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copied!<p>I had this same problem. I think what needs to be developed is a framework to have a HexagonalGrid object which can then be applied to many different data sets (and it would be awesome to do it for N dimensions). This is possible and it surprises me that neither Scipy or Numpy has anything for it (furthermore there seems to be nothing else like it except perhaps <a href="https://github.com/kevinschaul/binify" rel="nofollow">binify</a>) </p> <p>That said, I assume you want to use hexbinning to compare multiple binned data sets. This requires some common base. I got this to work using matplotlib's hexbin the following way:</p> <pre><code>import numpy as np import matplotlib.pyplot as plt def get_data (mean,cov,n=1e3): """ Quick fake data builder """ np.random.seed(101) points = np.random.multivariate_normal(mean=mean,cov=cov,size=int(n)) x, y = points.T return x,y def get_centers (hexbin_output): """ about 40% faster than previous post only cause you're not calculating the min/max every time """ paths = hexbin_output.get_paths() v = paths[0].vertices[:-1] # adds a value [0,0] to the end vx,vy = v.T idx = [3,0,5,2] # index for [xmin,xmax,ymin,ymax] xmin,xmax,ymin,ymax = vx[idx[0]],vx[idx[1]],vy[idx[2]],vy[idx[3]] half_width_x = abs(xmax-xmin)/2.0 half_width_y = abs(ymax-ymin)/2.0 centers = [] for i in xrange(len(paths)): cx = paths[i].vertices[idx[0],0]+half_width_x cy = paths[i].vertices[idx[2],1]+half_width_y centers.append((cx,cy)) return np.asarray(centers) # important parts ==> class Hexagonal2DGrid (object): """ Used to fix the gridsize, extent, and bins """ def __init__ (self,gridsize,extent,bins=None): self.gridsize = gridsize self.extent = extent self.bins = bins def hexbin (x,y,hexgrid): """ To hexagonally bin the data in 2 dimensions """ fig = plt.figure() ax = fig.add_subplot(111) # Note mincnt=0 so that it will return a value for every point in the # hexgrid, not just those with count>mincnt # Basically you fix the gridsize, extent, and bins to keep them the same # then the resulting count array is the same hexbin = plt.hexbin(x,y, mincnt=0, gridsize=hexgrid.gridsize, extent=hexgrid.extent, bins=hexgrid.bins) # you could close the figure if you don't want it # plt.close(fig.number) counts = hexbin.get_array().copy() return counts, hexbin # Example ===> if __name__ == "__main__": hexgrid = Hexagonal2DGrid((21,5),[-70,70,-20,20]) x_data,y_data = get_data((0,0),[[-40,95],[90,10]]) x_model,y_model = get_data((0,10),[[100,30],[3,30]]) counts_data, hexbin_data = hexbin(x_data,y_data,hexgrid) counts_model, hexbin_model = hexbin(x_model,y_model,hexgrid) # if you want the centers, they will be the same for both centers = get_centers(hexbin_data) # if you want to ignore the cells with zeros then use the following mask. # But if want zeros for some bins and not others I'm not sure an elegant way # to do this without using the centers nonzero = counts_data != 0 # now you can compare the two data sets variance_data = counts_data[nonzero] square_diffs = (counts_data[nonzero]-counts_model[nonzero])**2 chi2 = np.sum(square_diffs/variance_data) print(" chi2={}".format(chi2)) </code></pre>

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