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

Re: [Numpy-discussion] Array data ownership

[Marc P. <Mar...@on...>]

Konrad Hinsen wrote:
> 
> If I understand you correctly, you want NumPy to create an array
> object and allocate the data space, but make sure that the data space
> is not freed before you "allow" it. In that case, just create an
> ordinary array and keep an additional reference to it. When the data
> space may be destroyed, you remove the reference. However, there is no
> guarantee that the data space will be freed immediately, as there
> might still be other references around.
> 
No. I want to set the memory zone of the array but once this zone is set, I want
numpy to manage it as if it was owner of the memory.

I have an external lib which allocates the returned memory zone. I put this
memory zone into a PyArrayObject using PyArray_FromDimsAndData in order
to avoid memory copy. But I want now this array to be the owner of the
allocated zone. I mean I want this zone to be released if the Python object
is deleted. The ref count of Python is ok for me, as long as an array is
sharing the data, python won't release it. But I want Python to delete
the memory zone if the last reference is removed.

Marcvs [alias I'll have a try with myarray->flags |= OWN_DATA;
        and I'll let you know about my experiments...]

Re: [Numpy-discussion] Array data ownership

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

Marc Poinot <Mar...@on...> writes:

> That's what I want. Or more exactly, I want "global data that will never be
> freed by Numpy, until I tell it to do so !"

That option does not exist. The options are:

1) NumPy manages the data space of your array. It gets freed when the last
   array object referencing it is destroyed.

2) NumPy assumes that the data space is already allocated and is not
   freed as long as any array object might reference it (which, in
   practice, is until the end of the process).

PyArray_FromDimsAndData is used for allocating arrays that choose the
second option.

If I understand you correctly, you want NumPy to create an array
object and allocate the data space, but make sure that the data space
is not freed before you "allow" it. In that case, just create an
ordinary array and keep an additional reference to it. When the data
space may be destroyed, you remove the reference. However, there is no
guarantee that the data space will be freed immediately, as there
might still be other references around.

> [2] Can I use the PyArrayObject.flags bit "owns the data area" to set it
>     after the PyArray_FromDimsAndData call ? In the case of "yes", which

Whatever this does, it is not documented.

Konrad.
-- 
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-------------------------------------------------------------------------------

[Numpy-discussion] Array data ownership

[Marc P. <Mar...@on...>]

Hi all,

I use the Numpy C API to produce/use some PyArrayObjects. To set the
allocated memory zone, I use the PyArray_FromDimsAndData function, which
is described to "be used to access global data that will never be freed".
That's what I want. Or more exactly, I want "global data that will never be
freed by Numpy, until I tell it to do so !"

I mean some of my arrays are allocated and used as PyArrayObject data,
but I want some of them to be seen by Numpy as its own data. I want it to
deallocate the data at delete time. My questions are :

[1] Is PyArray_FromDimsAndData the right function or should I use another way ?
[2] Can I use the PyArrayObject.flags bit "owns the data area" to set it
    after the PyArray_FromDimsAndData call ? In the case of "yes", which
    is the bit rank ? The fourth starting from right ? Any macro already
    doing this ? Will I break the PyArrayObject consistency ?

Marcvs [alias yes I can go into the code... I though OO was reading docs 
        and using interfaces ;]

[Numpy-discussion] note on mail lists

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

Please note that the numpy-developers list is no longer active. Do not
send mail to this address; it will be discarded. Use numpy-discussion
for questions and problems and the bug list at Source Forge for actual
bugs only.

Thanks!

Re: [Numpy-discussion] Installing Numerical Python

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

>
> Hello,
>
> I'm have both Python 1.5 and Python2.2.1 on a Redhat 7.3 system, and
> when I try to install Numerical Python (v. 22), I get an error telling
> me that it cannot find the file /usr/lib/python2.2/config/Makefile. The
> command I use for the install is: python2 install.py build. The
> Python2.2 install comes directly from the RPMs on the python.org web
> site. Does anyone know how I might resolve this?

You probably need the python-devel package also.

-Travis O.

Re: [Numpy-discussion] Installing Numerical Python

[Francesc A. <fa...@op...>]

On Thu, Nov 07, 2002 at 10:37:31AM -0700, bondpaper wrote:
> I'm have both Python 1.5 and Python2.2.1 on a Redhat 7.3 system, and 
> when I try to install Numerical Python (v. 22), I get an error telling 
> me that it cannot find the file /usr/lib/python2.2/config/Makefile. The 
> command I use for the install is: python2 install.py build. The 
> Python2.2 install comes directly from the RPMs on the python.org web 
> site. Does anyone know how I might resolve this?

It should be. Maybe you need to install the development version packages of
python. For 2.2.1 you can find it at:

http://www.python.org/ftp/python/2.2.1/rpms/rh7.3/python2-devel-2.2.1-2.i386.rpm

Bye,

-- 
Francesc Alted                            PGP KeyID:      0x61C8C11F
Scientific aplications developer
Public PGP key available: http://www.openlc.org/falted_at_openlc.asc
Key fingerprint = 1518 38FE 3A3D 8BE8 24A0  3E5B 1328 32CC 61C8 C11F

[Numpy-discussion] Installing Numerical Python

[bondpaper <bon...@ea...>]

Hello,

I'm have both Python 1.5 and Python2.2.1 on a Redhat 7.3 system, and 
when I try to install Numerical Python (v. 22), I get an error telling 
me that it cannot find the file /usr/lib/python2.2/config/Makefile. The 
command I use for the install is: python2 install.py build. The 
Python2.2 install comes directly from the RPMs on the python.org web 
site. Does anyone know how I might resolve this?

Thanks.

Tom

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[Numpy-discussion] Segmentation fault when dealing with larga rank arrays

[Francesc A. <fa...@op...>]

Hi,

I'm having some segmentation faults when dealing with large rank arrays in
Numeric.

You can see some examples in the following:

>>> zeros((1,) * 21) == zeros((1,) * 21)
Segmentation fault

Of course, this kind of usage is quite unusual, but I'm interested on it
because I'm developing tests for a package based on Numerical and wanted to
check the larger rank supported.

>>> ones((1,) * 41)
Segmentation fault

In this case, I know that Numeric supports array ranks until 40, but in
opinion, an error should be raised instead of merely givin a "Segmentation
fault".


I'm having these problems using Numeric 20.2.1, 21.0 and 22.0 in both Python
2.1.x and 2.2.x. My platform is Intel under Linux Debian 3.0.

Thank you,

-- 
Francesc Alted                            PGP KeyID:      0x61C8C11F
Scientific aplications developer
Public PGP key available: http://www.openlc.org/falted_at_openlc.asc
Key fingerprint = 1518 38FE 3A3D 8BE8 24A0  3E5B 1328 32CC 61C8 C11F

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[Numpy-discussion] Fwd: RE: Problem with pyfort

[Don B. <bla...@ll...>]

Hello,

  I sent the following email to Paul Dubois.  Since he is not familiar with 
MS WINDOWS,
he suggested that I send my problem to this list. In short, I am unable to 
get the pyfdemo
to work in pyfort_8_.2 using python2.2 because my FORTRAN compiler, COMPAQ/DEC
Visual FORTRAN uses NMAKE.EXE rather than make. When I try to run the demo,
I get an error message saying that make is not recognized. What file or 
file(s) should I
change. I cannot run under LINUX, it must be WINDOWS2000.

Thanks for your assistance,

Don
>From: pa...@pf...
>Date: Wed, 16 Oct 2002 13:26:27 -0700
>Subject: RE: Problem with pyfort
>To: "Don Blackfield" <bla...@ll...>
>
>There is a subdirectory of the source that is devoted to how to do things
>on Microsoft. I don't have any experience of it myself. You can try the
>num...@li... list if you want to contact other users.
>
> >-- Original Message --
> >Date: Wed, 16 Oct 2002 12:10:54 -0700
> >To: "Paul F Dubois" <pa...@pf...>
> >From: Don Blackfield <bla...@ll...>
> >Subject: Problem with pyfort
> >
> >
> >  Paul,
> >
> >   You may be cursing Microsoft by now, I know that I am. I have
> >"successfully" installed pyfort. However,
> >when I try to run the pyfdemo script by typing
> >
> >pyfort.bat -i pyfdemo
> >
> >it begins to work. However it crashes because WINDOWS does not recognize
> >
> >make. The COMPAQ/DEC/Visual
> >Fortran uses a similar routine called NMAKE.EXE . Is there some place in
> >
> >some file(s) where I need to change
> >make to NMAKE.EXE ??
> >
> >  Sorry for the hassle.....
> >
> >Don
> >

==============================================
Donald T. Blackfield
Fusion Energy Program
New Technologies Engineering Division
Lawrence Livermore National Laboratory
P.O. Box 808, MailStop L-641
Livermore, CA 94550
(925)422-0496 / (925)422-7390 (fax)
dt...@ll...
==============================================

[Numpy-discussion] Re: Numpy-discussion -- confirmation of subscription -- request 961098

[Leonardo S. <re...@te...>]

On Tue, 15 Oct 2002 12:22:27 -0700
num...@li... wrote:

> Numpy-discussion -- confirmation of subscription -- request 961098
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RE: [Numpy-discussion] Dyadic Python

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

What took me a long while to appreciate is that inheriting from Numeric
wasn't going to do me any good precisely because any operation such as
a+b was going to produce an array not one of my new foos, unless I
overrode it. So in fact you end up having to override darn near
everything. I can only suggest MA as an example. People have succeeded
in similar tasks by starting with the MA source and changing it to suit.

> -----Original Message-----
> From: num...@li... 
> [mailto:num...@li...] On Behalf Of Rob
> Sent: Saturday, October 12, 2002 3:13 PM
> To: num...@li...
> Subject: [Numpy-discussion] Dyadic Python
> 
> 
> As a help for me learning Dyadic vector analysis, I have been 
> working on a Numpy class for manipulating Dyads.  I am 
> wondering if anyone else has any similar interests.  Most of 
> the impetus for the work comes from "Methods for 
> Electromagnetic Field Analysis" by Ismo Lindell.  In numerous 
> searches I found no computer code dealing with Dyads.
> 
> My biggest stumbling block so far is integrating the 
> symbolism of Dyadic analysis into something understandable.  
> Operator overloading isn't
> going to help me.   For example in my program:
> 
> A=dyad(a,b) where a and b are complex vectors
> B=dyad(c,d)       "
> A.dmmd(B) is equivalent to double dyadic multiplication of the dyads A
> and B.  :)   In a book this would be written something like  A xx B
> except that the x's would be aligned vertically.
> 
> Ok, its an insane project.    Rob.
>                           
> 
> -- 
> -----------------------------
> The Numeric Python EM Project
> 
www.pythonemproject.com


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[Numpy-discussion] Dyadic Python

[Rob <ro...@py...>]

As a help for me learning Dyadic vector analysis, I have been working on
a Numpy class for manipulating Dyads.  I am wondering if anyone else has
any similar interests.  Most of the impetus for the work comes from
"Methods for Electromagnetic Field Analysis" by Ismo Lindell.  In
numerous searches I found no computer code dealing with Dyads.

My biggest stumbling block so far is integrating the symbolism of Dyadic
analysis into something understandable.  Operator overloading isn't
going to help me.   For example in my program:

A=dyad(a,b) where a and b are complex vectors
B=dyad(c,d)       "
A.dmmd(B) is equivalent to double dyadic multiplication of the dyads A
and B.  :)   In a book this would be written something like  A xx B
except that the x's would be aligned vertically.

Ok, its an insane project.    Rob.
                          

-- 
-----------------------------
The Numeric Python EM Project

www.pythonemproject.com

[Numpy-discussion] Issues about class initialialization in with large numerical data

[Karthikesh R. <ka...@ja...>]

Hi All,

i am writing a class called communicate. Something like this:

class Communicate:
    """ This is the main communicate class : there will be more
    """

    def __init__(self):
        """This should hopefully read a file and set self's parameters
        """
        self.C = 10
        self.K = 6
        self.N = 8
        self.Na = 3
        self.L = 4
        self.modulation = "QPSK"


1. Shown is just one method in the class. Now there are lot of self
 variables (i understand they are something like private variables). Now i
 want to have an .ini file, so that i call something like this in the
  __init__ module :

def __init__(self, fileName):
    file = open(fileName, 'r')
    self = something(file)

and all the self's variables are set. i want the ini file to be the
interface between the class and the outside world. Some one suggested
ConfigParser , couldnt get an idea of what it is and how to use it..

2. Is this method of having all the data in self variables good.The size
of some of these 'self' could be very high 40000x40000 complex. Should i
create local variables in the methods (i need a lot of variables, but
their def life is too small .....)

for example one of the method could be:

def generateNoise(self):
        """ Generates AWGN
        """
        power = Numeric.sqrt(RandomArray.random()*10)
        Noise = Numeric.zeros(....)
        Noise.real = Random ......
        Noise.imag = RandomArray....

(Boared to type the whole thing that is why there are ......'s )

Any way power is a local variable, which i guess dies after
generateNoise() method is called in the object. Am i right..
Can i have variables like 'power'

3. Thirdly, i want an easy method like '.mat' that exists in matlab. Can i
use pickle (cPickle or Numeric.pickle) to store all the variables that i
want. These matrices could be 40000x40000 entries long. Assuming that all
the variables are in 'self' should i write an assignment module before
pickling them  (in case pickle is the right way to store these variables)

def writeToFile(self):
    Na = self.Na
    x =  self.x
    pickle the variables Na, x

Thankx in advance and in anticipation,

karthik 

PS: slowly moving from Matlab to Python, hence these stupid doubts

-----------------------------------------------------------------------
Karthikesh Raju,		    email: ka...@ja...		
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Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Todd M. <jm...@st...>]

Philip Austin wrote:

>David Abrahams writes:
> > 
> > Not sure what you're saying here. I /do/ remember when I was writing
> > the Numeric/NumArray support that there seemed no publicly-accessible
> > definition of the structure of the underlying array objects.
>
Numeric and numarray both have "arrayobject.h" which makes PyArrayObject 
public.  Numarray also has "libnumarray.h" which indirectly exposes NDInfo.

>
>As I understand it, the numarray now in CVS deprecates the NDInfo
>struct (see section 10.1 of numarray.pdf) in favor of PyArrayObject.
>
This is true,  but is currently neither released nor documented.  On the 
plus side, numarray is becoming more compatible with Numeric, getting 
faster, and simpler as well.

Todd

-- 
Todd Miller 			jm...@st...
STSCI / SSB

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Philip A. <pa...@eo...>]

David Abrahams writes:
 > 
 > Not sure what you're saying here. I /do/ remember when I was writing
 > the Numeric/NumArray support that there seemed no publicly-accessible
 > definition of the structure of the underlying array objects.

As I understand it, the numarray now in CVS deprecates the NDInfo
struct (see section 10.1 of numarray.pdf) in favor of PyArrayObject.

[snip]

 > See the note about pitfalls at
 > http://cvs.sourceforge.net/cgi-bin/viewcvs.cgi/*checkout*/boost/boost/libs/python/doc/tutorial/doc/derived_object_types.html
 > for the reasons why.

Thanks for the info -- Phil

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[David A. <da...@bo...>]

Philip Austin <pa...@eo...> writes:

>  > What I thought I would see, was some form of direct coupling
>  > between a module in boost and either the Numeric or numarray C-API.
>  > However, when I search the whole source tree (*.cpp *.hpp) I can
>  > find neither PyArrayObject, arrayobject.h, NDInfo, nor
>  > libnumarray.h.  So I'm not sure what part of any Numeric/numarray
>  > C-API you're using now (if any) 
> 
> The answer is -- no part of the C-API if he can get away with it.
> Your move towards Numeric compatibility for numarray means that he
> now can get away with it.

I've been getting away with it already. It's easy enough to access all
that functionality through the usual Python/C API.

> numeric wraps the module pointer, and calls functions
> through it -- e.g. (from boost/libs/python/src/numeric.cpp):
> 
>   void array_base::resize(object const& shape)
>   {
>       attr("resize")(shape);
>   }
> 
> which invokes PyObject_CallObject to call the array's resize method,
> with the tuple shape (similar to CXX). If the method isn't part of the
> library (say for Numeric and getflat) then a python exception is
> thrown.

Slow but effective. If you want to touch the raw PyObject*, of course,
you can do this:

        void f(numeric::array x)
        {
            PyObject* danger = x.ptr();
            // Cast to whatever you like.
        }

>  > and in particular, what isn't
>  > working for you.  Note: numarray and Numeric are not totally
>  > compatible at either the C or Python levels, so either area could
>  > have been a source of difficulty.
> 
> The problem was that, as long as a call to getNDInfo was required
> to access an array's data pointer, a Python-only approach
> like the above wouldn't work.  Since it is now possible to
> do this via a cast to PyArrayObject* for a numarray, the problem
> has disappeared.

Not sure what you're saying here. I /do/ remember when I was writing
the Numeric/NumArray support that there seemed no publicly-accessible
definition of the structure of the underlying array objects.

> (there's nothing stopping you from using the C-API if you want,
> though.  For example you can create a numarray with data
> copied from a C array with something like:
> 
> numeric::array makeNum(int * data, int n=0){
>   object obj(detail::new_reference(PyArray_FromDims(1, &n, PyArray_INT)));
>   char *arr_data = ((PyArrayObject*) obj.ptr())->data; 
>   memcpy(arr_data, data, 4 * n); 
>   return numeric::array(obj);
> }

Well, this uses undocumented implementation details. The supported
approach is:

   handle<> raw_array(PyArray_FromDims(1, &n, PyArray_INT));
   object obj(raw_array);

Note, though, that you can also write:

   handle<PyArrayObject> raw_array(PyArray_FromDims(1, &n, PyArray_INT));

Assuming, of course, that PyArray_FromDims returns a
PyArrayObject*. And now you have access to the PyArrayObject* through
raw_array.

Also note that it's probably smarter to use

   extract<numeric::array>(obj)

Than

   numeric::array(obj)

See the note about pitfalls at
http://cvs.sourceforge.net/cgi-bin/viewcvs.cgi/*checkout*/boost/boost/libs/python/doc/tutorial/doc/derived_object_types.html
for the reasons why.

-- 
           David Abrahams * Boost Consulting
da...@bo... * http://www.boost-consulting.com

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Philip A. <pa...@eo...>]

 > Philip Austin wrote:
[snip]
 > >
 > >boost/libs/python/test/numpy.cpp
 > >boost/libs/python/test/numpy.py
 > >boost/python/numeric.hpp
 > >
Todd Miller writes:

 > I took a look at the files above trying to see what you're doing.

(note that all of this was written by Dave Abrahams, I'm just
one of the first Numeric users.)

 > What I thought I would see, was some form of direct coupling
 > between a module in boost and either the Numeric or numarray C-API.
 > However, when I search the whole source tree (*.cpp *.hpp) I can
 > find neither PyArrayObject, arrayobject.h, NDInfo, nor
 > libnumarray.h.  So I'm not sure what part of any Numeric/numarray
 > C-API you're using now (if any) 

The answer is -- no part of the C-API if he can get away with it.
Your move towards Numeric compatibility for numarray means that he now
can get away with it.

Specifically, the extension tries to import the numarray module, and
if that fails, imports Numeric (the user can change this with

    numeric::set_module_and_type("Numeric", "ArrayType");
or:
    numeric::set_module_and_type("numarray", "NDArray");
)

numeric wraps the module pointer, and calls functions
through it -- e.g. (from boost/libs/python/src/numeric.cpp):

  void array_base::resize(object const& shape)
  {
      attr("resize")(shape);
  }

which invokes PyObject_CallObject to call the array's resize method,
with the tuple shape (similar to CXX). If the method isn't part of the
library (say for Numeric and getflat) then a python exception is
thrown.

 > and in particular, what isn't
 > working for you.  Note: numarray and Numeric are not totally
 > compatible at either the C or Python levels, so either area could
 > have been a source of difficulty.

The problem was that, as long as a call to getNDInfo was required
to access an array's data pointer, a Python-only approach
like the above wouldn't work.  Since it is now possible to
do this via a cast to PyArrayObject* for a numarray, the problem
has disappeared.

(there's nothing stopping you from using the C-API if you want,
though.  For example you can create a numarray with data
copied from a C array with something like:

numeric::array makeNum(int * data, int n=0){
  object obj(detail::new_reference(PyArray_FromDims(1, &n, PyArray_INT)));
  char *arr_data = ((PyArrayObject*) obj.ptr())->data; 
  memcpy(arr_data, data, 4 * n); 
  return numeric::array(obj);
}
)


Regards, Phil

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Todd M. <jm...@st...>]

Philip Austin wrote:

>Todd Miller writes:
> > Can you send me the Numeric wrapper code or let me know where to
> > get it?
>
>It's now on the cvs main trunk:
>
>cvs -d:pserver:ano...@cv...:/cvsroot/boost login
>cvs -z3 -d:pserver:ano...@cv...:/cvsroot/boost checkout boost
>
>You'll need all of boost if you want to try to compile/run the test
>code.  If you haven't worked with boost and boost.build (bjam)
>before, I could send my install notes.  If you'd just like to
>browse the code, the pertinent files are:
>
>boost/libs/python/test/numpy.cpp
>boost/libs/python/test/numpy.py
>boost/python/numeric.hpp
>
>and the new tutorial (which doesn't cover numpy yet) is
>quite good:
>
>boost/boost/libs/python/doc/tutorial
>
>as well as:
>
>boost/libs/python/doc/v2/reference.html
>(also without numpy documentation)
>
>Regards, Phil
>
I took a look at the files above trying to see what you're doing.  What 
I thought I would see, was some form of direct coupling between a module 
in boost and either the Numeric or numarray C-API.   However,  when I 
search the whole source tree (*.cpp *.hpp) I can find neither 
PyArrayObject, arrayobject.h, NDInfo, nor libnumarray.h.   So I'm not 
sure what part of any Numeric/numarray C-API you're using now (if any) 
and in particular, what isn't working for you.  Note:  numarray and 
Numeric are not totally compatible at either the C or Python levels, so 
either area could have been a source of difficulty.

Todd

-- 
Todd Miller 			jm...@st...
STSCI / SSB

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Philip A. <pa...@eo...>]

Todd Miller writes:
 > Can you send me the Numeric wrapper code or let me know where to
 > get it?

It's now on the cvs main trunk:

cvs -d:pserver:ano...@cv...:/cvsroot/boost login
cvs -z3 -d:pserver:ano...@cv...:/cvsroot/boost checkout boost

You'll need all of boost if you want to try to compile/run the test
code.  If you haven't worked with boost and boost.build (bjam)
before, I could send my install notes.  If you'd just like to
browse the code, the pertinent files are:

boost/libs/python/test/numpy.cpp
boost/libs/python/test/numpy.py
boost/python/numeric.hpp

and the new tutorial (which doesn't cover numpy yet) is
quite good:

boost/boost/libs/python/doc/tutorial

as well as:

boost/libs/python/doc/v2/reference.html
(also without numpy documentation)

Regards, Phil

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Todd M. <jm...@st...>]

Philip Austin wrote:

>
>Todd, thanks for the quick response. Regarding your header comments:
>
>/*
>Extensions constructed from seperate compilation units can access the
>C-API defined here by defining "libnumarray_UNIQUE_SYMBOL" to a global
>name unique to the extension.  Doing this circumvents the requirement
>to import libnumarray into each compilation unit, but is nevertheless
>mildly discouraged as "outside the Python norm" and potentially
>leading to problems.  Looking around at "existing Python art", most
>extension modules are monolithic C files, and likely for good reason.
>*/
>
>
>I'd agree with this sentiment, but with numarray's NDInfo interface I
>think we were stuck (we have an implementation file for a bunch of
>boost numarray helper functions that require the API but have static
>data initializations, so they can't go into a header).  
>
Long term, you would be better off ditching NDInfo and working with the 
Numeric compatability API.  Since it's a subset of the Numeric API, it 
may not support your purposes.  However, if it doesn't,  I want to 
extend it until it does.

>
>With Numeric, we could avoid import_array altogether, and get
>everything we needed from PyObject_CallObject calls to Numeric to
>create arrays using zeros or ones, followed by a cast to
>PyArrayObject* to get the data pointer and fill the array.  With
>numarray, we need (or needed) getNDInfo to do the same thing,
>mandating an import_libnumarray.  Are the plans for the revised
>interface to behave in the same way as NDInfo?
>
The current plan is to deprecate the "native" APIs which make use of 
NDInfo and to make numarray as compatible as possible with Numeric.   
 The bulk of the changes which make this reasonable are in numarray CVS 
now.  In a nutshell, prior to our use of C basetypes, we needed 
something like NDInfo.  Now that we're using C basetypes,  NDInfo 
appears less efficient, less compatible, and more complex than the 
"Numeric compatability" interface.   Its possible for our "basetypes" to 
appear to be Numeric arrays, so that's what we did.  If you guys find 
that you need missing parts of the Numeric C-API,  I'll add them to 
numarray.   So far, I've just added what I needed or knew someone else 
needed.

>  It's not a big deal to
>us one way or another, but it would simplify Dave Abraham's boost
>support for numeric/numarray if much of the functionality could be
>done through python calls from the C side.
>
Can you send me the Numeric wrapper code or let me know where to get it?

>
>Thanks, Phil
>
>
>
>-------------------------------------------------------
>This sf.net email is sponsored by:ThinkGeek
>Welcome to geek heaven.
>http://thinkgeek.com/sf
>_______________________________________________
>Numpy-discussion mailing list
>Num...@li...
>https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
Thanks,
Todd

-- 
Todd Miller 			jm...@st...
STSCI / SSB

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Philip A. <pa...@eo...>]

Todd Miller writes:
 > Philip Austin wrote:
 > 
 > >
 > >Currently libnumarray.h doesn't implement this -- is there
 > >another way to compile a multiple file project which
 > >initializes the API just once?
 > >
 > Hopefully you got my earlier message about this.   Since I CC'ed 
 > numpy-discussion and never saw it there,  I've included it below.  But, 
 > assuming you saw it,  I think what you need is in CVS now.

Todd, thanks for the quick response. Regarding your header comments:

/*
Extensions constructed from seperate compilation units can access the
C-API defined here by defining "libnumarray_UNIQUE_SYMBOL" to a global
name unique to the extension.  Doing this circumvents the requirement
to import libnumarray into each compilation unit, but is nevertheless
mildly discouraged as "outside the Python norm" and potentially
leading to problems.  Looking around at "existing Python art", most
extension modules are monolithic C files, and likely for good reason.
*/


I'd agree with this sentiment, but with numarray's NDInfo interface I
think we were stuck (we have an implementation file for a bunch of
boost numarray helper functions that require the API but have static
data initializations, so they can't go into a header).  

With Numeric, we could avoid import_array altogether, and get
everything we needed from PyObject_CallObject calls to Numeric to
create arrays using zeros or ones, followed by a cast to
PyArrayObject* to get the data pointer and fill the array.  With
numarray, we need (or needed) getNDInfo to do the same thing,
mandating an import_libnumarray.  Are the plans for the revised
interface to behave in the same way as NDInfo?  It's not a big deal to
us one way or another, but it would simplify Dave Abraham's boost
support for numeric/numarray if much of the functionality could be
done through python calls from the C side.

Thanks, Phil

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Todd M. <jm...@st...>]

Philip Austin wrote:

>
>Currently libnumarray.h doesn't implement this -- is there
>another way to compile a multiple file project which
>initializes the API just once?
>
Hopefully you got my earlier message about this.   Since I CC'ed 
numpy-discussion and never saw it there,  I've included it below.  But, 
assuming you saw it,  I think what you need is in CVS now.

Todd

-- 
Todd Miller             jm...@st...
STSCI / SSB



My earlier response:

Someone else asked for this recently, and the solution is half-way 
implemented in CVS now.   I'll finish it this morning so that it works 
the way you're used to, but the next "official" numarray release is 
still a ways off.   So, if you still want to port now, here are the 
caveats:

1.  Numarray CVS was working as of Friday on Windows, Linux, Solaris, 
and Tru64.

2.  Use the "Numeric compatible" portion of the C-API.  This API is a 
subset of the Numeric API which may cause you trouble.  Let me know if 
it does.  Don't use the "native" NDInfo based APIs because they're 
headed for deprecation.

3.  The version you'll be working with has not yet been widely used and 
may have significant bugs I don't know about.  It does pass all its unit 
tests on all our platforms.

Re: [Numpy-discussion] libnumarray.h and NO_IMPORT_ARRAY

[Todd M. <jm...@st...>]

Philip Austin wrote:

>We're currently porting several of our boost-wrapped Numeric classes
>to numarray.  The routines are spread across several cpp files, and
>**libnumarray_API is needed in each file.  In Numeric, we invoke
>import_array() in the module file, to initialize **PyArray_API, and
>def NO_IMPORT_ARRAY in the other files; this produces 
>extern void **PyArray_API in those files, and everyone's happy.
>
>Currently libnumarray.h doesn't implement this -- is there
>another way to compile a multiple file project which
>initializes the API just once?
>
>Thanks, Phil Austin
>
>
>
>-------------------------------------------------------
>This sf.net email is sponsored by:ThinkGeek
>Welcome to geek heaven.
>http://thinkgeek.com/sf
>_______________________________________________
>Numpy-discussion mailing list
>Num...@li...
>https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
Someone else asked for this recently, and the solution is half-way 
implemented in CVS now.   I'll finish it this morning so that it works 
the way you're used to, but the next "official" numarray release is 
still a ways off.   So, if you still want to port now, here are the caveats:

1.  Numarray CVS was working as of Friday on Windows, Linux, Solaris, 
and Tru64.

2.  Use the "Numeric compatible" portion of the C-API.  This API is a 
subset of the Numeric API which may cause you trouble.  Let me know if 
it does.  Don't use the "native" NDInfo based APIs because they're 
headed for deprecation.

3.  The version you'll be working with has not yet been widely used and 
may have significant bugs I don't know about.  It does pass all its unit 
tests on all our platforms.

Todd

-- 
Todd Miller 			jm...@st...
STSCI / SSB