig.colorbar(b,ax=ax,orientation='horizontal')
plt.show()

You'll also see I've changed the alpha and zorder

I hope this helps.