How to plot a density map in python?

Question

I have a .txt file containing the x,y values of regularly spaced points in a 2D map, the 3rd coordinate being the density at that point.

4.882812500000000E-004  4.882812500000000E-004  0.9072267 1.464843750000000E-003  4.882812500000000E-004   1.405174 2.441406250000000E-003  4.882812500000000E-004   24.32851 3.417968750000000E-003  4.882812500000000E-004   101.4136 4.394531250000000E-003  4.882812500000000E-004   199.1388 5.371093750000000E-003  4.882812500000000E-004   1278.898 6.347656250000000E-003  4.882812500000000E-004   1636.955 7.324218750000000E-003  4.882812500000000E-004   1504.590 8.300781250000000E-003  4.882812500000000E-004   814.6337 9.277343750000000E-003  4.882812500000000E-004   273.8610 

When I plot this density map in gnuplot, with the following commands:

set palette rgbformulae 34,35,0 set size square set pm3d map splot "dens_map.map" u 1:2:(log10($3+10.)) title "Density map"` 

Which gives me this beautiful image:

Now I would like to have the same result with matplotlib.

Answer 1

Here is my aim at a more complete answer including choosing the color map and a logarithmic normalization of the color axis.

import matplotlib.pyplot as plt import matplotlib.cm as cm from matplotlib.colors import LogNorm import numpy as np x, y, z = np.loadtxt('data.txt', unpack=True) N = int(len(z)**.5) z = z.reshape(N, N) plt.imshow(z+10, extent=(np.amin(x), np.amax(x), np.amin(y), np.amax(y)),         cmap=cm.hot, norm=LogNorm()) plt.colorbar() plt.show() 

I assume here that your data can be transformed into a 2d array by a simple reshape. If this is not the case than you need to work a bit harder on getting the data in this form. Using imshow and not pcolormesh is more efficient here if you data lies on a grid (as it seems to do). The above code snippet results in the following image, that comes pretty close to what you wanted: