Maintaining one colorbar for maptlotlib FuncAnimation

Question

I\'ve made a script which uses matplotlib\'s FuncAnimation function to animate a series of contour plots for paraboloid surface functions. I\'d lik

Answer 1

As usual, I got beaten to the punch by @ImportanceOfBeingErnest, but I have a slightly different approach, which I thinks works as well.

I created a separate axe for the color bar, and I created a standalone color bar using the example from matplotlib's documentation. This requires to know the extend of the color scale before hand though.

Then I just plot the contourf in the animation using the same colorbar and normalization.

#Generate some lists
def f(x,y,a):
    return a*(x**2+y**2)

avals = list(np.linspace(0,1,10))
xaxis = list(np.linspace(-2,2,9))
yaxis = list(np.linspace(-2,2,9))

xy = list(itertools.product(xaxis,yaxis))
xy = list(map(list,xy))
xy = np.array(xy)

x = xy[:,0]
y = xy[:,1]
x = list(x)
y = list(y)

zlist = []

for a in avals:
    z = []
    for i, xval in enumerate(x):
        z.append(f(x[i],y[i],a))
    zlist.append(z)

xi = np.linspace(min(x),max(x),len(x))
yi = np.linspace(min(y), max(y), len(y))

fig,[ax,cax] = plt.subplots(1,2, gridspec_kw={"width_ratios":[10,1]})


# Set the colormap and norm to correspond to the data for which
# the colorbar will be used.
cmap = mpl.cm.hsv
norm = mpl.colors.Normalize(vmin=0, vmax=10)

cb1 = mpl.colorbar.ColorbarBase(cax, cmap=cmap,
                                norm=norm,
                                orientation='vertical')

def animate(index):
    zi = ml.griddata(x, y, zlist[index], xi, yi, interp='linear')
    ax.clear()
    contourplot = ax.contourf(xi, yi, zi, cmap=cmap, norm=norm, origin='lower')
    #cbar = plt.colorbar(contourplot)
    ax.set_title('%03d'%(index))
    return ax

ani = animation.FuncAnimation(fig,animate,np.array([0,1,2,3,4,5,6,7,8,9]),interval=200,blit=False)