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

Re: [Numpy-discussion] PIL with numpy support!

[Charles R H. <cha...@gm...>]

On 10/10/06, Christopher Barker <Chr...@no...> wrote:
>
> Hi all:
>
> Fredrik Lundh wrote:
> > A little later that planned, but PIL 1.1.6 beta 2 is now available from
> SVN:
> >
> >      http://svn.effbot.python-hosting.com/tags/pil-1.1.6b2/
> >
> > A tarball will appear on effbot.org shortly:
> >
> >      http://effbot.org/downloads/#Imaging
> >
> > As usual, PIL 1.1.6 supports all Python versions from 1.5.2 and onwards,
> > including 2.5.
> >
> > For a hopefully complete list of changes, see:
> >
> >    http://effbot.org/zone/pil-changes-116.htm
>
> From there:
>
> """
> * Added "fromarray" function, which takes an object implementing the
> NumPy array interface and creates a PIL Image from it. (from Travis
> Oliphant).
>
> * Added NumPy array interface support (__array_interface__) to the Image
> class (based on code by Travis Oliphant). This allows you to easily
> convert between PIL image memories and NumPy arrays:
> import numpy, Image
>
> i = Image.open('lena.jpg')
> a = numpy.asarray(i) # a is readonly
> i = Image.fromarray(a)
> """


Fromarray wasn't there for me running latest PIL from svn last week. I had
to use another function whose name escapes me at the moment (I don't use PIL
very often), but yes, there is a way to use numpy arrays in PIL.

Chuck

[Numpy-discussion] PIL with numpy support!

[Christopher B. <Chr...@no...>]

Hi all:

Fredrik Lundh wrote:
> A little later that planned, but PIL 1.1.6 beta 2 is now available from SVN:
> 
>      http://svn.effbot.python-hosting.com/tags/pil-1.1.6b2/
> 
> A tarball will appear on effbot.org shortly:
> 
>      http://effbot.org/downloads/#Imaging
> 
> As usual, PIL 1.1.6 supports all Python versions from 1.5.2 and onwards,
> including 2.5.
> 
> For a hopefully complete list of changes, see:
> 
>    http://effbot.org/zone/pil-changes-116.htm

 From there:

"""
* Added "fromarray" function, which takes an object implementing the 
NumPy array interface and creates a PIL Image from it. (from Travis 
Oliphant).

* Added NumPy array interface support (__array_interface__) to the Image 
class (based on code by Travis Oliphant). This allows you to easily 
convert between PIL image memories and NumPy arrays:
import numpy, Image

i = Image.open('lena.jpg')
a = numpy.asarray(i) # a is readonly
i = Image.fromarray(a)
"""

I hope some of us numpy users will be able to test this new 
functionality while it's in beta.

-Chris




-- 
Christopher Barker, Ph.D.
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Chr...@no...

Re: [Numpy-discussion] ide for python/numpy/scipy/mpl, development ?

[Christopher B. <Chr...@no...>]

Eric Emsellem wrote:
> I have problems  getting binaries for wxPython (Suse10.1) and I
> don't want to attempt a full compilation of that package... 

Di you try the rpms at sourceforge? one of them may well be compatible.

If not, I've had good luck with building the source rpms:

rpmbuild --rebuild NameOfRPM.srpm


> installed Suse10.1 from the downloadable version and many many
> packages are missing there - 

That would be a trick -- you'll need relevant the dev packages. But that 
sounds like an issue you'll be coming up against over and over again anyway.

-Chris




-- 
Christopher Barker, Ph.D.
Oceanographer
                                     		
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7600 Sand Point Way NE   (206) 526-6329   fax
Seattle, WA  98115       (206) 526-6317   main reception

Chr...@no...

[Numpy-discussion] Constructing an array from memory address

[Daniel D. <dd...@br...>]

Hi,

I have an area of memory which is shared between processes (it is
actually a shared memory segment). The address of this memory is stored
in a python long variable, which I pass to various custom C/C++ python
modules.

I would like to construct a numpy array in this area. Is there any way I
can tell numpy to use a specific address (stored as a long) to use as
storage for the array? Is there any interest in adding this?

Thanks,
Daniel

[Numpy-discussion] Using Py2exe with numpy

[Charles H. <c....@se...>]

Hello,

=20

I'm trying to use py2exe but I have a problem :-) . I've already
compiled some program with it but this time I have a proble because my
program is using Numpy modules.

=20

My setup.py is the following

=20

from distutils.core import setup

import py2exe, sys

=20

sys.path.append("tools")

sys.path.append("report")

sys.path.append("spirent")

sys.path.append("numpydir")

=20

=20

=20

setup(

   console =3D ['checkfile.py'],

   )

=20

But when I enter the command "python setup.py py2exe", lots of numpy
files appear to be missing. Therefore, my executable file does not work.

=20

Is there something special to do with the setup.py in order to import
numpy modules?

=20

Thank you very much for you help

=20

Best Regards

=20



This e-mail communication contains information that is confidential and m=
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Re: [Numpy-discussion] suggestions for slices of matrices

[Alan G I. <ai...@am...>]

> JJ wrote: 
>> In my humble opinion, I think indexing is a weak spot in 
>> numpy. 

On Tue, 10 Oct 2006, Travis Oliphant apparently wrote: 
> I'm sorry you see it that way.  I think indexing is a strength of 
> numpy.  It's a little different then what you are used to 
> with Matlab, perhaps, but it is much more general-purpose 
> and capable

I am finding numpy indexing to be great, but it would be 
helpful perhaps to new users to have a few of the examples 
from this thread make it to the Cookbook
http://www.scipy.org/Cookbook/BuildingArrays
Sorry, I cannot do that at the moment.  Maybe JJ would find 
this a profitable exercise?

Cheers,
Alan Isaac

Re: [Numpy-discussion] suggestions for slices of matrices

[jj <jos...@ya...>]

> > --  If M is a nxm matrix and P and Z are nx1 (or 1xn)
> > matrices, then it would be nice if we could write
> > M[P==Z,:] to obtain all columns and only those rows
> > where P==Z.
> This works already if p and z are 1-d arrays.   That seems to be the 
> only issue.  you want this to work with p and z being 2-d arrays (i.e. 
> matrices).  The problem is there is already a defined behavior for this 
> case that would have to be ignored (i.e. special-cased to get what you 
> want).  This could be done within the special matrix sub-class of 
> course, but I'm not sure it is wise.  Too many special cases make life 
> difficult down the road.
> 


Thanks for the thoughtful reply Travis.  I see I will just have to get used to
using code such as M[(P==Z).A.ravel(),:], which I can live with.  Your comments
helped put this in perspective for me.  By the way, is there a web site that
lists all the current modules (as does
http://www.scipy.org/doc/api_docs/scipy.html)?
Best,

John

Re: [Numpy-discussion] Numpy with Py2exe module

[David D. <dav...@lo...>]

On Tue, Oct 10, 2006 at 01:11:28PM +0200, Charles Hanot wrote:
> Hi,
Hi
>=20
> =20
>=20
> my question is the following one. Is it possible to use Numpy with
> py2exe in order to compile my program?=20
>=20
I am right now finishing a dev (GUI app un pyGtk) that uses numpy, and
it have to be released as a win32 app. I used to have problems packing
it using py2exe with enthough version of numpy. Now I use numpy 1.0RC1
and it works fine (I'm only using numpy, not scipy). I use mpl too, and
packaging it using py2exe is a little more problematic. One thing I had
to do is to set the "optimize" py2exe option to 0 (because mpl does
automatic docstring stuffs, and docstrings are removed when optimizing
is used).

Note that I am usiing the latest py2exe too.

David


> =20
>=20
> In fact I'm trying to compile a program using py2exe but the problem is
> that the numpy function cannot be loaded by py2exe. I've already seen on
> the web that other users have the same problem but I've never seen any
> answer. Could you tell me if there is something to do? Is there any
> solution?
>=20
> =20
>=20
> Thank you very much,
>=20
> =20
>=20
> Best Regards,
>=20
> =20
>=20
> Charles Hanot=20
>=20
>=20
>=20
> This e-mail communication contains information that is confidential and m=
ay also be privileged. It is intended for the exclusive use of the addresse=
es. If you are not the person or organization to whom it is addressed, you =
must not copy, distribute or take any action in reliance upon it. If you re=
ceived this communication in error, please notify Septentrio nv immediately=
 [ telephone +32 [0] 16 300800 ]. Septentrio nv will not accept liability f=
or contractual commitments made by individuals employed by this company out=
side the scope of our business.

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[Numpy-discussion] Numpy with Py2exe module

[Charles H. <c....@se...>]

Hi,

=20

my question is the following one. Is it possible to use Numpy with
py2exe in order to compile my program?=20

=20

In fact I'm trying to compile a program using py2exe but the problem is
that the numpy function cannot be loaded by py2exe. I've already seen on
the web that other users have the same problem but I've never seen any
answer. Could you tell me if there is something to do? Is there any
solution?

=20

Thank you very much,

=20

Best Regards,

=20

Charles Hanot=20



This e-mail communication contains information that is confidential and m=
ay also be privileged. It is intended for the exclusive use of the addres=
sees. If you are not the person or organization to whom it is addressed, =
you must not copy, distribute or take any action in reliance upon it. If =
you received this communication in error, please notify Septentrio nv imm=
ediately [ telephone +32 [0] 16 300800 ]. Septentrio nv will not accept l=
iability for contractual commitments made by individuals employed by this=
 company outside the scope of our business.

Re: [Numpy-discussion] AMD & UMFpack in numpy

[Robert C. <cim...@nt...>]

Karol Langner wrote:
> Can someone give me a hint as to where in numpy the  AMD and UMFpack libraries 
> are used, if at all? I ask, because they have their respective sections in 
> site.cfg.example in the trunk.

AMD and UMFpack are optional parts of scipy.linsolve, so if you do not
want them you can freely ignore the entries in site.cfg (or remove them).

r.

Re: [Numpy-discussion] 1.0rc2: imag attribute of numpy.float64 causes segfault

[Travis O. <oli...@ie...>]

Peter Bienstman wrote:
> This is on an AMD64 platform:
>
> Python 2.4.3 (#1, Sep 27 2006, 14:14:48)
> [GCC 4.1.1 (Gentoo 4.1.1)] on linux2
> Type "help", "copyright", "credits" or "license" for more information.
>   
>>>> import numpy
>>>> print numpy.__version__
>>>>         
> 1.0rc2
>   
>>>> a = numpy.float64(1.0)
>>>> a
>>>>         
> 1.0
>   
>>>> a.real
>>>>         
> 1.0
>   
>>>> a.imag
>>>>         
> Segmentation fault
>
>   
Thanks for the test.   Fixed in SVN r3299

-Travis

[Numpy-discussion] 1.0rc2: imag attribute of numpy.float64 causes segfault

[Peter B. <Pet...@ug...>]

This is on an AMD64 platform:

Python 2.4.3 (#1, Sep 27 2006, 14:14:48)
[GCC 4.1.1 (Gentoo 4.1.1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> print numpy.__version__
1.0rc2
>>> a = numpy.float64(1.0)
>>> a
1.0
>>> a.real
1.0
>>> a.imag
Segmentation fault

Thanks!

Peter

Re: [Numpy-discussion] can this be made faster?

[Johannes L. <a.u...@gm...>]

Hi,

> > This seems like a rather common operation - I know I've needed it on
> > at least two occasions - is it worth creating some sort of C
> > implementation? What is the appropriate generalization?
>
> Some sort of indirect addressing infrastructure. But it looks like this
> could be tricky to make safe, it would need to do bounds checking at the
> least and would probably work best with a contiguous array as the target. I
> could see some sort of low-level function called argassign(target, indirect
> index, source) that could be used to build more complicated things in
> python.

This looks somehow like the behaviour of builtin map. One could do map(fn, 
index) with appropriate fn. But iirc this is not faster than a for loop if fn 
is not a builtin function.

An infrastructure like you imagine might use a similar syntax (with underlying 
C funcs). The main point is, how to tell it which operation to perform (add, 
multiply, average, whatever). Implementing a bunch of functions 
add_argassign, ... whatever_argassign contradicts my understanding of 
"generalized". ;)

Maybe it would be simpler to just have functions which handle the index arrays 
in advance. An example will show it best:

index = array([1, 2, 4, 2, 3, 1])   # 1 and 2 occur twice
data = array([1, 1, 1, 1, 1, 1])
newindex, newdata = filter_and_add(index, data)  # the kind of function I mean
print newindex
--> array([1, 2, 4, 3])  # duplicates have been removed
print newdata
--> array([2, 2, 1, 1])  # corresponding entries have been added
a[newindex] += newdata


Johannes

Re: [Numpy-discussion] suggestions for slices of matrices

[Travis O. <oli...@ie...>]

JJ wrote:
> Hello.
> I haven't been following development too closely
> lately, but I did just download and reinstall the
> current svn version.  For what its worth, I would like
> to again suggest two changes:
>   
Suggestions are nice.  Working code is better.   Many ideas are just too 
difficult to implement (and still work with the system as it exists) and 
so never get done.  I'm not saying these ideas fit into this category, 
but generally if a suggestion is not taken it's very likely seen in that 
light.
> --  If M is a nxm matrix and P and Z are nx1 (or 1xn)
> matrices, then it would be nice if we could write
> M[P==Z,:] to obtain all columns and only those rows
> where P==Z.
This works already if p and z are 1-d arrays.   That seems to be the 
only issue.  you want this to work with p and z being 2-d arrays (i.e. 
matrices).  The problem is there is already a defined behavior for this 
case that would have to be ignored (i.e. special-cased to get what you 
want).  This could be done within the special matrix sub-class of 
course, but I'm not sure it is wise.  Too many special cases make life 
difficult down the road.

It is better to just un-think the ambiguity between 1-d and 2-d arrays 
that was inspired by Matlab and recognize a 1-d situation when you have 
it.   But, that's just my opinion.  I'm not dead-set against 
special-casing in the matrix object if enough matrix-oriented people are 
in favor of it.  But, it would be a feature for a later NumPy (not 1.0).

>   Likewise, for 1xm (or mx1) matrices U and
> V, it would be nice to be able to use M[P==Z,U==V]. 
>   
Same issue as before + cross-product versus element-by-element. 
> Also, it would be nice to use M[P==Z,U==2], for
> example, to obtain selected rows where matrix U is
> equal to a constant.
>   
Again.  Form the cross-product using ix_().

> --  It would be nice to slice a matrix by using
> M[[1,2,3],[3,5,7]], for example.
>   
You can get the cross-product using M[ix_([1,2,3],[3,5,7])].  This was a 
design choice and I think a good one. It's been discussed before.

> I believe this would help make indexing more user
> friendly.  In my humble opinion, I think indexing is a
> weak spot in numpy. 
I'm sorry you see it that way.  I think indexing is a strength of 
numpy.  It's a little different then what you are used to with Matlab, 
perhaps, but it is much more general-purpose and capable (there is one 
weak-spot in that a certain boolean indexing operations uses more memory 
than it needs to, but that is a separate issue...).  The Matlab behavior 
can always be created in a sub-class.


Best regards,

-Travis

Re: [Numpy-discussion] AMD & UMFpack in numpy

[Karol L. <kar...@kn...>]

On Monday 09 of October 2006 20:15, Karol Langner wrote:
> Can someone give me a hint as to where in numpy the  AMD and UMFpack
> libraries are used, if at all? I ask, because they have their respective
> sections in site.cfg.example in the trunk.
>
> Karol

One more thing - should there be a [fft] section in this file?

Karol

-- 
written by Karol Langner
Tue Oct 10 08:45:05 CEST 2006

[Numpy-discussion] suggestions for slices of matrices

[JJ <jos...@ya...>]

Hello.
I haven't been following development too closely
lately, but I did just download and reinstall the
current svn version.  For what its worth, I would like
to again suggest two changes:

--  If M is a nxm matrix and P and Z are nx1 (or 1xn)
matrices, then it would be nice if we could write
M[P==Z,:] to obtain all columns and only those rows
where P==Z.  Likewise, for 1xm (or mx1) matrices U and
V, it would be nice to be able to use M[P==Z,U==V]. 
Also, it would be nice to use M[P==Z,U==2], for
example, to obtain selected rows where matrix U is
equal to a constant.

--  It would be nice to slice a matrix by using
M[[1,2,3],[3,5,7]], for example.

I believe this would help make indexing more user
friendly.  In my humble opinion, I think indexing is a
weak spot in numpy.  I find that most of my debugging
involves finding the right code to make a matrix slice
the way I want it.

John




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Re: [Numpy-discussion] problem with lapack_lite / BLAS

[Karol L. <kar...@kn...>]

On Saturday 07 of October 2006 20:32, Pauli Virtanen wrote:
> la, 2006-10-07 kello 20:01 +0200, Karol Langner kirjoitti:
> > I still get a floating point exception when running the numpy tests,
> > though. I haven't checked out numpy for some time, so I don't now if it's
> > a bug, or if it's my setup. The same thing happens when I use my manually
> > built atlas/lapack and the built-in debian atlas/lapack libraries. I'd be
> > grateful
> >
> > for a comment on if this is just me:
> > >>numpy.test(10,10)
> >
> > [output]
> > Check reading the top fields of a nested array ... ok
> > Check reading the nested fields of a nested array (1st level) ... ok
> > Check access nested descriptors of a nested array (1st level) ... ok
> > Check reading the nested fields of a nested array (2nd level) ... ok
> > Check access nested descriptors of a nested array (2nd level) ... ok
> > check_access_fields
> > (numpy.core.tests.test_numerictypes.test_read_values_plain_multiple) ...
> > ok check_access_fields
> > (numpy.core.tests.test_numerictypes.test_read_values_plain_single) ... ok
> > check_cdouble (numpy.tests.test_linalg.test_det)Floating point exception
>
> If you are using Debian stable (sarge), you might need to read
>
> http://www.its.caltech.edu/~astraw/coding.html#libc-patched-for-debian-sarg
>e-to-fix-floating-point-exceptions-on-sse2
>
> In short, libc in Debian stable has a bug that makes programs crash with
> SIGFPE when SSE instructions are invoked. The solution is to recompile
> libc from patched sources, and replace libm.so.6. At least for me this
> fixed crashes in numpy.
>
> 	Pauli Virtanen

Big thanks for that link. I took the leap and it does fix that SIGFPE. As a 
note, after building the glibc sources, you only have to install the libc6 
binary deb. There should be a link to that page somewhere on the wiki.

Karol

-- 
written by Karol Langner
Tue Oct 10 08:07:04 CEST 2006

[Numpy-discussion] =?GB2312?B?xvPStdPQwPs=?=

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Re: [Numpy-discussion] compatibility of extension modules

[Christian K. <ck...@ho...>]

Christian Kristukat <ckkart <at> hoc.net> writes:

> 
> Hi,
> i've got problems running a numpy/scipy extension module (scipy.sandbox.odr)
> built with cygwin/mingw32 on XP on other machines (windows 2000). I get those
> very informative 'windows encountered a problem' messages when calling the
> extension module - importing seems to work.

More precisely, a binary extension module built on a Celeron D crashes on an
Athlon Thunderbird, whereas the Atholon binary runs on the Celereon. So, I'm
wondering if I can turn off some processor specific optimization and if this 
might help here.

Christian

[Numpy-discussion] Problem buliding rc2 on OS X 10.3.9/Python2.5

[Jay P. <pa...@gm...>]

In the process of finally switching over to Python 2.5, and am trying
to build numpy. Unfortunately, it dies during the build:

Jay-Computer:~/Desktop/numpy-1.0rc2 jayparlar$ python setup.py build
Running from numpy source directory.
F2PY Version 2_3296
blas_opt_info:
  FOUND:
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    define_macros = [('NO_ATLAS_INFO', 3)]
    extra_compile_args = ['-faltivec',
'-I/System/Library/Frameworks/vecLib.framework/Headers']

lapack_opt_info:
  FOUND:
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    define_macros = [('NO_ATLAS_INFO', 3)]
    extra_compile_args = ['-faltivec']

running build
running config_fc
running build_src
building py_modules sources
building extension "numpy.core.multiarray" sources
Generating build/src.macosx-10.3-fat-2.5/numpy/core/config.h
customize NAGFCompiler
customize AbsoftFCompiler
customize IbmFCompiler
Could not locate executable g77
Could not locate executable f77
Could not locate executable gfortran
Could not locate executable f95
customize GnuFCompiler
customize Gnu95FCompiler
customize G95FCompiler
customize GnuFCompiler
customize Gnu95FCompiler
customize NAGFCompiler
customize NAGFCompiler using config
C compiler: gcc -arch ppc -arch i386 -isysroot
/Developer/SDKs/MacOSX10.4u.sdk -fno-strict-aliasing -Wno-long-double
-no-cpp-precomp -mno-fused-madd -fno-common -dynamic -DNDEBUG -g -O3

compile options:
'-I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5
-Inumpy/core/src -Inumpy/core/include
-I/Library/Frameworks/Python.framework/Versions/2.5/include/python2.5
-c'
gcc: _configtest.c
gcc: cannot specify -o with -c or -S and multiple compilations
gcc: cannot specify -o with -c or -S and multiple compilations
failure.
removing: _configtest.c _configtest.o
numpy/core/setup.py:50: DeprecationWarning: raising a string exception
is deprecated
  raise "ERROR: Failed to test configuration"
Traceback (most recent call last):
  File "setup.py", line 89, in <module>
    setup_package()
  File "setup.py", line 82, in setup_package
    configuration=configuration )
  File "/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/core.py",
line 174, in setup
    return old_setup(**new_attr)
  File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/core.py",
line 151, in setup
    dist.run_commands()
  File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py",
line 974, in run_commands
    self.run_command(cmd)
  File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py",
line 994, in run_command
    cmd_obj.run()
  File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/command/build.py",
line 112, in run
    self.run_command(cmd_name)
  File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/cmd.py",
line 333, in run_command
    self.distribution.run_command(command)
  File "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py",
line 994, in run_command
    cmd_obj.run()
  File "/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py",
line 87, in run
    self.build_sources()
  File "/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py",
line 106, in build_sources
    self.build_extension_sources(ext)
  File "/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py",
line 212, in build_extension_sources
    sources = self.generate_sources(sources, ext)
  File "/Users/jayparlar/Desktop/numpy-1.0rc2/numpy/distutils/command/build_src.py",
line 270, in generate_sources
    source = func(extension, build_dir)
  File "numpy/core/setup.py", line 50, in generate_config_h
    raise "ERROR: Failed to test configuration"
ERROR: Failed to test configuration


This is with the Universal 2.5 binary, and OS X 10.3.9.

Any ideas? Sorry if this one has been asked before, but I can't seem
to find a solution anywhere.

Jay P.

[Numpy-discussion] Numpy rc2 is out

[Travis O. <oli...@ie...>]

Release Candidate 2.0 is now out.  Thanks to all the great testing and 
fixes that were done between 1.0rc1 and 1.0rc2

The release date for NumPy 1.0 is Oct. 17.  

There will be a freeze on the trunk starting Monday Oct. 16 so any 
changes should be in by then.  If significant changes are made then we 
will release 1.0rc3 on Oct. 17 and push the release date of NumPy 1.0 to 
Oct 24.

-Travis

Re: [Numpy-discussion] can this be made faster?

[Charles R H. <cha...@gm...>]

On 10/9/06, A. M. Archibald <per...@gm...> wrote:
>
> > > > > c contains arbitray floats.
> > > > > essentially it is to compute class totals
> > > > > as in total[class[i]] += value[i]
>
> > > This seems like a rather common operation - I know I've needed it on
> > > at least two occasions - is it worth creating some sort of C
> > > implementation? What is the appropriate generalization?
> >
> > Some sort of indirect addressing infrastructure. But it looks like this
> > could be tricky to make safe, it would need to do bounds checking at the
> > least and would probably work best with a contiguous array as the
> target. I
> > could see some sort of low-level function called argassign(target,
> indirect
> > index, source) that could be used to build more complicated things in
> > python.
>
> If it were only assignment that was needed, fancy indexing could
> already handle it. The problem is that this is something that can't
> *quite* be done with the current fancy indexing infrastructure - every
> time an index comes up we want to add the value to what's there,
> rather than replacing it. I suppose histogram covers one major
> application; in fact if histogram allowed weightings ("count this
> point as -0.6") it would solve the OP's problem.


Sure, just add functions arg_addassign, etc., which means dest[ind[i]] +=
src[i], just as arg_assign would mean dest[ind[i]] = src[i]. If you covered
all the assign variants I think you could do most everything. Upper level
python routines could deal with shaping and such while the lower level
routines dealt with flat, contiguous arrays.

Chuck

Re: [Numpy-discussion] can this be made faster?

[A. M. A. <per...@gm...>]

> > > > c contains arbitray floats.
> > > > essentially it is to compute class totals
> > > > as in total[class[i]] += value[i]

> > This seems like a rather common operation - I know I've needed it on
> > at least two occasions - is it worth creating some sort of C
> > implementation? What is the appropriate generalization?
>
> Some sort of indirect addressing infrastructure. But it looks like this
> could be tricky to make safe, it would need to do bounds checking at the
> least and would probably work best with a contiguous array as the target. I
> could see some sort of low-level function called argassign(target, indirect
> index, source) that could be used to build more complicated things in
> python.

If it were only assignment that was needed, fancy indexing could
already handle it. The problem is that this is something that can't
*quite* be done with the current fancy indexing infrastructure - every
time an index comes up we want to add the value to what's there,
rather than replacing it. I suppose histogram covers one major
application; in fact if histogram allowed weightings ("count this
point as -0.6") it would solve the OP's problem.

A. M. Archibald

Re: [Numpy-discussion] can this be made faster?

[Charles R H. <cha...@gm...>]

On 10/9/06, A. M. Archibald <per...@gm...> wrote:
>
> On 09/10/06, Robert Kern <rob...@gm...> wrote:
> > Daniel Mahler wrote:
> > > In my case all a, b, c are large with b and c being orders of
> > > magnitude lareger than a.
> > > b is known to contain only, but potentially any, a-indexes,  reapeated
> > > many times.
> > > c contains arbitray floats.
> > > essentially it is to compute class totals
> > > as in total[class[i]] += value[i]
> >
> > In that case, a slight modification to Greg's suggestion will probably
> be fastest:
>
> If a is even moderately large and you don't care what's left behind in
> b and c you will probably accelerate the process by sorting b and c
> together (for cache coherency in a)
>
> This seems like a rather common operation - I know I've needed it on
> at least two occasions - is it worth creating some sort of C
> implementation? What is the appropriate generalization?


Some sort of indirect addressing infrastructure. But it looks like this
could be tricky to make safe, it would need to do bounds checking at the
least and would probably work best with a contiguous array as the target. I
could see some sort of low-level function called argassign(target, indirect
index, source) that could be used to build more complicated things in
python.

Chuck

A. M. Archibald
>
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Re: [Numpy-discussion] can this be made faster?

[A. M. A. <per...@gm...>]

On 09/10/06, Robert Kern <rob...@gm...> wrote:
> Daniel Mahler wrote:
> > In my case all a, b, c are large with b and c being orders of
> > magnitude lareger than a.
> > b is known to contain only, but potentially any, a-indexes,  reapeated
> > many times.
> > c contains arbitray floats.
> > essentially it is to compute class totals
> > as in total[class[i]] += value[i]
>
> In that case, a slight modification to Greg's suggestion will probably be fastest:

If a is even moderately large and you don't care what's left behind in
b and c you will probably accelerate the process by sorting b and c
together (for cache coherency in a)

This seems like a rather common operation - I know I've needed it on
at least two occasions - is it worth creating some sort of C
implementation? What is the appropriate generalization?

A. M. Archibald