Paul,
Note that by zoom the op means they are changing the bins, not actual
zooming(by just changing the x axis).
I was going to say we deal with normalization by delegating to numpy, but
we actually handle it internally (with a note that when we drop np 1.5 to
make numpy do it).
I think the best course of action here is to do that conversion and forward
this feature request to numpy (if it does not already do this).
Tom
On Sat, Mar 7, 2015, 18:29 Paul Hobson <pmhob...@gmail.com> wrote:
> IMO, this seems like a bug. I would expect bars to change height with
> zoom/limit levels.
> -p
>
> —
> Sent from Mailbox <https://www.dropbox.com/mailbox>
>
>
> On Sat, Mar 7, 2015 at 4:20 PM, Tomo Lazovich <lazov...@gmail.com> 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.png>
>>
>>
>> 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.png>
>>
>>
>> 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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