@Keith Goodman
I think I figured it out. I believe something like the following will do
what you want, iterating across one axis specially, so it can apply a median
function along an axis. This code in particular is for calculating a moving
average and seems to work (though I haven't checked my ma
> > import numpy as np
> > cimport numpy as cnp
>
> > cdef cnp.float64_t namean(cnp.ndarray[cnp.float64_t, ndim=1] a):
> >return np.nanmean(a) # just a placeholder
>
> > is not allowed? It works for me. Is it a cython version thing?
> > (I've got 0.13),
>
> Oh, that's nice! I'm using 0.11.2.
I think last time I looked into how to apply a function along an axis I
thought that the PyArray_IterAllButAxis would not work for that task (
http://docs.scipy.org/doc/numpy/reference/c-api.array.html#PyArray_IterAllButAxis),
but I think perhaps I misunderstood it. I'm looking into how to use it.
On Tue, Nov 30, 2010 at 11:58 AM, Matthew Brett wrote:
> Hi,
>
> On Tue, Nov 30, 2010 at 11:35 AM, Keith Goodman wrote:
>> On Tue, Nov 30, 2010 at 11:25 AM, John Salvatier
>> wrote:
>>> I am very interested in this result. I have wanted to know how to do an
>>
>> My first thought was to write th
Hi,
On Tue, Nov 30, 2010 at 11:35 AM, Keith Goodman wrote:
> On Tue, Nov 30, 2010 at 11:25 AM, John Salvatier
> wrote:
>> I am very interested in this result. I have wanted to know how to do an
>
> My first thought was to write the reducing function like this
>
> cdef np.float64_t namean(np.ndar
On Tue, Nov 30, 2010 at 11:25 AM, John Salvatier
wrote:
> I am very interested in this result. I have wanted to know how to do an
My first thought was to write the reducing function like this
cdef np.float64_t namean(np.ndarray[np.float64_t, ndim=1] a):
but cython doesn't allow np.ndarray in a
I am very interested in this result. I have wanted to know how to do an
apply_along_axis function for a while now.
On Tue, Nov 30, 2010 at 11:21 AM, Keith Goodman wrote:
> On Tue, Sep 1, 2009 at 2:37 PM, Sturla Molden wrote:
> > Dag Sverre Seljebotn skrev:
> >>
> >> Nitpick: This will fail on l
On Tue, Sep 1, 2009 at 2:37 PM, Sturla Molden wrote:
> Dag Sverre Seljebotn skrev:
>>
>> Nitpick: This will fail on large arrays. I guess numpy.npy_intp is the
>> right type to use in this case?
>>
> By the way, here is a more polished version, does it look ok?
>
> http://projects.scipy.org/numpy/
On Thu, Sep 3, 2009 at 12:14 AM, Sturla Molden wrote:
> Chad Netzer skrev:
> > That's right, Robert. Basically, I meant doing a median on a square
> > (or rectangle) "view" of an array, without first having to ravel(),
> > thus generally saving a copy. But actually, since my selection based
> >
Chad Netzer skrev:
> That's right, Robert. Basically, I meant doing a median on a square
> (or rectangle) "view" of an array, without first having to ravel(),
> thus generally saving a copy. But actually, since my selection based
> median overwrites the source array, it may not save a copy anyway.
Chad Netzer wrote:
> But Charles Harris's earlier suggestion of some hard coded medians for
> common filter template sizes (ie 3x3, 5x5, etc.) may be a nice
> addition to scipy, especially if it can be generalized somewhat to
> other filters.
>
For 2D images try looking into PIL : ImageFilter.M
Robert Kern skrev:
> When he is talking about 2D, I believe he is referring to median
> filtering rather than computing the median along an axis. I.e.,
> replacing each pixel with the median of a specified neighborhood
> around the pixel.
>
>
That's not something numpy's median function should b
On Wed, Sep 2, 2009 at 10:28 PM, Robert Kern wrote:
> When he is talking about 2D, I believe he is referring to median
> filtering rather than computing the median along an axis. I.e.,
> replacing each pixel with the median of a specified neighborhood
> around the pixel.
That's right, Robert. Ba
On Thu, Sep 3, 2009 at 00:09, Sturla Molden wrote:
> Chad Netzer skrev:
>> I'd also like to, if possible, have a specialized 2D version, since
>> image media filtering is one of my interests, and the C version works
>> on 1D (raveled) arrays only.
> I agree. NumPy (or SciPy) could have a select mo
Chad Netzer skrev:
> By the way, as far as I can tell, the above algorithm is exactly the
> same idea as a non-recursive Hoare (ie. quicksort) selection: Do the
> partition, then only proceed to the sub-partition that must contain
> the nth element.My version is a bit more general, allowing
>
On Wed, Sep 2, 2009 at 1:25 PM, Chad Netzer wrote:
> On Mon, Aug 31, 2009 at 9:06 PM, Sturla Molden wrote:
> >
> > We recently has a discussion regarding an optimization of NumPy's median
> > to average O(n) complexity. After some searching, I found out there is a
> > selection algorithm competit
On Mon, Aug 31, 2009 at 9:06 PM, Sturla Molden wrote:
>
> We recently has a discussion regarding an optimization of NumPy's median
> to average O(n) complexity. After some searching, I found out there is a
> selection algorithm competitive in speed with Hoare's quick select. It
> has the advantage
On Wed, 2 Sep 2009, Dag Sverre Seljebotn wrote:
> Sturla Molden wrote:
>> Dag Sverre Seljebotn skrev:
>>
>>> Nitpick: This will fail on large arrays. I guess numpy.npy_intp is the
>>> right type to use in this case?
>>>
>>>
>> By the way, here is a more polished version, does it look ok?
>>
>> htt
Citi, Luca skrev:
> Hello Sturla,
> In "_median" how can you, if n==2, use s[] if s is not defined?
> What if n==1?
>
That was a typo.
> Also, I think when returning an empty array, it should be of
> the same type you would get in the other cases.
Currently median returns numpy.nan for empty
Dag Sverre Seljebotn skrev:
> a) Is the cast to numpy.npy_intp really needed? I'm pretty sure shape is
>
> defined as numpy.npy_intp*.
I don't know Cython internals in detail but you do, I so take your word
for it. I thought shape was a tuple of Python ints.
> b) If you want higher perform
Sturla Molden wrote:
> Dag Sverre Seljebotn skrev:
>
>> Nitpick: This will fail on large arrays. I guess numpy.npy_intp is the
>> right type to use in this case?
>>
>>
> By the way, here is a more polished version, does it look ok?
>
> http://projects.scipy.org/numpy/attachment/ticket/1
Hello Sturla,
I had a quick look at your code.
Looks fine.
A few notes...
In "select" you should replace numpy with np.
In "_median" how can you, if n==2, use s[] if s is not defined?
What if n==1?
Also, I think when returning an empty array, it should be of
the same type you would get in the ot
Sturla Molden skrev:
>
> http://projects.scipy.org/numpy/attachment/ticket/1213/generate_qselect.py
> http://projects.scipy.org/numpy/attachment/ticket/1213/quickselect.pyx
My suggestion for a new median function is here:
http://projects.scipy.org/numpy/attachment/ticket/1213/median.py
The quickse
Sturla Molden skrev:
>
> By the way, here is a more polished version, does it look ok?
No it doesn't... Got to keep the GIL for the general case (sorting
object arrays). Fixing that.
SM
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Dag Sverre Seljebotn skrev:
>
> Nitpick: This will fail on large arrays. I guess numpy.npy_intp is the
> right type to use in this case?
>
By the way, here is a more polished version, does it look ok?
http://projects.scipy.org/numpy/attachment/ticket/1213/generate_qselect.py
http://projects.sc
Dag Sverre Seljebotn skrev:
> Nitpick: This will fail on large arrays. I guess numpy.npy_intp is the
> right type to use in this case?
>
>
Yup. You are right. Thanks.
Sturla
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Sturla Molden wrote:
> We recently has a discussion regarding an optimization of NumPy's median
> to average O(n) complexity. After some searching, I found out there is a
> selection algorithm competitive in speed with Hoare's quick select. It
> has the advantage of being a lot simpler to implem
Sturla Molden skrev:
> We recently has a discussion regarding an optimization of NumPy's median
> to average O(n) complexity. After some searching, I found out there is a
> selection algorithm competitive in speed with Hoare's quick select.
>
> Reference:
> http://ndevilla.free.fr/median/median.p
We recently has a discussion regarding an optimization of NumPy's median
to average O(n) complexity. After some searching, I found out there is a
selection algorithm competitive in speed with Hoare's quick select. It
has the advantage of being a lot simpler to implement. In plain Python:
impor
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