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

[Numpy-discussion] scipy.base (Numeric3) ready for extension modules to test

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

scipy.base is in a beta-quality state right now.  The Packages are not 
ported, but the basic object works  as far as I can tell.  If you have 
extension modules that you would like to try and compile with the new 
system, I would appreciate the feedback.

Note, this is for Numeric users only.  The C-API follows the Numeric 
convention.

There is a COMPATIBILITY file that lists a few of the changes (there are 
actually very few...), that may need to be made. 

Only if you used descr->zero, descr->one, or explicit typecode strings 
in your c-code should you need to make any changes.

There are changes you may wish to make to improve your code to take 
advantage of the extended C-API, but you shouldn't have to, except in a 
couple of cases.

You can check the code base out of anonymous CVS (wait a day) and try it....

-Travis O.

[Numpy-discussion] Re: Numeric - fromfile ?

[Scott D. D. <Sco...@Ac...>]

Arnd Baecker wrote:
  and bunch of good stuff, and
> ...
>     fp=file("x.dat","wb")
>     io.fwrite(fp,len(x),x)
>     fp.close()
> ...
>     # --- and read them back
>     fp=file("x.dat","rb")
>     x_read=io.fread(fp,10,"d")
>     fp.close()
> 
> Remark: `"wb"` and `"rb"` is only needed under Windows,
> normally `"w"` and `"r"` would be enough.

This is only true if you are talking about Unix/Linux vs.
Windows.  There are other systems where the 'b' is necessary,
and even where it makes no difference it explicitly states
your intent.  So, I'd always use "wb" or "rb" in these
cases.  On some filesystems, (VMS comes to mind) a text file
is a completely different format from a binary file.

--Scott David Daniels
Sco...@Ac...

[Numpy-discussion] Fast iteration ...

[Hari S. <hs...@gm...>]
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dHMgb3JpZ2luLApIZSwgd2hldGhlciBoZSBmYXNoaW9uZWQgaXQgb3Igd2hldGhlciBIZSBkaWQg
bm90LApIZSwgd2hvIHN1cnZleXMgaXQgYWxsIGZyb20gdGhlIGhpZ2hlc3QgaGVhdmVuLApIZSBr
bm93cyAtIG9yIG1heWJlIGV2ZW4gSGUgZG9lcyBub3Qga25vdy4gCgp+UmcgdmVkYQo=

Re: [Numpy-discussion] Numeric - fromfile ?

[Arnd B. <arn...@we...>]

On Tue, 23 Aug 2005, Alexandre Fayolle wrote:

> On Mon, Aug 22, 2005 at 01:27:55PM -0400, Hari Sundar wrote:
> > Hi,
> >
> > Is there a function similar to numarray's fromfile, to read a binary array
> > from a file ? What is the best way to read such a file into a Numeric array.

Another option is to use scipy.io, e.g.:

In [5]:scipy.io.fread?
Type:           builtin_function_or_method
Base Class:     <type 'builtin_function_or_method'>
String Form:    <built-in function fread>
Namespace:      Interactive
Docstring:
    g = numpyio.fread( fid, Num, read_type { mem_type, byteswap})

         fid =       open file pointer object (i.e. from fid =
open('filename') )
         Num =       number of elements to read of type read_type
         read_type = a character in 'cb1silfdFD' (PyArray types)
                     describing how to interpret bytes on disk.
    OPTIONAL
         mem_type =  a character (PyArray type) describing what kind of
                     PyArray to return in g.   Default = read_type
         byteswap =  0 for no byteswapping or a 1 to byteswap (to handle
                     different endianness).    Default = 0.


I use this a lot and it works very well.
For example:

    from scipy import *

    # --- Create some arrays:
    x=arange(10)/10.0

    z=zeros( (5,5),"d")
    z_complex=zeros( (5,7),"D")

    # --- write them as binary data:
    fp=file("x.dat","wb")
    io.fwrite(fp,len(x),x)
    fp.close()

    fp=file("z.dat","wb")
    io.fwrite(fp,z.shape[0]*z.shape[1],z)
    fp.close()

    fp=file("z_complex.dat","wb")
    io.fwrite(fp,z_complex.shape[0]*z_complex.shape[1],z_complex)
    fp.close()

    # --- and read them back
    fp=file("x.dat","rb")
    x_read=io.fread(fp,10,"d")
    fp.close()

    fp=file("z.dat","rb")
    z_read=reshape(io.fread(fp,5*5,"d"),(5,5))
    fp.close()

    fp=file("z_complex.dat","rb")
    z_complex_read=reshape(io.fread(fp,5*7,"D"),(5,7))
    fp.close()

Remark: `"wb"` and `"rb"` is only needed under Windows,
normally `"w"` and `"r"` would be enough.

Best,

Arnd

Re: [Numpy-discussion] Numeric - fromfile ?

[Alexandre F. <ale...@lo...>]

On Mon, Aug 22, 2005 at 01:27:55PM -0400, Hari Sundar wrote:
> Hi,
>=20
> Is there a function similar to numarray's fromfile, to read a binary arra=
y=20
> from a file ? What is the best way to read such a file into a Numeric arr=
ay.

It depends on the format of the file. If it's a pickled Numeric array,
then the pickle module can read it back. Otherwise, you get to read the
file (by opening it first and calling its read() method) and you can use
the fromstring() function in numeric. However you need to know the type
of the array and its shape.=20

--=20
Alexandre Fayolle                              LOGILAB, Paris (France).
http://www.logilab.com   http://www.logilab.fr  http://www.logilab.org

[Numpy-discussion] array reshaping?

[Daniel S. <she...@un...>]

Hello all

Question: Lets say I have two arrays dimensioned according 
to:

my_array = zeros((dim1,dim2,dim3)).astype(Complex32)
my_array_2 = zeros((dim3,dim2)).astype(Complex32)

where dim2 is not equal to dim3. Also Notice that dim2 and 
dim3
are swapped in my_array_2 relative to my_array. For some 
value of
dim1 I would like to write a chunk of data contained in 
my_array_2
to the chunk of memory represented by my_array[dim1,:,:]. 
How can
I best do this?

Cheers,
Daniel

[Numpy-discussion] Numeric - fromfile ?

[Hari S. <hs...@gm...>]

Hi,

Is there a function similar to numarray's fromfile, to read a binary array=
=20
from a file ? What is the best way to read such a file into a Numeric array=
.

thanks,
~Hari

[Numpy-discussion] Víagrra Works Very Good

[Ilmari M. <nvf...@ho...>]

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Re: [Numpy-discussion] Question about external libraries and ATLAS

[Matthew B. <mat...@gm...>]

Hi,

> But if you compile ATLAS as a dynamic library I'm guessing
> all these issues go away.=20

You probably saw this, but Clint Whaley is just in the process of
trying to optimize the shared library performace of ATLAS:

http://sourceforge.net/mailarchive/forum.php?thread_id=3D8003844&forum_id=
=3D426

Best,

Matthew

Re: [Numpy-discussion] Question about external libraries and ATLAS

[Piotr L. <lus...@cs...>]

Darren,

my experience with ATLAS and numarray on Gentoo (and other boxes)
tells me that if ATLAS is a static library then yes I need it at compile
time as the compiler/linker picks up needed symbol and puts
them in numarray's dynamic library.

On 64-bit boxes (Intel or AMD) the static libraries have to be
compiled with `-fPIC' in order to be useful for numarray's dynamic
library.

But if you compile ATLAS as a dynamic library I'm guessing
all these issues go away. The same applies to Goto BLAS
which already are distributed as shared libraries alas without
source code.

I'm sure others will have more to say about that.

Piotr

Darren Dale wrote:
> We are having a discussion on the gentoo-science mailing list about linking 
> Numeric and SciPy to external BLAS, LAPACK and ATLAS libraries. Can anyone 
> tell me, is it necessary to include atlas in the library list at compile time 
> in order to reap the rewards of atlas? Does Numeric use the atlas libraries 
> directly, or indirectly through the blas and lapack libraries?
> 
> Thanks,
> Darren
> 
> 
> -------------------------------------------------------
> SF.Net email is Sponsored by the Better Software Conference & EXPO
> September 19-22, 2005 * San Francisco, CA * Development Lifecycle Practices
> Agile & Plan-Driven Development * Managing Projects & Teams * Testing & QA
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> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion

[Numpy-discussion] Question about external libraries and ATLAS

[Darren D. <dd...@co...>]

We are having a discussion on the gentoo-science mailing list about linking 
Numeric and SciPy to external BLAS, LAPACK and ATLAS libraries. Can anyone 
tell me, is it necessary to include atlas in the library list at compile time 
in order to reap the rewards of atlas? Does Numeric use the atlas libraries 
directly, or indirectly through the blas and lapack libraries?

Thanks,
Darren

[Numpy-discussion] reading data from file

[Hari S. <hs...@gm...>]

Hi,

I have a small doubt and it is very difficult for me to word this for
google. So maybe someone here can help.

I use the 'fromfile' function to read data directly into arrays. This
works pretty well.

I have a special case, where I am reading in 2 files. The first one is
a full 3D file, (x,y,z). Each location tells me whether data is
available at that location in the other file or not. I call this the
mask.The other file contains information which should sit in x*y*z*D
space, i.e., I have a vector of length D at any given location (x,y,z)
where the mask is not zero.

How do I read this effectively ? I don't want to run a loop and test
for the mask being true and reading in the vector.

Also, I am currently thinking of keeping the information in memory as
an array of shape (x,y,z,D), even though the data is sparse. Is there
a more efficient way to do this ?

thanks,
~Hari

Re: [Numpy-discussion] freeing memory

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

Daniel Sheltraw wrote:

> Hello All
>
> Is their a reliable way to free memory allocated for an array in Numeric?
> My application uses a lot of memory and I need to free some as it goes 
> along.
>
Assuming this is all in Python, you just need to delete all names bound 
to the array.

 >>> del arr

This will remove the memory for arr assuming there are no other arrays 
referencing arr's memory (i.e. from indexing expresssions arr[3,:] and 
or name binds).  


-Travis

[Numpy-discussion] freeing memory

[Daniel S. <she...@un...>]

Hello All

Is their a reliable way to free memory allocated for an 
array in Numeric?
My application uses a lot of memory and I need to free 
some as it goes along.

Thanks,
Daniel

Re: [Numpy-discussion] still no cross product for new array type

[<co...@ph...>]

Greg Ewing <gre...@ca...> writes:

> Nadav Horesh wrote:
>> Gary Ruben <gr...@bi...>:
>>
>> > Just browsing through the new array PEP to see if there's support
>> for > 3-vector operations, such as cross product, norm, length
>> functions.
>> > Sadly I don't see any.
>>
>> Usually this is within the scope of higher level packages such as
>> ScientificPython.
>
> I'd like to see at least cross product included somewhere,
> since it's difficult to synthesize efficiently from the
> other operations provided.

I agree. I've added a cross_product() funtion to Numeric in CVS. It'll
do the cross product along any axes of the arrays passed, assuming
that they're of dimensions 2 or 3 (a 2d cross-product returns the
z-component of the equivalent 3d one).

The test cases look like this:

        a = Numeric.array([1,2,3])
        b = Numeric.array([4,5,6])
        assert_eq(Numeric.cross_product(a,b), [-3, 6, -3])
        a = Numeric.array([1,2])
        b = Numeric.array([4,5])
        assert_eq(Numeric.cross_product(a,b), -3)
        a = Numeric.array([[1,2,3], [4,5,6]])
        b = Numeric.array([7, 8, 9])
        assert_eq(Numeric.cross_product(a,b), [[-6,12,-6],[-3,6,-3]])
        a = Numeric.array([[1,2,3], [4,5,6]])
        b = Numeric.array([[10,11,12], [7,8,9]])
        assert_eq(Numeric.cross_product(a,b,axis1=0,axis2=0), [-33,-39,-45])
        assert_eq(Numeric.cross_product(a,b), [[-9,18,-9], [-3,6,-3]])

and the calling sequence like this:

def cross_product(a, b, axis1=-1, axis2=-1):
    """Return the cross product of two vectors.

    The cross product is performed over the last axes of a and b by default,
    and can handle axes with dimensions 2 and 3. For a dimension of 2,
    the z-component of the equivalent three-dimensional cross product is
    returned.
    """

Someone else can make the infix-operator module :-)

-- 
|>|\/|<
/--------------------------------------------------------------------------\
|David M. Cooke                      http://arbutus.physics.mcmaster.ca/dmc/
|co...@ph...

Re: [Numpy-discussion] still no cross product for new array type

[Robert K. <rk...@uc...>]

Perry Greenfield wrote:
> 
> On Aug 15, 2005, at 4:18 PM, Alan G Isaac wrote:
> 
>> On Mon, 15 Aug 2005, Chris Barker apparently wrote:
>>
>>> If we could ever get Python to include additional
>>> operators, we could have a Numeric array package and
>>> linear algebra package all in one.  both with nice
>>> notation, that would be nice.
>>
>> Yep.
> 
> I wonder if we shouldn't use the operator hack recently added to the 
> cookbook to give some sort of capability for this. It doesn't support 
> precedence that people would like, but it maybe useful enough. What do 
> people think?
> 
> http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/384122

If it's placed in a separate module with other such pseudo-operators 
(|dot|, |land|, |lor|) rather than in Numeric.py itself, I'm +1 on this. 
It's such a beautiful hack.

-- 
Robert Kern
rk...@uc...

"In the fields of hell where the grass grows high
  Are the graves of dreams allowed to die."
   -- Richard Harter

Re: Re[2]: [Numpy-discussion] still no cross product for new array type

[Perry G. <pe...@st...>]

On Aug 15, 2005, at 4:18 PM, Alan G Isaac wrote:

> On Mon, 15 Aug 2005, Chris Barker apparently wrote:
>> If we could ever get Python to include additional
>> operators, we could have a Numeric array package and
>> linear algebra package all in one.  both with nice
>> notation, that would be nice.
>
> Yep.

I wonder if we shouldn't use the operator hack recently added to the 
cookbook to give some sort of capability for this. It doesn't support 
precedence that people would like, but it maybe useful enough. What do 
people think?

http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/384122

Perry

Re[2]: [Numpy-discussion] still no cross product for new array type

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

On Mon, 15 Aug 2005, Chris Barker apparently wrote: 
> The new array type has nothing to do with linear algebra, 
> nor does Numeric or numarray.

"Nothing" seems a bit strong.

> If we could ever get Python to include additional 
> operators, we could have a Numeric array package and 
> linear algebra package all in one.  both with nice 
> notation, that would be nice. 

Yep.

> In the meantime, check out cvxopt: 
> http://www.ee.ucla.edu/~vandenbe/cvxopt/ 
> If you want a matrix package. The author is talking about using the new 
> array specification in future versions. 

There is also
http://www3.sympatico.ca/cjw/PyMatrix/
(I'm not using either at the moment so I cannot compare them.)

Cheers,
Alan Isaac

Re: [Numpy-discussion] still no cross product for new array type

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

Gary Ruben wrote:
> "Usually" means that that's where they are now, because they haven't 
> been implemented in numpy. Comparing with Matlab/IDL where their status 
> is slightly greater so as to earn operations of their own, I wonder why 
> equivalent status isn't afforded them in numpy.

Because numpy is NOT matlab or IDL, nor is it trying to be a close of 
either of them.

I've never used IDL, but MATLAB is (or at least was) a MATrix 
LABoratory. It was conceived, from the beginning, to be a tool to do 
linear algebra computations.

The new array type has nothing to do with linear algebra, nor does 
Numeric or numarray. they are general purpose array packages. As it 
happens, a handy way to store matrices is in a 2-d array, so it's 
natural to build a linear algebra (and vector arithmetic) package on top 
of NumPy arrays, but it should be higher level package.

One of the things I like most about NumPY, as opposed to MATLAB, is that 
it doesn't assume everything is a matrix!

If we could ever get Python to include additional operators, we could 
have a Numeric array package and linear algebra package all in one.. 
both with nice notation, that would be nice.

In the meantime, check out cvxopt:

http://www.ee.ucla.edu/~vandenbe/cvxopt/

If you want a matrix package. The author is talking about using the new 
array specification in future versions.

-Chris

-- 
Christopher Barker, Ph.D.
Oceanographer
                                     		
NOAA/OR&R/HAZMAT         (206) 526-6959   voice
7600 Sand Point Way NE   (206) 526-6329   fax
Seattle, WA  98115       (206) 526-6317   main reception

Chr...@no...

Re: [Numpy-discussion] storing magnitude data

[Tim H. <tim...@co...>]

Daniel Sheltraw wrote:

> Hello All
>
> Would someone please tell me how to store magnitude data in the first 
> half of
> a previously allocated complex array (I need to save memory). Memory 
> saving
> things like this are so simple and intuitive in C but less so in NumPy.

 >>> a = 3*na.arange(10) + 4*na.arange(10)*1j
 >>> na.absolute(a, a.real)
 >>> a.real
array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.])


>
> Thanks,
> Daniel
>
>
> -------------------------------------------------------
> SF.Net email is Sponsored by the Better Software Conference & EXPO
> September 19-22, 2005 * San Francisco, CA * Development Lifecycle 
> Practices
> Agile & Plan-Driven Development * Managing Projects & Teams * Testing 
> & QA
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> _______________________________________________
> Numpy-discussion mailing list
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> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>

[Numpy-discussion] storing magnitude data

[Daniel S. <she...@un...>]

Hello All

Would someone please tell me how to store magnitude data 
in the first half of
a previously allocated complex array (I need to save 
memory). Memory saving
things like this are so simple and intuitive in C but less 
so in NumPy.

Thanks,
Daniel

[Numpy-discussion] (no subject)

[Daniel S. <she...@un...>]

Hello All

Would someone please tell me how to write the magnitude of 
an
array to the first half of a previous declared complex 
valued
array without allocating more memory. This is the sort of 
thing
that is trivial in C but seems arcane in python/numpy.

Thanks,
Daniel

[Numpy-discussion] Numeric Python EM site back up

[rob <eur...@co...>]

Its at http://home.comcast.net/~europa100

I hope to morph it later with the latest numpy and/or numarray and/or 
scipy stuff.

I would also like a more "glamorous" location for the site.
Haha.

Rob

Re: [Numpy-discussion] still no cross product for new array type

[Greg E. <gre...@ca...>]

Nadav Horesh wrote:
> Gary Ruben <gr...@bi...>:
 >
> > Just browsing through the new array PEP to see if there's support for 
> > 3-vector operations, such as cross product, norm, length functions.
> > Sadly I don't see any.
 >
> Usually this is within the scope of higher level packages such as 
> ScientificPython.

I'd like to see at least cross product included somewhere,
since it's difficult to synthesize efficiently from the
other operations provided.

-- 
Greg Ewing, Computer Science Dept, +--------------------------------------+
University of Canterbury,	   | A citizen of NewZealandCorp, a	  |
Christchurch, New Zealand	   | wholly-owned subsidiary of USA Inc.  |
gre...@ca...	   +--------------------------------------+

Re: [Numpy-discussion] still no cross product for new array type

[Gary R. <gr...@bi...>]

"Usually" means that that's where they are now, because they haven't 
been implemented in numpy. Comparing with Matlab/IDL where their status 
is slightly greater so as to earn operations of their own, I wonder why 
equivalent status isn't afforded them in numpy.

Gary R.

Nadav Horesh wrote:
> Usually this is within the scope of higher level packages such as ScientificPython.
> 
>   Nadav
> 
> -----Original Message-----
> From:	num...@li... on behalf of Gary Ruben
> Sent:	Fri 12-Aug-05 14:44
> To:	num...@li...
> Cc:	
> Subject:	[Numpy-discussion] still no cross product for new array type
> Just browsing through the new array PEP to see if there's support for 
> 3-vector operations, such as cross product, norm, length functions. 
> Sadly I don't see any. It's something I think is lacking from Numeric 
> and numarray and would like to see implemented. Is it a deliberate 
> choice not to include any? I understand that vectors are sufficiently 
> different animals that you could argue that they shouldn't be supported. 
> I use them enough to think that they should be.
> 
> Gary Ruben