get bins coordinates with hexbin in matplotlib

Question

I use matplotlib's method hexbin to compute 2d histograms on my data. But I would like to get the coordinates of the centers of the hexagons in order to further process the results.

I got the values using get_array() method on the result, but I cannot figure out how to get the bins coordinates.

I tried to compute them given number of bins and the extent of my data but i don't know the exact number of bins in each direction. gridsize=(10,2) should do the trick but it does not seem to work.

Any idea?

Answer 1

I think this works.

from __future__ import division
import numpy as np
import math
import matplotlib.pyplot as plt

def generate_data(n):
    """Make random, correlated x & y arrays"""
    points = np.random.multivariate_normal(mean=(0,0),
        cov=[[0.4,9],[9,10]],size=int(n))
    return points

if __name__ =='__main__':

    color_map = plt.cm.Spectral_r
    n = 1e4
    points = generate_data(n)

    xbnds = np.array([-20.0,20.0])
    ybnds = np.array([-20.0,20.0])
    extent = [xbnds[0],xbnds[1],ybnds[0],ybnds[1]]

    fig=plt.figure(figsize=(10,9))
    ax = fig.add_subplot(111)
    x, y = points.T
    # Set gridsize just to make them visually large
    image = plt.hexbin(x,y,cmap=color_map,gridsize=20,extent=extent,mincnt=1,bins='log')
    # Note that mincnt=1 adds 1 to each count
    counts = image.get_array()
    ncnts = np.count_nonzero(np.power(10,counts))
    verts = image.get_offsets()
    for offc in xrange(verts.shape[0]):
        binx,biny = verts[offc][0],verts[offc][1]
        if counts[offc]:
            plt.plot(binx,biny,'k.',zorder=100)
    ax.set_xlim(xbnds)
    ax.set_ylim(ybnds)
    plt.grid(True)
    cb = plt.colorbar(image,spacing='uniform',extend='max')
    plt.show()

Answer 2

I would love to confirm that the code by Hooked using get_offsets() works, but I tried several iterations of the code mentioned above to retrieve center positions and, as Dave mentioned, get_offsets() remains empty. The workaround that I found is to use the non-empty 'image.get_paths()' option. My code takes the mean to find centers but which means it is just a smidge longer, but it does work.

The get_paths() option returns a set of x,y coordinates embedded that can be looped over and then averaged to return the center position for each hexagram.

The code that I have is as follows:

counts=image.get_array() #counts in each hexagon, works great
verts=image.get_offsets() #empty, don't use this
b=image.get_paths()   #this does work, gives Path([[]][]) which can be plotted

for x in xrange(len(b)):
    xav=np.mean(b[x].vertices[0:6,0]) #center in x (RA)
    yav=np.mean(b[x].vertices[0:6,1]) #center in y (DEC)
    plt.plot(xav,yav,'k.',zorder=100)