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

Re: [Numpy-discussion] matrix multiplication (newbie question)

[Nadav H. <na...@vi...>]

Make A,B,... matrices instead of arrays, so instead

A =3D array((.....))

Write

A =3D matrix((....))

=20

Assuming you had

>From numpy import *

=20

  Nadav

=20

  _____ =20

From: num...@li...
[mailto:num...@li...] On Behalf Of
izak marais
Sent: Wednesday, November 08, 2006 15:54
To: num...@li...
Subject: [Numpy-discussion] matrix multiplication (newbie question)

=20

Hi

Sorry if this is an obvious question, but what is the easiest way to
multiply matrices in numpy? Suppose I want to do A=3DB*C*D. The ' * '
operator apparently does element wise multiplication, as does the
'multiply' ufunc. All I could find was the numeric function
'matrix_multiply, but this only takes two arguments.

Thanks in advance!
Izak

 =20

  _____ =20

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2
BbXooR4YcdmONmTWaUizNjh6q2wGRcnxuw%3D%3D&PI=3Dy_mail_textlink>=20

Re: [Numpy-discussion] matrix multiplication (newbie question)

[Stefan v. d. W. <st...@su...>]

On Wed, Nov 08, 2006 at 05:54:17AM -0800, izak marais wrote:
> Hi
>=20
> Sorry if this is an obvious question, but what is the easiest way to mu=
ltiply
> matrices in numpy? Suppose I want to do A=3DB*C*D. The ' * ' operator a=
pparently
> does element wise multiplication, as does the 'multiply' ufunc. All I c=
ould
> find was the numeric function 'matrix_multiply, but this only takes two
> arguments.

You can also do N.dot(B, N.dot(C,D)).

Cheers
St=E9fan

Re: [Numpy-discussion] matrix multiplication (newbie question)

[Keith G. <kwg...@gm...>]

On 11/8/06, izak marais <iza...@ya...> wrote:

> Sorry if this is an obvious question, but what is the easiest way to
> multiply matrices in numpy? Suppose I want to do A=B*C*D. The ' * ' operator
> apparently does element wise multiplication, as does the 'multiply' ufunc.
> All I could find was the numeric function 'matrix_multiply, but this only
> takes two arguments.

If B and C and D are matrices, then '*' is matrix multiplication.

[Numpy-discussion] matrix multiplication (newbie question)

[izak m. <iza...@ya...>]

Hi

Sorry if this is an obvious question, but what is the easiest way to multiply matrices in numpy? Suppose I want to do A=B*C*D. The ' * ' operator apparently does element wise multiplication, as does the 'multiply' ufunc. All I could find was the numeric function 'matrix_multiply, but this only takes two arguments.

Thanks in advance!
Izak

 
---------------------------------
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Degrees online in as fast as 1 Yr - MBA, Bachelor's, Master's, Associate - Click now to apply

[Numpy-discussion] reading matrix from a file

[amit s. <ami...@ya...>]

Hi,
  i have a file with following format: 
 1 2 
 3 9 
 2 3 
 4 4 
   I want to read it and then store the values into two matrices, s.t. 
 A=[1 2;3 9] 
 B=[2 3;4 4] 
 
  Can anyone tell me how to do this in python?
 thanks 
 Amit 
  
 
---------------------------------
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Re: [Numpy-discussion] A reimplementation of MaskedArray

[Pierre GM <pgm...@gm...>]

Michael,
First of all, thanks for your interest in the exercise of style the new 
implementation of MaskedArray is basically nothing but.

On Tuesday 07 November 2006 20:11, Michael Sorich wrote:
> 1. It would be nice if the masked_singleton could be passed into a
> ndarray, as this would allow it to be passed into the MaskedArray e.g.
>
> import numpy as N
> import ma.maskedarray as MA
> test = N.array([1,2,MA.masked])
>
> >> ValueError: setting an array element with a sequence

I like your idea, but not its implementation. If MA.masked_singleton is 
defined as an object, as you suggest, then the dtype of the ndarray it is 
passed to becomes 'object', as you pointed out, and that is not something one 
would naturally expec, as basic numerical functions don't  work well  with the 
'object' dtype (just try  N.sqrt(N.array([1],dtype=N.object)) to see what I 
mean).
Even if we can construct a mask rather easily at the creation of the masked 
array, following your 'a==masked' suggestion, we still need to get the dtype 
of the non-masked section, and that doesn't seem trivial...

I guess that a simple solution is to use MA.masked_values. 
Make sure to use a numerical value for the masked data, else you'll end up 
with yet another object array.  


> 2. What happens if a masked array is passed into a ndarray or passed
> into a MaskedArray with another mask?
>>> test_ma1 = MA.array([1,2,3], mask=[False, False, True])
>>> print test_ma1, N.array(test_ma1), 
[1 2 --] [1 2 3]
>>> MA.array(test_ma1, mask=[True, False, False]) 
[-- 2 3]

Let me precise that my objective was to get an implementation as close to the 
original numpy.core.ma as possible, for 'backward compatibility'. I'm not 
sure it'd be wise to change it at this point, but that could be discussed.

As you've noticed, when creating a new masked array from an existing one, the 
'mask' argument supersedes the initial mask. That's ideal when you want to 
focus on a fraction of the initial data: you just mask what you don't need, 
and are still able to retrieve it when you need it. I agree that this default 
behavior is a bit strange when you have missing data: in that case, one would 
expect the new mask to be a combination of the 'mask' argument and the old 
mask. 
A possibility would then be to add a 'keep_mask' flag: a default of False 
would give the current behavior, a value of True would force the new mask to 
be a combination. I think that feelings are mixed on that list about extra 
flags, but the users of maskedarray are only a minority anyway (hopefully, 
only for the moment).

About the conversion to ndarray: 
By default, the result should have the same dtype as the _data section. 
For this reason, I disagree with your idea of "(returning) an object ndarray 
with the missing value containing the masked singleton". If you really want 
an object ndarray, you can use the filled method or the filled function, with 
your own definition of the filling value (such as your MaskedScalar). 

[Numpy-discussion] Profiling Python codes with hotshot and KCachegrind

[Fernando P. <fpe...@gm...>]

Hi all,

in the past, Arnd Baecker has made a number of very useful posts on
this matter, and provided some nice utilities to do it.  I now needed
to profile some fairly complex codes prior to a big optimization push,
so I went over his material and wrote a little tool to make the whole
process as painless as possible.  Here it is, hoping others may find
it useful:

http://amath.colorado.edu/faculty/fperez/python/profiling/

Cheers,

f

Re: [Numpy-discussion] Passing numpy arrays to matlab

[David C. <da...@ar...>]

Josh Marshall wrote:
> Hi David,
>
> Did you have a look at mlabwrap? It's quite hard to find on the net,  
> which is a shame, since it is a much more up to date version,  
> enhancing pymat with the things that you are trying to do. It allows  
> passing arrays and getting arrays back.
>
> http://mlabwrap.sourceforge.net/
>   
I didn't know that, thanks. Unfortunately, it is not really what I am 
trying to do: mlabwrap is just a python interface a bit more high level 
than pymat, with many fancy tricks, but still do copies. What I would 
like is to avoid completely the copying by using proxy classes around 
data from numpy so that I can pass "automatically" numpy arrays to 
matlab C api, and a proxy class around data from matlab so that they 
look like numpy arrays.
    I don't care that much about the actual api from python point of 
view, because I intend to use this mainly to compare matlab vs numpy 
implementation, not as a way to use matlab inside python regularly. And 
once the copy problem is solved, adding syntactic sugar using python is 
easy anyway, I think (it should be easy to do something similar to 
mlabwrap at that point),

    cheers,

    David

Re: [Numpy-discussion] Passing numpy arrays to matlab

[David C. <da...@ar...>]

Pauli Virtanen wrote:
> Hi all,
>
> ti, 2006-11-07 kello 11:23 +0900, David Cournapeau kirjoitti:
>   
>>     I am trying to find a nice way to communicate between matlab and 
>> python. I am aware of pymat, which does that, but the code is 
>> deprecated, and I thing basing the code on ctypes would lead to much 
>> more robust code.
>>
>>     http://claymore.engineer.gvsu.edu/%7Esteriana/Software/pymat.html
>>
>> I have a really simple prototype which can send and get back data from 
>> matlab, but I was wondering if it would be possible to use a scheme 
>> similar to ctypes instead of having to convert it by hand.
>>     
>
> A while ago I wrote a mex extension to embed the Python interpreter
> inside Matlab:
>
> 	http://www.iki.fi/pav/pythoncall
>
> I guess it's something like an inverse of pymat :)
>
>   
Yes, but at the end, I think they enable similar things. Thanks for the 
link !
> But I guess this is not really what you are looking for, since at
> present it just does a memory copy when passing arrays between Matlab
> and Python. Though, shared arrays might be just possible to implement if
> memory management is done carefully.
>   
In my case, it is much worse:

    1 first, you have numpy data that you have to copy to mxArray, the 
structure representing arrays in matlab C api.
    2 then when you "send" data to the matlab engine, this is done 
automatically through pipe  by the matlab engine API (maybe pipe does 
not imply copying; I don't know much about pipe from a programming point 
of view, actually)
    3 The arrays you get back from matlab are in matlab mxArray 
structures: right now, I copy their data to new numpy arrays.

    At first, I just developed a prototype without thinking too much, 
and the result was much slower than I thought: sending a numpy  with 
2e5x10 double takes around 100 ms on my quite powerful machine (around 
14 cycles per item for the best case). I suspect it is because I copy 
memory in a non contiguous manner (matlab arrays have a internal F 
storage for real arrays, but complex arrays are really two different 
arrays, which is different than Fortran convention I think, making the 
copy cost really expensive for complex arrays). To see if I was doing 
something wrong, I compared with numpy.require(ar, requirements = 
'F_CONTIGUOUS'), which is even much slower
    There is not much I can do about 2, it looks like there is a way to 
avoid copying for 1, and my question was more specific to 3 (but 
reusable in 1, maybe, if I am smart enough). Basically:

     * how to create an object which has the same interface than numpy 
arrays, but owns the data from a foreign structure, which data are 
availble when building the object (The idea was to create a class which 
implements the array interface from python, kind of proxy class, which 
owns the data from mxArray; owns here is from a memory management point 
of view).

    David

Re: [Numpy-discussion] "Slicing" recarrays

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

Christopher Barker wrote:

>Hi all,
>
>I'm using a recarray to read a bunch of binary data out of a file. It's 
>working great, but it seems there should be a more efficient way to 
>"slice" the data. Here's what I've got:
>
>The binary data is essentially a dump of a 2-d array of structs. So I 
>read it like this:
>
>DataType = N.dtype([("long","i4"), ("lat", "i4"), ("flag","b1")])
>
>data = N.fromfile(file, DataType)
>
>data.shape = (M, N)
>
>So I now have a MxN array of the structs. What I would like to do is 
>extract a MxNx2 array of just the two 4-byte integers. It seems that I 
>should be able to do that without copying -- by using a view into the 
>original data. I can't figure out how, however. What I am doing is:
>
>LEs = N.empty((M, N, 2), dtype=N.int32)
>LEs[:,:,0] = data['long']
>LEs[:,:,1] = data['lat']
>
>This works, but these are BIG files, so it would be nice not to be doing 
>that extra copying. Is that possible?
>  
>

How about

newdtype = N.dtype([('both','2i4'),('','b1')])
res = data.view(newdtype)['both']


-Travis

Re: [Numpy-discussion] A reimplementation of MaskedArray

[Michael S. <mic...@gm...>]

On 10/25/06, Pierre GM <pgm...@gm...> wrote:
> On Tuesday 24 October 2006 02:50, Michael Sorich wrote:
> > I am currently running numpy rc2 (I haven't tried your
> > reimplementation yet as I am still using python 2.3). I am wondering
> > whether the new maskedarray is able to handle construction of arrays
> > from masked scalar values (not sure if this is the correct term).
>
> The answer is no, unfortunately: both the new and old implementations fail at
> the same point, raising a TypeError: lists are processed through
> numpy.core.numeric.array, which doesn't handle that.

I have finally gotten around to upgrading to python 2.4 and have had a
chance to play with your new version of the MaskedArray. It is great
to see that someone is working on this. I have a few thoughts on
masked arrays that may or may no warrant discusion

1. It would be nice if the masked_singleton could be passed into a
ndarray, as this would allow it to be passed into the MaskedArray e.g.

import numpy as N
import ma.maskedarray as MA
test = N.array([1,2,MA.masked])
>> ValueError: setting an array element with a sequence

If the masked_singleton was implemented as an object that is not a
MakedArray (which is a sequence that numpy.array chokes on), then a
valid numpy array with an object dtype could be produced. e.g.

class MaskedScalar:
    def __str__(self):
        return 'masked'
masked = MaskedScalar()

test = N.array([1,2,masked])
print test.dtype, test
>>object [1 2 masked]
print test == masked
>>[False False True]
print test[2] == masked
>>True
print test[2] is masked
>>True

Then it would be possible to alternatively define a masked array as
MA.array([1,2,masked]) or MA.array(N.array([1,2,masked])). In the
__init__ of the MaskedArray if the ndarray has an object dtype simply
calculate the mask from a==masked.

2. What happens if a masked array is passed into a ndarray or passed
into a MaskedArray with another mask?

test_ma1 = MA.array([1,2,3], mask=[False, False, True])
print test_ma1
>>[1 2 --]

print N.array(test_ma1)
>>[1 2 3]

test_ma2 = MA.array(test_ma1, mask=[True, False, False])
print test_ma2
>>[-- 2 3]

I suppose it depends on whether you are masking useful data, or the
masks represent missing data. In the former it may make sense to
change or remove the mask. However in the latter case the original
data entered is a bogus value which should never be unmasked. In this
case, when converting to a ndarray I think it make more sense to make
an object ndarray with the missing value containing the masked
singleton. Additionally, if the MaskedArray is holding missing data,
it does not make much sense to be able to pass in to the MA
constructor both an existing ma and a mask.

[Numpy-discussion] "Slicing" recarrays

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

Hi all,

I'm using a recarray to read a bunch of binary data out of a file. It's 
working great, but it seems there should be a more efficient way to 
"slice" the data. Here's what I've got:

The binary data is essentially a dump of a 2-d array of structs. So I 
read it like this:

DataType = N.dtype([("long","i4"), ("lat", "i4"), ("flag","b1")])

data = N.fromfile(file, DataType)

data.shape = (M, N)

So I now have a MxN array of the structs. What I would like to do is 
extract a MxNx2 array of just the two 4-byte integers. It seems that I 
should be able to do that without copying -- by using a view into the 
original data. I can't figure out how, however. What I am doing is:

LEs = N.empty((M, N, 2), dtype=N.int32)
LEs[:,:,0] = data['long']
LEs[:,:,1] = data['lat']

This works, but these are BIG files, so it would be nice not to be doing 
that extra copying. Is that possible?

Thanks,

-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] Stacking arrays...

[Bill B. <wb...@gm...>]

dstack maybe? or does that add a dim on the other end?
--bb

On 11/8/06, Christopher Barker <Chr...@no...> wrote:
>
> HI all,
>
> I'm trying to get the hang of this new r_ and c_ stuff.
>
> I need to take two MxN arrays, and stack them together into one MxNx2
> arrays. I found this works:
>
> >>> a = N.ones((3,4))
> >>> b = N.ones((3,4)) * 2
> >>> a
> array([[ 1.,  1.,  1.,  1.],
>         [ 1.,  1.,  1.,  1.],
>         [ 1.,  1.,  1.,  1.]])
> >>> b
> array([[ 2.,  2.,  2.,  2.],
>         [ 2.,  2.,  2.,  2.],
>         [ 2.,  2.,  2.,  2.]])
> >>> a.shape = (3,4,1)
> >>> b.shape = (3,4,1)
> >>> c = N.c_[a,b]
> >>> c.shape
> (3, 4, 2)
> >>> c[0,0]
> array([ 1.,  2.])
>
>
> What I'm wondering is if there is a way to do that without explicitly
> adding the extra dimension to a and b before calling c_ ?
>
> -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...
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job
> easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
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> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] Stacking arrays...

[Pau G. <pau...@gm...>]

On 11/7/06, Christopher Barker <Chr...@no...> wrote:
> HI all,
>
> I'm trying to get the hang of this new r_ and c_ stuff.
>
> I need to take two MxN arrays, and stack them together into one MxNx2
> arrays. I found this works:
>
>  >>> a = N.ones((3,4))
>  >>> b = N.ones((3,4)) * 2
>  >>> a
> array([[ 1.,  1.,  1.,  1.],
>         [ 1.,  1.,  1.,  1.],
>         [ 1.,  1.,  1.,  1.]])
>  >>> b
> array([[ 2.,  2.,  2.,  2.],
>         [ 2.,  2.,  2.,  2.],
>         [ 2.,  2.,  2.,  2.]])
>  >>> a.shape = (3,4,1)
>  >>> b.shape = (3,4,1)
>  >>> c = N.c_[a,b]
>  >>> c.shape
> (3, 4, 2)
>  >>> c[0,0]
> array([ 1.,  2.])
>
>
> What I'm wondering is if there is a way to do that without explicitly
> adding the extra dimension to a and b before calling c_ ?
>

c=N.dstack([a,b]) seems to do the trick.

pau



> -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...
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] More on Numexpr-unsupported objects

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

Ivan Vilata i Balaguer wrote:


[SNIP]
>
> Though I admit it may be strange for this error to be triggered.
>
> (I had a little mess with using ``expressions.ExpressionNode`` instead
> of ``expr.ExpressionNode``...  I still don't see why the private copy of
> the module is necessary.)
>
>   
It's an attempt to make things thread safe. Since we are mucking around 
with the internals of the expression module, if there were multiple 
threads one might thread might change get_context out from under the 
other one. Giving each call to stringToExpression its own copy of the 
expression module prevents this.

It is,  admittedly, a kind of stupid way to do this. A more sane thing 
to do would probably to have some sort of expression object that we 
instantiate for each call to stringToExpression instead of mucking 
around with modules, which really aren't meant to be (ab)used this way. 
However, this would require a major rewrite of the expression module and 
I was feeling lazy at the time (and still am), so I resorted to 
questionable hackery.

-tim

Re: [Numpy-discussion] Passing numpy arrays to matlab

[Pauli V. <pau...@ik...>]

Hi all,

ti, 2006-11-07 kello 11:23 +0900, David Cournapeau kirjoitti:
>     I am trying to find a nice way to communicate between matlab and=20
> python. I am aware of pymat, which does that, but the code is=20
> deprecated, and I thing basing the code on ctypes would lead to much=20
> more robust code.
>=20
>     http://claymore.engineer.gvsu.edu/%7Esteriana/Software/pymat.html
>=20
> I have a really simple prototype which can send and get back data from=20
> matlab, but I was wondering if it would be possible to use a scheme=20
> similar to ctypes instead of having to convert it by hand.

A while ago I wrote a mex extension to embed the Python interpreter
inside Matlab:

	http://www.iki.fi/pav/pythoncall

I guess it's something like an inverse of pymat :)

But I guess this is not really what you are looking for, since at
present it just does a memory copy when passing arrays between Matlab
and Python. Though, shared arrays might be just possible to implement if
memory management is done carefully.

BR,

	Pauli Virtanen

Re: [Numpy-discussion] Passing numpy arrays to matlab

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

Hi,

> Thank you very much, I think this added documentation is pretty recent;
> I have never seen it before, and I did a lot a mex programming at some
> point... This whole mxarray nonsense reminds me why I gave up on matlab :),

I would be very happy to help with this.  It would be great if we
could get a standard well-maintained library of some sort towards
scipy - we (http://neuroimaging.scipy.org/) have a great deal of
matlab integration to do.

Best,

Matthew

[Numpy-discussion] Stacking arrays...

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

HI all,

I'm trying to get the hang of this new r_ and c_ stuff.

I need to take two MxN arrays, and stack them together into one MxNx2 
arrays. I found this works:

 >>> a = N.ones((3,4))
 >>> b = N.ones((3,4)) * 2
 >>> a
array([[ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.],
        [ 1.,  1.,  1.,  1.]])
 >>> b
array([[ 2.,  2.,  2.,  2.],
        [ 2.,  2.,  2.,  2.],
        [ 2.,  2.,  2.,  2.]])
 >>> a.shape = (3,4,1)
 >>> b.shape = (3,4,1)
 >>> c = N.c_[a,b]
 >>> c.shape
(3, 4, 2)
 >>> c[0,0]
array([ 1.,  2.])


What I'm wondering is if there is a way to do that without explicitly 
adding the extra dimension to a and b before calling c_ ?

-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...

[Numpy-discussion] More on Numexpr-unsupported objects

[Ivan V. i B. <iv...@ca...>]

This is somehow related with this previous thread about raising
``TypeError`` on unsupported objects in Numexpr:
http://www.mail-archive.com/num...@li.../msg03146.html

There is still a case where unsupported objects can get into expressions
without Numexpr noticing.  For instance:

>>> import numexpr
>>> numexpr.evaluate('[]')
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
  File "numexpr/compiler.py", line 594, in evaluate
    _names_cache[expr_key] = getExprNames(ex, context)
  File "numexpr/compiler.py", line 570, in getExprNames
    ast = expressionToAST(ex)
  File "numexpr/compiler.py", line 84, in expressionToAST
    this_ast = ASTNode(ex.astType, ex.astKind, ex.value,
AttributeError: 'list' object has no attribute 'astType'

The attached patch makes the error clearer and more consistent with the
error added in the aforementioned thread:

>>> import numpy
>>> import numexpr
>>> numexpr.evaluate('[]')
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
  File "numexpr/compiler.py", line 596, in evaluate
    _names_cache[expr_key] = getExprNames(ex, context)
  File "numexpr/compiler.py", line 571, in getExprNames
    ex = stringToExpression(text, {}, context)
  File "numexpr/compiler.py", line 229, in stringToExpression
    raise TypeError("unsupported expression type: %s" % type(ex))
TypeError: unsupported expression type: <type 'list'>

Though I admit it may be strange for this error to be triggered.

(I had a little mess with using ``expressions.ExpressionNode`` instead
of ``expr.ExpressionNode``...  I still don't see why the private copy of
the module is necessary.)

::

	Ivan Vilata i Balaguer   >qo<   http://www.carabos.com/
	       Cárabos Coop. V.  V  V   Enjoy Data
	                          ""

[Numpy-discussion] help installing numpy1.0 on sgi irix

[IV <iva...@gm...>]

Dear All,

has anyone successfully install numpy-1.0 on irix 6.5?

The GCC is 3.3 and is the latest from freeware.sgi.com.
Building gcc 4.1.1 from source did not work for me, and I am afraid to
force it.

Latest Python 2.5 installed fine from source.

Numpy gives this error(s):
$ python setup.py install
Running from numpy source directory.
F2PY Version 2_3396
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /usr/local/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
  libraries lapack,blas not found in /usr/local/lib
  NOT AVAILABLE

atlas_blas_info:
  libraries lapack,blas not found in /usr/local/lib
  NOT AVAILABLE

/usr/people/ivan/numpy-1.0/numpy/distutils/system_info.py:1301:
UserWarning:
    Atlas (http://math-atlas.sourceforge.net/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [atlas]) or by setting
    the ATLAS environment variable.
  warnings.warn(AtlasNotFoundError.__doc__)
blas_info:
  libraries blas not found in /usr/local/lib
  NOT AVAILABLE

/usr/people/ivan/numpy-1.0/numpy/distutils/system_info.py:1310:
UserWarning:
    Blas (http://www.netlib.org/blas/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [blas]) or by setting
    the BLAS environment variable.
  warnings.warn(BlasNotFoundError.__doc__)
blas_src_info:
  NOT AVAILABLE

/usr/people/ivan/numpy-1.0/numpy/distutils/system_info.py:1313:
UserWarning:
    Blas (http://www.netlib.org/blas/) sources not found.
    Directories to search for the sources can be specified in the
    numpy/distutils/site.cfg file (section [blas_src]) or by setting
    the BLAS_SRC environment variable.
  warnings.warn(BlasSrcNotFoundError.__doc__)
  NOT AVAILABLE

lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /usr/local/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
  libraries lapack,blas not found in /usr/local/lib
  libraries lapack_atlas not found in /usr/local/lib
numpy.distutils.system_info.atlas_threads_info
  NOT AVAILABLE

atlas_info:
  libraries lapack,blas not found in /usr/local/lib
  libraries lapack_atlas not found in /usr/local/lib
numpy.distutils.system_info.atlas_info
  NOT AVAILABLE

/usr/people/ivan/numpy-1.0/numpy/distutils/system_info.py:1210:
UserWarning:
    Atlas (http://math-atlas.sourceforge.net/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [atlas]) or by setting
    the ATLAS environment variable.
  warnings.warn(AtlasNotFoundError.__doc__)
lapack_info:
  libraries lapack not found in /usr/local/lib
  NOT AVAILABLE

/usr/people/ivan/numpy-1.0/numpy/distutils/system_info.py:1221:
UserWarning:
    Lapack (http://www.netlib.org/lapack/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [lapack]) or by setting
    the LAPACK environment variable.
  warnings.warn(LapackNotFoundError.__doc__)
lapack_src_info:
  NOT AVAILABLE

/usr/people/ivan/numpy-1.0/numpy/distutils/system_info.py:1224:
UserWarning:
    Lapack (http://www.netlib.org/lapack/) sources not found.
    Directories to search for the sources can be specified in the
    numpy/distutils/site.cfg file (section [lapack_src]) or by setting
    the LAPACK_SRC environment variable.
  warnings.warn(LapackSrcNotFoundError.__doc__)
  NOT AVAILABLE

running install
running build
running config_fc
running build_src
building py_modules sources
building extension "numpy.core.multiarray" sources
Generating build/src.irix64-6.5-2.5/numpy/core/config.h
customize MipsFCompiler
customize MipsFCompiler
customize MipsFCompiler using config
C compiler: gcc -fno-strict-aliasing -DNDEBUG -g -O3 -Wall
-Wstrict-prototypes -shared

compile options: '-I/usr/local/include/python2.5 -Inumpy/core/src
-Inumpy/core/include -I/usr/local/include/python2.5 -c'
gcc: _configtest.c
gcc _configtest.o -L/usr/local/lib -L/usr/lib -o _configtest
ld32: FATAL   12 : Expecting n32 objects: /usr/lib/libc.so is o32.
collect2: ld returned 4 exit status
ld32: FATAL   12 : Expecting n32 objects: /usr/lib/libc.so is o32.
collect2: ld returned 4 exit status
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 "/usr/people/ivan/numpy-1.0/numpy/distutils/core.py", line 174,
in setup
    return old_setup(**new_attr)
  File "/usr/local/lib/python2.5/distutils/core.py", line 151, in setup
    dist.run_commands()
  File "/usr/local/lib/python2.5/distutils/dist.py", line 974, in
run_commands
    self.run_command(cmd)
  File "/usr/local/lib/python2.5/distutils/dist.py", line 994, in
run_command
    cmd_obj.run()
  File "/usr/people/ivan/numpy-1.0/numpy/distutils/command/install.py",
line 16, in run
    r = old_install.run(self)
  File "/usr/local/lib/python2.5/distutils/command/install.py", line
506, in run
    self.run_command('build')
  File "/usr/local/lib/python2.5/distutils/cmd.py", line 333, in
run_command
    self.distribution.run_command(command)
  File "/usr/local/lib/python2.5/distutils/dist.py", line 994, in
run_command
    cmd_obj.run()
  File "/usr/local/lib/python2.5/distutils/command/build.py", line 112,
in run
    self.run_command(cmd_name)
  File "/usr/local/lib/python2.5/distutils/cmd.py", line 333, in
run_command
    self.distribution.run_command(command)
  File "/usr/local/lib/python2.5/distutils/dist.py", line 994, in
run_command
    cmd_obj.run()
  File
"/usr/people/ivan/numpy-1.0/numpy/distutils/command/build_src.py", line
87, in run
    self.build_sources()
  File
"/usr/people/ivan/numpy-1.0/numpy/distutils/command/build_src.py", line
106, in build_sources
    self.build_extension_sources(ext)
  File
"/usr/people/ivan/numpy-1.0/numpy/distutils/command/build_src.py", line
212, in build_extension_sources
    sources = self.generate_sources(sources, ext)
  File
"/usr/people/ivan/numpy-1.0/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 


Please advise :)

[Numpy-discussion] numpy on wince

[Emanuele O. <oli...@it...>]

Hi all,
do you know if numpy/Numeric is available on WinCE ?
I'm thinking about the use of a numpy/Numeric app on a palmtop
and I'm not sure it will be deployed on linux only devices.

Thanks in advance,

Emanuele

P.S.: I know that Python is available on many many platforms, but
I not so confident about many external libraries.

Re: [Numpy-discussion] Passing numpy arrays to matlab

[David C. <da...@ar...>]

Andrew Straw wrote:
> David Cournapeau wrote:
>   
>> Andrew Straw wrote:
>>   
>>     
>>> David Cournapeau wrote:
>>>   
>>>     
>>>       
>>>>     - To send data from the calling process to matlab, you first have to 
>>>> create a mxArray, which is the basic matlab handler of a matlab array, 
>>>> and populating it. Using mxArray is very ackward : you cannot create 
>>>> mxArray from existing data, you have to copy data to them, etc... 
>>>>     
>>>>       
>>>>         
>>> My understanding, never having done it, but from reading the docs, is
>>> that you can create a "hybrid array" where you manage the memory. Thus,
>>> you can create an mxArray from existing data. However, the docs
>>> basically say that this is too hard for most mortals (and they may well
>>> be right -- too painful for me, anyway)!
>>>   
>>>     
>>>       
>> Would you mind telling me where you found that information ? Because 
>> right now, I am wasting a lot of cycles because of memory copy in both 
>> directions, and it is sometimes slow enough so that it is annoying,
>>   
>>     
> I found it reading through the in-program help (the C-API section,
> whatever it's called) on a Matlab installation at my university. I guess
> this was Matlab 2006A. A quick Google search turns this up:
>
> http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_external/index.html?/access/helpdesk/help/techdoc/matlab_external/f25255.html
>
> They give the following example, which seems to create a Matlab array
> "pArray" with data owned by the C variable "data":
>
> mxArray *pArray = mxCreateDoubleMatrix(0, 0, mxREAL);
> double data[10];
>
> mxSetPr(pArray, data);
> mxSetM(pArray, 1);
> mxSetN(pArray, 10);
>   
Thank you very much, I think this added documentation is pretty recent; 
I have never seen it before, and I did a lot a mex programming at some 
point... This whole mxarray nonsense reminds me why I gave up on matlab :),

cheers,

David

Re: [Numpy-discussion] Passing numpy arrays to matlab

[Andrew S. <str...@as...>]

David Cournapeau wrote:
> Andrew Straw wrote:
>   
>> David Cournapeau wrote:
>>   
>>     
>>>     - To send data from the calling process to matlab, you first have to 
>>> create a mxArray, which is the basic matlab handler of a matlab array, 
>>> and populating it. Using mxArray is very ackward : you cannot create 
>>> mxArray from existing data, you have to copy data to them, etc... 
>>>     
>>>       
>> My understanding, never having done it, but from reading the docs, is
>> that you can create a "hybrid array" where you manage the memory. Thus,
>> you can create an mxArray from existing data. However, the docs
>> basically say that this is too hard for most mortals (and they may well
>> be right -- too painful for me, anyway)!
>>   
>>     
> Would you mind telling me where you found that information ? Because 
> right now, I am wasting a lot of cycles because of memory copy in both 
> directions, and it is sometimes slow enough so that it is annoying,
>   
I found it reading through the in-program help (the C-API section,
whatever it's called) on a Matlab installation at my university. I guess
this was Matlab 2006A. A quick Google search turns this up:

http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_external/index.html?/access/helpdesk/help/techdoc/matlab_external/f25255.html

They give the following example, which seems to create a Matlab array
"pArray" with data owned by the C variable "data":

mxArray *pArray = mxCreateDoubleMatrix(0, 0, mxREAL);
double data[10];

mxSetPr(pArray, data);
mxSetM(pArray, 1);
mxSetN(pArray, 10);

Re: [Numpy-discussion] new 874

[Fran K. <bu...@ca...>]

Hi,

PHAjjRMACY for LESS http://www.mitujunhertshieasde.com

=20

Your slightest wish is our command.

Re: [Numpy-discussion] Passing numpy arrays to matlab

[David C. <da...@ar...>]

Andrew Straw wrote:
> David Cournapeau wrote:
>   
>>     - To send data from the calling process to matlab, you first have to 
>> create a mxArray, which is the basic matlab handler of a matlab array, 
>> and populating it. Using mxArray is very ackward : you cannot create 
>> mxArray from existing data, you have to copy data to them, etc... 
>>     
> My understanding, never having done it, but from reading the docs, is
> that you can create a "hybrid array" where you manage the memory. Thus,
> you can create an mxArray from existing data. However, the docs
> basically say that this is too hard for most mortals (and they may well
> be right -- too painful for me, anyway)!
>   
Would you mind telling me where you found that information ? Because 
right now, I am wasting a lot of cycles because of memory copy in both 
directions, and it is sometimes slow enough so that it is annoying,

cheers,

David