Hi,
I have been investigating Ticket #605 'Incorrect behavior of
numpy.histogram' (http://scipy.org/scipy/numpy/ticket/605 ).
The fix for this ticket really depends on what the expectations are
for the bin limits and different applications have different behavior.
Consequently, I think that feedba
The matlab behaviour is to extend the first bin to include all data
down to -inf and extend the last bin to handle all data to inf. This
is probably the behaviour with least suprise.
Therefor, I would vote +1 for behaviour #1 by default, +1 for keeping
the old behaviour #2 around as an option and
On 05/04/2008, Bruce Southey <[EMAIL PROTECTED]> wrote:
> 1) Should the first bin contain all values less than or equal to the
> value of the first limit and the last bin contain all values greater
> than the value of the last limit?
> This produced the counts as: array([3, 3, 9]) (I termed th
On Apr 5, 2008, at 2:01 PM, Bruce Southey wrote:
> Hi,
> I have been investigating Ticket #605 'Incorrect behavior of
> numpy.histogram' (http://scipy.org/scipy/numpy/ticket/605 ).
I think that my preference depends on the definition of what
the bin number means. If the bin numbers are the lower
Am Samstag, 05. April 2008 21:54:27 schrieb Anne Archibald:
> There's also a fourth option - raise an exception if any points are
> outside the range.
+1
I think this should be the default. Otherwise, I tend towards "exclude", in
order to have comparable bin sizes (when plotting, I always find
+1 for an outlier keyword. Note, that this implies that when bins are passed
explicitly, the edges are given (nbins+1), not simply the left edges
(nbins).
While we are refactoring histogram, I'd suggest adding an axis keyword. This
is pretty straightforward to implement using the np.apply_along_ax
Hi,
Thanks David for pointing the piece of information I forgot to add in
my original email.
-1 for 'raise an exception' because, as Dan points out, the problem
stems from user providing bins.
+1 for the outliers keyword. Should 'exclude' distinguish points that
are too low and those that are too
+1 for axis and +1 for a keyword to define what to do with values
outside the range.
For the keyword, ather than 'outliers', I would propose 'discard' or
'exclude', because it could be used to describe the four
possibilities :
- discard='low' => values lower than the range are discarded,
va
On Apr 7, 2008, at 4:14 PM, LB wrote:
> +1 for axis and +1 for a keyword to define what to do with values
> outside the range.
>
> For the keyword, ather than 'outliers', I would propose 'discard' or
> 'exclude', because it could be used to describe the four
> possibilities :
> - discard='low'
> On Apr 7, 2008, at 4:14 PM, LB wrote:
> > +1 for axis and +1 for a keyword to define what to do with values
> > outside the range.
> >
> > For the keyword, ather than 'outliers', I would propose 'discard' or
> > 'exclude', because it could be used to describe the four
> > possibilities :
> > - d
Am Montag, 07. April 2008 14:34:08 schrieb Hans Meine:
> Am Samstag, 05. April 2008 21:54:27 schrieb Anne Archibald:
> > There's also a fourth option - raise an exception if any points are
> > outside the range.
>
> +1
>
> I think this should be the default. Otherwise, I tend towards "exclude",
>
Hans,
Note that the current histogram is buggy, in the sense that it assumes that
all bins have the same width and computes db = bins[1]-bin[0]. This is why
you get zeros everywhere.
The current behavior has been heavily criticized and I think we should
change it. My proposal is to have for histo
Hi,
I agree that the current histogram should be changed. However, I am not
sure 1.0.5 is the correct release for that.
David, this doesn't work for your code:
r= np.array([1,2,2,3,3,3,4,4,4,4,5,5,5,5,5])
dbin=[2,3,4]
rc, rb=histogram(r, bins=dbin, discard=None)
Returns:
rc=[3 3] # Really should
2008/4/8, Bruce Southey <[EMAIL PROTECTED]>:
>
> Hi,
> I agree that the current histogram should be changed. However, I am not
> sure 1.0.5 is the correct release for that.
We both agree.
David, this doesn't work for your code:
> r= np.array([1,2,2,3,3,3,4,4,4,4,5,5,5,5,5])
> dbin=[2,3,4]
> rc,
Hi,
I should have asked first (I hope that you don't mind), but I created a
ticket Ticket #728 (http://scipy.org/scipy/numpy/ticket/728 ) for
numpy.r_ because this incorrectly casts based on the array types.
The bug is that -inf and inf are numpy floats but dbin is an array of
ints. Unfortunate
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