Hi,
I would like to revitalize the discussion on including PR#7804 (atleast_nd
function) at Stephan Hoyer's request. atleast_nd has come up as a
convenient workaround for #8206 (adding padding options to diff) to be able
to do broadcasting with the required dimensions reversed.
Regards,
-Joe
I would like to follow up on my original PR (7804). While there
appears to be some debate as to whether the PR is numpy material to
begin with, there do not appear to be any technical issues with it. To
make the decision more straightforward, I factored out the
non-controversial bug fixes to masked
On Thu, Jul 7, 2016 at 4:34 AM, Sebastian Berg
wrote:
> On Mi, 2016-07-06 at 15:30 -0400, Benjamin Root wrote:
>> I don't see how one could define a spec that would take an arbitrary
>> array of indices at which to place new dimensions. By definition, you
>>
>
> You just give a reordered range, so
On Mi, 2016-07-06 at 15:30 -0400, Benjamin Root wrote:
> I don't see how one could define a spec that would take an arbitrary
> array of indices at which to place new dimensions. By definition, you
>
You just give a reordered range, so that (1, 0, 2) would be the current
3D version. If 1D, fill i
On Wed, Jul 6, 2016 at 1:56 PM, Ralf Gommers wrote:
>
>
> On Wed, Jul 6, 2016 at 6:26 PM, Nathaniel Smith wrote:
>
>> On Jul 5, 2016 11:21 PM, "Ralf Gommers" wrote:
>> >
>> >
>> >
>> > On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith wrote:
>> >
>> >> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovi
Joseph Fox-Rabinovitz
gmail.com> writes:
>
> On Wed, Jul 6, 2016 at 2:57 PM, Eric
Firing hawaii.edu> wrote:
> > On 2016/07/06 8:25 AM, Benjamin Root
wrote:
> >>
> >> I wouldn't have the keyword be
"where", as that collides with the notion
> >> of "where" elsewhere in numpy.
> >
> >
> > Agre
On Wed, Jul 6, 2016 at 4:56 PM, Ralf Gommers wrote:
>
>
> On Wed, Jul 6, 2016 at 6:26 PM, Nathaniel Smith wrote:
>
>> On Jul 5, 2016 11:21 PM, "Ralf Gommers" wrote:
>> >
>> >
>> >
>> > On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith wrote:
>> >
>> >> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovi
On Wed, Jul 6, 2016 at 6:26 PM, Nathaniel Smith wrote:
On Jul 5, 2016 11:21 PM, "Ralf Gommers" wrote:
> >
> >
> >
> > On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith wrote:
> >
> >> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz" <
> jfoxrabinov...@gmail.com> wrote:
> >> >
> >> > Hi,
> >> >
> >
I don't see how one could define a spec that would take an arbitrary array
of indices at which to place new dimensions. By definition, you don't know
how many dimensions are going to be added. If you knew, then you wouldn't
be calling this function. I can only imagine simple rules such as 'left' or
On Wed, Jul 6, 2016 at 2:57 PM, Eric Firing wrote:
> On 2016/07/06 8:25 AM, Benjamin Root wrote:
>>
>> I wouldn't have the keyword be "where", as that collides with the notion
>> of "where" elsewhere in numpy.
>
>
> Agreed. Maybe "side"?
I have tentatively changed it to "pos". The reason that I
On Wed, Jul 6, 2016 at 3:01 PM, Juan Nunez-Iglesias wrote:
> at_leastnd would be useful for nd image processing in a very analogous way
> to how at_least2d is used by scikit-image, assuming it prepends. The
> at_least3d choice is baffling, seems analogous to the 0.5-based indexing
> presented at P
at_leastnd would be useful for nd image processing in a very analogous way
to how at_least2d is used by scikit-image, assuming it prepends. The
at_least3d choice is baffling, seems analogous to the 0.5-based indexing
presented at PyCon, and should be "fun" to deprecate. =P
On 6 July 2016 at 2:57
On 2016/07/06 8:25 AM, Benjamin Root wrote:
I wouldn't have the keyword be "where", as that collides with the notion
of "where" elsewhere in numpy.
Agreed. Maybe "side"?
(I find atleast_1d and atleast_2d to be very helpful for handling
inputs, as Ben noted; I'm skeptical as to the value of a
Agreed. I was originally going with "side", but I want something that
can be changed to accepting arbitrary specs without changing the word.
Perhaps "pos"? I am open to suggestion.
-Joe
On Wed, Jul 6, 2016 at 2:25 PM, Benjamin Root wrote:
> I wouldn't have the keyword be "where", as that col
I wouldn't have the keyword be "where", as that collides with the notion of
"where" elsewhere in numpy.
On Wed, Jul 6, 2016 at 2:21 PM, Joseph Fox-Rabinovitz <
jfoxrabinov...@gmail.com> wrote:
> I still think this function is useful. I have made a change so that it
> only accepts one array, as Ma
I still think this function is useful. I have made a change so that it
only accepts one array, as Marten suggested, making the API much
cleaner than that of its siblings. The side on which the new
dimensions will be added is configurable via the `where` parameter,
which currently accepts 'before' a
On Tue, Jul 5, 2016 at 10:06 PM, Nathaniel Smith wrote:
> I don't know how typical I am in this. But it does make me wonder if the
> atleast_* functions act as an attractive nuisance, where new users take
> their presence as an implicit recommendation that they are actually a
> useful thing to re
I was using "reduce" in an abstract sense. I put in a 4D array in, get
a 1-3D array out, depending on some other parameters (not strictly
just by reduction, although that is the net effect). The placement of
the dimensions is irrelevant, I just need to make the output 4D again
for further calculati
On Jul 6, 2016 6:12 AM, "Joseph Fox-Rabinovitz"
wrote:
>
> I can add a keyword-only argument that lets you put the new dims
> before or after the existing ones. I am not sure how to specify
> arbitrary patterns for the new dimensions, but that should take care
> of most use cases.
>
> The use case
On Jul 5, 2016 11:21 PM, "Ralf Gommers" wrote:
>
>
>
> On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith wrote:
>
>> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz"
wrote:
>> >
>> > Hi,
>> >
>> > I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a
>> > function np.atleast_nd in P
On Mi, 2016-07-06 at 10:22 -0400, Marten van Kerkwijk wrote:
> Hi All,
>
> I'm with Nathaniel here, in that I don't really see the point of
> these routines in the first place: broadcasting takes care of many of
> the initial use cases one might think of, and others are generally
> not all that we
We use np.at_least2d extensively in scikit-image, and I also use it in a
*lot* of my own code now that scikit-learn stopped accepting 1D arrays as
feature vectors.
> what is the advantage of np.at_leastnd` over `np.array(a, copy=False,
ndim=n)`
Readability, clearly.
My only concern is the descri
Hi All,
I'm with Nathaniel here, in that I don't really see the point of these
routines in the first place: broadcasting takes care of many of the initial
use cases one might think of, and others are generally not all that well
served by them: the examples from scipy to me do not really support
`a
While atleast_1d/2d/3d predates my involvement in numpy, I am probably
partly to blame for popularizing them as I helped to fix them up a fair
amount. I wouldn't call its use "guessing". Rather, I would treat them as
useful input sanitizers. If your function is going to be doing 2d indexing
on an i
I can add a keyword-only argument that lets you put the new dims
before or after the existing ones. I am not sure how to specify
arbitrary patterns for the new dimensions, but that should take care
of most use cases.
The use case that motivated this function in the first place is that I
am doing s
On Wed, Jul 6, 2016 at 3:29 AM, wrote:
>
>
> On Wed, Jul 6, 2016 at 2:21 AM, Ralf Gommers
> wrote:
>
>>
>>
>> On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith wrote:
>>
>> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz"
>>> wrote:
>>> >
>>> > Hi,
>>> >
>>> > I have generalized np.atleast_1d, np
On Wed, Jul 6, 2016 at 2:21 AM, Ralf Gommers wrote:
>
>
> On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith wrote:
>
> On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz"
>> wrote:
>> >
>> > Hi,
>> >
>> > I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a
>> > function np.atleast_n
On Wed, Jul 6, 2016 at 7:06 AM, Nathaniel Smith wrote:
On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz"
> wrote:
> >
> > Hi,
> >
> > I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a
> > function np.atleast_nd in PR#7804
> > (https://github.com/numpy/numpy/pull/7804).
> >
> >
On Jul 5, 2016 9:09 PM, "Joseph Fox-Rabinovitz"
wrote:
>
> Hi,
>
> I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a
> function np.atleast_nd in PR#7804
> (https://github.com/numpy/numpy/pull/7804).
>
> As a result of this PR, I have a couple of questions about
> `np.atleast_3d
Hi,
I have generalized np.atleast_1d, np.atleast_2d, np.atleast_3d with a
function np.atleast_nd in PR#7804
(https://github.com/numpy/numpy/pull/7804).
As a result of this PR, I have a couple of questions about
`np.atleast_3d`. `np.atleast_3d` appears to do something weird with
the dimensions: If
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