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

[Numpy-discussion] Small Bug in multiarray.c

[<ba...@st...>]

When attempting to compile the CVS version of Numpy using MSVC 6
under Windows 2000 I found a small error in multiarray.c: the doc
string for arange contains newlines. The offending code begins one
line # 1168. Simple removing the newlines from the string fixes the
error.

John

********************************
* John Barnard, Ph.D.
* Senior Research Statistician
* deCODE genetics
* 1000 Winter Str., Suite 3100
* Waltham, MA 02451
* Phone (Direct)  : (781) 290-5771 Ext. 27
* Phone (General) : (781) 466-8833
* Fax             : (781) 466-8686
* Email: j.b...@de...
********************************

[Numpy-discussion] Total least squares problem

[Nils W. <nw...@me...>]

Hi,

How do I solve a Total Least Squares problem in Numpy ?
A small example would be appreciated.

The TLS problem assumes an overdetermined set of linear equations
AX = B, where both the data matrix A as well as the observation
matrix B are inaccurate:

Nils

Reference:

R.D.Fierro, G.H. Golub, P.C. Hansen, D.P.O'Leary,
Regularization by truncated total least squares,
SIAM J. Sci. Comput. Vol.18(4) 1997 pp. 1223-1241

[Numpy-discussion] Building Numeric with Intel KML and mingw32

[Krishnaswami, N. <ne...@cs...>]

Hello, 

I'm trying to rebuild Numeric with the Intel Kernel Math Libraries. 

I've gotten Numeric building normally with the default BLAS libraries,
but I'm not sure what I need to put into the libraries_dir_list and
libraries_list variables in the setup.py file. 

I have the directories mkl\ia32\bin (contains the DLLs), mkl\ia32\lib 
(contains the lib*.a files), and mkl\include (contains the *.h files). 

Can anyone tells me what goes where? 

--
Neel Krishnaswami
ne...@cs...
 

Re: [Numpy-discussion] RandomArray - random

[Nils W. <nw...@me...>]

"Paul F. Dubois" schrieb:
> 
> Your reference to random is not fully qualified so I suppose you could
> be picking up some other random. But I just tried
> RandomArray.random((2,3)) and it worked fine.
> 
> BTW you could just do 2.0*random((n,m))-1.0.
> 
It seems to be a conflict with Vpython formerly Visualpython. 
http://cil.andrew.cmu.edu/projects/visual/index.html

Python 2.1.1 (#1, Sep 24 2001, 05:28:47)
[GCC 2.95.3 20010315 (SuSE)] on linux2
Type "copyright", "credits" or "license" for more information.
>>> from Numeric import *
>>> from RandomArray import *
>>> random((2,3))
array([[ 0.68769461,  0.33015978,  0.07285815],
       [ 0.20514929,  0.81925279,  0.50694615]])
>>> from visual import *
Visual-2001-09-24
>>> random((2,3))
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
TypeError: random() takes exactly 1 argument (2 given)
>>>

Nils

> -----Original Message-----
> From: num...@li...
> [mailto:num...@li...] On Behalf Of Nils
> Wagner
> Sent: Sunday, November 11, 2001 8:34 AM
> To: num...@li...
> Subject: [Numpy-discussion] RandomArray - random
> 
> Hi,
> 
> I tried to produce a random matrix say Q (2ndof \times nsamp+1) with
> Numpy 20.2 and Python 2.1.1 (#1, Sep 24 2001, 05:28:47) [GCC 2.95.3
> 20010315 (SuSE)] on linux2 Type "copyright", "credits" or "license" for
> more information.
> 
> Traceback (most recent call last):
>   File "modal.py", line 192, in ?
>     Q = 2.0*random((2*ndof,nsamp+1))-ones((2*ndof,nsamp+1))
> TypeError: random() takes exactly 1 argument (2 given)
> 
> Does it require a new syntax to obtain a matrix consisting of uniformly
> distributed random numbers in the range +/- 1 ?
> 
> Nils
> 
> _______________________________________________
> Numpy-discussion mailing list Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
> 
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion

RE: [Numpy-discussion] RandomArray - random

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

Your reference to random is not fully qualified so I suppose you could
be picking up some other random. But I just tried
RandomArray.random((2,3)) and it worked fine.

BTW you could just do 2.0*random((n,m))-1.0.


-----Original Message-----
From: num...@li...
[mailto:num...@li...] On Behalf Of Nils
Wagner
Sent: Sunday, November 11, 2001 8:34 AM
To: num...@li...
Subject: [Numpy-discussion] RandomArray - random


Hi,

I tried to produce a random matrix say Q (2ndof \times nsamp+1) with
Numpy 20.2 and Python 2.1.1 (#1, Sep 24 2001, 05:28:47) [GCC 2.95.3
20010315 (SuSE)] on linux2 Type "copyright", "credits" or "license" for
more information.

Traceback (most recent call last):
  File "modal.py", line 192, in ?
    Q = 2.0*random((2*ndof,nsamp+1))-ones((2*ndof,nsamp+1))
TypeError: random() takes exactly 1 argument (2 given)

Does it require a new syntax to obtain a matrix consisting of uniformly
distributed random numbers in the range +/- 1 ?

Nils

_______________________________________________
Numpy-discussion mailing list Num...@li...
https://lists.sourceforge.net/lists/listinfo/numpy-discussion

[Numpy-discussion] RandomArray - random

[Nils W. <nw...@me...>]

Hi,

I tried to produce a random matrix say Q (2ndof \times nsamp+1) with
Numpy 20.2
and
Python 2.1.1 (#1, Sep 24 2001, 05:28:47)
[GCC 2.95.3 20010315 (SuSE)] on linux2
Type "copyright", "credits" or "license" for more information.

Traceback (most recent call last):
  File "modal.py", line 192, in ?
    Q = 2.0*random((2*ndof,nsamp+1))-ones((2*ndof,nsamp+1))
TypeError: random() takes exactly 1 argument (2 given)

Does it require a new syntax to obtain a matrix consisting of uniformly
distributed random numbers in the range +/- 1 ?

Nils

Re: [Numpy-discussion] Sparse matrices

[Jochen <jo...@jo...>]

On Tue, 6 Nov 2001 15:01:18 -1000 Herbert L Roitblat wrote:

Herbert> Travis Oliphant has one.

Isn't that the one in SciPy?

Herbert> ----- Original Message -----
Herbert> From: "R.M.Everson" <R.M...@ex...>
Herbert> To: <num...@li...>
Herbert> Sent: Tuesday, November 06, 2001 11:03 AM
Herbert> Subject: [Numpy-discussion] Sparse matrices

>> Does anyone have a working sparse matrix module for Numeric 20.2.0
>> and Python 2.1 (or similar).  I'm tryinng to get the version in the
>> SciPy CVS tree to work - so far without success.

Herbert,

this inverse citing really is counterproductive on mls.

Greetings,
Jochen
-- 
Einigkeit und Recht und Freiheit                http://www.Jochen-Kuepper.de
    Liberté, Égalité, Fraternité                GnuPG key: 44BCCD8E
        Sex, drugs and rock-n-roll

Re: [Numpy-discussion] Sparse matrices

[Herbert L. R. <roi...@ha...>]

Travis Oliphant has one.
H.
----- Original Message -----
From: "R.M.Everson" <R.M...@ex...>
To: <num...@li...>
Sent: Tuesday, November 06, 2001 11:03 AM
Subject: [Numpy-discussion] Sparse matrices


>
> Hello,
>
> Does anyone have a working sparse matrix module for Numeric 20.2.0 and
> Python 2.1 (or similar).  I'm tryinng to get the version in the SciPy
> CVS tree to work - so far without success.
>
> I don't want anything particularly fancy -- not even sparse matrix
> inversion.  Addition and multiplication would be fine.
>
> Thanks for any ideas/pointers/software etc!
>
> Cheers,
>
> Richard.
>
> --
> Department of Computer Science, Exeter University    Voice: +44 1392
264065
> R.M...@ex...                         Secretary: +44 1392
264061
> http://www.dcs.ex.ac.uk/people/reverson                Fax: +44 1392
264067
>
>
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

[Numpy-discussion] MA - math operations do not preserve fill_value

[Joe V. A. <van...@at...>]

Using Python 2.1 and Numeric 20.2.1 on Redhat Linux 7.1

Shouldn't masked arrays preserve the fill value of their operands, if
both operands have the same fill value?  Otherwise, if I want to
preserve the value of the fill_value, I have to write expressions like:


d=masked_values((a+b),a.fill_value()) 

Here's a demonstration of the problem:

>>> a = masked_values((1.0,2.0,3.0,4.0,-999.0), -999)
>>> b = masked_values((-999.0,1.0,2.0,3.0,4.0), -999)

>>> a
array(data =
 [   1.,   2.,   3.,   4.,-999.,],
      mask =
 [0,0,0,0,1,],
      fill_value=-999)
 
>>> b
array(data =
 [-999.,   1.,   2.,   3.,   4.,],
      mask =
 [1,0,0,0,0,],
      fill_value=-999)

>>> c=a+b
>>> c
array(data =
 [  1.00000002e+20,  3.00000000e+00,  5.00000000e+00,  7.00000000e+00,
        1.00000002e+20,],
      mask =
 [1,0,0,0,1,],
      fill_value=[  1.00000002e+20,])

>>> d=masked_values((a+b),a.fill_value())
>>> d
array(data =
 [-999.,   3.,   5.,   7.,-999.,],
      mask =
 [1,0,0,0,1,],
      fill_value=-999)
-- 
Joe VanAndel  	          
National Center for Atmospheric Research
http://www.atd.ucar.edu/~vanandel/
Internet: van...@uc...

[Numpy-discussion] Sparse matrices

[<R.M...@ex...>]

Hello,

Does anyone have a working sparse matrix module for Numeric 20.2.0 and
Python 2.1 (or similar).  I'm tryinng to get the version in the SciPy
CVS tree to work - so far without success.

I don't want anything particularly fancy -- not even sparse matrix
inversion.  Addition and multiplication would be fine.

Thanks for any ideas/pointers/software etc!

Cheers,

Richard.

-- 
Department of Computer Science, Exeter University    Voice: +44 1392 264065
R.M...@ex...                         Secretary: +44 1392 264061
http://www.dcs.ex.ac.uk/people/reverson                Fax: +44 1392 264067

Re: [Numpy-discussion] Assembly optimized numerical packages?

[Jon S. <js...@wm...>]

On Fri, 2 Nov 2001, Hung Jung Lu wrote:

> Can someone give me pointers where to find
> freeware/shareware/commercial packages for linear
> algebra and probability calculations (e.g: Cholesky
> decomposition, eigenvalue & eigenvectors in
> diagonalization, interpolation, normal distribution,
> beta distribution, inverse cumulative normal function,
> etc.), and such that it uses assembly level
> optimization (I need highspeed, but on mundane Pentium
> 3 or Pentium 4 machines) and can be used in Windows
> platform and from Microsoft's Visual C++?
For statistical distribution functions, you can check DCDFLIB.C:
http://odin.mdacc.tmc.edu/anonftp/page_2.html

It is C, not assembler.

Jon Saenz.				| Tfno: +34 946012445
Depto. Fisica Aplicada II               | Fax:  +34 944648500
Facultad de Ciencias.   \\ Universidad del Pais Vasco \\
Apdo. 644   \\ 48080 - Bilbao  \\ SPAIN

Re: [Numpy-discussion] Assembly optimized numerical packages?

[Chris B. <chr...@ho...>]

Hung Jung Lu wrote:

> Can someone give me pointers where to find
> freeware/shareware/commercial packages for linear
> algebra and probability calculations (e.g: Cholesky
> decomposition, eigenvalue & eigenvectors in
> diagonalization,

This sounds likeyou are looking for is LAPACK with a good BLAS. Do a web
search, and you'll find lot's of pointers.

interpolation, normal distribution,
> beta distribution, inverse cumulative normal function,
> etc.)

I'm lost here. Perhaps someone else will have some pointers.

-Chris



-- 
Christopher Barker,
Ph.D.                                                           
Chr...@ho...                 ---           ---           ---
http://members.home.net/barkerlohmann ---@@       -----@@       -----@@
                                   ------@@@     ------@@@     ------@@@
Oil Spill Modeling                ------   @    ------   @   ------   @
Water Resources Engineering       -------      ---------     --------    
Coastal and Fluvial Hydrodynamics --------------------------------------
------------------------------------------------------------------------

[Numpy-discussion] Assembly optimized numerical packages?

[Hung J. Lu <hun...@ya...>]

Hi,

This is a tangential topic.

Can someone give me pointers where to find
freeware/shareware/commercial packages for linear
algebra and probability calculations (e.g: Cholesky
decomposition, eigenvalue & eigenvectors in
diagonalization, interpolation, normal distribution,
beta distribution, inverse cumulative normal function,
etc.), and such that it uses assembly level
optimization (I need highspeed, but on mundane Pentium
3 or Pentium 4 machines) and can be used in Windows
platform and from Microsoft's Visual C++?

I know mtxvec from www.dewresearch.com does something
along these lines, but it seems like they are aiming
for specific dev platforms (CBuilder and Delphi).

thanks!

Hung Jung


__________________________________________________
Do You Yahoo!?
Find a job, post your resume.
http://careers.yahoo.com

Re: [Numpy-discussion] RE: Numeric2

[John J. L. <jj...@po...>]

On Tue, 30 Oct 2001, Perry Greenfield wrote:
[...]
> > What is the current status of Numeric2?
> >
> We are in the process of putting it up on sourceforge now.
[...]

What does it do??


John

Re: [Numpy-discussion] Undefined symbol in lapack_lite.so

[Gerard V. <gve...@la...>]

Do you have lapack-3.0? Numeric-20.2.0 (better get 20.2.1) is
interfaced to new faster lapack routines.

You could check it by
[packer@taco lib]$ strings liblapack.so.3 | grep dgesdd
dgesdd_
[packer@taco lib]$

best regards -- Gerard

On Wednesday 31 October 2001 16:50, Edward C. Jones wrote:
> The following Python program:
>
> import Numeric, LinearAlgebra
>
> a = Numeric.identity(5)
> v, s, wt = LinearAlgebra.singular_value_decomposition(a)
>
> gave the following error:
>
> Traceback (most recent call last):
>   File "./silly.py", line 3, in ?
>     import Numeric, LinearAlgebra
>   File "/usr/lib/python2.2/site-packages/Numeric/LinearAlgebra.py", line
> 8, in ?    import lapack_lite
> ImportError: /usr/lib/python2.2/site-packages/Numeric/lapack_lite.so:
> undefined symbol: dgesdd_
>
> When I installed Numeric-20.2.0, I changed Setup.py to:
>
> # delete all but the first one in this list if using your own LAPACK/BLAS
> sourcelist = ['Src/lapack_litemodule.c',]
> # set these to use your own BLAS
> library_dirs_list = ['usr/lib']
> libraries_list = ['/usr/lib/libblas.so', '/usr/lib/liblapack.so']
>
> What is the problem?
>
> Thanks,
> Ed Jones
>
>
>
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion

[Numpy-discussion] Undefined symbol in lapack_lite.so

[Edward C. J. <edc...@er...>]

The following Python program:

import Numeric, LinearAlgebra

a = Numeric.identity(5)
v, s, wt = LinearAlgebra.singular_value_decomposition(a)

gave the following error:

Traceback (most recent call last):
  File "./silly.py", line 3, in ?
    import Numeric, LinearAlgebra
  File "/usr/lib/python2.2/site-packages/Numeric/LinearAlgebra.py", line 
8, in ?    import lapack_lite
ImportError: /usr/lib/python2.2/site-packages/Numeric/lapack_lite.so: 
undefined symbol: dgesdd_

When I installed Numeric-20.2.0, I changed Setup.py to:

# delete all but the first one in this list if using your own LAPACK/BLAS
sourcelist = ['Src/lapack_litemodule.c',]
# set these to use your own BLAS
library_dirs_list = ['usr/lib']
libraries_list = ['/usr/lib/libblas.so', '/usr/lib/liblapack.so']

What is the problem?

Thanks,
Ed Jones

[Numpy-discussion] RE: Numeric2

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

> Date: Tue, 30 Oct 2001 14:58:38 -0500
> From: "Edward C. Jones" <edc...@er...>
> To: num...@li...
> Subject: [Numpy-discussion] Numeric2
> 
> What is the current status of Numeric2?
> 
We are in the process of putting it up on sourceforge now.
While you can actually download it from there now, I would
strongly recommend waiting until we put some more current
and up-to-date documentation about it there as well. We
will announce it on this mailing list when it is ready
(within a week?). But it isn't called Numeric2 any longer.
We've called it numarray and it will be found under
"python arrays" on Sourceforge. 

Perry Greenfield

[Numpy-discussion] Numeric2

[Edward C. J. <edc...@er...>]

What is the current status of Numeric2?

RE: [Numpy-discussion] Rank deficient matrices

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

Use LinearAlgebra.singular_value_decomposition

-----Original Message-----
From: num...@li...
[mailto:num...@li...] On Behalf Of Nils
Wagner
Sent: Tuesday, October 30, 2001 12:24 AM
To: num...@li...
Subject: [Numpy-discussion] Rank deficient matrices


Hi,

Let us assume that

r = rank(C) < N               (1)

where C is a symmetric NxN matrix. This implies that the number of
non-zero eigenvalues of C is r. Because C is a symmetric matrix there
exists an orthogonal matrix U whose columns are the eigenvectors of C
such that

U^\top C U = [ d , O
               O , O].         (2) 

In the above equation d \in rxr is a diagonal matrix consisting of only
the non-zero eigenvalues of C. For convenience, partition U as

U = [U_1 | U_2]                (3)

where the columns of U_1 (Nxr) are the eigenvectors corresponding to the
non-zero block d and the columns of U_2 are the eigenvectors
corresponding to the rest
(N-r) number
of zero eigenvalues.

Defining a rectangular transformation matrix

R = U_1                        (4)

it is easy to show that

R^\top C R = d.                 (5)

Therefore, the matrix R in equation (4) transforms the originally rank
deficient matrix C to a full-rank (diagonal) matrix of rank r.

I am looking for an efficient Numpy implementation of this
transformation.

Thanks in advance 

                   Nils Wagner

_______________________________________________
Numpy-discussion mailing list Num...@li...
https://lists.sourceforge.net/lists/listinfo/numpy-discussion

[Numpy-discussion] Rank deficient matrices

[Nils W. <nw...@me...>]

Hi,

Let us assume that

r = rank(C) < N               (1)

where C is a symmetric NxN matrix. This implies that the number
of non-zero eigenvalues of C is r. Because C is a symmetric
matrix there exists an orthogonal matrix U whose columns are
the eigenvectors of C such that

U^\top C U = [ d , O
               O , O].         (2) 

In the above equation d \in rxr is a diagonal matrix consisting
of only the non-zero eigenvalues of C.
For convenience, partition U as

U = [U_1 | U_2]                (3)

where the columns of U_1 (Nxr) are the eigenvectors corresponding to the
non-zero block
d and the columns of U_2 are the eigenvectors corresponding to the rest
(N-r) number
of zero eigenvalues.

Defining a rectangular transformation matrix

R = U_1                        (4)

it is easy to show that

R^\top C R = d.                 (5)

Therefore, the matrix R in equation (4) transforms the originally rank
deficient
matrix C to a full-rank (diagonal) matrix of rank r.

I am looking for an efficient Numpy implementation of this
transformation.

Thanks in advance 

                   Nils Wagner

[Numpy-discussion] (real)**(2x2 general real matrix)

[Jean-Bernard A. <jb...@ph...>]

Hey numpy people!

Do you know the better way to compute with numpy:

r**M

where r is a positive real and M a 2x2 (rank 2) matrix, symmetric or not,
with real or not eigenvalues.

Help or comment are appreciated,

	Jean-Bernard

[Numpy-discussion] Problem with Numeric?

[<sa...@hy...>]

 I am using Numeric 20.2.0.  I have found the following 
inconsistency.  I am not a python guru and have no idea how I 
would fix it myself.

The following code creates a series of arrays of data, and then 
applies maximum.reduce to the array to get the largest value.  It then 
prints the type of returned max value.

Most of the returns are typed as an array with a shape of ().  If  the 
array is created as a type of 'l' (long) or 'd' (double), the return type 
is not array.  This seems wrong.  Can someone tell me if this is 
expected, and if not, where would I look to change/fix it.

  --- CODE ---
  from Numeric import *
  print "++++"
  for tc in ('b', 'i', 'l', 's', 'f', 'd'):
       smallArray = array([1,2,3,4], tc)    
       amax = maximum.reduce(smallArray)
       print smallArray.typecode(), smallArray, "Max:", amax, 		   
                type(amax) 
    if type(amax) == type(smallArray): 
       print amax.shape


  --- OUTPUT ---
>>>> ++++
 b [1 2 3 4] Max: 4 <type 'array'>
  ()
 i [1 2 3 4] Max: 4 <type 'array'>
 ()
 l [1 2 3 4] Max: 4 <type 'int'>
 s [1 2 3 4] Max: 4 <type 'array'>
 ()
 f [ 1.  2.  3.  4.] Max: 4.0 <type 'array'>
 ()
  d [ 1.  2.  3.  4.] Max: 4.0 <type 'float'>

[Numpy-discussion] RE: activity on NumPy

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

No, I just have a major release coming up of cdat (cdat.sf.net) so I've
been busy.

-----Original Message-----
From: nobody [mailto:no...@so...] On Behalf Of Alessandro
MIRONE
Sent: Tuesday, October 16, 2001 2:22 AM
To: pa...@pf...
Subject: activity on NumPy


Hi,
 I have noticed that that developer activity
has almost stopped ( bugs and patches are no more processed) after 11/9.
Did you get some restriction at your lab? Best regards
   Alessandro Mirone

[Numpy-discussion] Problems with pickling an array with type Object

[<sa...@hy...>]

Greetings,

I am trying to pickle an array of objects with a typecode of Object.  
Ultimately this array will be in a class I create based on UserArray, 
but the following illustrates a problem I am having.  See code at the 
end of the message.

I am working in Windows 2000, Service Pack 1, using PythonWin, 
win32all build 140.  I am using the following versions of python stuff

Python:  2.1.1 (#20, Jul 20 2001, 01:19:29) [MSC 32 bit (Intel)]
Numeric:  20.2.0
cPickle:  1.71

I create a simple array with a type of Object, pickle it to a file using 
cPickle.  I then run this script in PythonWin. I can unpickle this file 
from PythonWin, but not from DOS window.  Alternatively, I can 
pickle the array from DOS, and unpickle it from DOS, but not from 
PythonWin.  If I save both versions of the file and compare them, 
they are the same size, but not the same bytes.  This does not 
happen with other types for the array.

When unpickling, I get a GPF, either from PythonWin.exe or 
Python.exe.

Any ideas?

My ultimate goal is to be able to store simple class objects in the 
array and be able to pickle and unpickle them.

----- CODE ----

# simple test for pickling problem
import Numeric
import cPickle
import sys

basePath = "c:\\python21\\scripts\\pickle\\"

print "++++++"
# create a simple array of object, populated by integer data
ds1 = Numeric.array([1,2,3,4], 'O')
print ds1, ds1.typecode()
print "Python: ", sys.version
print "Numeric: " , Numeric.__version__
print "cPickle: ", cPickle.__version__
pickle = 1 # change to 0 to skip the pickling step
for ii in (range(1,2)):
    if pickle:
        fileName = "%s%s%d%s" % (basePath, "simple", ii, ".pck")
        print "pickling ", fileName
        fp = open(fileName, 'wb')
        cPickle.dump(ds1, fp, 1)
        fp.close()
    
for ii in range(1,2):
    fileName = "%s%s%d%s" % (basePath, "simple", ii, ".pck")
    print "unpickling ", fileName
    fp = open(fileName, 'rb')
    obj = cPickle.load(fp)      
    fp.close()
    print obj, obj.typecode()

Re: [Numpy-discussion] sign of modulo

[Warren F. <fo...@SL...>]

On Fri, 12 Oct 2001, Eric Nodwell wrote:
> > Numpy delegates to the C platform's / and % operators, Python does it
> > "right".
> Wouldn't it be preferable to change Numpy so it is also "right"?

Speed.  In Python, there's so much overhead from symbol resolution,
looking up __mod__s and __rmod__s and whatnot that the extra work is lost
in the noise.  Numpy would take a bigger hit when working with large
arrays.  One could always write python_compatible_div and
python_compatible_mod functions, if one wanted.  Could even submit a patch
to add them, if truly motivated.

Warren Focke