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

Re: [Numpy-discussion] Failure to install

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

On 6/22/06, Jocelyn E. Renner <j-r...@no...> wrote:

> python setup.py install
>
> when I was in the numarray directory.  When I executed this, I received
> the following error message:
> error: could not create
> '/System/Library/Frameworks/Python.framework/Versions/2.3/include/
> python2.3/numarray': Permission denied
>
> I have tried to unlock this folder with little to no luck (I must
> confess I am not the most computer savvy person ever).

Try

sudo python setup.py install

[Numpy-discussion] unique() should return a sorted array

[David H. <dav...@gm...>]

Hi,

Numpy's unique(x) returns an array x with repetitions removed. However,
since it returns asarray(dict.keys()), the resulting array is not sorted,
worse, the original order may not be conserved. I think that unique() should
return a sorted array, like its matlab homonym.

Regards,

David Huard

[Numpy-discussion] Failure to install

[Jocelyn E. R. <j-r...@no...>]

Hello!
I am attempting to install numarray on my Mac OX 10.3, and I  
successfully downloaded it.  Since I am attempting to use this with  
Cantera, I followed their recommendations as to installing which  
included typing:

python setup.py install

when I was in the numarray directory.  When I executed this, I received  
the following error message:
error: could not create  
'/System/Library/Frameworks/Python.framework/Versions/2.3/include/ 
python2.3/numarray': Permission denied

I have tried to unlock this folder with little to no luck (I must  
confess I am not the most computer savvy person ever).  If anyone could  
give me some advice as to how to get this to install properly, I'd  
appreciate it!  If it does not need to be in this folder, is there  
anyway to bypass this?

Thanks so much!
Jocelyn

Jocelyn Renner
Mechanical Engineering, Northwestern University
------------------------------------------------------------------------ 
-----------------
No man is an island, entire of itself...any man's death diminishes me,  
because I am involved in mankind; and therefore never send to know for  
whom the bell tolls; it tolls for thee. ---John Donne Meditation XVII

Re: [Numpy-discussion] Matrix construction

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

On 6/22/06, Ed Schofield <sch...@ft...> wrote:
>
>
> On 22/06/2006, at 12:40 AM, Bill Baxter wrote:
>
> > Actually I think using mat() (just an alias for the matrix
> > constructor) is a bad way to do it.  That mat() (and most others on
> > that page) should probably be replaced with asmatrix() to avoid the
> > copy.
>
> Perhaps the 'mat' function should become an alias for 'asmatrix'.
> I've thought this for a while.


That makes sense to me.  As far as I know, asmatrix() defaults to calling
the constructor if it can't snarf the memory of the object being passed in.

So, go on, shoot Ed and me down! :-)

--Bill

[Numpy-discussion] Numeric and Python 2.5b1

[Konrad H. <kon...@la...>]

Those who try out Python 2.5b1 and add Numeric might be annoyed by =20
the warning message that Python issues when Numeric is imported the =20
first time. This is due to the fact that Numeric lives inside a =20
directory called "Numeric" without being a package - Numeric has been =20=

around for longer than packages in Python.

You can get rid of this warning by adding the following lines to =20
sitecustomize.py:

import warnings
try:
     warnings.filterwarnings("ignore", category=3DImportWarning)
except NameError:
     pass
del warnings

The try statement ensures that the code will work for older Python =20
releases as well.

Konrad.
--
---------------------------------------------------------------------
Konrad Hinsen
Centre de Biophysique Mol=E9culaire, CNRS Orl=E9ans
Synchrotron Soleil - Division Exp=E9riences
Saint Aubin - BP 48
91192 Gif sur Yvette Cedex, France
Tel. +33-1 69 35 97 15
E-Mail: hinsen =E4t cnrs-orleans.fr
---------------------------------------------------------------------

Re: [Numpy-discussion] Selecting columns of a matrix

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

'''
The following mail is a bit long and tedious to read, sorry about
that. Here is the abstract:
   "I would like boolean indexing to work like slices and not like
arrays of indices"
'''


hi,

I'm _really_ sorry to insist, but I have been thinking on it and I
don't feel like replacing <boolean> with nonzero(<boolean>) is what we
want.

For me this is a bad trick equivalent to replacing slices to arrays of
indices with r_[<slice>]:
- it works only if you do that for a single axis.

Let me explain:
if i have an array,

>>> from numpy import *
>>> a = arange(12).reshape(3,4)

i can slice it:

>>> a[1:3,0:3]
array([[ 4,  5,  6],
       [ 8,  9, 10]])

i can define boolean arrays 'equivalent' to this slices

>>> b1 = array([False,True,True])             # equivalent to 1:3
>>> b2 = array([True,True,True,False])      # equivalent to 0:3

now if i use one of this boolean arrays for indexing, all work like with slices:

>>> a[b1,:]                     #same as a[1:3,:]
array([[ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> a[:,b2]                     # same as a[:,0:3]
array([[ 0,  1,  2],
       [ 4,  5,  6],
       [ 8,  9, 10]])

but if I use both at the same time:

>>> a[b1,b2]                 # not equivalent to a[1:3,0:3] but to
a[r_[1:3],r_[0:3]]
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
ValueError: shape mismatch: objects cannot be broadcast to a single shape

it doesn't work because nonzero(b1) and nonzero(b2) have different shapes.
if I want the equivalent to a[1:3,1:3], i can do

>>> a[ix_(b1,b2)]
array([[ 4,  5,  6],
       [ 8,  9, 10]])

I can not see when the current behaviour of a[b1,b2] would be used.
>From my (probably naive) point of view, <boolean> should not be
converted to nonzero(<boolean>), but to some kind of slicing object.
In that way boolean indexing could work like slices and not like
arrays of integers, which will be more intuitive for me.

Converting slices to arrays of indices is a trick that only works for one axis:

>>> a[r_[1:3],0:3]           #same as a[1:3,0:3]
array([[ 4,  5,  6],
       [ 8,  9, 10]])
>>> a[1:3,r_[0:3]]            #same as a[1:3,0:3]
array([[ 4,  5,  6],
       [ 8,  9, 10]])
>>> a[r_[1:3],r_[0:3]]       # NOT same as a[1:3,0:3]
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
ValueError: shape mismatch: objects cannot be broadcast to a single shape


am I completly wrong??
may be the current behaviour (only usefull for one axis) is enought??

sorry for asking things and not giving solutions and thanks for everything.

pau


PD: I noticed that the following code works
>>> a[a>4,:,:,:,:,1:2:3,...,4:5:6]
array([ 5,  6,  7,  8,  9, 10, 11])

Re: [Numpy-discussion] Numexpr does broadcasting.

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

Hi Tim

On Wed, Jun 21, 2006 at 12:02:27PM -0700, Tim Hochberg wrote:
>=20
> Numexpr can now handle broadcasting. As an example, check out this=20
> implementation of the distance-in-a-bunch-of-dimenstions function that'=
s=20
> been going around. This is 80% faster than the most recent one posted o=
n=20
> my box and considerably easier to read.

This looks really cool.  However, it does seem to break scalar
operation:

a =3D 3.
b =3D 4.
expr =3D numexpr("2*a+3*b",[('a',float),('b'.float)])
expr.run(a,b)

Out[41]: array(-7.1680117685147315e-39)

I havn't used numexpr before, so I could be doing something silly
(although I did verify that the above works on r1986).

Cheers
St=E9fan

Re: [Numpy-discussion] Matrix construction

[Ed S. <sch...@ft...>]

On 22/06/2006, at 12:40 AM, Bill Baxter wrote:

> Actually I think using mat() (just an alias for the matrix  
> constructor) is a bad way to do it.  That mat() (and most others on  
> that page) should probably be replaced with asmatrix() to avoid the  
> copy.

Perhaps the 'mat' function should become an alias for 'asmatrix'.   
I've thought this for a while.  Then code and documentation like this  
page could remain short and simple without incurring the performance  
penalty.

Go on, shoot me down! :)

-- Ed

Re: [Numpy-discussion] Selecting columns of a matrix

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

Simon Burton wrote:
> On Wed, 21 Jun 2006 10:50:26 -0600
> Travis Oliphant <oli...@ie...> wrote:
>
>   
>> So, in SVN NumPy, you will be able to do
>>
>> a[:,V>0]
>> a[V>0,:] 
>>
>> The V>0 will be replaced with integer arrays as if nonzero(V>0) had been 
>> called.
>>     
>
> OK.
> But just for the record, we should note how to
> do the operation that this used to do, eg.
>
>   
>>>> a=numpy.array([1,2])
>>>> a[[numpy.bool_(1)]]
>>>>         
> array([2]
>   
This behavior hasn't changed...

All that's changed is that what used to raise an error (boolean arrays 
in a tuple) now works in the same way that boolean arrays worked before.
>
> So, with the new implementation, is it possible to cast
> the bool array to an integer type without incurring a copy overhead ?
>   

I'm not sure what you mean.  What copy overhead?   There is still 
copying going on.  The way it's been implemented, the boolean arrays get 
replaced with integer index arrays under the hood so it is really nearly 
identical to replacing the boolean array with nonzero(<boolean>).
> And finally, is someone keeping track of the performance
> of array getitem ? It seems that as travis overloads it more and
> more it might then slow down in some cases.
>   
Actually, I'm very concientious of the overhead of getitem in code that 
I add.  I just today found a memory leak in code that was added that I 
did not review carefully that was also slowing down all accesses of 
arrays > 1d that resulted in array scalars.  I added an optimization 
that should speed that up.

But, it would be great if others could watch the speed changes for basic 
operations.
> I must admit my vision is blurring and head is spining as numpy 
> goes through these growing pains
The 1.0 beta release is coming shortly.   I would like to see the first 
beta by the first of July.   The final 1.0 release won't occur, though, 
until after SciPy 2006.

Thanks for your patience.   We've been doing a lot of house-cleaning 
lately to separate the "old but compatible" interface from the "new."   
This has resulted in some confusion, to be sure.   Please don't hesitate 
to voice your concerns.

-Travis

[Numpy-discussion] f.p. powers and masked arrays

[Michael F. <fi...@as...>]

Hello all,

I'm encountering some (relatively new?) behavior with masked arrays that 
strikes me as bizarre.  Raising zero to a floating-point value is triggering 
a mask to be set, even though the result should be well-defined.  When using 
fixed-point integers for powers, everything works as expected.  I'm seeing 
this with both numarray and numpy.  Take the case of 0**1, illustrated below:

>>> import numarray as n1
>>> import numarray.ma as n1ma
>>> n1.array(0.)**1
array(0.0)
>>> n1.array(0.)**1.
array(0.0)
>>> n1ma.array(0.)**1
array(0.0)
>>> n1ma.array(0.)**1.
array(data =
 [1.0000000200408773e+20],
      mask =
 1,
      fill_value=[  1.00000002e+20])

>>> import numpy as n2
>>> import numpy.core.ma as n2ma
>>> n2.array(0.)**1
array(0.0)
>>> n2.array(0.)**1.
array(0.0)
>>> n2ma.array(0.)**1
array(0.0)
>>> n2ma.array(0.)**1.
array(data =
 1e+20,
      mask =
 True,
      fill_value=1e+20)

I've been using python v2.3.5 & v.2.4.3, numarray v1.5.1, and numpy v0.9.8, 
and tested this on an x86 Debian box and a PPC OSX box.  It may be the case 
that this issue has manifested in the past several months, as it's causing a 
new problem with some of my older code.  Any thoughts?

Thanks in advance,
Mike

Re: [Numpy-discussion] Selecting columns of a matrix

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

On 6/22/06, Alan G Isaac <ai...@am...> wrote:
>
> > Alan G Isaac wrote:
> >> M.transpose()[V>0]
> >> If you want the columns as columns,
> >> you can transpose again.
>
>
> On Wed, 21 Jun 2006, Keith Goodman apparently wrote:
> > I can't get that to work when M is a n by m matrix:
>
> The problem is not M being a matrix.
> You made V a matrix (i.e., 2d).
> So you need to ravel() it first.
> >> M.transpose()[V.ravel()>0]


No dice, V.ravel() returns a matrix still.  Looks like you'll need
M.T[V.A.ravel()>0].T

Just lovely.  Is the new bool conversion thingy going to help make the
syntax more reasonable for matrices, too?  Seems like it will still require
M[:,V.A.ravel() > 0]  or M[:, V.A.squeeze() > 0] or M[:,V.A[:,0]>0]

Anyway, this seems to me just more evidence that one is better off getting
used to the 'array' way of doing things rather than clinging to Matlab ways
by using 'matrix'.  Is it worth dealing with the extra A's and asmatrix()'s
and squeeze()'s that seem to crop up just to be able to write A*B instead of
dot(A,B)  (*)?

--Bill

(*) Ok, there's also the bit about being able to tell column vectors from
row vectors and getting useful errors when you try to use a row that should
have been a column.  And then there's also the .T, .I, .H convenience
factor.

Re: [Numpy-discussion] Selecting columns of a matrix

[Simon B. <si...@ar...>]

On Wed, 21 Jun 2006 10:50:26 -0600
Travis Oliphant <oli...@ie...> wrote:

> 
> So, in SVN NumPy, you will be able to do
> 
> a[:,V>0]
> a[V>0,:] 
> 
> The V>0 will be replaced with integer arrays as if nonzero(V>0) had been 
> called.

OK.
But just for the record, we should note how to
do the operation that this used to do, eg.

>>> a=numpy.array([1,2])
>>> a[[numpy.bool_(1)]]
array([2])
>>> 

This could be a way of, say, maping a large
boolean array onto some other values (1 or 2 in the
above case).

So, with the new implementation, is it possible to cast
the bool array to an integer type without incurring a copy overhead ?

And finally, is someone keeping track of the performance
of array getitem ? It seems that as travis overloads it more and
more it might then slow down in some cases.

I must admit my vision is blurring and head is spining as numpy 
goes through these growing pains. I hope it's over soon. Not
because I have trouble keeping up (although i do) but it's
my matlab/R/numarray entrenched co-workers who cannot
be exposed to this unstable development (they will run
screaming to the woods).

cheers,

Simon.

-- 
Simon Burton, B.Sc.
Licensed PO Box 8066
ANU Canberra 2601
Australia
Ph. 61 02 6249 6940
http://arrowtheory.com 

Re: [Numpy-discussion] MA bug or feature?

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

I was setting the fill_value as 'NA' when constructing the array so
the masked values would be printed as 'NA'. It is not a big deal to
avoid doing this.

Nevertheless, the differences between a masked array with a boolean
mask and a mask of booleans have caused me trouble before. Especially
when there are hidden in-place conversions of a mask which is a array
of False to a mask which is False. e.g.

import numpy
print numpy.version.version

ma1 = numpy.ma.array(((1.,2,3),(4,5,6)), mask=((0,0,0),(0,0,0)))
print ma1.mask
a1 = numpy.asarray(ma1)
print ma1.mask

----------------------
0.9.9.2538
[[False False False]
 [False False False]]
False

On 6/21/06, Pierre GM <pgm...@ma...> wrote:
> On Wednesday 21 June 2006 04:46, Michael Sorich wrote:
> > When transposing a masked array of dtype '<f8' I noticed that an
> > ndarray of dtype '|O4' was returned.
>
>
> OK, I see where the problem is:
> When your fill_value has a type that cannot be converted to the type of your
> data, the `filled` method (used internally in many functions, such as
> `transpose`) raises a TypeError, which is caught and your array is converted
> to 'O'.
>
> That's what happen here: your fill_value is a string, your data are integer,
> the types don't match, hence the conversion. So, no, I don't think that's a
> bug.
>
> Why filling when you don't have any masked values, then ? Well, there's a
> subtle difference between a boolean mask and a mask of booleans.
> When the mask is boolean (mask=nomask=False), there's no masked value, and
> `filled` returns the data.
> Now, when your mask is an array of boolean (your first case), MA doesn't check
> whether mask.any()==False to determine whether there are some missing data or
> not, it just processes the whole array of boolean.
>
> I agree that's a bit confusing here, and there might be some room for
> improvement (for example, changing the current
> `if m is nomask` to `if m is nomask or m.any()==False`, or better, forcing
> mask to nomask if mask.any()==False). But I don;t think that qualifies as
> bug.
>
> In short:
> when you have an array of numbers, don't try to fill it with characters.
>

[Numpy-discussion] flatiter and inequality comparison

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

I do not understand how to think about this:

    >>> x=arange(3).flat
    >>> x
    <numpy.flatiter object at 0x01BD0C58>
    >>> x>2
    True
    >>> x>10
    True

Why?  (I realize this behaves like xrange,
so this may not be a numpy question,
but I do not understand that behavior either.)

What I expected:
that a flatiter object would iterate through
its values and return either
- a flatiter of the resulting comparisons, or
- an array of the resulting comparisons

Thank you,
Alan Isaac

Re: [Numpy-discussion] Selecting columns of a matrix

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

> Alan G Isaac wrote: 
>> M.transpose()[V>0] 
>> If you want the columns as columns, 
>> you can transpose again. 


On Wed, 21 Jun 2006, Keith Goodman apparently wrote: 
> I can't get that to work when M is a n by m matrix: 

The problem is not M being a matrix.
You made V a matrix (i.e., 2d).
So you need to ravel() it first.
>> M.transpose()[V.ravel()>0] 

hth,
Alan Isaac

Re: [Numpy-discussion] Element-by-element matrix multiplication

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

Actually I think using mat() (just an alias for the matrix constructor) is a
bad way to do it.  That mat() (and most others on that page) should probably
be replaced with asmatrix() to avoid the copy.

--bb

On 6/22/06, Keith Goodman <kwg...@gm...> wrote:
>
> On 6/21/06, Robert Kern <rob...@gm...> wrote:
>
> > Keith Goodman wrote:
> > > The NumPy for Matlab Users page suggests mat(a.A * b.A) for
> > > element-by-element matrix multiplication. I think it would be helpful
> > > to also include multiply(a, b).
> > >
> > > a.*b
> > >
> > > mat(a.A * b.A) or
> > > multiply(a, b)
> >
> > It is a wiki page. You may edit it yourself without needing to ask
> permission.
>
> OK. Done. I also added a notice about SciPy's PayPal account being
> suspended.
>
> All the advantages of Linux Managed Hosting--Without the Cost and Risk!
> Fully trained technicians. The highest number of Red Hat certifications in
> the hosting industry. Fanatical Support. Click to learn more
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=107521&bid=248729&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>



-- 
William V. Baxter III
OLM Digital
Kono Dens Building Rm 302
1-8-8 Wakabayashi Setagaya-ku
Tokyo, Japan  154-0023
+81 (3) 3422-3380

[Numpy-discussion] oucyc good

[Emelia L. <eme...@ho...>]

Hi
=20
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Save over 50% on your medications with our online STORE
<http://fonakilastoe.com>=20
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  _____ =20

the river-bank bent and sighed. I dont know what river it was, a
rushing red one, swollen with the rains of the last few days, that came
down from the hills and mountains in front of them. Soon it was nearly
dark. The winds broke up the grey clouds, and a waning moon appeared
above the hills between the flying rags. Then they stopped, and Thorin
muttered something about supper, and where shall we get a dry patch to

[Numpy-discussion] Numexpr does broadcasting.

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

Numexpr can now handle broadcasting. As an example, check out this 
implementation of the distance-in-a-bunch-of-dimenstions function that's 
been going around. This is 80% faster than the most recent one posted on 
my box and considerably easier to read.

    expr = numexpr("(a - b)**2", [('a', float), ('b', float)])
    def dist_numexpr(A, B):
        return sqrt(sum(expr(A[:,newaxis], B[newaxis,:]), axis=2))

Now, if we just could do 'sum' inside the numexpr, I bet that this would 
really scream.  This is something that David has talked about adding at 
various points. I just made his life a bit harder by supporting 
broadcasting, but I still don't think it would be all that hard to add 
reduction operations like sum and product as long as they were done at 
the outermost level of the expression. That is, "sum(x*2 + 5)" should be 
doable, but "5 + sum(x**2)" would likely be difficult.

Anyway, I thought that was cool, so I figured I'd share ;-)

[Bizzarely, numexpr seems to run faster on my box when compiled with 
"-O1" than when compiled with "-O2" or "-O2 -funroll-all-loops". Go figure.]

-tim

Re: [Numpy-discussion] memory leak in array

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

saa...@sf... wrote:
> Hi Travis
>
> Not sure if you've had a chance to look at the previous code I sent or not,
> but I was able to reduce the code (see below) to its smallest size and still
> have the problem, albeit at a slower rate. The problem appears to come from
> changing values in the array. Does this create another reference to the
> array,	which can't be released? If this problem does not have a work-around
> or "fix", please let me know.
>   
This is now fixed in SVN.

-Travis

Re: [Numpy-discussion] Element-by-element matrix multiplication

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

On 6/21/06, Robert Kern <rob...@gm...> wrote:

> Keith Goodman wrote:
> > The NumPy for Matlab Users page suggests mat(a.A * b.A) for
> > element-by-element matrix multiplication. I think it would be helpful
> > to also include multiply(a, b).
> >
> > a.*b
> >
> > mat(a.A * b.A) or
> > multiply(a, b)
>
> It is a wiki page. You may edit it yourself without needing to ask permission.

OK. Done. I also added a notice about SciPy's PayPal account being suspended.

Re: [Numpy-discussion] Selecting columns of a matrix

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

On 6/21/06, Travis Oliphant <oli...@ie...> wrote:
> Pau Gargallo wrote:
> > On 6/21/06, Travis Oliphant <oli...@ie...> wrote:
> >
> >> Johannes Loehnert wrote:
> >>
> >>> Hi,
> >>>
> >>>
> >>>
> >>>> I'm not sure why bool arrays cannot be used as indices.
> >>>> The "natural" solution to the original problem seemed to be:
> >>>> M[:,V>0]
> >>>> but this is not allowed.
> >>>>
> >>>>
> >>> I started a thread on this earlier this year. Try searching the archive for
> >>> "boolean indexing" (if it comes back online somewhen).
> >>>
> >>> Travis had some reason for not implementing this, but unfortunately I do not
> >>> remember what it was. The corresponding message might still linger on my home
> >>>
> >>> PC, which I can access this evening....
> >>>
> >>>
> >> I suspect my reason was just not being sure if it could be explained
> >> consistently.  But, after seeing this come up again.   I decided it was
> >> easy enough to implement.
> >>
> >> So, in SVN NumPy, you will be able to do
> >>
> >> a[:,V>0]
> >> a[V>0,:]
> >>
> >> The V>0 will be replaced with integer arrays as if nonzero(V>0) had been
> >> called.
> >>
> >>
> >
> > does it work for a[<boolean>,<boolean>] ?
> >
> Sure, it will work.  Basically all boolean arrays will be interpreted as
> nonzero(V>0), everywhere.
> > what about a[ix_( nonzero(<boolean>), nonzero(<boolean>) )] ?
> >
> > maybe the <boolean> to nonzero(<boolean>) conversion would be more
> > coherently done by the ix_ function than by the []
> >
> >
> I've just added support for <boolean> inside ix_  so that the nonzero
> will be done automatically as well.
>
> So
>
> a[ix_(<boolean>,<boolean>)]  will give the cross-product selection.
>


ok so: a[ b1, b2 ] will be different than a[ ix_(b1,b2) ] just like
with integer indices.
Make sense to me.

also, a[b] will be as before (a[where(b)]) ?
maybe a trailing coma could lunch the new behaviour?
a[b] -> a[where(b)]
a[b,] -> a[b,...] -> a[nonzero(b)]

Thanks,

pau

[Numpy-discussion] Problem installing numpy on Gentoo

[Webb S. <web...@gm...>]

I am trying to install numpy on Gentoo (see my info below for version
etc).  It all seems to go fine, but when I try to import it and run
the tests, I get the following error (in ipython):

In [1]: import numpy
import linalg -> failed: libg2c.so.0: cannot open shared object file:
No such file or directory

I have gfortran on my system, but libg2c is not part of the gcc-4.1.1
distribution anymore (maybe that is a bug with Gentoo?).  I also get
the same error when I run f2py from the command line.

Here is the bug I filed: http://bugs.gentoo.org/show_bug.cgi?id=136988

Info that might help:

cowboy ~ # ls /usr/lib/gcc/i686-pc-linux-gnu/4.1.1/
crtbegin.o     libgcc.a        libgfortran.so.1      libobjc.so.1.0.0
crtbeginS.o    libgcc_eh.a     libgfortran.so.1.0.0  libstdc++.a
crtbeginT.o    libgcc_s.so     libgfortranbegin.a    libstdc++.so
crtend.o       libgcc_s.so.1   libgfortranbegin.la   libstdc++.so.6
crtendS.o      libgcov.a       libobjc.a             libstdc++.so.6.0.8
crtfastmath.o  libgfortran.a   libobjc.la            libstdc++_pic.a
include        libgfortran.la  libobjc.so            libsupc++.a
install-tools  libgfortran.so  libobjc.so.1          libsupc++.la

cowboy ~ # ls /usr/lib/gcc/i686-pc-linux-gnu/3.4.6/
SYSCALLS.c.X            libffi.la        libobjc.la
crtbegin.o              libffi.so        libobjc.so
crtbeginS.o             libfrtbegin.a    libobjc.so.1
crtbeginT.o             libg2c.a         libobjc.so.1.0.0
crtend.o                libg2c.la        libstdc++.a
crtendS.o               libg2c.so        libstdc++.la
hardened.specs          libg2c.so.0      libstdc++.so
hardenednopie.specs     libg2c.so.0.0.0  libstdc++.so.6
hardenednopiessp.specs  libgcc.a         libstdc++.so.6.0.3
hardenednossp.specs     libgcc_eh.a      libstdc++_pic.a
include                 libgcc_s.so      libsupc++.a
install-tools           libgcc_s.so.1    libsupc++.la
libffi-2.00-beta.so     libgcov.a        specs
libffi.a                libobjc.a        vanilla.specs
cowboy ~ # emerge --info
Portage 2.1.1_pre1-r1 (default-linux/x86/2006.0, gcc-4.1.1/vanilla,
glibc-2.4-r3, 2.6.11-gentoo-r9 i686)
=================================================================
System uname: 2.6.11-gentoo-r9 i686 AMD Athlon(tm) Processor
Gentoo Base System version 1.12.1
distcc 2.18.3 i686-pc-linux-gnu (protocols 1 and 2) (default port
3632) [disabled]
ccache version 2.4 [enabled]
dev-lang/python:     2.4.3-r1
dev-python/pycrypto: 2.0.1-r5
dev-util/ccache:     2.4-r2
dev-util/confcache:  [Not Present]
sys-apps/sandbox:    1.2.18.1
sys-devel/autoconf:  2.13, 2.59-r7
sys-devel/automake:  1.4_p6, 1.5, 1.6.3, 1.7.9-r1, 1.8.5-r3, 1.9.6-r2
sys-devel/binutils:  2.16.1-r2
sys-devel/gcc-config: 2.0.0_rc1
sys-devel/libtool:   1.5.22
virtual/os-headers:  2.6.11-r5
ACCEPT_KEYWORDS="x86 ~x86"
AUTOCLEAN="yes"
CBUILD="i686-pc-linux-gnu"
CFLAGS=" -march=athlon -O2 -pipe -fomit-frame-pointer"
CHOST="i686-pc-linux-gnu"
CONFIG_PROTECT="/etc /usr/share/X11/xkb"
CONFIG_PROTECT_MASK="/etc/env.d /etc/eselect/compiler /etc/gconf
/etc/revdep-rebuild /etc/terminfo /etc/texmf/web2c"
CXXFLAGS=" -march=athlon -O2 -pipe -fomit-frame-pointer"
DISTDIR="/usr/portage/distfiles"
FEATURES="autoconfig ccache distlocks metadata-transfer sandbox sfperms"
GENTOO_MIRRORS="http://distfiles.gentoo.org
http://distro.ibiblio.org/pub/linux/distributions/gentoo"
PKGDIR="/usr/portage/packages"
PORTAGE_RSYNC_OPTS="--recursive --links --safe-links --perms --times
--compress --force --whole-file --delete --delete-after --stats
--timeout=180 --exclude='/distfiles' --exclude='/local'
--exclude='/packages'"
PORTAGE_TMPDIR="/var/tmp"
PORTDIR="/usr/portage"
PORTDIR_OVERLAY="/usr/local/portage"
SYNC="rsync://rsync.gentoo.org/gentoo-portage"
USE="x86 X alsa apache2 apm arts avi berkdb bitmap-fonts blas cli
crypt cups dba dri eds emacs emboss encode esd f77 fftw foomaticdb
fortran g77 gdbm gif gnome gpm gstreamer gtk gtk2 imlib ipv6 isdnlog
jpeg lapack libg++ libwww mad mikmod mime mmap motif mp3 mpeg ncurses
nls nptl nptlonly objc ogg opengl oss pam pcre pdflib perl png
postgres pppd python quicktime readline reflection sdl session spell
spl ssl svg tcltk tcpd tidy truetype truetype-fonts type1-fonts udev
unicode vorbis xml xmms xorg xv zlib elibc_glibc kernel_linux
userland_GNU"
Unset:  CTARGET, EMERGE_DEFAULT_OPTS, INSTALL_MASK, LANG, LC_ALL,
LDFLAGS, LINGUAS, MAKEOPTS, PORTAGE_RSYNC_EXTRA_OPTS

cowboy ~ # gcc --version
i686-pc-linux-gnu-gcc (GCC) 4.1.1 (Gentoo 4.1.1)
Copyright (C) 2006 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Re: [Numpy-discussion] Selecting columns of a matrix

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

Pau Gargallo wrote:
> On 6/21/06, Travis Oliphant <oli...@ie...> wrote:
>   
>> Johannes Loehnert wrote:
>>     
>>> Hi,
>>>
>>>
>>>       
>>>> I'm not sure why bool arrays cannot be used as indices.
>>>> The "natural" solution to the original problem seemed to be:
>>>> M[:,V>0]
>>>> but this is not allowed.
>>>>
>>>>         
>>> I started a thread on this earlier this year. Try searching the archive for
>>> "boolean indexing" (if it comes back online somewhen).
>>>
>>> Travis had some reason for not implementing this, but unfortunately I do not
>>> remember what it was. The corresponding message might still linger on my home
>>>
>>> PC, which I can access this evening....
>>>
>>>       
>> I suspect my reason was just not being sure if it could be explained
>> consistently.  But, after seeing this come up again.   I decided it was
>> easy enough to implement.
>>
>> So, in SVN NumPy, you will be able to do
>>
>> a[:,V>0]
>> a[V>0,:]
>>
>> The V>0 will be replaced with integer arrays as if nonzero(V>0) had been
>> called.
>>
>>     
>
> does it work for a[<boolean>,<boolean>] ?
>   
Sure, it will work.  Basically all boolean arrays will be interpreted as 
nonzero(V>0), everywhere.
> what about a[ix_( nonzero(<boolean>), nonzero(<boolean>) )] ?
>
> maybe the <boolean> to nonzero(<boolean>) conversion would be more
> coherently done by the ix_ function than by the []
>
>   
I've just added support for <boolean> inside ix_  so that the nonzero 
will be done automatically as well.  

So

a[ix_(<boolean>,<boolean>)]  will give the cross-product selection.


-Travis

Re: [Numpy-discussion] Element-by-element matrix multiplication

[Robert K. <rob...@gm...>]

Keith Goodman wrote:
> The NumPy for Matlab Users page suggests mat(a.A * b.A) for
> element-by-element matrix multiplication. I think it would be helpful
> to also include multiply(a, b).
> 
> a.*b
> 
> mat(a.A * b.A) or
> multiply(a, b)

It is a wiki page. You may edit it yourself without needing to ask permission.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
 that is made terrible by our own mad attempt to interpret it as though it had
 an underlying truth."
  -- Umberto Eco

[Numpy-discussion] Element-by-element matrix multiplication

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

The NumPy for Matlab Users page suggests mat(a.A * b.A) for
element-by-element matrix multiplication. I think it would be helpful
to also include multiply(a, b).

a.*b

mat(a.A * b.A) or
multiply(a, b)