I\'ve got a bunch of regularly distributed points (θ = n*π/6, r=1...8), each having a value in [0, 1]. I can plot them with their values in matplotlib using
Sure! Just use pcolormesh on a polar axes.
E.g.
import matplotlib.pyplot as plt
import numpy as np
# Generate some data...
# Note that all of these are _2D_ arrays, so that we can use meshgrid
# You'll need to "grid" your data to use pcolormesh if it's un-ordered points
theta, r = np.mgrid[0:2*np.pi:20j, 0:1:10j]
z = np.random.random(theta.size).reshape(theta.shape)
fig, (ax1, ax2) = plt.subplots(ncols=2, subplot_kw=dict(projection='polar'))
ax1.scatter(theta.flatten(), r.flatten(), c=z.flatten())
ax1.set_title('Scattered Points')
ax2.pcolormesh(theta, r, z)
ax2.set_title('Cells')
for ax in [ax1, ax2]:
ax.set_ylim([0, 1])
ax.set_yticklabels([])
plt.show()

If your data isn't already on a regular grid, then you'll need to grid it to use pcolormesh.
It looks like it's on a regular grid from your plot, though. In that case, gridding it is quite simple. If it's already ordered, it may be as simple as calling reshape. Otherwise, a simple loop or exploiting numpy.histogram2d with your z values as weights will do what you need.