Re: [Numpydiscussion] np.histogramdd of empty data
 Forwarded message 
From: Ralf Gommers
Hi Ralf,
I cloned numpy/master and played around a little.
when giving the bins explicitely, now histogram2d and histogramdd work as expected in all tests i tried.
However, some of the cases with missing bin specification appear somewhat inconsistent.
The first question is if creating arbitrary bins for empty data and empty bin specification is better than raising an Exception:
Specifically:
Bins of size 0 should give a meaningful error, I was just fixing that as part of #1788 in https://github.com/rgommers/numpy/tree/ticket1788histogramdd
numpy.histogram2d([],[],bins=[0,0])
(array([ 0., 0.]), array([ 0.]), array([ 0.]))
Now gives: ValueError: Element at index 0 in `bins` should be a positive integer.
numpy.histogram([],bins=0)
ValueError: zerosize array to minimum.reduce without identity
Now gives: ValueError: `bins` should be a positive integer.
so 1d and 2d behave not quite the same.
also, these work (although with arbitrary bin edges):
numpy.histogram2d([],[],bins=[1,1])
(array([ 0., 0.]), array([ 0., 1.]), array([ 0., 1.]))
numpy.histogram2d([],[],bins=[0,1])
(array([ 0., 0.]), array([ 0.]), array([ 0., 1.]))
while this raises an error:
numpy.histogram([],bins=1)
ValueError: zerosize array to minimum.reduce without identity
Now gives: (array([0]), array([ 0., 1.]))
another thing with nonempty data:
numpy.histogram([1],bins=1)
(array([1]), array([ 0.5, 1.5]))
That is the correct answer.
numpy.histogram([1],bins=0)
(array([], dtype=int64), array([ 0.5]))
Now gives: ValueError: `bins` should be a positive integer.
while
numpy.histogram2d([1],[1],bins=A)
ValueError: zerosize array to minimum.reduce without identity
(here A==[0,0] or A==[0,1] but not A==[1,1] which gives a result)
Same sensible errors now, telling you bins elements shouldn't be 0. Cheers, Ralf
On Thu, Mar 31, 2011 at 12:32 PM, Ralf Gommers
 Forwarded message  From: Ralf Gommers
Date: Thu, Mar 31, 2011 at 7:31 PM Subject: Re: [Numpydiscussion] np.histogramdd of empty data To: Nils Becker On Thu, Mar 31, 2011 at 12:33 PM, Nils Becker
wrote: Hi Ralf,
I cloned numpy/master and played around a little.
when giving the bins explicitely, now histogram2d and histogramdd work as expected in all tests i tried.
However, some of the cases with missing bin specification appear somewhat inconsistent.
The first question is if creating arbitrary bins for empty data and empty bin specification is better than raising an Exception:
Specifically:
Bins of size 0 should give a meaningful error, I was just fixing that as part of #1788 in https://github.com/rgommers/numpy/tree/ticket1788histogramdd
numpy.histogram2d([],[],bins=[0,0])
(array([ 0., 0.]), array([ 0.]), array([ 0.]))
Now gives: ValueError: Element at index 0 in `bins` should be a positive integer.
numpy.histogram([],bins=0)
ValueError: zerosize array to minimum.reduce without identity
Now gives: ValueError: `bins` should be a positive integer.
so 1d and 2d behave not quite the same.
also, these work (although with arbitrary bin edges):
numpy.histogram2d([],[],bins=[1,1])
(array([ 0., 0.]), array([ 0., 1.]), array([ 0., 1.]))
numpy.histogram2d([],[],bins=[0,1])
(array([ 0., 0.]), array([ 0.]), array([ 0., 1.]))
while this raises an error:
numpy.histogram([],bins=1)
ValueError: zerosize array to minimum.reduce without identity
Now gives: (array([0]), array([ 0., 1.]))
Just for consistency's sake, maybe the same should be done for np.bincount() and np.digitize() for empty data (but known bins)? I don't know if your fix to histogram does this or not, but the latest pull from numpy master doesn't do this. Thanks, Ben Root
On Thu, Mar 31, 2011 at 8:00 PM, Benjamin Root
On Thu, Mar 31, 2011 at 12:32 PM, Ralf Gommers
wrote:  Forwarded message  From: Ralf Gommers
Date: Thu, Mar 31, 2011 at 7:31 PM Subject: Re: [Numpydiscussion] np.histogramdd of empty data To: Nils Becker On Thu, Mar 31, 2011 at 12:33 PM, Nils Becker
wrote: Hi Ralf,
I cloned numpy/master and played around a little.
when giving the bins explicitely, now histogram2d and histogramdd work as expected in all tests i tried.
However, some of the cases with missing bin specification appear somewhat inconsistent.
The first question is if creating arbitrary bins for empty data and empty bin specification is better than raising an Exception:
Specifically:
Bins of size 0 should give a meaningful error, I was just fixing that as part of #1788 in https://github.com/rgommers/numpy/tree/ticket1788histogramdd
numpy.histogram2d([],[],bins=[0,0])
(array([ 0., 0.]), array([ 0.]), array([ 0.]))
Now gives: ValueError: Element at index 0 in `bins` should be a positive integer.
numpy.histogram([],bins=0)
ValueError: zerosize array to minimum.reduce without identity
Now gives: ValueError: `bins` should be a positive integer.
so 1d and 2d behave not quite the same.
also, these work (although with arbitrary bin edges):
numpy.histogram2d([],[],bins=[1,1])
(array([ 0., 0.]), array([ 0., 1.]), array([ 0., 1.]))
numpy.histogram2d([],[],bins=[0,1])
(array([ 0., 0.]), array([ 0.]), array([ 0., 1.]))
while this raises an error:
numpy.histogram([],bins=1)
ValueError: zerosize array to minimum.reduce without identity
Now gives: (array([0]), array([ 0., 1.]))
Just for consistency's sake, maybe the same should be done for np.bincount() and np.digitize() for empty data (but known bins)? I don't know if your fix to histogram does this or not, but the latest pull from numpy master doesn't do this.
It doesn't. There was a ticket for that already, #1387. I added the desired behavior there. Since those functions are written in C it's a little more timeconsuming to fix. Ralf
participants (2)

Benjamin Root

Ralf Gommers