Re: [matplotlib-devel] Histogram normalization and overflow bins

[Paul H. <pmh...@gm...>]

IMO, this seems like a bug. I would expect bars to change height with zoom/limit levels. 

-p



—
Sent from Mailbox

On Sat, Mar 7, 2015 at 4:20 PM, Tomo Lazovich <laz...@gm...> wrote:

> 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.