numpy-discussion — Discussion list for all users of Numerical Python

Re: [Numpy-discussion] Nieghbourhood functions

[Charles G W. <cg...@al...>]

 > Rob...@dn... wrote:
 > > 
 > > I am looking for efficient ways to code neighbourhood functions.  For
 > > example a neighbourhod add for an element in an array will simply be the sum
 > > of the neighbours:
 > > 
 > > 1 0 2
 > > 3 x 3   , then x becomes 7 (first order neighbour), 11 (2nd order) etc.
 > > 1 1 0
 > > 
 > > I would be interested in efficient ways of doing this for a whole array,
 > > something like  a_nsum = neighbour_sum(a, order=1), where each element in
 > > a_nsum is the sum of the corresponding element in a.

As Scott Ransom already mentioned, these "neighborhood functions" are
usually referred to as convolutions and are widely used in
signal/image processing.  For large convolution kernels, the most
efficient implementation is to use Fourier methods, since a
convolution in the spatial domain maps to a multiplication in the
Fourier domain.  However for small kernels this is inefficient because
the time spent doing the forward and inverse FFT's dwarfs the time
that it would take to just do the convolution.

There is a convolve function built into Numeric but it only is
implemented for 1-d arrays.  It would be nice if this were
generalized... when somebody gets the time.

In the meanwhile - here are two more comments which may help.
If your kernel is separable (i.e. a rank-one matrix, or equivalently,
the outer product of a column and a row vector) then the 2-d
convolution is equivalent to doing 2 1-d convolutions in sequence.
For your "first order neighborhood function" the kernel is 

0 1 0
1 0 1
0 1 0

which is not separable.  But for the "second order" case, the kernel
is

1 1 1
1 0 1
1 1 1

which is *almost* separable if you made that middle 0 into a 1.  But
if you were to convolve with the kernel 

1 1 1
1 1 1
1 1 1

then subtract off the value of the original array, then you'd have
what you were looking for.  And convolving with this kernel is
essentially the same as convolving with the 1-d kernel [1 1 1], then
transposing, then convolving with [1 1 1] again, and transposing a
second time.  This scales up to larger separable kernels.  I'm not
sure how efficient this winds up being - transposing large arrays can
be slow, if the arrays are too large to sit in physical memory - the
access pattern of a transpose is hell on virtual memory.

Finally, I would suggest that you look at the file Demo/life.py in the
Numeric Python distribution - in particular the functions shift_up,
shift_down, shift_left, and shift_right.  Using these you could write:

def first_order(arr):
    return shift_left(arr) + shift_right(arr) + shift_up(arr) +
	   shift_down(arr)

which is nice and terse and understandable, but unfortunately not very
memory-efficient.

If speed and memory usage are both critical, your best bet is probably
to write the functions in C and use SWIG to create Python bindings.


Charles G. Waldman
Object Environments
cg...@ob...

Re: [Numpy-discussion] Nieghbourhood functions

[Scott R. <ra...@cf...>]

Hi Robert,

Isn't this just a convolution?  For your first order case you are
convolving the array with a kernel that looks like:

0 1 0
1 0 1
0 1 0

and for second order:

1 1 1
1 0 1
1 1 1

If this is what you are looking for, there is a convolve function built
in to Numeric but it is for rank-1 arrays only.  You can use 2D FFTs for
a 2D case -- although the efficiency won't be the greatest unless you
can use the FFT of the kernel array and/or the data array over and over
again (since in general a convolution by FFTs takes 3 FFTs -- 1 for the
data, 1 for the kernel, and 1 inverse one after you have multiplied the
first two together).  It would work well for higher order cases...(but
beware of the wrapping that goes on accross the boundaries!)

For lower order cases or when you can't re-use the FFTs, you'll probably
want a brute force technique -- which I'll leave for someone else...

Scott

Rob...@dn... wrote:
> 
> I am looking for efficient ways to code neighbourhood functions.  For
> example a neighbourhod add for an element in an array will simply be the sum
> of the neighbours:
> 
> 1 0 2
> 3 x 3   , then x becomes 7 (first order neighbour), 11 (2nd order) etc.
> 1 1 0
> 
> I would be interested in efficient ways of doing this for a whole array,
> something like  a_nsum = neighbour_sum(a, order=1), where each element in
> a_nsum is the sum of the corresponding element in a.

-- 
Scott M. Ransom                   Address:  Harvard-Smithsonian CfA
Phone:  (617) 495-4142                      60 Garden St.  MS 10 
email:  ra...@cf...              Cambridge, MA  02138
GPG Fingerprint: 06A9 9553 78BE 16DB 407B  FFCA 9BFA B6FF FFD3 2989

[Numpy-discussion] Nieghbourhood functions

[<Rob...@dn...>]

I am looking for efficient ways to code neighbourhood functions.  For
example a neighbourhod add for an element in an array will simply be the sum
of the neighbours:

1 0 2
3 x 3   , then x becomes 7 (first order neighbour), 11 (2nd order) etc.
1 1 0 

I would be interested in efficient ways of doing this for a whole array,
something like  a_nsum = neighbour_sum(a, order=1), where each element in
a_nsum is the sum of the corresponding element in a.

There must be some work done on neighbourhood functions for arrays, so I
would be grateful for some pointers.

Thanks,
Robert Denham



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[Numpy-discussion] Optimisation of a Numpy set intersection function

[Tim C. <tc...@op...>]

I have been trying to optimise a simple set intersection function 
for a Numpy-based application on which I am working. The function needs
to
find the common elements (i.e the intersection) of two or more 
Numpy rank-1 arrays of integers passed to it as arguments - call them
array A, array B etc. My first attempt used searchsorted() to see where
in array A each element of array B would fit and then check if the
element in array A at that position equalled the element in array B - if
it did,
then that element was part of the intersection:

def intersect(A,B):
	c = searchsorted(A,B)
	d = compress(less(c,len(A)),B)
	return compress(equal(take(A,compress(less(c,len(A)),c)),d),d)

This works OK but it requires the arrays to be stored in sorted order
(which is what
we did) or to be sorted by the function (not shown).

I should add that the arrays passed to the function may have up to a few
million elements each.

Ole Nielsen at ANU (Australian National University), who gave a paper on
data
mining using Python at IPC9, suggested an improvement, shown below.
Provided the arrays are pre-sorted, Ole's function is about 40% faster
than my attempt, and about the same speed if the arrays aren't
pre-sorted.
However, Ole's function can be generalised to find the intersection of
more than
two arrays at a time. Most of the time is spent sorting the arrays, and
this
increases as N.log(N) as the total size of the concatenated arrays (N)
increases
(I checked this empirically).

def intersect(Arraylist):
	C = sort(concatenate(Arraylist))
        D = subtract(C[0:-n], C[n:])   #or
        # D = convolve(C,[n,-n])   
        return compress(equal(D,0),C)     #or
        # return take(C,nonzero(equal(D,0)))   

Paul Dubois suggested the following elegant alternative:

def intersect(x,y):
	return compress(sum(equal(subtract.outer(x,y),0),1),x)

Unfortunately, perhaps due to the overhead of creating the rank-2 array
to
hold the results of the subtract.outer() method call, it turns out to be 
slower than Ole's function, as well as using huge tracts of memory.

My questions for the list, are:

a) can anyone suggest any further optimisation of Ole's function, or
some
alternative?

b) how much do you think would be gained by re-implementing this
function in C using the Numpy C API? If such an effort would be
worthwhile,
are there any things we should consider while tackling this task?

Regards,

Tim Churches
Sydney, Australia

RE: [Numpy-discussion] Broken Win32 Package for 19.0

[Paul F. D. <pa...@pf...>]

Done. Erroneous file removed.

-----Original Message-----
From: num...@li...
[mailto:num...@li...]On Behalf Of Pete
Shinners
Sent: Thursday, April 05, 2001 12:09 AM
To: num...@li...
Subject: [Numpy-discussion] Broken Win32 Package for 19.0


hey folks. i'm the guy who put the win32 package for Numeric-19.0
for Python-2.0 together. turns out the package is missing one
small and important file, the Numeric.pth file.

i've just found out about this and created a new archive that
actually works. hopefully this can replace the current file
available for download on the sourceforge pages.

http://pygame.seul.org/ftp/contrib/Numeric-19.0.0b-Python-2.0.zip


sorry about the foul-up, hopefully no one got stuck on it. i
only just received an email about the problem tonight.





_______________________________________________
Numpy-discussion mailing list
Num...@li...
http://lists.sourceforge.net/lists/listinfo/numpy-discussion

[Numpy-discussion] Broken Win32 Package for 19.0

[Pete S. <pe...@sh...>]

hey folks. i'm the guy who put the win32 package for Numeric-19.0
for Python-2.0 together. turns out the package is missing one
small and important file, the Numeric.pth file.

i've just found out about this and created a new archive that
actually works. hopefully this can replace the current file
available for download on the sourceforge pages.

http://pygame.seul.org/ftp/contrib/Numeric-19.0.0b-Python-2.0.zip


sorry about the foul-up, hopefully no one got stuck on it. i
only just received an email about the problem tonight.

[Numpy-discussion] Citing NumPy

[Konrad H. <hi...@cn...>]

I am just working on an article describing a program which makes heavy
use of NumPy, so I should cite some reference to it. Did anyone do
this before? What is the most appropriate reference? I think URLs
are acceptable these days, but it should be a rather stable one.

Konrad.
-- 
-------------------------------------------------------------------------------
Konrad Hinsen                            | E-Mail: hi...@cn...
Centre de Biophysique Moleculaire (CNRS) | Tel.: +33-2.38.25.56.24
Rue Charles Sadron                       | Fax:  +33-2.38.63.15.17
45071 Orleans Cedex 2                    | Deutsch/Esperanto/English/
France                                   | Nederlands/Francais
-------------------------------------------------------------------------------

Re: [Numpy-discussion] curious FFFT timing

[Eric H. <eha...@ho...>]

Scott,
    Right you are.  My brain fried -- I forgot about the other "small prime"
stuff

Inverse FFT (      4096) Number of seconds     5.508000 Time per FFT
0.005508
Forward FFT (      4096) Number of seconds     3.225000 Time per FFT
0.003225
Inverse FFT (      4097) Number of seconds    29.723000 Time per FFT
0.029723
Forward FFT (      4097) Number of seconds    26.788000 Time per FFT
0.026788

Cheers
Eric


----- Original Message -----
From: "Scott Ransom" <ra...@cf...>
To: "Eric Hagemann" <eha...@ho...>
Cc: <Num...@li...>
Sent: Tuesday, April 03, 2001 9:05 PM
Subject: Re: [Numpy-discussion] curious FFFT timing


> Hi Eric,
>
> > Any one want to speculate on the timing difference for the 4095 vice
> > 4096 long transforms ?
> > Since 4095 is not a power of two I would have expected a greater time
> > difference (DFT vice FFT)
>
> You are correct in that 4095 is not a power of two, but is is the
> product of only small prime factors:
> 3 * 3 * 5 * 7 * 13 = 4095
>
> Since the FFT code implements a N*log_2(N) algorithm for numbers that
> contain only small prime factors, the difference in times is rather
> small.  FFTs that have lengths that are powers-of-two tend to be more
> efficient in general (since the decimation routines are cleaner for this
> case).
>
> If you test with 4097 (17 * 241) it will be quite a bit slower I'd
> guess...
>
> Scott
>
>
> --
> Scott M. Ransom                   Address:  Harvard-Smithsonian CfA
> Phone:  (617) 495-4142                      60 Garden St.  MS 10
> email:  ra...@cf...              Cambridge, MA  02138
> GPG Fingerprint: 06A9 9553 78BE 16DB 407B  FFCA 9BFA B6FF FFD3 2989
>
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> http://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] curious FFFT timing

[Scott R. <ra...@cf...>]

Hi Eric,

> Any one want to speculate on the timing difference for the 4095 vice
> 4096 long transforms ?
> Since 4095 is not a power of two I would have expected a greater time
> difference (DFT vice FFT)

You are correct in that 4095 is not a power of two, but is is the
product of only small prime factors:
3 * 3 * 5 * 7 * 13 = 4095

Since the FFT code implements a N*log_2(N) algorithm for numbers that
contain only small prime factors, the difference in times is rather
small.  FFTs that have lengths that are powers-of-two tend to be more
efficient in general (since the decimation routines are cleaner for this
case).

If you test with 4097 (17 * 241) it will be quite a bit slower I'd
guess...

Scott


-- 
Scott M. Ransom                   Address:  Harvard-Smithsonian CfA
Phone:  (617) 495-4142                      60 Garden St.  MS 10 
email:  ra...@cf...              Cambridge, MA  02138
GPG Fingerprint: 06A9 9553 78BE 16DB 407B  FFCA 9BFA B6FF FFD3 2989

[Numpy-discussion] curious FFFT timing

[Eric H. <eha...@ho...>]

I have an application that needs to perform close 1/2 million FFT's =
(actually inverse FFTs)  I was stymied by the time it was taking so I =
created a simple benchmark program to measure raw fft speed - hoping to =
extrapolate and see if the FFT algorithm was the time consuming portion =
of the algorithm.

The result I obtained surprised me

Inverse FFT (      4096) Number of seconds     5.608000 Time per FFT     =
0.005608
Forward FFT (      4096) Number of seconds     3.164000 Time per FFT     =
0.003164
Inverse FFT (      4095) Number of seconds     8.222000 Time per FFT     =
0.008222
Forward FFT (      4095) Number of seconds     5.468000 Time per FFT     =
0.005468
Inverse FFT (      8192) Number of seconds    12.578000 Time per FFT     =
0.012578
Forward FFT (      8192) Number of seconds     7.551000 Time per FFT     =
0.007551

Inverse FFTs were taking almost twice as long as forward FFTs !

Bottom line I found that in the inverse code the multiplication of 1/N =
at the end was the culprit.

When I removed the /n from the line=20

return _raw_fft(a, n, axis, fftpack.cffti, fftpack.cfftb, _fft_cache)/n  =


the code timing was more along the line of what was expected.

Any one want to speculate on the timing difference for the 4095 vice =
4096 long transforms ?
Since 4095 is not a power of two I would have expected a greater time =
difference (DFT vice FFT)

N*N / N*log2(N) =3D N/log2(N) =3D 4096/12 =3D 341.33 which is >> than =
the 1.72 seen above

Cheers
Eric

------------------------------------ Code Snippet =
---------------------------------------------

import time
from Numeric import *
from FFT import *


def f_fft(number,length):

    a =3D arange(float(length))
    start_time =3D time.time()
   =20
    for i in xrange(number):
        b =3D fft(a)

    durat =3D time.time() - start_time
    durat_per =3D durat / number
    print "Forward FFT (%10d) Number of seconds %12.6f Time per FFT =
%12.6f" % (length,durat,durat_per)
   =20
def i_fft(number,length):

    a =3D arange(float(length))
    start_time =3D time.time()
   =20
    for i in xrange(number):
        b =3D inverse_fft(a)

    durat =3D time.time() - start_time
    durat_per =3D durat / number
    print "Inverse FFT (%10d) Number of seconds %12.6f Time per FFT =
%12.6f" % (length,durat,durat_per)



i_fft(1000,4096)
f_fft(1000,4096)
i_fft(1000,4095)
f_fft(1000,4095)
i_fft(1000,8192)
f_fft(1000,8192)

RE: [Numpy-discussion] redirection?

[Paul F. D. <du...@us...>]

It works! Thanks for coming to my aid.

-----Original Message-----
From: Karl Bellve [mailto:Kar...@um...]
Sent: Tuesday, April 03, 2001 8:46 AM
To: Paul F. Dubois; Numpy
Subject: Re: [Numpy-discussion] redirection?



Here is a template html page. Change the appropiate content/urls and put
this in www.python.org/numeric/


<html>
<head>
<meta http-equiv="Refresh" content="0; URL=http://new_url.com">
<title>You are being redirected</title>
</head>

<body>
This page was been moved. If you aren't redirected automatically, please
<a
href="http://new_url.com">click here.</a>
</body>
</html>




"Paul F. Dubois" wrote:
>
> Since I made that typo I have people going to pfdubois.com/numeric when I
> actually made it /numpy. I can create a numerc -- but I think it would be
nicer
> if I did the automatic redirection trick. So what does one do to get a
browser
> to switch to a different site?
>
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> http://lists.sourceforge.net/lists/listinfo/numpy-discussion

--
Cheers,



Karl Bellve, Ph.D.                   ICQ # 13956200
Biomedical Imaging Group             TLCA# 7938
University of Massachusetts
Email: Kar...@um...
Phone: (508) 856-6514
Fax:   (508) 856-1840
PGP Public key: finger kd...@mo...

RE: [Numpy-discussion] Documentation typos

[Paul F. D. <pa...@pf...>]

Changes in my master copy, will appear next release. Thanks very much.

The last point was interesting as due to rich comparisons the buggy example
no longer had an exception, but it didn't work "right" either. I expanded
the example to explain.

-----Original Message-----
From: num...@li...
[mailto:num...@li...]On Behalf Of
Christos Siopis <si...@as...>
Sent: Tuesday, April 03, 2001 8:10 AM
To: num...@li...
Subject: [Numpy-discussion] Documentation typos


I was reading some sections of the latest NumPy tutorial (the one
currently listed as http://pfdubois.com/numpy/numdoc.pdf ) and spotted
these typos, listed below in case you want to fix them. Page numbers refer
to the actual page numbers printed on the page (not the PDF document's
numbers).

- p.14: "A very useful features of arrays..." (should be "feature").

- p.15, top-of-page NumPy example: arrays are shown printed before the
  corresponding "print" command (see "print a" and "print b").

- p.17: in the "Advanced Users" section: the explanation of what these
  sections are for should perhaps be included in the previous page's
  "Advanced Users" section, since that one is the first such occurrence
  in the tutorial.

- p.19, near the end: Above "identity": "The first example BELOW
satisfies...
  while the second array ...": there is only one example "below", the
  buggy example. The first, correct example is probably the one given
  previously on the  same page.

Christos




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Numpy-discussion mailing list
Num...@li...
http://lists.sourceforge.net/lists/listinfo/numpy-discussion

Re: [Numpy-discussion] Using Numpy inside an application...

[Karl B. <Kar...@um...>]

Karl Bellve wrote:
> 
> Since I can't search the web site, here is a question...
> 
> 
> "a" appears to be an list of numbers. I am not sure if Python knows it
> is a PyArrayObject. This is where I am getting into trouble. I can't do
> something like a=PyRun_SimpleString("a = a + 100"); I get an error
> "Value Error: Function not Supported".
> 
>

I corrected a few things.

First, I wasn't importing Numeric but just import_array (thanks to Pete
Shinners). Although I am getting DLL collisions and relocations, it
still works.

I am able to do the following now: a=a+100.  I need to figure out why I
am getting the DLL collisions...


-- 
Cheers,



Karl Bellve

[Numpy-discussion] Documentation typos

[Christos S. <si...@as...>]

I was reading some sections of the latest NumPy tutorial (the one
currently listed as http://pfdubois.com/numpy/numdoc.pdf ) and spotted
these typos, listed below in case you want to fix them. Page numbers refer
to the actual page numbers printed on the page (not the PDF document's
numbers).

- p.14: "A very useful features of arrays..." (should be "feature").

- p.15, top-of-page NumPy example: arrays are shown printed before the
  corresponding "print" command (see "print a" and "print b").

- p.17: in the "Advanced Users" section: the explanation of what these
  sections are for should perhaps be included in the previous page's
  "Advanced Users" section, since that one is the first such occurrence
  in the tutorial.

- p.19, near the end: Above "identity": "The first example BELOW satisfies...
  while the second array ...": there is only one example "below", the
  buggy example. The first, correct example is probably the one given
  previously on the  same page.

Christos

Re: [Numpy-discussion] numpy newbie question

[Jon S. <js...@wm...>]

Try SWIG (http://www.swig.org), to see whether it supports your needs.


Jon Saenz.				| Tfno: +34 946012470
Depto. Fisica Aplicada II               | Fax:  +34 944648500
Facultad de Ciencias.   \\ Universidad del Pais Vasco \\
Apdo. 644   \\ 48080 - Bilbao  \\ SPAIN

On Tue, 3 Apr 2001, Michael Drumheller wrote:

> Hi,
> I am pretty new to numpy and am using it for 
> image processing.  (Also, I am not 100% sure
> that this is the appropriate email address for
> this question--feel free to redirect me.)
> My problem is that I would like to be able
> to dive down into C and write my own filters etc.,
> but I do not want to have to surmount the
> somewhat daunting Python/C-extension learning curve if
> that is at all possible.  I am wondering if
> anybody has "packaged" this ability anywhere,
> sort of like Perl's Inline.pm.
> Thanks,
> Mike Drumheller
> 
> =====
> Michael Drumheller
>         2735 NE 87th Street, Seattle, Washington 98115
>              206/523-7865 drumheller@alum..mit.edu
> 
> __________________________________________________
> Do You Yahoo!?
> Get email at your own domain with Yahoo! Mail. 
> http://personal.mail.yahoo.com/
> 
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> http://lists.sourceforge.net/lists/listinfo/numpy-discussion
> 

[Numpy-discussion] numpy newbie question

[Michael D. <mdr...@ya...>]

Hi,
I am pretty new to numpy and am using it for 
image processing.  (Also, I am not 100% sure
that this is the appropriate email address for
this question--feel free to redirect me.)
My problem is that I would like to be able
to dive down into C and write my own filters etc.,
but I do not want to have to surmount the
somewhat daunting Python/C-extension learning curve if
that is at all possible.  I am wondering if
anybody has "packaged" this ability anywhere,
sort of like Perl's Inline.pm.
Thanks,
Mike Drumheller

=====
Michael Drumheller
        2735 NE 87th Street, Seattle, Washington 98115
             206/523-7865 dru...@al...

__________________________________________________
Do You Yahoo!?
Get email at your own domain with Yahoo! Mail. 
http://personal.mail.yahoo.com/

Re: [Numpy-discussion] redirection?

[Karl B. <Kar...@um...>]

Here is a template html page. Change the appropiate content/urls and put
this in www.python.org/numeric/


<html>
<head>
<meta http-equiv="Refresh" content="0; URL=http://new_url.com">
<title>You are being redirected</title>
</head>

<body>
This page was been moved. If you aren't redirected automatically, please
<a
href="http://new_url.com">click here.</a>
</body>
</html>




"Paul F. Dubois" wrote:
> 
> Since I made that typo I have people going to pfdubois.com/numeric when I
> actually made it /numpy. I can create a numerc -- but I think it would be nicer
> if I did the automatic redirection trick. So what does one do to get a browser
> to switch to a different site?
> 
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> http://lists.sourceforge.net/lists/listinfo/numpy-discussion

-- 
Cheers,



Karl Bellve, Ph.D.                   ICQ # 13956200
Biomedical Imaging Group             TLCA# 7938 		
University of Massachusetts
Email: Kar...@um...
Phone: (508) 856-6514
Fax:   (508) 856-1840
PGP Public key: finger kd...@mo...

[Numpy-discussion] redirection?

[Paul F. D. <du...@ll...>]

Since I made that typo I have people going to pfdubois.com/numeric when I
actually made it /numpy. I can create a numerc -- but I think it would be=
 nicer
if I did the automatic redirection trick. So what does one do to get a br=
owser
to switch to a different site?=20

[Numpy-discussion] Using Numpy inside an application...

[Karl B. <Kar...@um...>]

Since I can't search the web site, here is a question...

I am using the following calls inside my C++ app:

import_array();
a=PyRun_SimpleString("from image_module import *"); // my module for
wrapping a C++ class
a=PyRun_SimpleString("image=imagemanager()\n");     // load a new
instance of the class into Python
a=PyRun_SimpleString("a=image.GetImage(0)");	    // assign image 0 to a

Basically, GetImage() returns a numpy Array Object for image 0, properly
initialized by PyArray_FromDimsAndData(). I can't return all the images
because they might not be sequential in memory (off loaded to the
filesystem).

"a" appears to be an list of numbers. I am not sure if Python knows it
is a PyArrayObject. This is where I am getting into trouble. I can't do
something like a=PyRun_SimpleString("a = a + 100"); I get an error
"Value Error: Function not Supported". 

I am trying to figure out what namespace did "import_array()" import
Numeric into.

Also, I am using _numpy_d.pyd that I compiled under Windows NT and MSVC
6.0.


-- 
Cheers,



Karl Bellve, Ph.D.                   ICQ # 13956200
Biomedical Imaging Group             TLCA# 7938 		
University of Massachusetts
Email: Kar...@um...
Phone: (508) 856-6514
Fax:   (508) 856-1840
PGP Public key: finger kd...@mo...

[Numpy-discussion] Web Server

[Karl B. <Kar...@um...>]

When you go to www.numpy.org, it tries to redirect you to
www.numpy.org/numeric/ which it can't find...

-- 
Cheers,



Karl Bellve, Ph.D.                   ICQ # 13956200
Biomedical Imaging Group             TLCA# 7938 		
University of Massachusetts
Email: Kar...@um...
Phone: (508) 856-6514
Fax:   (508) 856-1840
PGP Public key: finger kd...@mo...

Re: [Numpy-discussion] Update to Numeric home page

[arne k. <ar...@no...>]

"Paul F. Dubois" wrote:
> 
> The Numeric home page http://pfdubois.com/numeric has been updated to
> include:
> 
> a. New HTML and PDF version of the documentation
> b. New Happydoc-generated documentation for the modules, using the new
> Happydoc.
> c. Description of and link to Scientific Python.
> d. Navigation buttons and a hit counter.
> 
> numpy.sourceforge.net still exists and links to this page.
> The "Homepage" button on the project page goes directly to it. I moved the
> contents to a web site I control so that I can work on it more easily --
> sourceforge sites are hard to work on due to security.
> http://pfdubois.com/numeric
> This site is hosted by Yahoo and seems to perform well. Like any site, it
> can be down, but I think it is down less than SF.
> 
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> http://lists.sourceforge.net/lists/listinfo/numpy-discussion

there is a problem with this url:
http://pfdubois.com/numeric

Not Found

The requested URL /numeric was not found on this server.


-- 
Arne Keller
Laboratoire de Photophysique Moleculaire du CNRS, Bat. 213.
Universite de Paris-Sud, 91405 Orsay Cedex, France.
tel.: (33) 1 69 15 82 83 -- fax. : (33) 1 69 15 67 77

RE: [Numpy-discussion] arg matrix max question

[Paul F. D. <pa...@pf...>]

>>> x
array([[   0,    1,    2,    3,    4,    5],
       [   6,    7,    8,    9,   10,   11],
       [  12,   13,   14,   15, 1000,   17],
       [  18,   19,   20,   21,   22,   23]])
>>> n = argmax(ravel(x))
>>> divmod(n, x.shape[1])
(2, 4)
>>> x[2,4]
1000
>>>
divmod is a builtin -- the rest are from Numeric.
  -----Original Message-----
  From: num...@li...
[mailto:num...@li...]On Behalf Of Eric
Hagemann
  Sent: Tuesday, May 01, 2001 5:26 PM
  To: Num...@li...
  Subject: [Numpy-discussion] arg matrix max question


  I need to find the location of the maximum value in a two dimensional
Numeric array

  The best I have come up with uses a couple of calls to argmax and then
cross checking the results

  I know there must be a better way and that I am not the only one who needs
to do such a thing

  Anybody got a code snippet to share ?

[Numpy-discussion] arg matrix max question

[Eric H. <eha...@ho...>]

I need to find the location of the maximum value in a two dimensional =
Numeric array

The best I have come up with uses a couple of calls to argmax and then =
cross checking the results

I know there must be a better way and that I am not the only one who =
needs to do such a thing

Anybody got a code snippet to share ?

RE: [Numpy-discussion] Update to Numeric home page

[Paul F. D. <pa...@pf...>]

Sorry, as a subsequent messgage said, that was a typo. It is
http://pfdubois.com/numpy.
You don't have to remember it, however; there are links on
http://numpy.sourceforge.net and on http://sourceforge.net/projects/numpy
and www.numpy.org points to it as well.

-----Original Message-----
From: arne [mailto:arne]On Behalf Of arne keller
Sent: Saturday, March 31, 2001 5:04 AM
To: Paul F. Dubois
Cc: num...@li...
Subject: Re: [Numpy-discussion] Update to Numeric home page


"Paul F. Dubois" wrote:
>
> The Numeric home page http://pfdubois.com/numeric has been updated to
> include:
>
> a. New HTML and PDF version of the documentation
> b. New Happydoc-generated documentation for the modules, using the new
> Happydoc.
> c. Description of and link to Scientific Python.
> d. Navigation buttons and a hit counter.
>
> numpy.sourceforge.net still exists and links to this page.
> The "Homepage" button on the project page goes directly to it. I moved the
> contents to a web site I control so that I can work on it more easily --
> sourceforge sites are hard to work on due to security.
> http://pfdubois.com/numeric
> This site is hosted by Yahoo and seems to perform well. Like any site, it
> can be down, but I think it is down less than SF.
>
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> http://lists.sourceforge.net/lists/listinfo/numpy-discussion

there is a problem with this url:
http://pfdubois.com/numeric

Not Found

The requested URL /numeric was not found on this server.


--
Arne Keller
Laboratoire de Photophysique Moleculaire du CNRS, Bat. 213.
Universite de Paris-Sud, 91405 Orsay Cedex, France.
tel.: (33) 1 69 15 82 83 -- fax. : (33) 1 69 15 67 77

Re: [Numpy-discussion] Update to Numeric home page

[John A. T. <tu...@bl...>]

>>>>> "PFD" == Paul F Dubois <pa...@pf...>:

PFD> The Numeric home page http://pfdubois.com/numeric has been updated to
PFD> include:
PFD> 
PFD> a. New HTML and PDF version of the documentation
PFD> b. New Happydoc-generated documentation for the modules, using the new
PFD> Happydoc.
PFD> c. Description of and link to Scientific Python.
PFD> d. Navigation buttons and a hit counter.
PFD> 
PFD> numpy.sourceforge.net still exists and links to this page.
PFD> The "Homepage" button on the project page goes directly to it. I moved the
PFD> contents to a web site I control so that I can work on it more easily --
PFD> sourceforge sites are hard to work on due to security.
PFD> 
PFD> This site is hosted by Yahoo and seems to perform well. Like any site, it
PFD> can be down, but I think it is down less than SF.

and now http://www.numpy.org/ points there as well (or it will shortly)

-- 
John A. Turner, Ph.D.           Senior Research Associate
Blue Sky Studios, 44 S. Broadway, White Plains, NY  10601
http://www.blueskystudios.com/             (914) 259-6319