How to put matplotlib.pyplot.contourf result in a circle?

Question

Here is my code for building some data:

from scipy.interpolate import griddata from numpy import linspace import matplotlib.pyplot as plt meanR = [9.95184937, 9.87947708, 9.87628496, 9.78414422, 9.79365258, 9.96168969, 9.87537519, 9.74536093, 10.16686878, 10.04425475, 10.10444126, 10.2917172 , 10.16745917, 10.0235203 , 9.89914 , 10.11263505, 9.99756449, 10.17861254, 10.04704248] koord = [[1,4],[3,4],[1,3],[3,3],[2,3],[1,2],[3,2],[2,2],[1,1],[3,1],[2,1],[1,0],[3,0],[0,3],[4,3],[0,2],[4,2],[0,1],[4,1]] x,y=[],[] for i in koord: x.append(i[0]) y.append(i[1]) z = meanR xi = linspace(-2,6,300); yi = linspace(-2,6,300); zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='cubic') CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet) plt.scatter(x,y,marker='o',c='b',s=15) plt.xlim(min(x),max(x)) plt.ylim(min(y),max(y)) plt.show() 

As a result, we have:

How can I write it in a circle? something like that

Answer 1

Since you don't need any axes, you can also use normal projection, delete the axes, and draw a circle. I had some fun and added some bonus ears, a nose and a color bar. I made a code annotation, hope this is clear.

 from __future__ import print_function from __future__ import division from __future__ import absolute_import import scipy.interpolate import numpy import matplotlib import matplotlib.pyplot as plt # close old plots plt.close("all") # some parameters N = 300 # number of points for interpolation xy_center = [2,2] # center of the plot radius = 2 # radius # mostly original code meanR = [9.95184937, 9.87947708, 9.87628496, 9.78414422, 9.79365258, 9.96168969, 9.87537519, 9.74536093, 10.16686878, 10.04425475, 10.10444126, 10.2917172 , 10.16745917, 10.0235203 , 9.89914 , 10.11263505, 9.99756449, 10.17861254, 10.04704248] koord = [[1,4],[3,4],[1,3],[3,3],[2,3],[1,2],[3,2],[2,2],[1,1],[3,1],[2,1],[1,0],[3,0],[0,3],[4,3],[0,2],[4,2],[0,1],[4,1]] x,y = [],[] for i in koord: x.append(i[0]) y.append(i[1]) z = meanR xi = numpy.linspace(-2, 6, N) yi = numpy.linspace(-2, 6, N) zi = scipy.interpolate.griddata((x, y), z, (xi[None,:], yi[:,None]), method='cubic') # set points > radius to not-a-number. They will not be plotted. # the dr/2 makes the edges a bit smoother dr = xi[1] - xi[0] for i in range(N): for j in range(N): r = numpy.sqrt((xi[i] - xy_center[0])**2 + (yi[j] - xy_center[1])**2) if (r - dr/2) > radius: zi[j,i] = "nan" # make figure fig = plt.figure() # set aspect = 1 to make it a circle ax = fig.add_subplot(111, aspect = 1) # use different number of levels for the fill and the lines CS = ax.contourf(xi, yi, zi, 60, cmap = plt.cm.jet, zorder = 1) ax.contour(xi, yi, zi, 15, colors = "grey", zorder = 2) # make a color bar cbar = fig.colorbar(CS, ax=ax) # add the data points # I guess there are no data points outside the head... ax.scatter(x, y, marker = 'o', c = 'b', s = 15, zorder = 3) # draw a circle # change the linewidth to hide the circle = matplotlib.patches.Circle(xy = xy_center, radius = radius, edgecolor = "k", facecolor = "none") ax.add_patch(circle) # make the axis invisible for loc, spine in ax.spines.iteritems(): # use ax.spines.items() in Python 3 spine.set_linewidth(0) # remove the ticks ax.set_xticks([]) ax.set_yticks([]) # Add some body parts. Hide unwanted parts by setting the zorder low # add two ears circle = matplotlib.patches.Ellipse(xy = [0,2], width = 0.5, height = 1.0, angle = 0, edgecolor = "k", facecolor = "w", zorder = 0) ax.add_patch(circle) circle = matplotlib.patches.Ellipse(xy = [4,2], width = 0.5, height = 1.0, angle = 0, edgecolor = "k", facecolor = "w", zorder = 0) ax.add_patch(circle) # add a nose xy = [[1.5,3], [2,4.5],[2.5,3]] polygon = matplotlib.patches.Polygon(xy = xy, facecolor = "w", zorder = 0) ax.add_patch(polygon) # set axes limits ax.set_xlim(-0.5, 4.5) ax.set_ylim(-0.5, 4.5) plt.show() 

Answer 2

If you replace the part where you are building, do the following:

 fig = plt.figure() ax = fig.add_subplot(111, polar=True) CS = ax.contourf(xi,yi,zi,15,cmap=plt.cm.jet) ax.scatter(x,y,marker='o',c='b',s=15) ax.set_xlim(min(x),max(x)) ax.set_ylim(min(y),max(y)) 

you get it

To get what you want, you must rescale x , y , xi , yi so that the image is centered to zero. You may also need to convert to polar coordinates. Now I don’t have time to provide additional information, but I hope this helps you get started