Hello matplotlib developers,
I'm not sure if this is the right mailing list for this question, so please
re-direct me if it is not.
I am wondering whether it is possible to have a histogram in pyplot
normalized to the total length of the list input, rather than just the bins
showing on the plot (i.e. include those entries in the "overflow" and
"underflow", off the right and left edges of the plot). As far as I can
tell, the normed option of pyplot.hist currently makes it so that the area
under the bins showing is 1. This can lead to a situation like the one
pasted below, where when I look at the whole histogram the bins have
certain values but when I try to zoom in to see one part of the plot better
those values change.
I can think of two ways to solve this as of now:
1) Use the weights option to scale each entry by 1/len(input) rather than
using normed=True.
2) Somehow add the contents of the overflow to the last bin of the plot,
which would keep the normalizations constant for earlier bins even if you
extend the axes.
Is there a better way of doing this? If the option does not currently
exist, I am also happy to help implement it if the community would find it
desirable.
Thanks for your help!
Tomo Lazovich
P.S. Here is a toy example of what I mean:
>> import numpy as np
>> import matplotlib.pyplot as plt
>> h1 = [0, 0, 0, 1, 1, 2, 3]
>> my_bins = np.linspace(-0.5, 4.5, 6)
>> plt.hist(h1, bins=my_bins, normed=True)
>> plt.show()
gives
[image: Inline image 1]
Now, if I change the range on the x axis that I would like plot:
>> my_bins2 = np.linspace(-0.5, 1.5, 3)
>> plt.hist(h1, bins=my_bins2, normed=True)
>> plt.show()
[image: Inline image 2]
The y values have changed to 0.6 and 0.4 because the normalization does not
include the values that are cut off to the right of the plot.
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