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
On Wed, Sep 2, 2009 at 23:59, Gökhan Sever wrote:
> Robert,
>
> You must have thrown a couple RTFM's while replying my emails :)
Not really. There's no manual for this. Greg Wilson's _Data Crunching_
may be a good general introduction to how to think about these
problems.
http://www.pragprog.com
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 1:58 PM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 13:28, Gökhan Sever wrote:
> > Put the reference manual in:
> >
> > http://drop.io/1plh5rt
> >
> > First few pages describe the data format they use.
>
> Ah. The fields are *not* delimited by a fixed value. Regexes are no
On Wed, Sep 2, 2009 at 4:23 PM, Jeremy Mayes wrote:
> This one line causes python to core dump on linux.
> numpy.lexsort([
> numpy.array(['-','-','-','-','-','-','-','-','-','-','-','-','-'])[::-1],numpy.array([732685.,
> 732685., 732685., 732685., 732685., 732685.,732685., 732685.,
> 732685
On Wed, Sep 2, 2009 at 19:11, Tim Michelsen wrote:
> Hello Robert and Josef,
> thanks for the quick answers! I really appreciate this.
>
I am trying to create a inverse cumulative histogram [3] which shall
look like [4] but with the higher values at the left.
>>> Okay. That is completely
Hello Robert and Josef,
thanks for the quick answers! I really appreciate this.
>>> I am trying to create a inverse cumulative histogram [3] which shall
>>> look like [4] but with the higher values at the left.
>> Okay. That is completely different from what you've asked before.
You are right.
But
On Wed, Sep 2, 2009 at 7:26 PM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 18:15, Tim Michelsen
> wrote:
>> Hello fellow numy users,
>> I posted some questions on histograms recently [1, 2] but still couldn't
>> find a solution.
>>
>> I am trying to create a inverse cumulative histogram [3] whic
31/08/09 @ 14:37 (-0400), thus spake Pierre GM:
> On Aug 31, 2009, at 2:33 PM, Ernest Adrogué wrote:
>
> > 30/08/09 @ 13:19 (-0400), thus spake Pierre GM:
> >> I can't reproduce that with a recent SVN version (r7348). What
> >> version
> >> of numpy are you using ?
> >
> > Version 1.2.1
>
> Tha
On Wed, Sep 2, 2009 at 5:19 PM, Citi, Luca wrote:
> I experience the same problem.
> A few more additional test cases:
>
> In [1]: import numpy
>
> In [2]: numpy.lexsort([numpy.arange(5)[::-1].copy(), numpy.arange(5)])
> Out[2]: array([0, 1, 2, 3, 4])
>
> In [3]: numpy.lexsort([numpy.arange(5)[::
On Wed, Sep 2, 2009 at 18:15, Tim Michelsen wrote:
> Hello fellow numy users,
> I posted some questions on histograms recently [1, 2] but still couldn't
> find a solution.
>
> I am trying to create a inverse cumulative histogram [3] which shall
> look like [4] but with the higher values at the lef
On Wed, Sep 2, 2009 at 5:19 PM, Citi, Luca wrote:
> I experience the same problem.
> A few more additional test cases:
>
> In [1]: import numpy
>
> In [2]: numpy.lexsort([numpy.arange(5)[::-1].copy(), numpy.arange(5)])
> Out[2]: array([0, 1, 2, 3, 4])
>
> In [3]: numpy.lexsort([numpy.arange(5)[::
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
I experience the same problem.
A few more additional test cases:
In [1]: import numpy
In [2]: numpy.lexsort([numpy.arange(5)[::-1].copy(), numpy.arange(5)])
Out[2]: array([0, 1, 2, 3, 4])
In [3]: numpy.lexsort([numpy.arange(5)[::-1].copy(), numpy.arange(5.)])
Out[3]: array([0, 1, 2, 3, 4])
In [
Hello fellow numy users,
I posted some questions on histograms recently [1, 2] but still couldn't
find a solution.
I am trying to create a inverse cumulative histogram [3] which shall
look like [4] but with the higher values at the left.
The classification shall follow this exemplary rule:
cl
On Wed, Sep 2, 2009 at 4:37 PM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 17:23, Jeremy Mayes wrote:
> > This one line causes python to core dump on linux.
> > numpy.lexsort([
> >
> numpy.array(['-','-','-','-','-','-','-','-','-','-','-','-','-'])[::-1],numpy.array([732685.,
> > 732685., 7326
I am unable to build numpy on Snow Leopard. The error that I am getting is
shown below. It is a linking issue related to the change in the the default
behavior of gcc under Snow Leopard. Before it used to compile for the 32 bit
i386 architecture, now the default is the 64 bit x86_64 architecture.
On Wed, Sep 2, 2009 at 17:23, Jeremy Mayes wrote:
> This one line causes python to core dump on linux.
> numpy.lexsort([
> numpy.array(['-','-','-','-','-','-','-','-','-','-','-','-','-'])[::-1],numpy.array([732685.,
> 732685., 732685., 732685., 732685., 732685.,732685., 732685.,
> 732685.,
This one line causes python to core dump on linux.
numpy.lexsort([
numpy.array(['-','-','-','-','-','-','-','-','-','-','-','-','-'])[::-1],numpy.array([732685.,
732685., 732685., 732685., 732685., 732685.,732685., 732685.,
732685., 732685., 732685., 732685., 732679.])[::-1]])
Here's some
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, Sep 2, 2009 at 13:28, Gökhan Sever wrote:
> Put the reference manual in:
>
> http://drop.io/1plh5rt
>
> First few pages describe the data format they use.
Ah. The fields are *not* delimited by a fixed value. Regexes are no
help to you for pulling out the information you need, except perhap
On Wed, Sep 2, 2009 at 12:46 PM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 12:33, Gökhan Sever wrote:
> > How your find suggestion work? It just returns the location of the first
> > occurrence.
>
> http://docs.python.org/library/stdtypes.html#str.find
>
> str.find(sub[, start[, end]])
>Ret
On Wed, Sep 2, 2009 at 12:29 PM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 12:27, Gökhan Sever wrote:
> >
> > On Wed, Sep 2, 2009 at 12:01 PM, Citi, Luca wrote:
> >>
> >> If I understand the problem...
> >> if you are 100% sure that "', '" only occurs between fields
> >> and never within, you
On Wed, Sep 2, 2009 at 12:29 PM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 12:27, Gökhan Sever wrote:
> >
> > On Wed, Sep 2, 2009 at 12:01 PM, Citi, Luca wrote:
> >>
> >> If I understand the problem...
> >> if you are 100% sure that "', '" only occurs between fields
> >> and never within, you
On Wed, Sep 2, 2009 at 12:33, Gökhan Sever wrote:
> How your find suggestion work? It just returns the location of the first
> occurrence.
http://docs.python.org/library/stdtypes.html#str.find
str.find(sub[, start[, end]])
Return the lowest index in the string where substring sub is
found, su
On Wed, Sep 2, 2009 at 12:27, Gökhan Sever wrote:
>
> On Wed, Sep 2, 2009 at 12:01 PM, Citi, Luca wrote:
>>
>> If I understand the problem...
>> if you are 100% sure that "', '" only occurs between fields
>> and never within, you can use the 'split' method of the string
>> which could be faster th
On Wed, Sep 2, 2009 at 12:04 PM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 11:53, Gökhan Sever wrote:
>
> > How to use recarrays with variable-length data fields as well as
> metadata?
>
> You don't.
>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
On Wed, Sep 2, 2009 at 12:01 PM, Citi, Luca wrote:
> If I understand the problem...
> if you are 100% sure that "', '" only occurs between fields
> and never within, you can use the 'split' method of the string
> which could be faster than regexp in this simple case.
> ___
Thank you, Robert, for the quick reply.
I just saw the line
#define PyUFunc_None -1
in the ufuncobject.h file.
It is always the same, you choose a sentinel thinking
that it doesn't conflict with any possible value and
you later find there is one such case.
As said it is not a big deal.
I wouldn't
If I understand the problem...
if you are 100% sure that "', '" only occurs between fields
and never within, you can use the 'split' method of the string
which could be faster than regexp in this simple case.
___
NumPy-Discussion mailing list
NumPy-Discus
On Wed, Sep 2, 2009 at 11:53, Gökhan Sever wrote:
> How to use recarrays with variable-length data fields as well as metadata?
You don't.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
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
On Wed, Sep 2, 2009 at 10:34 AM, Sturla Molden wrote:
> Gökhan Sever skrev:
> > What would be wisest and fastest way to tackle this issue?
> Get the format, read the binary data directly, skip the ascii/regex part.
>
> I sometimes use recarrays with formatted binary data; just constructing
> a dt
On Wed, Sep 2, 2009 at 10:11 AM, Robert Kern wrote:
> On Wed, Sep 2, 2009 at 09:38, Gökhan Sever wrote:
> > Hello,
> >
> > I want to be able to parse a binary file which hold information regarding
> to
> > experiment configuration and data obviously. Both configuration and data
> > sections are v
I forgot to mention I also support transpose.
-S.
On Wed, Sep 2, 2009 at 5:23 PM, Sturla Molden wrote:
> Sebastian Haase skrev:
>> A mockarray is initialized with a list of nd-arrays. The result is a
>> mock array having one additional dimention "in front".
> This is important, because often in t
On Wed, Sep 2, 2009 at 11:33, denis bzowy wrote:
> Robert Kern gmail.com> writes:
>
>> Looks good! Where can we get the code? Can this be specialized for 1D
> functions?
>
>
>
> Re code: sure, I'll be happy to post it if anyone points me to a real test
> case or two, to help me understand the enve
Robert Kern gmail.com> writes:
> Looks good! Where can we get the code? Can this be specialized for 1D
functions?
Re code: sure, I'll be happy to post it if anyone points me to a real test
case or two, to help me understand the envelope -- 100^2 -> 500^2 grid ?
(Splines on regular grids are fa
On Wed, Sep 2, 2009 at 11:11, Citi, Luca wrote:
> Hello,
> I know I am splitting the hair, but should not
> np.bitwise_and.identity be -1 instead of 1?
> I mean, something with all the bits set?
Probably. However, the .identity parts of ufuncs were designed mostly
to support multiply and add, so .
Hello,
I know I am splitting the hair, but should not
np.bitwise_and.identity be -1 instead of 1?
I mean, something with all the bits set?
I am checking whether all elements of a vector 'v'
have a certain bit 'b' set:
if np.bitwise_and.reduce(v) & (1 << b):
# do something
If v is empty, the ex
Gökhan Sever skrev:
> What would be wisest and fastest way to tackle this issue?
Get the format, read the binary data directly, skip the ascii/regex part.
I sometimes use recarrays with formatted binary data; just constructing
a dtype and use numpy.fromfile to read. That works when the binary fi
Sebastian Haase skrev:
> A mockarray is initialized with a list of nd-arrays. The result is a
> mock array having one additional dimention "in front".
This is important, because often in the case of 'concatenation' a real
concatenation is not needed. But then there is a common tool called
Matlab
On Wed, Sep 2, 2009 at 09:38, Gökhan Sever wrote:
> Hello,
>
> I want to be able to parse a binary file which hold information regarding to
> experiment configuration and data obviously. Both configuration and data
> sections are variable-length. A chuck this data is shown as below (after a
> binar
V. Armando Solé skrev:
> I am looking for a way to have a non contiguous array C in which the
> "left" (1, 2000) elements point to A and the "right" (1, 4000)
> elements point to B.
>
> Any hint will be appreciated.
If you know in advance that A and B are going to be duplicated, you can
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
Hello,
I want to be able to parse a binary file which hold information regarding to
experiment configuration and data obviously. Both configuration and data
sections are variable-length. A chuck this data is shown as below (after a
binary read operation)
'\x00\...@\x00$\x00\x02\x00\x12\x00\xff\x0
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
Hi everyone,
In case anyone is interested, I just set up a google group to discuss
GPU-based simulation for our Python neural simulator Brian:
http://groups.google.fr/group/brian-on-gpu
Our simulator relies heavily Numpy. I would be very happy if the GPU
experts here would like to share their ex
> >
> > Is there a way to constrain an old-style compilation just to make a code
> > work? I have similar problems with other old pieces of code.
>
> Use "-arch i686" in the CFLAGS and LDFLAGS. I think.
>
Unfortunately, it seems not to have any effect.
I'll try something else.
Thanks anyway.
Hi,
depending on the needs you have you might be interested in my "minimal
implementation" of what I call a
mock-ndarray.
I needed somthing like this to analyze higher dimensional stacks of 2d
images and what I needed was mostly the indexing features of
nd-arrays.
A mockarray is initialized with a
A Wednesday 02 September 2009 11:20:55 Jorge Scandaliaris escrigué:
> Thanks David, Robert and Francesc for comments and suggestions. It's nice
> having options, but that also means one has to choose ;)
> I will have a closer look at pytables. The thing that got me "scared" about
> it was the word
Citi, Luca wrote:
> As Gaël pointed out you cannot create A, B and then C
> as the concatenation of A and B without duplicating
> the vectors.
>
> But you can still re-link A to the left elements
> and B to the right ones afterwards by using views into C.
>
Thanks for the hint. In my case th
Thanks David, Robert and Francesc for comments and suggestions. It's nice having
options, but that also means one has to choose ;)
I will have a closer look at pytables. The thing that got me "scared" about it
was the word database. I have close to zero experience using or, even worst,
designing da
Gael Varoquaux wrote:
> You cannot in the numpy memory model. The numpy memory model defines an
> array as something that has regular strides to jump from an element to
> the next one.
>
I expected problems in the suggested case (concatenating columns) but I
did not expect the problem would be
As Gaël pointed out you cannot create A, B and then C
as the concatenation of A and B without duplicating
the vectors.
> I am looking for a way to have a non contiguous array C in which the
> "left" (1, 2000) elements point to A and the "right" (1, 4000)
> elements point to B.
But you
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
On Wed, Sep 02, 2009 at 09:40:49AM +0200, "V. Armando Solé" wrote:
> Let's say we have two arrays A and B of shapes (1, 2000) and (1,
> 4000).
> If I do C=numpy.concatenate((A, B), axis=1), I get a new array of
> dimension (1, 6000) with duplication of memory.
> I am looking for a w
A Wednesday 02 September 2009 05:50:57 Robert Kern escrigué:
> On Tue, Sep 1, 2009 at 21:11, Jorge Scandaliaris
wrote:
> > David Warde-Farley cs.toronto.edu> writes:
> >> If you actually want to save multiple arrays, you can use
> >> savez('fname', *[a,b,c]) and they will be accessible under the
Hello,
Let's say we have two arrays A and B of shapes (1, 2000) and (1,
4000).
If I do C=numpy.concatenate((A, B), axis=1), I get a new array of
dimension (1, 6000) with duplication of memory.
I am looking for a way to have a non contiguous array C in which the
"left" (1, 2000
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