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.
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,
in order to
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
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
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,
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
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
+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,
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 :
- discard='low'
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
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
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 this
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