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

[Tomo L. <laz...@gm...>]

Thanks for the suggestion...I will see how numpy handles this.

Sorry for not being clearer earlier. Tom is right that by "zooming" I meant
changing the bins so that they covered a smaller range. Is there a better
way of "zooming" in on an axis so that I don't have this issue?

Thanks!

Tomo

On Sat, Mar 7, 2015 at 7:39 PM, Thomas Caswell <tca...@gm...> wrote:

> 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 <pmh...@gm...> 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 <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.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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