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POPython: how to make an histogram with equally *sized* bins
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  1. POPython: how to make an histogram with equally *sized* bins
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    copied!<p>I have a set of data, and want to make an histogram of it. I need the bins to have the same <em>size</em>, by which I mean that they must contain the same number of objects, rather than the more common (numpy.histogram) problem of having <em>equally spaced</em> bins. This will naturally come at the expenses of the bins widths, which can - and in general will - be different.</p> <p>I will specify the number of desired bins and the data set, obtaining the bins edges in return.</p> <pre><code>Example: data = numpy.array([1., 1.2, 1.3, 2.0, 2.1, 2.12]) bins_edges = somefunc(data, nbins=3) print(bins_edges) &gt;&gt; [1.,1.3,2.1,2.12] </code></pre> <p>So the bins all contain 2 points, but their widths (0.3, 0.8, 0.02) are different.</p> <p>There are two limitations: - if a group of data is identical, the bin containing them could be bigger. - if there are N data and M bins are requested, there will be N/M bins plus one if N%M is not 0.</p> <p>This piece of code is some cruft I've written, which worked nicely for small data sets. What if I have 10**9+ points and want to speed up the process?</p> <pre><code> 1 import numpy as np 2 3 def def_equbin(in_distr, binsize=None, bin_num=None): 4 5 try: 6 7 distr_size = len(in_distr) 8 9 bin_size = distr_size / bin_num 10 odd_bin_size = distr_size % bin_num 11 12 args = in_distr.argsort() 13 14 hist = np.zeros((bin_num, bin_size)) 15 16 for i in range(bin_num): 17 hist[i, :] = in_distr[args[i * bin_size: (i + 1) * bin_size]] 18 19 if odd_bin_size == 0: 20 odd_bin = None 21 bins_limits = np.arange(bin_num) * bin_size 22 bins_limits = args[bins_limits] 23 bins_limits = np.concatenate((in_distr[bins_limits], 24 [in_distr[args[-1]]])) 25 else: 26 odd_bin = in_distr[args[bin_num * bin_size:]] 27 bins_limits = np.arange(bin_num + 1) * bin_size 28 bins_limits = args[bins_limits] 29 bins_limits = in_distr[bins_limits] 30 bins_limits = np.concatenate((bins_limits, [in_distr[args[-1]]])) 31 32 return (hist, odd_bin, bins_limits) </code></pre>
 

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