From chris at fonnesbeck.org  Mon Jun  2 21:39:29 2003
From: chris at fonnesbeck.org (Christopher Fonnesbeck)
Date: Mon, 2 Jun 2003 21:39:29 -0400
Subject: [SciPy-user] weave failures on OSX
Message-ID: <37896545-9564-11D7-840A-000A956FDAC0@fonnesbeck.org>

Just got a new Powerbook, and am trying to get scipy and weave running. 
I built weave from CVS, but the test yields 31 errors ... I'll post the 
first and last here:

======================================================================
ERROR: result[1:-1,1:-1] = (b[1:-1,1:-1] + b[2:,1:-1] + b[:-2,1:-1]
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"/usr/lib/python2.2/site-packages/weave/tests/test_blitz_tools.py", 
line 155, in check_5point_avg_2d_double
     self.generic_2d(expr,Float64)
   File 
"/usr/lib/python2.2/site-packages/weave/tests/test_blitz_tools.py", 
line 130, in generic_2d
     mod_location)
   File 
"/usr/lib/python2.2/site-packages/weave/tests/test_blitz_tools.py", 
line 80, in generic_test
     blitz_tools.blitz(expr,arg_dict,{},verbose=0) #,
   File "/usr/lib/python2.2/site-packages/weave/blitz_tools.py", line 
72, in blitz
     type_converters = converters.blitz,
   File "/usr/lib/python2.2/site-packages/weave/inline_tools.py", line 
445, in compile_function
     exec 'import ' + module_name
   File "<string>", line 1, in ?
ImportError: Failure linking new module

...etc ...

======================================================================
ERROR: check_exceptions (test_inline_tools.test_inline)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"/usr/lib/python2.2/site-packages/weave/tests/test_inline_tools.py", 
line 25, in check_exceptions
     result = inline_tools.inline(code,['a'])
   File "/usr/lib/python2.2/site-packages/weave/inline_tools.py", line 
335, in inline
     auto_downcast = auto_downcast,
   File "/usr/lib/python2.2/site-packages/weave/inline_tools.py", line 
445, in compile_function
     exec 'import ' + module_name
   File "<string>", line 1, in ?
ImportError: Failure linking new module

----------------------------------------------------------------------
Ran 186 tests in 354.412s

FAILED (errors=31)
<unittest.TextTestRunner instance at 0x60c140>


Any ideas?



From chris at fonnesbeck.org  Mon Jun  2 21:42:00 2003
From: chris at fonnesbeck.org (Christopher Fonnesbeck)
Date: Mon, 2 Jun 2003 21:42:00 -0400
Subject: [SciPy-user] more OSX trouble
Message-ID: <91957B2B-9564-11D7-840A-000A956FDAC0@fonnesbeck.org>

While I'm at it, scipy.test() also fails, although a little more 
quietly. Again, scipy and weave appeared to have built without errors. 
Here's the test for scipy:

======================================================================
ERROR: check_complex (test_array_import.test_read_array)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"/usr/lib/python2.2/site-packages/scipy/io/tests/test_array_import.py", 
line 39, in check_complex
     a = rand(13,4) + 1j*rand(13,4)
   File "/usr/lib/python2.2/site-packages/scipy/common.py", line 222, in 
rand
     return stats.random(args)
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
222, in __getattr__
     module = self._ppimport_importer()
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
190, in _ppimport_importer
     raise PPImportError,\
PPImportError: Traceback (most recent call last):
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
200, in _ppimport_importer
     module = __import__(name,None,None,['*'])
   File "/usr/lib/python2.2/site-packages/scipy/stats/__init__.py", line 
9, in ?
     from stats import *
   File "/usr/lib/python2.2/site-packages/scipy/stats/stats.py", line 
1500, in ?
     import distributions
   File "/usr/lib/python2.2/site-packages/scipy/stats/distributions.py", 
line 20, in ?
     errp = special.errprint
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
222, in __getattr__
     module = self._ppimport_importer()
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
200, in _ppimport_importer
     module = __import__(name,None,None,['*'])
   File "/usr/lib/python2.2/site-packages/scipy/special/__init__.py", 
line 10, in ?
     from basic import *
   File "/usr/lib/python2.2/site-packages/scipy/special/basic.py", line 
5, in ?
     from cephes import *
ImportError: Failure linking new module


======================================================================
ERROR: check_float (test_array_import.test_read_array)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"/usr/lib/python2.2/site-packages/scipy/io/tests/test_array_import.py", 
line 47, in check_float
     a = rand(3,4)*30
   File "/usr/lib/python2.2/site-packages/scipy/common.py", line 222, in 
rand
     return stats.random(args)
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
222, in __getattr__
     module = self._ppimport_importer()
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
190, in _ppimport_importer
     raise PPImportError,\
PPImportError: Traceback (most recent call last):
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
200, in _ppimport_importer
     module = __import__(name,None,None,['*'])
   File "/usr/lib/python2.2/site-packages/scipy/stats/__init__.py", line 
9, in ?
     from stats import *
   File "/usr/lib/python2.2/site-packages/scipy/stats/stats.py", line 
1500, in ?
     import distributions
   File "/usr/lib/python2.2/site-packages/scipy/stats/distributions.py", 
line 20, in ?
     errp = special.errprint
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
222, in __getattr__
     module = self._ppimport_importer()
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
200, in _ppimport_importer
     module = __import__(name,None,None,['*'])
   File "/usr/lib/python2.2/site-packages/scipy/special/__init__.py", 
line 10, in ?
     from basic import *
   File "/usr/lib/python2.2/site-packages/scipy/special/basic.py", line 
5, in ?
     from cephes import *
ImportError: Failure linking new module


======================================================================
ERROR: check_integer (test_array_import.test_read_array)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"/usr/lib/python2.2/site-packages/scipy/io/tests/test_array_import.py", 
line 55, in check_integer
     a = stats.randint(1,20,size=(3,4))
AttributeError: 'module' object has no attribute 'randint'

======================================================================
ERROR: check_basic (test_array_import.test_numpyio)
----------------------------------------------------------------------
Traceback (most recent call last):
   File 
"/usr/lib/python2.2/site-packages/scipy/io/tests/test_array_import.py", 
line 22, in check_basic
     a = 255*rand(20)
   File "/usr/lib/python2.2/site-packages/scipy/common.py", line 222, in 
rand
     return stats.random(args)
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
222, in __getattr__
     module = self._ppimport_importer()
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
190, in _ppimport_importer
     raise PPImportError,\
PPImportError: Traceback (most recent call last):
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
200, in _ppimport_importer
     module = __import__(name,None,None,['*'])
   File "/usr/lib/python2.2/site-packages/scipy/stats/__init__.py", line 
9, in ?
     from stats import *
   File "/usr/lib/python2.2/site-packages/scipy/stats/stats.py", line 
1500, in ?
     import distributions
   File "/usr/lib/python2.2/site-packages/scipy/stats/distributions.py", 
line 20, in ?
     errp = special.errprint
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
222, in __getattr__
     module = self._ppimport_importer()
   File "/usr/lib/python2.2/site-packages/scipy_base/ppimport.py", line 
200, in _ppimport_importer
     module = __import__(name,None,None,['*'])
   File "/usr/lib/python2.2/site-packages/scipy/special/__init__.py", 
line 10, in ?
     from basic import *
   File "/usr/lib/python2.2/site-packages/scipy/special/basic.py", line 
5, in ?
     from cephes import *
ImportError: Failure linking new module


----------------------------------------------------------------------
Ran 5 tests in 0.088s

FAILED (errors=4)
<unittest.TextTestRunner instance at 0x4383d0>



From andrew.straw at adelaide.edu.au  Mon Jun  2 23:33:30 2003
From: andrew.straw at adelaide.edu.au (Andrew Straw)
Date: Tue, 3 Jun 2003 13:03:30 +0930
Subject: [SciPy-user] more OSX trouble
In-Reply-To: <91957B2B-9564-11D7-840A-000A956FDAC0@fonnesbeck.org>
Message-ID: <2505298A-9574-11D7-AE4C-00039311EA24@adelaide.edu.au>

Christopher Fonnesbeck wrote:

> While I'm at it, scipy.test() also fails, although a little more 
> quietly. Again, scipy and weave appeared to have built without errors. 
> Here's the test for scipy:

Weird.

I've done little since the recent email I wrote about getting scipy to 
work.  I'm now using Jack Jansen's 2.3b1 binary distribution, but 
everything else should be the same...

 >>> scipy.test(level=1)
<snip>
Ran 739 tests in 22.075s

FAILED (failures=66, errors=2)
<unittest.TextTestRunner object at 0x624b30>

 >>> weave.test(level=1)
<snip>
Ran 132 tests in 3.893s

OK
<unittest.TextTestRunner object at 0x6186f0>

I had to quit the interpreter between those 2 runs, but even most of 
the scipy "failures" were floating point numbers not being the same at 
about the 10th decimal place...



From pearu at scipy.org  Tue Jun  3 02:55:51 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Tue, 3 Jun 2003 09:55:51 +0300 (EEST)
Subject: [SciPy-user] more OSX trouble
In-Reply-To: <91957B2B-9564-11D7-840A-000A956FDAC0@fonnesbeck.org>
Message-ID: <Pine.LNX.4.21.0306030949280.12649-100000@cens.kybi>

On Mon, 2 Jun 2003, Christopher Fonnesbeck wrote:

> While I'm at it, scipy.test() also fails, although a little more 
> quietly. Again, scipy and weave appeared to have built without errors. 
> Here's the test for scipy:
> 
> ======================================================================
> ERROR: check_complex (test_array_import.test_read_array)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>    File 
> "/usr/lib/python2.2/site-packages/scipy/io/tests/test_array_import.py", 

<snip>

>      from cephes import *
> ImportError: Failure linking new module

See also earlier scipy-user thread on 'Failure during testing'.
Currently those import errors I would relate to Python 2.2
Mac related bugs. Try upgrading to Python 2.2.3. You could
also try Python 2.3xx but currently some scipy tests are known
to fail with Python 2.3 for some other reasons.

Pearu



From h.jansen at fel.tno.nl  Tue Jun  3 08:08:54 2003
From: h.jansen at fel.tno.nl (H Jansen)
Date: Tue, 03 Jun 2003 14:08:54 +0200
Subject: [SciPy-user] blas/lapack extension
References: <Pine.LNX.4.21.0306030949280.12649-100000@cens.kybi>
Message-ID: <3EDC8FD6.DD14A106@fel.tno.nl>

I would appreciate some help on the following:

In need for a pivoting QR decomposition of a rectangular matrix, I set
out to extend scipy.linalg.lapack by writing f2py the wrapper code for
the  <tchar=s,d,c,z>geqp3(...). Having mastered the basic f2py skills of
writing python extensions for C code (I'm using ATLAS with full lapack
library), I discovered that the (optimized) ATlAS/lapack routines are
prefixed with "clapack_" (e.g. clapack_gesv), which in turn are wrappers
for "ATL_" (ATLAS) optimized C functions. According to this philosophy,
for each lapack function two more layers are therefore needed between a
python wrapper function and the original lapack C code: one
"clapack_"-like wrapper and one "ATL_"-like wrapper. Am I correct? Is
this what the scipy authors have in mind when planning further
extensions of scipy.linalg?

Or can it be done simpler ...? How about the storage order then?

Another question: why are there two "*.pyf" files: a clapack.pyf file
and a generic_clapack.pyf file?

I've appended my _geqp3 code for the clapack.pyf and generic_clapack.pyf
files.

>>>>>>>>>>>>>>>>>>>>> generic_clapack.pyf <<<<<<<<<<<<<<<<<<<<<<<<<<<<<


  function <tchar=s,d,c,z>geqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

   ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R
   ! using Level 3 BLAS.
   ! A  (input/output) REAL array, dimension (LDA,N)
   !    On entry, the M-by-N matrix A.
   !    On exit, the upper triangle of the array contains the
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below
   !    the diagonal, together with the array TAU, represent the
   !    orthogonal matrix Q as a product of min(M,N) elementary
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry:
   !          if JPVT(J).ne.0, the J-th column of A is permuted to
   !                           the front of A*P (a leading column);
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the
   !                           K-th column of A.
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

     fortranname  <tchar=s,d,c,z>geqp3_
     integer intent(c,hide) ::  <tchar=s,d,c,z>geqp3
     callstatement <tchar=s,d,c,z>geqp3_return_value = info =
(*f2py_func)
                      (102-rowmajor,     m,        n,          
a,        m, piv,         tau,        work,    lwork)
     callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m   = shape(a,0)
     integer depend(a),intent(hide):: n   = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     <type_in> dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a

 end function <tchar=s,d,c,z>geqp3


>>>>>>>>>>>>>>>>>>>>> clapack.pyf (s,d,c and z codes) <<<<<<<<<<<<<<<<<<<<<<<<<<<<<

function sgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname  sgeqp3
  integer intent(c,hide) :: sgeqp3
  callstatement sgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)), intent(out) :: tau
     intent(c,in,out,copy,out=qr) a

end function sgeqp3

function dgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname  dgeqp3
  integer intent(c,hide) :: dgeqp3
  callstatement dgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a

end function dgeqp3

function cgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname cgeqp3
  integer intent(c,hide) :: cgeqp3
  callstatement cgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a

end function cgeqp3

function zgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  d
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname  zgeqp3
  integer intent(c,hide) :: zgeqp3
  callstatement zgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a

end function zgeqp3
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From h.jansen at fel.tno.nl  Tue Jun  3 12:49:13 2003
From: h.jansen at fel.tno.nl (H Jansen)
Date: Tue, 03 Jun 2003 18:49:13 +0200
Subject: [SciPy-user] wrapping lapack
Message-ID: <3EDCD189.2CDA7CEE@fel.tno.nl>

<< earlier message was posted in the wrong thread >>

I would appreciate some help on the following:

In need for a pivoting QR decomposition of a rectangular matrix, I set
out to extend scipy.linalg.lapack by writing f2py the wrapper code for
the  <tchar=s,d,c,z>geqp3(...). Having mastered the basic f2py skills of
writing python extensions for C code (I'm using ATLAS with full lapack
library), I discovered that the (optimized) ATlAS/lapack routines are
prefixed with "clapack_" (e.g. clapack_gesv), which in turn are wrappers
for "ATL_" (ATLAS) optimized C functions. According to this philosophy,
for each lapack function two more layers are therefore needed between a
python wrapper function and the original lapack C code: one
"clapack_"-like wrapper and one "ATL_"-like wrapper. Am I correct? Is
this what the scipy authors have in mind when planning further
extensions of scipy.linalg?

Or can it be done simpler ...? How about the storage order then?

Another question: why are there two "*.pyf" files: a clapack.pyf file
and a generic_clapack.pyf file?

I've appended my _geqp3 code for the clapack.pyf and generic_clapack.pyf
files.

>>>>>>>>>>>>>>>>>>>>> generic_clapack.pyf <<<<<<<<<<<<<<<<<<<<<<<<<<<<<


  function <tchar=s,d,c,z>geqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

   ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R
   ! using Level 3 BLAS.
   ! A  (input/output) REAL array, dimension (LDA,N)
   !    On entry, the M-by-N matrix A.
   !    On exit, the upper triangle of the array contains the
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below
   !    the diagonal, together with the array TAU, represent the
   !    orthogonal matrix Q as a product of min(M,N) elementary
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry:
   !          if JPVT(J).ne.0, the J-th column of A is permuted to
   !                           the front of A*P (a leading column);
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the
   !                           K-th column of A.
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

     fortranname  <tchar=s,d,c,z>geqp3_
     integer intent(c,hide) ::  <tchar=s,d,c,z>geqp3
     callstatement <tchar=s,d,c,z>geqp3_return_value = info =
(*f2py_func)
                      (102-rowmajor,     m,        n,          
a,        m, piv,         tau,        work,    lwork)
     callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m   = shape(a,0)
     integer depend(a),intent(hide):: n   = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     <type_in> dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a

 end function <tchar=s,d,c,z>geqp3


>>>>>>>>>>>>>>>>>>>>> clapack.pyf (s,d,c and z codes) <<<<<<<<<<<<<<<<<<<<<<<<<<<<<

function sgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname  sgeqp3
  integer intent(c,hide) :: sgeqp3
  callstatement sgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)), intent(out) :: tau
     intent(c,in,out,copy,out=qr) a

end function sgeqp3

function dgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname  dgeqp3
  integer intent(c,hide) :: dgeqp3
  callstatement dgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a

end function dgeqp3

function cgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname cgeqp3
  integer intent(c,hide) :: cgeqp3
  callstatement cgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a

end function cgeqp3

function zgeqp3(m,n,a,piv,tau,work,lwork,info,rowmajor)

  ! qr,tau,piv,info = geqp3(a, piv=0, rowmajor=1, overwrite_a=0)
   !
   ! Computes a QR factorization with column pivoting of matrix a so
that
   !        A*P = Q*R  d
   ! using Level 3 BLAS. 
   ! A  (input/output) REAL array, dimension (LDA,N)   
   !    On entry, the M-by-N matrix A.   
   !    On exit, the upper triangle of the array contains the   
   !    min(M,N)-by-N upper trapezoidal matrix R; the elements below   
   !    the diagonal, together with the array TAU, represent the   
   !    orthogonal matrix Q as a product of min(M,N) elementary   
   !    reflectors.
   ! piv pivots columns (input/output) INTEGER array, dimension (N)
   !     On entry: 
   !          if JPVT(J).ne.0, the J-th column of A is permuted to 
   !                           the front of A*P (a leading column); 
   !          if JPVT(J)=0,    the J-th column of A is a free column.
   !     On exit:
   !          if JPVT(J)=K,    then the J-th column of A*P was the the 
   !                           K-th column of A. 
   ! TAU (output) REAL array, dimension (min(M,N))
   !     The scalar factors of the elementary reflectors.

  fortranname  zgeqp3
  integer intent(c,hide) :: zgeqp3
  callstatement zgeqp3_return_value = info =
(*f2py_func)(102-rowmajor,m,n,a,m,piv,tau,work,lwork)
  callprotoargument const int,const int,const int,<type_in_c>*,const
int,int*,<type_in_c>*,<type_in_c>*,const int

     integer optional,intent(in),check(rowmajor==1||rowmajor==0) ::
rowmajor = 1

     integer depend(a),intent(hide):: m = shape(a,0)
     integer depend(a),intent(hide):: n = shape(a,1)
     <type_in> dimension(m,n)      :: a
     integer dimension(n),depend(n),intent(in,out) :: piv
     integer depend(work),intent(hide),check(lwork>=3*n+1):: lwork =
shape(work,0)
     <type_in> dimension(lwork),intent(in) :: work
     integer intent(out)::info
     integer dimension(MIN(m,n)),intent(out,hide) :: tau
     intent(c,in,out,copy,out=qr) a
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From Ralf_Ahlbrink at web.de  Tue Jun  3 18:19:11 2003
From: Ralf_Ahlbrink at web.de (Ralf Ahlbrink)
Date: Wed, 4 Jun 2003 00:19:11 +0200
Subject: [SciPy-user] Debian package of scipy
In-Reply-To: <200305292321.55110.Ralf_Ahlbrink@web.de>
References: <3ED62486.DC437FE8@mecha.uni-stuttgart.de>
	<200305292321.55110.Ralf_Ahlbrink@web.de>
Message-ID: <200306040019.11247.Ralf_Ahlbrink@web.de>

Hallo!

I've uploaded new debian source files to scipy.org, have a look at
http://www.scipy.org/Members/RalfA/packages/Debian-woody-DEB/

For compilation on woody release you have to compile and install 
python2.3(-numeric) from the unstable tree. Or modify the debian/control and 
debian/rules files to omit the python2.3 packages - or give me a hint...

I hope, that someone will test this on sid -- I can't. And I'm aware of the 
lintian messages...

Because f2py needs distutils, I've also made an appropriate package for 
initial use. So, you have to build and install pythonX-distutils and then 
pythonX-f2py -- substitute X with '', '2.1', '2.2', '2.3'  -- before you 
start with the big rest.

I tried to make one big scipy source package. After packaging and compilation 
(see hints in above given URL) you should get pythonX-scipy-core, -scipy, 
-weave and -chaco.

Ralf.




From jamalytis at hotmail.com  Tue Jun  3 18:43:25 2003
From: jamalytis at hotmail.com (James Analytis)
Date: Tue, 03 Jun 2003 22:43:25 +0000
Subject: [SciPy-user] Segmentation Faults
Message-ID: <Law14-F109pFuiHV5EI0000ca34@hotmail.com>

Hi,
I am having trouble with my scipy installation.  I keep getting these 
segmentation faults, but I think they only occur when modules with common 
names that come from different packages are called. Any ideas?
J

eg.
Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>from Numeric import *
>>>import scipy
>>>x=arange(10)
>>>y = exp(-x/3.0)
Segmentation fault

AND

Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>from scipy import *
Segmentation fault

BUT Numeric alone is fine

Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>from Numeric import *
>>>x=arange(10)
>>>y = exp(-x/3.0)
>>>

AND "import scipy" alone is fine

Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>import scipy
>>>from scipy import *
>>>

_________________________________________________________________
Surf the net and talk on the phone with Xtra Jetstream @  
http://www.xtra.co.nz/products/0,,5803,00.html !



From fperez at colorado.edu  Tue Jun  3 20:45:57 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Tue, 03 Jun 2003 18:45:57 -0600
Subject: [SciPy-user] io.read_array only works for positive numbers?
Message-ID: <3EDD4145.5000900@colorado.edu>

Hi all,

I'm wondering if the following behavior is supposed to be normal:

In [99]: cat legpols2.dat
1    1.                     1.      1.                     1.
-1   -0.49999999999999994   0.      0.49999999999999994    1.
1    -0.125                 -0.5    -0.125                 1.
-1   0.43749999999999994    0.      -0.43749999999999994   1.
1    -0.2890625             0.375   -0.2890625             1.

In [100]: scipy.io.read_array("legpols2.dat")
Out[100]:
array([[ 1.       ,  1.       ,  1.       ,  1.       ,  1.       ],
        [ 1.       ,  0.5      ,  0.       ,  0.5      ,  1.       ],
        [ 1.       ,  0.125    ,  0.5      ,  0.125    ,  1.       ],
        [ 1.       ,  0.4375   ,  0.       ,  0.4375   ,  1.       ],
        [ 1.       ,  0.2890625,  0.375    ,  0.2890625,  1.       ]])

read_array seems to fit the bill for my simple needs quite nicely, but I'd 
like to be able to use negative numbers as well as positive ones.  Any hints?

TIA,

f.



From nwagner at mecha.uni-stuttgart.de  Wed Jun  4 02:29:49 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Wed, 04 Jun 2003 08:29:49 +0200
Subject: [SciPy-user] io.read_array only works for positive numbers?
References: <3EDD4145.5000900@colorado.edu>
Message-ID: <3EDD91DD.C15FEB0E@mecha.uni-stuttgart.de>

Fernando Perez schrieb:
> 
> Hi all,
> 
> I'm wondering if the following behavior is supposed to be normal:
> 
> In [99]: cat legpols2.dat
> 1    1.                     1.      1.                     1.
> -1   -0.49999999999999994   0.      0.49999999999999994    1.
> 1    -0.125                 -0.5    -0.125                 1.
> -1   0.43749999999999994    0.      -0.43749999999999994   1.
> 1    -0.2890625             0.375   -0.2890625             1.
> 
> In [100]: scipy.io.read_array("legpols2.dat")
> Out[100]:
> array([[ 1.       ,  1.       ,  1.       ,  1.       ,  1.       ],
>         [ 1.       ,  0.5      ,  0.       ,  0.5      ,  1.       ],
>         [ 1.       ,  0.125    ,  0.5      ,  0.125    ,  1.       ],
>         [ 1.       ,  0.4375   ,  0.       ,  0.4375   ,  1.       ],
>         [ 1.       ,  0.2890625,  0.375    ,  0.2890625,  1.       ]])
> 
> read_array seems to fit the bill for my simple needs quite nicely, but I'd
> like to be able to use negative numbers as well as positive ones.  Any hints?
> 
> TIA,
> 
> f.
> 
Afaik, it is fixed in CVS.

 Nils

> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user



From pearu at scipy.org  Wed Jun  4 03:06:41 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Wed, 4 Jun 2003 10:06:41 +0300 (EEST)
Subject: [SciPy-user] Segmentation Faults
In-Reply-To: <Law14-F109pFuiHV5EI0000ca34@hotmail.com>
Message-ID: <Pine.LNX.4.21.0306040958380.29583-100000@cens.kybi>

On Tue, 3 Jun 2003, James Analytis wrote:

> Hi,
> I am having trouble with my scipy installation.  I keep getting these 
> segmentation faults, but I think they only occur when modules with common 
> names that come from different packages are called. Any ideas?

Did you recently upgraded Numeric? May be scipy was built using
older version of Numeric headers (IMHO, it's a known distutils problem
that when upgrading Numeric, distutils does not update Numeric header
files). So, check that the Numeric header files installed in your system 
correspond to the Numeric version you are using.

> Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
> [GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
> Type "help", "copyright", "credits" or "license" for more information.
> >>>from scipy import *
> Segmentation fault

Use `python -v`  to find out at what point the import segfaults.

HTH,
	Pearu




From nwagner at mecha.uni-stuttgart.de  Wed Jun  4 03:17:25 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Wed, 04 Jun 2003 09:17:25 +0200
Subject: [SciPy-user] Support of FORTRAN binary files in scipy
Message-ID: <3EDD9D05.CEDDA1CF@mecha.uni-stuttgart.de>

Hi all,

is it possible to read and write FORTRAN binary files with scipy ?
How can I do that ?

Nils

Fortran:

open(UNIT=11,FILE='mass.mat',STATUS='OLD',FORM='UNFORMATTED',ERR=90,
IOSTAT=IRET)
open(UNIT=12,FILE='massmod.mat',STATUS='NEW',FORM='UNFORMATTED';ERR=99, 
IOSTAT=IRET)

Scipy :

?



From virge at blammo.its.monash.edu.au  Wed Jun  4 08:53:52 2003
From: virge at blammo.its.monash.edu.au (virge at blammo.its.monash.edu.au)
Date: Wed, 04 Jun 2003 22:53:52 +1000 (EST)
Subject: [SciPy-user] VIRUS NOTIFICATION
Message-ID: <20030604125352.68AEE12C007@blammo.its.monash.edu.au>

                        V I R U S  A L E R T

Our virus scanner found a virus in your email to the following
recipient(s):

Ron.Grant at BusEco.monash.edu.au

Your email was not delivered. Please check your system for viruses, or
ask your technical support person to do so, and then re-send your email.

For more information on viruses, please refer to the following URL:

http://www.its.monash.edu.au/software/information/viruses/

The following technical data was produced by the virus scanning
software. Your technical support person may request this information to
help diagnose the problem.

SENDER     : scipy-user at scipy.net
DATE       : Wed, 4 Jun 2003 22:53:33 +1000
SUBJECT    : Re: 45443-343556
VIRUS      : W32/Sobig-C



From jgatzi at hotmail.com  Wed Jun  4 09:04:19 2003
From: jgatzi at hotmail.com (jgatzi at hotmail.com)
Date: Wed, 4 Jun 2003 23:04:19 +1000
Subject: [SciPy-user] Re: Movie
Message-ID: <20030604141338.8D0973EB0C@www.scipy.com>

Please see the attached file.
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From chris at fonnesbeck.org  Tue Jun  3 16:50:56 2003
From: chris at fonnesbeck.org (Christopher Fonnesbeck)
Date: Tue, 3 Jun 2003 16:50:56 -0400
Subject: [SciPy-user] OS X using 2.3
Message-ID: <7225B910-9690-11D7-AE88-000A956FDAC0@fonnesbeck.org>

Well, things are better now that I am using the python2.3 framework,  
rather than 2.2 that comes with OS X 10.2.6. Weave runs the test  
without error (and pretty quickly, at that!). Scipy, however, still  
dies, now during the statistical section:

======================================================================
ERROR: check_chebyc (test_basic.test_chebyc)
----------------------------------------------------------------------
Traceback (most recent call last):
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/site- 
packages/scipy/special/tests/test_basic.py", line 214, in check_chebyc
     chebc = chebyc(1)(.2)
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/site- 
packages/scipy/special/orthogonal.py", line 398, in chebyc
     p = p * 2.0/p(2)
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/site- 
packages/scipy_base/polynomial.py", line 445, in __getattr__
     raise KeyError
KeyError

======================================================================
ERROR: check_chebys (test_basic.test_chebys)
----------------------------------------------------------------------
Traceback (most recent call last):
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/site- 
packages/scipy/special/tests/test_basic.py", line 220, in check_chebys
     chebs = chebys(1)(.2)
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/site- 
packages/scipy/special/orthogonal.py", line 429, in chebys
     p = p * (n+1.0)/p(2)
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/site- 
packages/scipy_base/polynomial.py", line 445, in __getattr__
     raise KeyError
KeyError

... plus about 70 failures that I will not trouble you with.

Are these the errors that were anticipated?

The other problem I get is with plotting using scipy.gplt. I get a  
broken pipe error, even though I have gnuplot installed:

Traceback (most recent call last):
   File "<stdin>", line 1, in ?
   File "Mallard.py", line 1155, in plot_history
     gplt.surf(ma,po,mat)
   File "/usr/lib/python2.2/site-packages/scipy/gplt/interface.py", line  
164, in surf
     apply(_active.surf,data)
   File "/usr/lib/python2.2/site-packages/scipy/gplt/pyPlot.py", line  
437, in surf
     self._init_plot()
   File "/usr/lib/python2.2/site-packages/scipy/gplt/pyPlot.py", line  
702, in _init_plot
     self._send('reset')
   File "/usr/lib/python2.2/site-packages/scipy/gplt/pyPlot.py", line  
820, in _send
     self.g.flush()
IOError: [Errno 32] Broken pipe



From fperez at colorado.edu  Wed Jun  4 12:21:01 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Wed, 04 Jun 2003 10:21:01 -0600
Subject: [SciPy-user] io.read_array only works for positive numbers?
In-Reply-To: <3EDD91DD.C15FEB0E@mecha.uni-stuttgart.de>
References: <3EDD4145.5000900@colorado.edu>
	<3EDD91DD.C15FEB0E@mecha.uni-stuttgart.de>
Message-ID: <3EDE1C6D.2090400@colorado.edu>

Nils Wagner wrote:

> 
> Afaik, it is fixed in CVS.

Ah, thanks.  I'm running cvs, but haven't updated in about a month or so.

Best,

f.




From haase at msg.ucsf.edu  Wed Jun  4 12:39:28 2003
From: haase at msg.ucsf.edu (Sebastian Haase)
Date: Wed, 4 Jun 2003 09:39:28 -0700
Subject: [SciPy-user] io.read_array  in SciPy vs the one in "scientific"
References: 	<3EDD4145.5000900@colorado.edu><3EDD91DD.C15FEB0E@mecha.uni-stuttgart.de>
	<3EDE1C6D.2090400@colorado.edu>
Message-ID: <006f01c32ab7$ddbe3dc0$421ee6a9@rodan>

Hi,
Quick question: How does SciPy's read_array compare to Scientific.IO.ArrayIO
(cf. http://starship.python.net/~hinsen/ScientificPython/) ?
And why are there these two (independent !?) projects anyway ...?

Thanks
Sebastian Haase

----- Original Message ----- 
From: "Fernando Perez" <fperez at colorado.edu>
To: <scipy-user at scipy.net>
Sent: Wednesday, June 04, 2003 9:21 AM
Subject: Re: [SciPy-user] io.read_array only works for positive numbers?


> Nils Wagner wrote:
>
> >
> > Afaik, it is fixed in CVS.
>
> Ah, thanks.  I'm running cvs, but haven't updated in about a month or so.
>
> Best,
>
> f.
>
>
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user
>



From fperez at colorado.edu  Wed Jun  4 12:43:22 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Wed, 04 Jun 2003 10:43:22 -0600
Subject: [SciPy-user] io.read_array  in SciPy vs the one in "scientific"
In-Reply-To: <006f01c32ab7$ddbe3dc0$421ee6a9@rodan>
References: 	<3EDD4145.5000900@colorado.edu><3EDD91DD.C15FEB0E@mecha.uni-stuttgart.de>
	<3EDE1C6D.2090400@colorado.edu> <006f01c32ab7$ddbe3dc0$421ee6a9@rodan>
Message-ID: <3EDE21AA.7050002@colorado.edu>

Sebastian Haase wrote:
> Hi,
> Quick question: How does SciPy's read_array compare to Scientific.IO.ArrayIO
> (cf. http://starship.python.net/~hinsen/ScientificPython/) ?
> And why are there these two (independent !?) projects anyway ...?

Well, for one, the Scientific readArray allows negative numbers, so it's an 
improvement :)

However, the issue of the two projects is there.  After my little run-in with 
the negative numbers issue, I turned over to Scientific as plan B.  I view 
SciPy as the 'lead' scientific computing project for python, but I wish there 
were a bit more code sharing, especially in areas where Scientific already 
paved the way (such as the readarray case).  But there may be licensing issues 
I'm not aware of, so please don't read too much into my opinion on this matter.

Best,

f.



From oliphant.travis at ieee.org  Wed Jun  4 15:02:13 2003
From: oliphant.travis at ieee.org (Travis E. Oliphant)
Date: Wed, 04 Jun 2003 13:02:13 -0600
Subject: [SciPy-user] io.read_array only works for positive numbers?
References: <3EDD4145.5000900@colorado.edu>
Message-ID: <3EDE4235.8030609@ieee.org>

This was fixed recently in CVS.  At least I was pretty sure I checked in 
the fix.  Do you have a recent CVS version of scipy?

-Travis



From oliphant.travis at ieee.org  Wed Jun  4 15:03:46 2003
From: oliphant.travis at ieee.org (Travis E. Oliphant)
Date: Wed, 04 Jun 2003 13:03:46 -0600
Subject: [SciPy-user] io.read_array only works for positive numbers?
References: <3EDD4145.5000900@colorado.edu>
Message-ID: <3EDE4292.7050301@ieee.org>

Here's what I get:

 >>> scipy.__version__
'0.2.0_alpha_197.4144'

 >>> io.read_array("legpols2.dat")
array([[ 1.    ,  1.    ,  1.    ,  1.    ,  1.    ],
        [-1.    , -0.5   ,  0.    ,  0.5   ,  1.    ],
        [ 1.    , -0.125 , -0.5   , -0.125 ,  1.    ],
        [-1.    ,  0.4375,  0.    , -0.4375,  1.    ],
        [ 1.    , -0.2891,  0.375 , -0.2891,  1.    ]])


Fernando Perez wrote:
> Hi all,
> 
> I'm wondering if the following behavior is supposed to be normal:
> 
> In [99]: cat legpols2.dat
> 1    1.                     1.      1.                     1.
> -1   -0.49999999999999994   0.      0.49999999999999994    1.
> 1    -0.125                 -0.5    -0.125                 1.
> -1   0.43749999999999994    0.      -0.43749999999999994   1.
> 1    -0.2890625             0.375   -0.2890625             1.
> 
> In [100]: scipy.io.read_array("legpols2.dat")
> Out[100]:
> array([[ 1.       ,  1.       ,  1.       ,  1.       ,  1.       ],
>        [ 1.       ,  0.5      ,  0.       ,  0.5      ,  1.       ],
>        [ 1.       ,  0.125    ,  0.5      ,  0.125    ,  1.       ],
>        [ 1.       ,  0.4375   ,  0.       ,  0.4375   ,  1.       ],
>        [ 1.       ,  0.2890625,  0.375    ,  0.2890625,  1.       ]])
> 
> read_array seems to fit the bill for my simple needs quite nicely, but 
> I'd like to be able to use negative numbers as well as positive ones.  
> Any hints?
> 
> TIA,
> 
> f.
> 
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user




From oliphant.travis at ieee.org  Wed Jun  4 15:23:07 2003
From: oliphant.travis at ieee.org (Travis E. Oliphant)
Date: Wed, 04 Jun 2003 13:23:07 -0600
Subject: [SciPy-user] Support of FORTRAN binary files in scipy
References: <3EDD9D05.CEDDA1CF@mecha.uni-stuttgart.de>
Message-ID: <3EDE471B.7070505@ieee.org>


Nils Wagner wrote:
> Hi all,
> 
> is it possible to read and write FORTRAN binary files with scipy ?
> How can I do that ?
> 
> Nils
> 
> Fortran:
> 
> open(UNIT=11,FILE='mass.mat',STATUS='OLD',FORM='UNFORMATTED',ERR=90,
> IOSTAT=IRET)
> open(UNIT=12,FILE='massmod.mat',STATUS='NEW',FORM='UNFORMATTED';ERR=99, 
> IOSTAT=IRET)

Try:

fid = io.fopen(<filename>)

fid.fort_read()
fid.fort_write()

These methods read and write fortran records (you need one command for 
each corresponding fortran read or write command used).  They have 
docstrings.   I have not tested them extensively for all fortran 
compilers, but they should work at least for g77.

-Travis






From oliphant.travis at ieee.org  Wed Jun  4 15:35:44 2003
From: oliphant.travis at ieee.org (Travis E. Oliphant)
Date: Wed, 04 Jun 2003 13:35:44 -0600
Subject: [SciPy-user] io.read_array  in SciPy vs the one in "scientific"
References: 	<3EDD4145.5000900@colorado.edu><3EDD91DD.C15FEB0E@mecha.uni-stuttgart.de>
	<3EDE1C6D.2090400@colorado.edu> <006f01c32ab7$ddbe3dc0$421ee6a9@rodan>
	<3EDE21AA.7050002@colorado.edu>
Message-ID: <3EDE4A10.8080105@ieee.org>

Fernando Perez wrote:
> Sebastian Haase wrote:
> 
>> Hi,
>> Quick question: How does SciPy's read_array compare to 
>> Scientific.IO.ArrayIO
>> (cf. http://starship.python.net/~hinsen/ScientificPython/) ?
>> And why are there these two (independent !?) projects anyway ...?
> 

For one, scipy is larger than ScientificPython by a large margin (a much 
larger code base).  The naming conventions are different.  We would 
gladly include code from ScientificPython (and have done so in fact) but 
need to change the naming conventions.

io.read_array is based somewhat on ArrayIO but goes beyond it in terms 
of what is supported.   SciPy is trying to gather code together in one 
super-package.  Code that could be adapted from ScientificPython is 
always welcome.

> 
> Well, for one, the Scientific readArray allows negative numbers, so it's 
> an improvement :)

A bug that was fixed over a month ago.

> 
> However, the issue of the two projects is there.  After my little run-in 
> with the negative numbers issue, I turned over to Scientific as plan B.  
> I view SciPy as the 'lead' scientific computing project for python, but 
> I wish there were a bit more code sharing, especially in areas where 
> Scientific already paved the way (such as the readarray case).  But 
> there may be licensing issues I'm not aware of, so please don't read too 
> much into my opinion on this matter.

read_array did borrow from Scientific.  It's just that significant 
changes had to be made in order to support the full flexibility desired 
from read_array.  io.read_array is quite a bit more flexible.

We have not ignored ScientificPython but borrow from in when possible. 
Contributions are always welcome.

-Travis O.






From fperez at colorado.edu  Wed Jun  4 16:26:08 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Wed, 04 Jun 2003 14:26:08 -0600
Subject: [SciPy-user] io.read_array only works for positive numbers?
In-Reply-To: <3EDE4235.8030609@ieee.org>
References: <3EDD4145.5000900@colorado.edu> <3EDE4235.8030609@ieee.org>
Message-ID: <3EDE55E0.6010009@colorado.edu>

Travis E. Oliphant wrote:
> This was fixed recently in CVS.  At least I was pretty sure I checked in 
> the fix.  Do you have a recent CVS version of scipy?
> 

Not terribly recent (about 1 1/2 months old, I think).  So I'll update.

Thanks for the info.

Regards,

Fernando.



From fperez at colorado.edu  Wed Jun  4 16:31:15 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Wed, 04 Jun 2003 14:31:15 -0600
Subject: [SciPy-user] io.read_array  in SciPy vs the one in "scientific"
In-Reply-To: <3EDE4A10.8080105@ieee.org>
References: 	<3EDD4145.5000900@colorado.edu><3EDD91DD.C15FEB0E@mecha.uni-stuttgart.de>
	<3EDE1C6D.2090400@colorado.edu> <006f01c32ab7$ddbe3dc0$421ee6a9@rodan>
	<3EDE21AA.7050002@colorado.edu> <3EDE4A10.8080105@ieee.org>
Message-ID: <3EDE5713.5000800@colorado.edu>

Travis E. Oliphant wrote:
>>> Well, for one, the Scientific readArray allows negative numbers, so it's 
>>> an improvement :)
> 
> 
> A bug that was fixed over a month ago.

Hence the smiley - I'd already seen the replies about it being fixed in more 
recent cvs and I was just joking about it.  Sorry if it came across as 
criticism, it wasn't meant to.

> read_array did borrow from Scientific.  It's just that significant 
> changes had to be made in order to support the full flexibility desired 
> from read_array.  io.read_array is quite a bit more flexible.
> 
> We have not ignored ScientificPython but borrow from in when possible. 
> Contributions are always welcome.

Thanks for this info.  I knew you guys were aware of Scientific, of course, 
but I'd always wondered how much cross-pollination did in fact take place. 
I'm glad to see that you _are_ in fact getting as much as possible from 
Scientific, when it suits the design goals of Scipy.


Regards,

Fernando.



From pearu at scipy.org  Thu Jun  5 02:05:48 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Thu, 5 Jun 2003 09:05:48 +0300 (EEST)
Subject: [SciPy-user] OS X using 2.3
In-Reply-To: <7225B910-9690-11D7-AE88-000A956FDAC0@fonnesbeck.org>
Message-ID: <Pine.LNX.4.21.0306050901500.7833-100000@cens.kybi>

On Tue, 3 Jun 2003, Christopher Fonnesbeck wrote:

> Well, things are better now that I am using the python2.3 framework,  
> rather than 2.2 that comes with OS X 10.2.6. Weave runs the test  
> without error (and pretty quickly, at that!). Scipy, however, still  
> dies, now during the statistical section:
> 
> ======================================================================
> ERROR: check_chebyc (test_basic.test_chebyc)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
...
> KeyError
> 
> ... plus about 70 failures that I will not trouble you with.
> 
> Are these the errors that were anticipated?

Yes, they are known to appear with Python 2.3 and probably releated
to new attribute rules in 2.3. The fix shouldn't be hard though..

Pearu




From nwagner at mecha.uni-stuttgart.de  Thu Jun  5 08:34:53 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Thu, 05 Jun 2003 14:34:53 +0200
Subject: [SciPy-user] tic toc in Matlab versus genutils.clock() in IPython
Message-ID: <3EDF38ED.44248A27@mecha.uni-stuttgart.de>

Hi all,

I wonder, if I can compare the results of 
Matlab, that is

tic
big_calculation()
toc

with scipy (ipython)

from IPython import genutils

t0 = genutils.clock()
big_calculcation()
t1 = genutils.clock()

Any comments ?

Nils



From nwagner at mecha.uni-stuttgart.de  Thu Jun  5 08:46:37 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Thu, 05 Jun 2003 14:46:37 +0200
Subject: [SciPy-user] Interactive user input in scipy
Message-ID: <3EDF3BAD.E99B009E@mecha.uni-stuttgart.de>

Hi all,

How can I build an interactive user input in scipy ?

print 'Please enter the number of matrices'

print 'Please enter the file name'

A small scipy example with respect to this feature is appreciated.

Thanks in advance

                  Nils



From nwagner at mecha.uni-stuttgart.de  Thu Jun  5 09:03:26 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Thu, 05 Jun 2003 15:03:26 +0200
Subject: [SciPy-user] Large generalized eigenvalue problems
Message-ID: <3EDF3F9E.BF95E6A5@mecha.uni-stuttgart.de>

Hi all,

Is there an upper limit (restriction due to memory) for the matrix
dimension in

w,vl,vr = linalg.eig(A,B, left=1, right=1, overwrite_a=0, overwrite_b=0)
?

 
Nils



From james.analytis at physics.ox.ac.uk  Thu Jun  5 09:50:15 2003
From: james.analytis at physics.ox.ac.uk (James Analytis)
Date: Thu, 5 Jun 2003 14:50:15 +0100 (BST)
Subject: [SciPy-user] Probs with scipy.test() and seg faults
Message-ID: <Pine.LNX.4.55.0306051429270.725@nodens.physics.ox.ac.uk>

Hi,
I'm having problems with my scipy installation. Most of the time, I get
Seg faults whenever I try to import scipy. When it
finally does work, it seg faults on the first test:

Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> scipy.test()
No test suite found for  scipy.__cvs_version__
 creating test suite for: scipy.common
Segmentation fault

I'm using ATLAS 3.5.2,F2PY-2.32.225-1419 on RedHat 9.
How can I find out what's going wrong?
Thanks,
J



From h.jansen at fel.tno.nl  Thu Jun  5 10:14:27 2003
From: h.jansen at fel.tno.nl (H Jansen)
Date: Thu, 05 Jun 2003 16:14:27 +0200
Subject: [SciPy-user] Probs with scipy.test() and seg faults
References: <Pine.LNX.4.55.0306051429270.725@nodens.physics.ox.ac.uk>
Message-ID: <3EDF5043.630D476C@fel.tno.nl>

I have the same on RedHat 8.0:
	- can't find any test suites
	- and terminates with Segmentation fault

I'm using ATLAS 3.2.1 and same F2PY version.

Henk

James Analytis wrote:
> 
> Hi,
> I'm having problems with my scipy installation. Most of the time, I get
> Seg faults whenever I try to import scipy. When it
> finally does work, it seg faults on the first test:
> 
> Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
> [GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
> Type "help", "copyright", "credits" or "license" for more information.
> >>> import scipy
> >>> scipy.test()
> No test suite found for  scipy.__cvs_version__
>  creating test suite for: scipy.common
> Segmentation fault
> 
> I'm using ATLAS 3.5.2,F2PY-2.32.225-1419 on RedHat 9.
> How can I find out what's going wrong?
> Thanks,
> J
> 
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user
-------------- next part --------------
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From pearu at scipy.org  Thu Jun  5 10:28:42 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Thu, 5 Jun 2003 17:28:42 +0300 (EEST)
Subject: [SciPy-user] Probs with scipy.test() and seg faults
In-Reply-To: <3EDF5043.630D476C@fel.tno.nl>
Message-ID: <Pine.LNX.4.21.0306051718520.7833-100000@cens.kybi>

On Thu, 5 Jun 2003, H Jansen wrote:

> I have the same on RedHat 8.0:
> 	- can't find any test suites
> 	- and terminates with Segmentation fault
> 
> I'm using ATLAS 3.2.1 and same F2PY version.

> James Analytis wrote:
> > 
> > I'm having problems with my scipy installation. Most of the time, I get
> > Seg faults whenever I try to import scipy. When it
> > finally does work, it seg faults on the first test:
> > 
> > Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
> > [GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
> > Type "help", "copyright", "credits" or "license" for more information.
> > >>> import scipy
> > >>> scipy.test()
> > No test suite found for  scipy.__cvs_version__
> >  creating test suite for: scipy.common
> > Segmentation fault

Usually such mysterious segfaults are cause by building
scipy against different (older) Numeric headers than
was used for building Numeric.

Could you confirm that by checking whether installed
Numeric header files correspond to ones in the 
Numeric source tree or not? E.g.

ls -l /path/to/src/Numeric-23.0/Include/Numeric/
ls -l /usr/local/include/python2.2/Numeric/

Also using `python -v` or importing libwadpy might give 
more information on the source of these crashes.

HTH,
	Pearu



From james.analytis at physics.ox.ac.uk  Thu Jun  5 11:21:28 2003
From: james.analytis at physics.ox.ac.uk (James Analytis)
Date: Thu, 5 Jun 2003 16:21:28 +0100
Subject: [SciPy-user] Probs with scipy.test() and seg faults
Message-ID: <200306051621.28891.james.analytis@physics.ox.ac.uk>

Hey,
Apologies for not seeing your first message. Here are the outputs from the 
commands you gave me.
The seg fault seems to occur on 

import scipy_base.type_check # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/type_check.pyc
Segmentation fault

Where to now?
J


[analytis at toomey Numeric]$ ls -l /usr/lib/python2.2/site-packages/Numeric
total 2080
-rwxr-xr-x    1 root     root        72871 Jun  5 15:24 arrayfns.so
-rw-r--r--    1 root     root         7981 Nov  1  2002 ArrayPrinter.py
-rw-r--r--    1 root     root         9810 Jun  5 15:27 ArrayPrinter.pyc
drwxr-xr-x    2 root     root         4096 Jun  5 15:31 FFT
-rwxr-xr-x    1 root     root      1113300 Jun  5 15:27 lapack_lite.so
-rw-r--r--    1 root     root        18119 Feb 27 23:37 LinearAlgebra.py
-rw-r--r--    1 root     root        20974 Jun  5 15:27 LinearAlgebra.pyc
drwxr-xr-x    2 root     root         4096 Jun  5 15:27 MA
-rw-r--r--    1 root     root         6260 Feb 27 23:37 Matrix.py
-rw-r--r--    1 root     root         9971 Jun  5 15:27 Matrix.pyc
-rw-r--r--    1 root     root        12294 Aug 28  2002 MLab.py
-rw-r--r--    1 root     root        19491 Jun  5 15:27 MLab.pyc
-rwxr-xr-x    1 root     root        84054 Jun  5 15:24 multiarray.so
-rw-r--r--    1 root     root        27530 Mar  6 17:01 Numeric.py
-rw-r--r--    1 root     root        38854 Jun  5 15:27 Numeric.pyc
-rw-r--r--    1 root     root           15 Mar  6 17:01 numeric_version.py
-rw-r--r--    1 root     root          174 Jun  5 15:27 numeric_version.pyc
-rwxr-xr-x    1 root     root       219837 Jun  5 15:23 _numpy.so
-rw-r--r--    1 root     root         3236 Jun  9  2002 Precision.py
-rw-r--r--    1 root     root         4550 Jun  5 15:27 Precision.pyc
-rw-r--r--    1 root     root        14259 Aug 28  2002 RandomArray.py
-rw-r--r--    1 root     root        18316 Jun  5 15:27 RandomArray.pyc
-rwxr-xr-x    1 root     root       131114 Jun  5 15:24 ranlib.so
drwxr-xr-x    2 root     root         4096 Jun  5 15:27 RNG
-rwxr-xr-x    1 root     root       182242 Jun  5 15:24 umath.so
-rw-r--r--    1 root     root         7582 Mar 10  2002 UserArray.py
-rw-r--r--    1 root     root        19380 Jun  5 15:27 UserArray.pyc
[analytis at toomey Numeric]$ ls -l 
/usr/lib/python2.2/site-packages/Numeric-23.0/Include/Numeric/
total 36
-rw-r--r--    1 909      users       14370 Jun 11  2002 arrayobject.h
-rw-r--r--    1 909      users        4228 Nov 19  2002 f2c.h
-rw-r--r--    1 909      users        1285 Sep  2  2002 ranlib.h
-rw-r--r--    1 909      users        6702 May 10  2001 ufuncobject.h
[analytis at toomey Numeric]$

[analytis at toomey analytis]$ python -v
# /usr/lib/python2.2/site.pyc matches /usr/lib/python2.2/site.py
import site # precompiled from /usr/lib/python2.2/site.pyc
# /usr/lib/python2.2/os.pyc matches /usr/lib/python2.2/os.py
import os # precompiled from /usr/lib/python2.2/os.pyc
import posix # builtin
# /usr/lib/python2.2/posixpath.pyc matches /usr/lib/python2.2/posixpath.py
import posixpath # precompiled from /usr/lib/python2.2/posixpath.pyc
# /usr/lib/python2.2/stat.pyc matches /usr/lib/python2.2/stat.py
import stat # precompiled from /usr/lib/python2.2/stat.pyc
# /usr/lib/python2.2/UserDict.pyc matches /usr/lib/python2.2/UserDict.py
import UserDict # precompiled from /usr/lib/python2.2/UserDict.pyc
# /usr/lib/python2.2/copy_reg.pyc matches /usr/lib/python2.2/copy_reg.py
import copy_reg # precompiled from /usr/lib/python2.2/copy_reg.pyc
# /usr/lib/python2.2/types.pyc matches /usr/lib/python2.2/types.py
import types # precompiled from /usr/lib/python2.2/types.pyc
# /usr/lib/python2.2/__future__.pyc matches /usr/lib/python2.2/__future__.py
import __future__ # precompiled from /usr/lib/python2.2/__future__.pyc
import japanese # directory /usr/lib/python2.2/lib-dynload/japanese
# /usr/lib/python2.2/lib-dynload/japanese/__init__.pyc matches 
/usr/lib/python2.2/lib-dynload/japanese/__init__.py
import japanese # precompiled from 
/usr/lib/python2.2/lib-dynload/japanese/__init__.pyc
import japanese.aliases # directory 
/usr/lib/python2.2/lib-dynload/japanese/aliases
# /usr/lib/python2.2/lib-dynload/japanese/aliases/__init__.pyc matches 
/usr/lib/python2.2/lib-dynload/japanese/aliases/__init__.py
import japanese.aliases # precompiled from 
/usr/lib/python2.2/lib-dynload/japanese/aliases/__init__.pyc
import encodings # directory /usr/lib/python2.2/encodings
# /usr/lib/python2.2/encodings/__init__.pyc matches 
/usr/lib/python2.2/encodings/__init__.py
import encodings # precompiled from /usr/lib/python2.2/encodings/__init__.pyc
# /usr/lib/python2.2/codecs.pyc matches /usr/lib/python2.2/codecs.py
import codecs # precompiled from /usr/lib/python2.2/codecs.pyc
dlopen("/usr/lib/python2.2/lib-dynload/structmodule.so", 2);
import struct # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/structmodule.so
dlopen("/usr/lib/python2.2/lib-dynload/_codecsmodule.so", 2);
import _codecs # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/_codecsmodule.so
# /usr/lib/python2.2/encodings/aliases.pyc matches 
/usr/lib/python2.2/encodings/aliases.py
import encodings.aliases # precompiled from 
/usr/lib/python2.2/encodings/aliases.pyc
Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
dlopen("/usr/lib/python2.2/lib-dynload/readline.so", 2);
import readline # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/readline.so
>>> import scipy
import scipy # directory /usr/lib/python2.2/site-packages/scipy
# /usr/lib/python2.2/site-packages/scipy/__init__.pyc matches 
/usr/lib/python2.2/site-packages/scipy/__init__.py
import scipy # precompiled from 
/usr/lib/python2.2/site-packages/scipy/__init__.pyc
# /usr/lib/python2.2/site-packages/scipy/scipy_version.pyc matches 
/usr/lib/python2.2/site-packages/scipy/scipy_version.py
import scipy.scipy_version # precompiled from 
/usr/lib/python2.2/site-packages/scipy/scipy_version.pyc
# /usr/lib/python2.2/site-packages/scipy/__cvs_version__.pyc matches 
/usr/lib/python2.2/site-packages/scipy/__cvs_version__.py
import scipy.__cvs_version__ # precompiled from 
/usr/lib/python2.2/site-packages/scipy/__cvs_version__.pyc
import scipy_base # directory /usr/lib/python2.2/site-packages/scipy_base
# /usr/lib/python2.2/site-packages/scipy_base/__init__.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/__init__.py
import scipy_base # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/__init__.pyc
# /usr/lib/python2.2/site-packages/scipy_base/scipy_base_version.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/scipy_base_version.py
import scipy_base.scipy_base_version # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/scipy_base_version.pyc
# /usr/lib/python2.2/site-packages/scipy_base/__cvs_version__.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/__cvs_version__.py
import scipy_base.__cvs_version__ # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/__cvs_version__.pyc
# /usr/lib/python2.2/site-packages/scipy_base/ppimport.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/ppimport.py
import scipy_base.ppimport # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/ppimport.pyc
# /usr/lib/python2.2/string.pyc matches /usr/lib/python2.2/string.py
import string # precompiled from /usr/lib/python2.2/string.pyc
dlopen("/usr/lib/python2.2/lib-dynload/strop.so", 2);
import strop # dynamically loaded from /usr/lib/python2.2/lib-dynload/strop.so
# /usr/lib/python2.2/traceback.pyc matches /usr/lib/python2.2/traceback.py
import traceback # precompiled from /usr/lib/python2.2/traceback.pyc
# /usr/lib/python2.2/linecache.pyc matches /usr/lib/python2.2/linecache.py
import linecache # precompiled from /usr/lib/python2.2/linecache.pyc
# /usr/lib/python2.2/pydoc.pyc matches /usr/lib/python2.2/pydoc.py
import pydoc # precompiled from /usr/lib/python2.2/pydoc.pyc
import imp # builtin
# /usr/lib/python2.2/re.pyc matches /usr/lib/python2.2/re.py
import re # precompiled from /usr/lib/python2.2/re.pyc
# /usr/lib/python2.2/sre.pyc matches /usr/lib/python2.2/sre.py
import sre # precompiled from /usr/lib/python2.2/sre.pyc
# /usr/lib/python2.2/sre_compile.pyc matches /usr/lib/python2.2/sre_compile.py
import sre_compile # precompiled from /usr/lib/python2.2/sre_compile.pyc
import _sre # builtin
# /usr/lib/python2.2/sre_constants.pyc matches 
/usr/lib/python2.2/sre_constants.py
import sre_constants # precompiled from /usr/lib/python2.2/sre_constants.pyc
# /usr/lib/python2.2/sre_parse.pyc matches /usr/lib/python2.2/sre_parse.py
import sre_parse # precompiled from /usr/lib/python2.2/sre_parse.pyc
# /usr/lib/python2.2/inspect.pyc matches /usr/lib/python2.2/inspect.py
import inspect # precompiled from /usr/lib/python2.2/inspect.pyc
# /usr/lib/python2.2/dis.pyc matches /usr/lib/python2.2/dis.py
import dis # precompiled from /usr/lib/python2.2/dis.pyc
# /usr/lib/python2.2/tokenize.pyc matches /usr/lib/python2.2/tokenize.py
import tokenize # precompiled from /usr/lib/python2.2/tokenize.pyc
# /usr/lib/python2.2/token.pyc matches /usr/lib/python2.2/token.py
import token # precompiled from /usr/lib/python2.2/token.pyc
# /usr/lib/python2.2/repr.pyc matches /usr/lib/python2.2/repr.py
import repr # precompiled from /usr/lib/python2.2/repr.pyc
dlopen("/usr/lib/python2.2/site-packages/scipy_base/fastumath.so", 2);
dlopen("/usr/lib/python2.2/site-packages/Numeric/_numpy.so", 2);
import _numpy # dynamically loaded from 
/usr/lib/python2.2/site-packages/Numeric/_numpy.so
import scipy_base.fastumath # dynamically loaded from 
/usr/lib/python2.2/site-packages/scipy_base/fastumath.so
# /usr/lib/python2.2/site-packages/Numeric/Numeric.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/Numeric.py
import Numeric # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/Numeric.pyc
# /usr/lib/python2.2/site-packages/Numeric/numeric_version.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/numeric_version.py
import numeric_version # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/numeric_version.pyc
dlopen("/usr/lib/python2.2/site-packages/Numeric/multiarray.so", 2);
import multiarray # dynamically loaded from 
/usr/lib/python2.2/site-packages/Numeric/multiarray.so
# /usr/lib/python2.2/site-packages/Numeric/Precision.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/Precision.py
import Precision # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/Precision.pyc
dlopen("/usr/lib/python2.2/lib-dynload/mathmodule.so", 2);
import math # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/mathmodule.so
# /usr/lib/python2.2/site-packages/Numeric/ArrayPrinter.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/ArrayPrinter.py
import ArrayPrinter # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/ArrayPrinter.pyc
# /usr/lib/python2.2/pickle.pyc matches /usr/lib/python2.2/pickle.py
import pickle # precompiled from /usr/lib/python2.2/pickle.pyc
import marshal # builtin
dlopen("/usr/lib/python2.2/lib-dynload/cStringIO.so", 2);
import cStringIO # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/cStringIO.so
# /usr/lib/python2.2/copy.pyc matches /usr/lib/python2.2/copy.py
import copy # precompiled from /usr/lib/python2.2/copy.pyc
# /usr/lib/python2.2/StringIO.pyc matches /usr/lib/python2.2/StringIO.py
import StringIO # precompiled from /usr/lib/python2.2/StringIO.pyc
import errno # builtin
# /usr/lib/python2.2/site-packages/scipy_base/limits.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/limits.py
import scipy_base.limits # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/limits.pyc
# /usr/lib/python2.2/site-packages/scipy_base/type_check.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/type_check.py
import scipy_base.type_check # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/type_check.pyc
Segmentation fault
[analytis at toomey analytis]$ python
[analytis at toomey analytis]$ python -v
# /usr/lib/python2.2/site.pyc matches /usr/lib/python2.2/site.py
import site # precompiled from /usr/lib/python2.2/site.pyc
# /usr/lib/python2.2/os.pyc matches /usr/lib/python2.2/os.py
import os # precompiled from /usr/lib/python2.2/os.pyc
import posix # builtin
# /usr/lib/python2.2/posixpath.pyc matches /usr/lib/python2.2/posixpath.py
import posixpath # precompiled from /usr/lib/python2.2/posixpath.pyc
# /usr/lib/python2.2/stat.pyc matches /usr/lib/python2.2/stat.py
import stat # precompiled from /usr/lib/python2.2/stat.pyc
# /usr/lib/python2.2/UserDict.pyc matches /usr/lib/python2.2/UserDict.py
import UserDict # precompiled from /usr/lib/python2.2/UserDict.pyc
# /usr/lib/python2.2/copy_reg.pyc matches /usr/lib/python2.2/copy_reg.py
import copy_reg # precompiled from /usr/lib/python2.2/copy_reg.pyc
# /usr/lib/python2.2/types.pyc matches /usr/lib/python2.2/types.py
import types # precompiled from /usr/lib/python2.2/types.pyc
# /usr/lib/python2.2/__future__.pyc matches /usr/lib/python2.2/__future__.py
import __future__ # precompiled from /usr/lib/python2.2/__future__.pyc
import japanese # directory /usr/lib/python2.2/lib-dynload/japanese
# /usr/lib/python2.2/lib-dynload/japanese/__init__.pyc matches 
/usr/lib/python2.2/lib-dynload/japanese/__init__.py
import japanese # precompiled from 
/usr/lib/python2.2/lib-dynload/japanese/__init__.pyc
import japanese.aliases # directory 
/usr/lib/python2.2/lib-dynload/japanese/aliases
# /usr/lib/python2.2/lib-dynload/japanese/aliases/__init__.pyc matches 
/usr/lib/python2.2/lib-dynload/japanese/aliases/__init__.py
import japanese.aliases # precompiled from 
/usr/lib/python2.2/lib-dynload/japanese/aliases/__init__.pyc
import encodings # directory /usr/lib/python2.2/encodings
# /usr/lib/python2.2/encodings/__init__.pyc matches 
/usr/lib/python2.2/encodings/__init__.py
import encodings # precompiled from /usr/lib/python2.2/encodings/__init__.pyc
# /usr/lib/python2.2/codecs.pyc matches /usr/lib/python2.2/codecs.py
import codecs # precompiled from /usr/lib/python2.2/codecs.pyc
dlopen("/usr/lib/python2.2/lib-dynload/structmodule.so", 2);
import struct # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/structmodule.so
dlopen("/usr/lib/python2.2/lib-dynload/_codecsmodule.so", 2);
import _codecs # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/_codecsmodule.so
# /usr/lib/python2.2/encodings/aliases.pyc matches 
/usr/lib/python2.2/encodings/aliases.py
import encodings.aliases # precompiled from 
/usr/lib/python2.2/encodings/aliases.pyc
Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
dlopen("/usr/lib/python2.2/lib-dynload/readline.so", 2);
import readline # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/readline.so
>>> import scipy
import scipy # directory /usr/lib/python2.2/site-packages/scipy
# /usr/lib/python2.2/site-packages/scipy/__init__.pyc matches 
/usr/lib/python2.2/site-packages/scipy/__init__.py
import scipy # precompiled from 
/usr/lib/python2.2/site-packages/scipy/__init__.pyc
# /usr/lib/python2.2/site-packages/scipy/scipy_version.pyc matches 
/usr/lib/python2.2/site-packages/scipy/scipy_version.py
import scipy.scipy_version # precompiled from 
/usr/lib/python2.2/site-packages/scipy/scipy_version.pyc
# /usr/lib/python2.2/site-packages/scipy/__cvs_version__.pyc matches 
/usr/lib/python2.2/site-packages/scipy/__cvs_version__.py
import scipy.__cvs_version__ # precompiled from 
/usr/lib/python2.2/site-packages/scipy/__cvs_version__.pyc
import scipy_base # directory /usr/lib/python2.2/site-packages/scipy_base
# /usr/lib/python2.2/site-packages/scipy_base/__init__.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/__init__.py
import scipy_base # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/__init__.pyc
# /usr/lib/python2.2/site-packages/scipy_base/scipy_base_version.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/scipy_base_version.py
import scipy_base.scipy_base_version # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/scipy_base_version.pyc
# /usr/lib/python2.2/site-packages/scipy_base/__cvs_version__.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/__cvs_version__.py
import scipy_base.__cvs_version__ # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/__cvs_version__.pyc
# /usr/lib/python2.2/site-packages/scipy_base/ppimport.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/ppimport.py
import scipy_base.ppimport # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/ppimport.pyc
# /usr/lib/python2.2/string.pyc matches /usr/lib/python2.2/string.py
import string # precompiled from /usr/lib/python2.2/string.pyc
dlopen("/usr/lib/python2.2/lib-dynload/strop.so", 2);
import strop # dynamically loaded from /usr/lib/python2.2/lib-dynload/strop.so
# /usr/lib/python2.2/traceback.pyc matches /usr/lib/python2.2/traceback.py
import traceback # precompiled from /usr/lib/python2.2/traceback.pyc
# /usr/lib/python2.2/linecache.pyc matches /usr/lib/python2.2/linecache.py
import linecache # precompiled from /usr/lib/python2.2/linecache.pyc
# /usr/lib/python2.2/pydoc.pyc matches /usr/lib/python2.2/pydoc.py
import pydoc # precompiled from /usr/lib/python2.2/pydoc.pyc
import imp # builtin
# /usr/lib/python2.2/re.pyc matches /usr/lib/python2.2/re.py
import re # precompiled from /usr/lib/python2.2/re.pyc
# /usr/lib/python2.2/sre.pyc matches /usr/lib/python2.2/sre.py
import sre # precompiled from /usr/lib/python2.2/sre.pyc
# /usr/lib/python2.2/sre_compile.pyc matches /usr/lib/python2.2/sre_compile.py
import sre_compile # precompiled from /usr/lib/python2.2/sre_compile.pyc
import _sre # builtin
# /usr/lib/python2.2/sre_constants.pyc matches 
/usr/lib/python2.2/sre_constants.py
import sre_constants # precompiled from /usr/lib/python2.2/sre_constants.pyc
# /usr/lib/python2.2/sre_parse.pyc matches /usr/lib/python2.2/sre_parse.py
import sre_parse # precompiled from /usr/lib/python2.2/sre_parse.pyc
# /usr/lib/python2.2/inspect.pyc matches /usr/lib/python2.2/inspect.py
import inspect # precompiled from /usr/lib/python2.2/inspect.pyc
# /usr/lib/python2.2/dis.pyc matches /usr/lib/python2.2/dis.py
import dis # precompiled from /usr/lib/python2.2/dis.pyc
# /usr/lib/python2.2/tokenize.pyc matches /usr/lib/python2.2/tokenize.py
import tokenize # precompiled from /usr/lib/python2.2/tokenize.pyc
# /usr/lib/python2.2/token.pyc matches /usr/lib/python2.2/token.py
import token # precompiled from /usr/lib/python2.2/token.pyc
# /usr/lib/python2.2/repr.pyc matches /usr/lib/python2.2/repr.py
import repr # precompiled from /usr/lib/python2.2/repr.pyc
dlopen("/usr/lib/python2.2/site-packages/scipy_base/fastumath.so", 2);
dlopen("/usr/lib/python2.2/site-packages/Numeric/_numpy.so", 2);
import _numpy # dynamically loaded from 
/usr/lib/python2.2/site-packages/Numeric/_numpy.so
import scipy_base.fastumath # dynamically loaded from 
/usr/lib/python2.2/site-packages/scipy_base/fastumath.so
# /usr/lib/python2.2/site-packages/Numeric/Numeric.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/Numeric.py
import Numeric # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/Numeric.pyc
# /usr/lib/python2.2/site-packages/Numeric/numeric_version.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/numeric_version.py
import numeric_version # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/numeric_version.pyc
dlopen("/usr/lib/python2.2/site-packages/Numeric/multiarray.so", 2);
import multiarray # dynamically loaded from 
/usr/lib/python2.2/site-packages/Numeric/multiarray.so
# /usr/lib/python2.2/site-packages/Numeric/Precision.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/Precision.py
import Precision # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/Precision.pyc
dlopen("/usr/lib/python2.2/lib-dynload/mathmodule.so", 2);
import math # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/mathmodule.so
# /usr/lib/python2.2/site-packages/Numeric/ArrayPrinter.pyc matches 
/usr/lib/python2.2/site-packages/Numeric/ArrayPrinter.py
import ArrayPrinter # precompiled from 
/usr/lib/python2.2/site-packages/Numeric/ArrayPrinter.pyc
# /usr/lib/python2.2/pickle.pyc matches /usr/lib/python2.2/pickle.py
import pickle # precompiled from /usr/lib/python2.2/pickle.pyc
import marshal # builtin
dlopen("/usr/lib/python2.2/lib-dynload/cStringIO.so", 2);
import cStringIO # dynamically loaded from 
/usr/lib/python2.2/lib-dynload/cStringIO.so
# /usr/lib/python2.2/copy.pyc matches /usr/lib/python2.2/copy.py
import copy # precompiled from /usr/lib/python2.2/copy.pyc
# /usr/lib/python2.2/StringIO.pyc matches /usr/lib/python2.2/StringIO.py
import StringIO # precompiled from /usr/lib/python2.2/StringIO.pyc
import errno # builtin
# /usr/lib/python2.2/site-packages/scipy_base/limits.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/limits.py
import scipy_base.limits # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/limits.pyc
# /usr/lib/python2.2/site-packages/scipy_base/type_check.pyc matches 
/usr/lib/python2.2/site-packages/scipy_base/type_check.py
import scipy_base.type_check # precompiled from 
/usr/lib/python2.2/site-packages/scipy_base/type_check.pyc
Segmentation fault
[analytis at toomey analytis]$



From pearu at scipy.org  Thu Jun  5 12:06:21 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Thu, 5 Jun 2003 19:06:21 +0300 (EEST)
Subject: [SciPy-user] Probs with scipy.test() and seg faults
In-Reply-To: <200306051621.28891.james.analytis@physics.ox.ac.uk>
Message-ID: <Pine.LNX.4.21.0306051901370.7833-100000@cens.kybi>

On Thu, 5 Jun 2003, James Analytis wrote:

> [analytis at toomey Numeric]$ ls -l 
> /usr/lib/python2.2/site-packages/Numeric-23.0/Include/Numeric/
> total 36
> -rw-r--r--    1 909      users       14370 Jun 11  2002 arrayobject.h
> -rw-r--r--    1 909      users        4228 Nov 19  2002 f2c.h
> -rw-r--r--    1 909      users        1285 Sep  2  2002 ranlib.h
> -rw-r--r--    1 909      users        6702 May 10  2001 ufuncobject.h

These header files seem to be the recent ones but I doubt
that scipy build process uses them.

How did you acctually install Numeric? When installing Numeric using

  setup.py install

then the header files should go into

  /usr/include/python2.2/Numeric/

not to

  /usr/lib/python2.2/site-packages/Numeric-23.0/Include/Numeric/

Pearu



From fperez at colorado.edu  Thu Jun  5 12:27:28 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Thu, 05 Jun 2003 10:27:28 -0600
Subject: [SciPy-user] tic toc in Matlab versus genutils.clock() in IPython
In-Reply-To: <3EDF38ED.44248A27@mecha.uni-stuttgart.de>
References: <3EDF38ED.44248A27@mecha.uni-stuttgart.de>
Message-ID: <3EDF6F70.10906@colorado.edu>

Nils Wagner wrote:
> Hi all,
> 
> I wonder, if I can compare the results of 
> Matlab, that is
> 
> tic
> big_calculation()
> toc
> 
> with scipy (ipython)
> 
> from IPython import genutils
> 
> t0 = genutils.clock()
> big_calculcation()
> t1 = genutils.clock()

I don't know how matlab works, so my comments are of limited value. 
genutils.clock() in ipython gives you:

- under unix, the user+system time as returned by the getrusage system call. 
It does NOT use the clock() system call, which has wraparound problems. 
Please see the respective manpages for those calls for all the details 
(there's also info in the standard python docs).

- under windows, it is simply time.clock(). I don't know what the timing 
subtleties are in that platform.

Cheers,

f.



From james.analytis at physics.ox.ac.uk  Thu Jun  5 12:38:05 2003
From: james.analytis at physics.ox.ac.uk (James Analytis)
Date: Thu, 5 Jun 2003 17:38:05 +0100
Subject: [SciPy-user] Probs with scipy.test() and seg faults
Message-ID: <200306051738.05841.james.analytis@physics.ox.ac.uk>

Hey,
I removed the existing Numeric and installed Numeric 23.0
from the tarball Numeric-23.0.tar.gz

cd /usr/lib/python2.2/site-packages
tar zxvf /home/analytis/trials/TAR/Numeric-23.0.tar.gz
cd Numeric-23.0/
python setup.py install

I then removed all the scipy files and reinstalled them

cd /usr/lib/python2.2/site-packages/
tar zxvf /home/analytis/trials/TAR/SciPy-0.2.0_alpha_197.4144.linux2_py.tar.gz

The output is now:

[analytis at toomey analytis]$ ls -l 
/usr/lib/python2.2/site-packages/Numeric-23.0/Include/Numeric/
total 36
-rw-r--r--    1 909      users       14370 Jun 11  2002 arrayobject.h
-rw-r--r--    1 909      users        4228 Nov 19  2002 f2c.h
-rw-r--r--    1 909      users        1285 Sep  2  2002 ranlib.h
-rw-r--r--    1 909      users        6702 May 10  2001 ufuncobject.h
[analytis at toomey analytis]$ ls -l /usr/include/python2.2/Numeric/
total 36
-rw-r--r--    1 root     root        14370 Jun 11  2002 arrayobject.h
-rw-r--r--    1 root     root         4228 Nov 19  2002 f2c.h
-rw-r--r--    1 root     root         1285 Sep  2  2002 ranlib.h
-rw-r--r--    1 root     root         6702 May 10  2001 ufuncobject.h
[analytis at toomey analytis]$

What do you think?
J



From chris at fonnesbeck.org  Thu Jun  5 12:45:14 2003
From: chris at fonnesbeck.org (Christopher Fonnesbeck)
Date: Thu, 5 Jun 2003 12:45:14 -0400
Subject: [SciPy-user] gplot on OS X
Message-ID: <14AA9BFA-9775-11D7-917C-000A956FDAC0@fonnesbeck.org>

Most of scipy seems to be working, except for gplot, which gives a 
broken pipe when used (even though gnuplot is installed):

Traceback (most recent call last):
   File "<stdin>", line 1, in ?
   File "Mallard.py", line 1155, in plot_history
     gplt.surf(ma,po,mat)
   File "/usr/lib/python2.2/site-packages/scipy/gplt/interface.py", line 
164, in surf
     apply(_active.surf,data)
   File "/usr/lib/python2.2/site-packages/scipy/gplt/pyPlot.py", line 
437, in surf
     self._init_plot()
   File "/usr/lib/python2.2/site-packages/scipy/gplt/pyPlot.py", line 
702, in _init_plot
     self._send('reset')
   File "/usr/lib/python2.2/site-packages/scipy/gplt/pyPlot.py", line 
820, in _send
     self.g.flush()
IOError: [Errno 32] Broken pipe



From pearu at scipy.org  Thu Jun  5 13:12:11 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Thu, 5 Jun 2003 20:12:11 +0300 (EEST)
Subject: [SciPy-user] Probs with scipy.test() and seg faults
In-Reply-To: <200306051738.05841.james.analytis@physics.ox.ac.uk>
Message-ID: <Pine.LNX.4.21.0306052004230.7833-100000@cens.kybi>

On Thu, 5 Jun 2003, James Analytis wrote:

> Hey,
> I removed the existing Numeric and installed Numeric 23.0
> from the tarball Numeric-23.0.tar.gz
> 
> cd /usr/lib/python2.2/site-packages
> tar zxvf /home/analytis/trials/TAR/Numeric-23.0.tar.gz
> cd Numeric-23.0/
> python setup.py install
> 
> I then removed all the scipy files and reinstalled them
> 
> cd /usr/lib/python2.2/site-packages/
> tar zxvf /home/analytis/trials/TAR/SciPy-0.2.0_alpha_197.4144.linux2_py.tar.gz
> 
> The output is now:
> 
> [analytis at toomey analytis]$ ls -l 
> /usr/lib/python2.2/site-packages/Numeric-23.0/Include/Numeric/
> total 36
> -rw-r--r--    1 909      users       14370 Jun 11  2002 arrayobject.h
> -rw-r--r--    1 909      users        4228 Nov 19  2002 f2c.h
> -rw-r--r--    1 909      users        1285 Sep  2  2002 ranlib.h
> -rw-r--r--    1 909      users        6702 May 10  2001 ufuncobject.h
> [analytis at toomey analytis]$ ls -l /usr/include/python2.2/Numeric/
> total 36
> -rw-r--r--    1 root     root        14370 Jun 11  2002 arrayobject.h
> -rw-r--r--    1 root     root         4228 Nov 19  2002 f2c.h
> -rw-r--r--    1 root     root         1285 Sep  2  2002 ranlib.h
> -rw-r--r--    1 root     root         6702 May 10  2001 ufuncobject.h
> [analytis at toomey analytis]$
> 
> What do you think?

That looks good. Though, since you are using linux2 binaries, it
does not matter at all. For some reason I assumed that you built scipy
from sources yourself but obviously you were not.

If you still get segfaults, I suggest building scipy from sources,
preferably from scipy CVS tree.

Note that SciPy-0.2.0_alpha_197.4144.linux2_py.tar.gz contains
binaries built under RedHat 7.3 and if you are running on some
other RedHat version/Linux distribution then I am not very 
surprised that such segfaults occur.

Pearu



From pearu at scipy.org  Thu Jun  5 13:23:20 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Thu, 5 Jun 2003 20:23:20 +0300 (EEST)
Subject: [SciPy-user] tic toc in Matlab versus genutils.clock() in IPython
In-Reply-To: <3EDF6F70.10906@colorado.edu>
Message-ID: <Pine.LNX.4.21.0306052012410.7833-100000@cens.kybi>

On Thu, 5 Jun 2003, Fernando Perez wrote:

> I don't know how matlab works, so my comments are of limited value. 
> genutils.clock() in ipython gives you:
> 
> - under unix, the user+system time as returned by the getrusage system call. 

IMHO, the returned user+system time is not very reliable, especially on
multiuser system or when for some reason swapping is under progress or 
.... In such cases I wouldn't use genutils.clock() for measuring the
efficiency of the code as the timing results may be is influenced by
unrelated system activities.

Recall that under linux scipy_test provides jiffies function that returns
the user time, not the user+system time. With jiffies one should get
the same results every time the same code is run, no matter if
the system is idle (up to a current python process) or swapping as crazy.

Regards,
	Pearu



From fperez at colorado.edu  Thu Jun  5 13:34:36 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Thu, 05 Jun 2003 11:34:36 -0600
Subject: [SciPy-user] tic toc in Matlab versus genutils.clock() in IPython
In-Reply-To: <Pine.LNX.4.21.0306052012410.7833-100000@cens.kybi>
References: <Pine.LNX.4.21.0306052012410.7833-100000@cens.kybi>
Message-ID: <3EDF7F2C.60403@colorado.edu>

Pearu Peterson wrote:
> On Thu, 5 Jun 2003, Fernando Perez wrote:
> 
> 
>>I don't know how matlab works, so my comments are of limited value. 
>>genutils.clock() in ipython gives you:
>>
>>- under unix, the user+system time as returned by the getrusage system call. 
> 
> 
> IMHO, the returned user+system time is not very reliable, especially on
> multiuser system or when for some reason swapping is under progress or 
> .... In such cases I wouldn't use genutils.clock() for measuring the
> efficiency of the code as the timing results may be is influenced by
> unrelated system activities.
> 
> Recall that under linux scipy_test provides jiffies function that returns
> the user time, not the user+system time. With jiffies one should get
> the same results every time the same code is run, no matter if
> the system is idle (up to a current python process) or swapping as crazy.

I actually debated a lot whether to add system time or not to the returned 
value.  While it is true that there may be corner cases where your process 
gets charged for system time, I decided to leave it in for a specific reason: 
  if your code is consistently using a significant amount of system time, it 
may well have issues of  over-hungry memory usage.

I agree, however, that without having a way to distinguish user/system time, 
the current implementation of genutils.clock() is not enough to distinguish 
these issues.  Perhaps I'll change it to return _only_ user time in the next 
release, it's a trivial change.

However, when I'm timing my own numerical codes, what I use is the timer class 
here included. This is unix-only, but it allows me to take readings along the 
way conveniently, and I can distinguish between user and system time.

I include it here in case anyone wants to use it.  It is not meant to time 
short code snippets, but rather to instrument timing into a large code which 
runs for a long time, with a minimal amount of effort.

Comments/improvements are welcome.

Best,

f.
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From j.analytis1 at physics.ox.ac.uk  Fri Jun  6 08:24:15 2003
From: j.analytis1 at physics.ox.ac.uk (James Analytis)
Date: Fri, 6 Jun 2003 13:24:15 +0100
Subject: [SciPy-user] Probs with scipy.test() and seg faults
Message-ID: <200306061324.15087.j.analytis1@physics.ox.ac.uk>

Hey,
I built scipy from the sources as you suggested. On scipy.test() i got these 
as the last few lines:
----------------------------------------------------------------------
Ran 640 tests in 10.232s

FAILED (failures=1, errors=4)
<unittest.TextTestRunner instance at 0x827c944>
>>
----------------------------------------------------------------------
No seg faults! So apart from that one failure things seems to be ok (right?)
Thanks,
J

-- 
"I'm a simple man, Hobbes."
"You?? Yesterday you wanted a nuclear powered car that could turn into a
jet with laser-guided heat-seeking missiles!"
"I'm a simple man with complex tastes." -Calvin and Hobbes



From pearu at scipy.org  Fri Jun  6 08:33:23 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Fri, 6 Jun 2003 15:33:23 +0300 (EEST)
Subject: [SciPy-user] Probs with scipy.test() and seg faults
In-Reply-To: <200306061324.15087.j.analytis1@physics.ox.ac.uk>
Message-ID: <Pine.LNX.4.21.0306061531090.7833-100000@cens.kybi>

On Fri, 6 Jun 2003, James Analytis wrote:

> Hey,
> I built scipy from the sources as you suggested. On scipy.test() i got these 
> as the last few lines:
> ----------------------------------------------------------------------
> Ran 640 tests in 10.232s
> 
> FAILED (failures=1, errors=4)
> <unittest.TextTestRunner instance at 0x827c944>
> >>
> ----------------------------------------------------------------------
> No seg faults! So apart from that one failure things seems to be ok (right?)

Not too bad. Though I wonder what are these errors? Certain scipy tests
(e.g. stats ones) are known to fail occasionally and that is ok, but in
principle there should not be any `errors` in scipy tests.

Pearu




From james.analytis at physics.ox.ac.uk  Fri Jun  6 09:03:06 2003
From: james.analytis at physics.ox.ac.uk (J Analytis)
Date: Fri, 6 Jun 2003 14:03:06 +0100
Subject: [SciPy-user] Probs with scipy.test() and seg faults
In-Reply-To: <Pine.LNX.4.21.0306061531090.7833-100000@cens.kybi>
References: <Pine.LNX.4.21.0306061531090.7833-100000@cens.kybi>
Message-ID: <200306061403.06359.analytis@nodens.physics.ox.ac.uk>

Hey,
I've copied the two relevant sections from the scipy.test() output below. The 
first is to do with finding clapack, but has corrected the problem by using 
flapack.
The errors are related to incomplete BLAS sources. INSTALL.txt says I can cure 
the prob by using ATLAS or the official release of BLAS libraries. But I'm 
using atlas 3.5.2 so I should be ok.
How do i find out if scipy is finding the ATLAS libraries correctly (assuming 
that i've installed atlas properly!)? Is ATLAS incomplete? (liblapack.a is 
about 5MB, and I wasn't able to find it using the command 
python scipy_core/scipy_distutils/system.py
as suggested in the release notes.)
Also do I need to remove the existing scipy if I need to rebuild/reinstall it?

Thanks,
J



###################
****************************************************************
WARNING: Importing clapack failed with the following exception:
-----------
exceptions.ImportError: cannot import name clapack
-----------
See scipy/INSTALL.txt for troubleshooting.
Notes:
* If atlas library is not found by scipy/system_info.py,
  then scipy skips building clapack and uses flapack instead.
****************************************************************

No test suite found for  scipy.linalg.linalg_version
 creating test suite for: scipy.linalg.matfuncs
   !! FAILURE building test for  scipy.linalg.matfuncs
    <string>:1: ImportError: No module named test_matfuncs (in ?)


###################
======================================================================
ERROR: check_random (test_decomp.test_qr)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.2/site-packages/scipy/linalg/tests/test_decomp.py", 
line 295, in check_random
    q,r = qr(a)
  File "/usr/lib/python2.2/site-packages/scipy/linalg/decomp.py", line 359, in 
qr
    gemm, = get_blas_funcs(('gemm',),(qr,))
  File "/usr/lib/python2.2/site-packages/scipy/linalg/blas.py", line 43, in 
get_blas_funcs
    if ordering and fblas.has_column_major_storage(arrays[ordering[0][1]]):
  File "/usr/lib/python2.2/site-packages/scipy_distutils/misc_util.py", line 
44, in __getattr__
    raise self._info[0],self._info[1]
ImportError: /usr/lib/python2.2/site-packages/scipy/linalg/fblas.so: undefined 
symbol: srotmg_

======================================================================
ERROR: check_random_complex (test_decomp.test_qr)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.2/site-packages/scipy/linalg/tests/test_decomp.py", 
line 302, in check_random_complex
    q,r = qr(a)
  File "/usr/lib/python2.2/site-packages/scipy/linalg/decomp.py", line 359, in 
qr
    gemm, = get_blas_funcs(('gemm',),(qr,))
  File "/usr/lib/python2.2/site-packages/scipy/linalg/blas.py", line 43, in 
get_blas_funcs
    if ordering and fblas.has_column_major_storage(arrays[ordering[0][1]]):
  File "/usr/lib/python2.2/site-packages/scipy_distutils/misc_util.py", line 
44, in __getattr__
    raise self._info[0],self._info[1]
ImportError: /usr/lib/python2.2/site-packages/scipy/linalg/fblas.so: undefined 
symbol: srotmg_

======================================================================
ERROR: check_simple (test_decomp.test_qr)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.2/site-packages/scipy/linalg/tests/test_decomp.py", 
line 283, in check_simple
    q,r = qr(a)
  File "/usr/lib/python2.2/site-packages/scipy/linalg/decomp.py", line 359, in 
qr
    gemm, = get_blas_funcs(('gemm',),(qr,))
  File "/usr/lib/python2.2/site-packages/scipy/linalg/blas.py", line 43, in 
get_blas_funcs
    if ordering and fblas.has_column_major_storage(arrays[ordering[0][1]]):
  File "/usr/lib/python2.2/site-packages/scipy_distutils/misc_util.py", line 
44, in __getattr__
    raise self._info[0],self._info[1]
ImportError: /usr/lib/python2.2/site-packages/scipy/linalg/fblas.so: undefined 
symbol: srotmg_

======================================================================
ERROR: check_simple_complex (test_decomp.test_qr)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.2/site-packages/scipy/linalg/tests/test_decomp.py", 
line 288, in check_simple_complex
    q,r = qr(a)
  File "/usr/lib/python2.2/site-packages/scipy/linalg/decomp.py", line 359, in 
qr
    gemm, = get_blas_funcs(('gemm',),(qr,))
  File "/usr/lib/python2.2/site-packages/scipy/linalg/blas.py", line 43, in 
get_blas_funcs
    if ordering and fblas.has_column_major_storage(arrays[ordering[0][1]]):
  File "/usr/lib/python2.2/site-packages/scipy_distutils/misc_util.py", line 
44, in __getattr__
    raise self._info[0],self._info[1]
ImportError: /usr/lib/python2.2/site-packages/scipy/linalg/fblas.so: undefined 
symbol: srotmg_

======================================================================
FAIL: check_basic (test_morestats.test_shapiro)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.2/site-packages/scipy/stats/tests/test_morestats.py", 
line 37, in check_basic
    assert_almost_equal(pw,0.52459925413131714,6)
  File "/usr/lib/python2.2/site-packages/scipy_test/testing.py", line 349, in 
assert_almost_equal
    assert round(abs(desired - actual),decimal) == 0, msg
AssertionError:
Items are not equal:
DESIRED: 0.52459925413131714
ACTUAL: 0.5246044397354126

----------------------------------------------------------------------
Ran 640 tests in 8.418s

FAILED (failures=1, errors=4)
<unittest.TextTestRunner instance at 0x841022c>
>>>



On Friday 06 June 2003 13:33, Pearu Peterson wrote:
> On Fri, 6 Jun 2003, James Analytis wrote:
> > Hey,
> > I built scipy from the sources as you suggested. On scipy.test() i got
> > these as the last few lines:
> > ----------------------------------------------------------------------
> > Ran 640 tests in 10.232s
> >
> > FAILED (failures=1, errors=4)
> > <unittest.TextTestRunner instance at 0x827c944>
> >
> > ----------------------------------------------------------------------
> > No seg faults! So apart from that one failure things seems to be ok
> > (right?)
>
> Not too bad. Though I wonder what are these errors? Certain scipy tests
> (e.g. stats ones) are known to fail occasionally and that is ok, but in
> principle there should not be any `errors` in scipy tests.
>
> Pearu
>
>
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user



From pearu at scipy.org  Fri Jun  6 11:35:07 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Fri, 6 Jun 2003 18:35:07 +0300 (EEST)
Subject: [SciPy-user] Probs with scipy.test() and seg faults
In-Reply-To: <200306061403.06359.analytis@nodens.physics.ox.ac.uk>
Message-ID: <Pine.LNX.4.21.0306061826110.12860-100000@cens.kybi>

On Fri, 6 Jun 2003, J Analytis wrote:

> Hey,
> I've copied the two relevant sections from the scipy.test() output below. 

Thanks.

> The 
> first is to do with finding clapack, but has corrected the problem by using 
> flapack.
> The errors are related to incomplete BLAS sources. INSTALL.txt says I can cure 
> the prob by using ATLAS or the official release of BLAS libraries. But I'm 
> using atlas 3.5.2 so I should be ok.

These undefined srotmg_ errors indicate that ATLAS was not found.

> How do i find out if scipy is finding the ATLAS libraries correctly (assuming 
> that i've installed atlas properly!)? Is ATLAS incomplete? (liblapack.a is 
> about 5MB, and I wasn't able to find it using the command 
> python scipy_core/scipy_distutils/system.py
> as suggested in the release notes.)

If system_info.py does not show that it detected ATLAS libraries then
also scipy installation cannot find ATLAS.

Where did you install ATLAS libraries, what is its directory contents? 
You can use ATLAS environment variable to indicate the location of ATLAS
libraries.

> Also do I need to remove the existing scipy if I need to rebuild/reinstall it?

No. However removing scipy build directory might be a good idea before 
rebuilding scipy, also
  rm -f Lib/linalg/{clapack,flapack,cblas,fblas}.pyf
might be required, especially when having issues with finding ATLAS
libraries. See also comments in the header of Lib/linalg/setup_linalg.py.

Pearu



From vperel at yahoo.com  Fri Jun  6 12:52:42 2003
From: vperel at yahoo.com (Victor P.)
Date: Fri, 6 Jun 2003 09:52:42 -0700 (PDT)
Subject: [SciPy-user] linalg.eig for solving transcendental eigenvalue problems
Message-ID: <20030606165242.47437.qmail@web11306.mail.yahoo.com>

Mr. Pearu Peterson,
I saw your message regarding use of linalg.eig for
solving transcendental eigenvalue problems on the web
site

http://scipy.net/pipermail/scipy-dev/2002-August/001131.html

Can you let me know, please, where the program (or
subroutine) linalg.eig can be bought or downloaded?

Victor

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From james.analytis at physics.ox.ac.uk  Fri Jun  6 13:02:08 2003
From: james.analytis at physics.ox.ac.uk (J Analytis)
Date: Fri, 6 Jun 2003 18:02:08 +0100
Subject: [SciPy-user] Probs with scipy.test() and seg faults
In-Reply-To: <Pine.LNX.4.21.0306061826110.12860-100000@cens.kybi>
References: <Pine.LNX.4.21.0306061826110.12860-100000@cens.kybi>
Message-ID: <200306061802.08437.analytis@nodens.physics.ox.ac.uk>

Hey,
So I went through the INSTALL.txt instructions once more and I think the key 
step to redo was to make the optimised lapack library. It turned out that 
neither clapack.py nor cblas.pyf were in Lib/linalg/ so I only needed to 
remove fblas.pyf and flapack.pyf
After removing build/, I rebuilt and installed scipy.
Now, the output for scipy.test() has one failure:

............................................
======================================================================
FAIL: check_basic (test_morestats.test_shapiro)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.2/site-packages/scipy/stats/tests/test_morestats.py", 
line 37, in check_basic
    assert_almost_equal(pw,0.52459925413131714,6)
  File "/usr/lib/python2.2/site-packages/scipy_test/testing.py", line 349, in 
assert_almost_equal
    assert round(abs(desired - actual),decimal) == 0, msg
AssertionError:
Items are not equal:
DESIRED: 0.52459925413131714
ACTUAL: 0.5246044397354126

----------------------------------------------------------------------
Ran 739 tests in 8.960s

FAILED (failures=1)
<unittest.TextTestRunner instance at 0x8269e0c>
>>>

No reference to srotmg_ errors, so I guess that means the ATLAS libraries were 
found.
Good enough?
Thanks again,
J


On Friday 06 June 2003 16:35, Pearu Peterson wrote:
> On Fri, 6 Jun 2003, J Analytis wrote:
> > Hey,
> > I've copied the two relevant sections from the scipy.test() output below.
>
> Thanks.
>
> > The
> > first is to do with finding clapack, but has corrected the problem by
> > using flapack.
> > The errors are related to incomplete BLAS sources. INSTALL.txt says I can
> > cure the prob by using ATLAS or the official release of BLAS libraries.
> > But I'm using atlas 3.5.2 so I should be ok.
>
> These undefined srotmg_ errors indicate that ATLAS was not found.
>
> > How do i find out if scipy is finding the ATLAS libraries correctly
> > (assuming that i've installed atlas properly!)? Is ATLAS incomplete?
> > (liblapack.a is about 5MB, and I wasn't able to find it using the command
> > python scipy_core/scipy_distutils/system.py
> > as suggested in the release notes.)
>
> If system_info.py does not show that it detected ATLAS libraries then
> also scipy installation cannot find ATLAS.
>
> Where did you install ATLAS libraries, what is its directory contents?
> You can use ATLAS environment variable to indicate the location of ATLAS
> libraries.
>
> > Also do I need to remove the existing scipy if I need to
> > rebuild/reinstall it?
>
> No. However removing scipy build directory might be a good idea before
> rebuilding scipy, also
>   rm -f Lib/linalg/{clapack,flapack,cblas,fblas}.pyf
> might be required, especially when having issues with finding ATLAS
> libraries. See also comments in the header of Lib/linalg/setup_linalg.py.
>
> Pearu
>
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user



From oliphant.travis at ieee.org  Fri Jun  6 13:13:08 2003
From: oliphant.travis at ieee.org (Travis Oliphant)
Date: Fri, 06 Jun 2003 11:13:08 -0600
Subject: [SciPy-user] linalg.eig for solving transcendental eigenvalue
 problems
References: <20030606165242.47437.qmail@web11306.mail.yahoo.com>
Message-ID: <3EE0CBA4.2060809@ieee.org>


Victor P. wrote:
> Mr. Pearu Peterson,
> I saw your message regarding use of linalg.eig for
> solving transcendental eigenvalue problems on the web
> site
> 
> http://scipy.net/pipermail/scipy-dev/2002-August/001131.html
> 
> Can you let me know, please, where the program (or
> subroutine) linalg.eig can be bought or downloaded?
>

linalg.eig is in the linalg subpackage of SciPy.  Installing SciPy is 
all you need to do.




From p.berkes at biologie.hu-berlin.de  Wed Jun 11 05:09:23 2003
From: p.berkes at biologie.hu-berlin.de (Pietro Berkes)
Date: Wed, 11 Jun 2003 11:09:23 +0200
Subject: [SciPy-user] matrix multiplication
In-Reply-To: <Pine.LNX.4.21.0305262348470.26769-100000@cens.kybi>
References: <Pine.LNX.4.21.0305262348470.26769-100000@cens.kybi>
Message-ID: <200306111109.23528.p.berkes@biologie.hu-berlin.de>


I did some benchmarks using the attached code (which is actually a 
cut-and-paste from linalg/test_basic.py and scipy_test/testing.py). The test 
function multiplies contiguous/non-contiguous arrays of type 'f'/'d'. Here is 
the result:


           Multiplying matrices of type f
      ==================================
      |    contiguous     |   non-contiguous
----------------------------------------------
 size |  scipy  | matmult |  scipy  | matmult
   20 |   0.11  |   0.20  |   0.11  |   0.20     (secs for 2000 calls)
   50 |   1.00  |   0.57  |   1.00  |   0.57     (secs for 2000 calls)
   75 |   3.11  |   1.40  |   3.12  |   1.40     (secs for 2000 calls)
  100 |   3.74  |   0.81  |   3.74  |   0.83     (secs for 1000 calls)
  500 |   5.04  |   0.25  |   5.04  |   0.25     (secs for 4 calls)
 1000 |  27.67  |   0.92  |  27.75  |   0.92     (secs for 2 calls)

           Multiplying matrices of type d
      ==================================
      |    contiguous     |   non-contiguous
----------------------------------------------
 size |  scipy  | matmult |  scipy  | matmult
   20 |   0.11  |   0.22  |   0.10  |   0.22     (secs for 2000 calls)
   50 |   1.01  |   0.61  |   1.02  |   0.61     (secs for 2000 calls)
   75 |   3.43  |   1.78  |   3.42  |   1.78     (secs for 2000 calls)
  100 |   3.78  |   1.52  |   3.80  |   1.56     (secs for 1000 calls)
  500 |   7.96  |   0.50  |   7.95  |   0.50     (secs for 4 calls)
 1000 |  35.71  |   1.82  |  35.65  |   1.82     (secs for 2 calls)


Calling the lapack routine is always is always faster but for small (20x20) 
arrays. For large matrices it can get up to 30 time faster on my machine 
(Pentium 4, 1.8 GHz, 1.5Gb memory)!

Pietro.


On Monday 26 May 2003 22:56, Pearu Peterson wrote:
> On Mon, 26 May 2003, eric jones wrote:
> > This seems like a reasonable alias to make, though, to the Numeric
> > routine.  With the following code, I get a marked speed up:
> >
> > C:\temp>python matmult_tst.py
> > std matmult: 1.28100001812
> > gemm matmult: 0.171999931335
> >
> > Other opinions?
>
> I get speed up factor around 5.
> Before making an alias to Numeric.dot, we should test
> how gemm matmult measures on non-contiguous arrays
> as linalg.gemm will make a contiguous copy of its arguments
> while Numeric.dot just runs the multiplication loop.
>
> Pearu
>
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user
-------------- next part --------------
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From nwagner at mecha.uni-stuttgart.de  Wed Jun 11 05:34:03 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Wed, 11 Jun 2003 11:34:03 +0200
Subject: [SciPy-user] How can I improve the efficiency linalg.eig with respect to certain
 properties of the matrices
Message-ID: <3EE6F78B.28A43EBE@mecha.uni-stuttgart.de>

Hi all,

I wonder, if I can speed up the computation of a standard/generalized
large scale eigenvalue problem (n>10^3) in scipy.
I guess it should be possible somehow, since linalg.eig ignores the
special structure
(symmetrie, band, definiteness) of the matrix. Are there any efforts in
adapting 
linalg.eig with respect to certain properties of the matrix A in
(Ax=\lambda x) and matrices for (Ax = \lambda Bx) ?


Any comments or suggestions ?

Nils
-------------- next part --------------
from scipy import *           
from IPython import genutils
n = 255

def eberlein():

  tmp=zeros((n,n),Float)
  for k in arange(0,n):
    tmp[k,k] = 1.0+(1.0+(1.0+2.0*(k+1.0)**2-2*(k+1.0))/n-2.0*(k+1))/n
  for k in arange(0,n-1):
    tmp[k,k+1] = (k+1.0)*(n-(k+1.0))/n**2
    tmp[k+1,k] = tmp[k,k+1]

  return tmp
t0 = genutils.clock()
A = eberlein()
w, vr = linalg.eig(A)
t1 = genutils.clock()
print
print 'CPU time',t1-t0
print
print 'Eigenvalues by linalg.eig'
print
print sort(abs(w))
#
# Analytical eigenvalues
#
wa = zeros(n,Float)
for j in arange(0,n):
  wa[j] = 1.0-(j+1.0)*j/n**2
print
print 'Analytical eigenvalues'
print
print sort(wa)
  

From p.berkes at biologie.hu-berlin.de  Wed Jun 11 05:44:07 2003
From: p.berkes at biologie.hu-berlin.de (Pietro Berkes)
Date: Wed, 11 Jun 2003 11:44:07 +0200
Subject: [SciPy-user] How can I improve the efficiency linalg.eig with
	respect to certain properties of the matrices
In-Reply-To: <3EE6F78B.28A43EBE@mecha.uni-stuttgart.de>
References: <3EE6F78B.28A43EBE@mecha.uni-stuttgart.de>
Message-ID: <200306111144.07233.p.berkes@biologie.hu-berlin.de>

We have written some f2py wrappers for the most important LAPACK routines 
concerning standard and generalized symmetrical eigenvalue problems. I have 
no time now, but I could mail something on this argument next week.

Pietro.


On Wednesday 11 June 2003 11:34, Nils Wagner wrote:
> Hi all,
>
> I wonder, if I can speed up the computation of a standard/generalized
> large scale eigenvalue problem (n>10^3) in scipy.
> I guess it should be possible somehow, since linalg.eig ignores the
> special structure
> (symmetrie, band, definiteness) of the matrix. Are there any efforts in
> adapting
> linalg.eig with respect to certain properties of the matrix A in
> (Ax=\lambda x) and matrices for (Ax = \lambda Bx) ?
>
>
> Any comments or suggestions ?
>
> Nils



From arnd.baecker at web.de  Wed Jun 11 06:01:04 2003
From: arnd.baecker at web.de (Arnd Baecker)
Date: Wed, 11 Jun 2003 12:01:04 +0200 (CEST)
Subject: [SciPy-user] test_morestats.test_shapiro failure
Message-ID: <Pine.LNX.4.51.0306111159480.7702@ptpcp2.phy.tu-dresden.de>

Hi,

I just did a fresh reinstall with python 2.2.3, Numeric CVS and scipy from
cvs.

With scipy.test(1) I get the error below
(which actually seems unproblematic, as the two numbers
are close -  but not close enough ?).

Arnd

======================================================================
FAIL: check_basic (test_morestats.test_shapiro)
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/home/abaecker/morepub/PYTHON/lib/python2.2/site-packages/scipy/stats/tests/test_morestats.py",
line 37, in check_basic
    assert_almost_equal(pw,0.52459925413131714,6)
  File
"/home/abaecker/morepub/PYTHON/lib/python2.2/site-packages/scipy_test/testing.py",
line 349, in assert_almost_equal
    assert round(abs(desired - actual),decimal) == 0, msg
AssertionError:
Items are not equal:
DESIRED: 0.52459925413131714
ACTUAL: 0.5246044397354126

----------------------------------------------------------------------
Ran 739 tests in 7.620s



From joe at enthought.com  Thu Jun 12 13:02:45 2003
From: joe at enthought.com (Joe Cooper)
Date: Thu, 12 Jun 2003 12:02:45 -0500
Subject: [SciPy-user] testing.scipy.org
Message-ID: <3EE8B235.4030403@enthought.com>

Hi all,

I have just installed the latest Zope and CMF for the SciPy community 
portal.  This upgrade will make it easier to add new products to the 
portal (think ZWiki!), and will resolve some minor but annoying 
outstanding issues with the current installation.

I would like to test the new installation for a few days before making 
the final database transition and shutting down the old site.  To that 
end, I'd like to ask folks to have a look at the new site, login, create 
some content, browse around, and look for troubles.  Please keep in mind 
that when the testing is done, the database will be overwritten with the 
current live database, so don't create content that you don't expect to 
lose, as it /will/ go away during the final migration.  Right now the 
anticipated migration date is one week from today, on June 19th, unless 
serious problems are uncovered during the test period.

The URL for the test Zope is:

http://testing.scipy.org

Please let me know if you spot any problems.

PS-To those folks who have ZMI access to either the SciPy CMF instance, 
or the SciPy Zope root folder, please be aware that modifying CMF 
content within the ZMI is incompatible with the new Zope/CMF version. 
New content should be published using the CMF interfaces (all recent 
content has been created this way, so I don't think this will be a 
problem, but there was some older content that was broken in this way so 
I thought it worth mentioning).

Thanks!
-- 
Joe Cooper <joe at enthought.com>



From beleza at celulas.com.br  Thu Jun 12 19:42:20 2003
From: beleza at celulas.com.br (beleza)
Date: Thu, 12 Jun 2003 20:42:20 -0300
Subject: [SciPy-user] < SAÚDE - NUTRIÇÃO - BEM ESTAR >
Message-ID: <20030613005046.EF1EA3EB09@www.scipy.com>


     Combate a m? alimenta??o, que nos leva a adquirir doen?as!
     Combate aos maus h?bitos alimentares e ao engano propagado 
	 na M?DIA.

     Desculpe-nos se voc? n?o concorda com tais coloca??es !
     
     Mas se desejar tomar conhecimento de algo NUTRITIVO realmente!
	 
     Observe o endere?o eletr?nico a seguir;

                         www.redebiz.com.br/lojajemc

    
    Se voc? conhece algu?m que precisa de nutri??o interna e externa,
	informe este endere?o, pois a SA?DE  p?blica agradece!



   Controle Total
   Regi?o Sul
   Brasil
   

   
  Para remover:
  urgente at remover.com
  N?s combatemos a obesidade, seja ela o n?vel que for!



From dmorrill at enthought.com  Fri Jun 13 20:07:55 2003
From: dmorrill at enthought.com (David C. Morrill)
Date: Fri, 13 Jun 2003 19:07:55 -0500
Subject: [SciPy-user] Python 2.2.3 for Windows (Enthought Edition)
Message-ID: <005001c33209$0184f110$8201a8c0@dellbert>

Enthought, Inc. is pleased to announce the availability of Python 2.2.3 for
Windows (Enthought Edition) at:

http://www.enthought.com/python

Python 2.2.3 for Windows (Enthought Edition) is an easily installed and
freely downloadable Python distribution incorporating many of the packages
that we find useful in our Python programming endeavors:

- wxPython 2.4.0.7
- PIL 1.1.4
- VTK 4.2.2
- Numeric 23.0
- SciPy 0.2
- Chaco 0.1.0
- Traits 1.0.2
- PyCrust 0.7.2

Many of these packages should be familiar, if not near and dear, to most of
the subscribers to this list :-) We are hoping that people will find this a
convenient bundling of many useful Python packages, and we hope to increase
both the number of platforms supported as well as the scope of the
distribution's content as time goes on.

This distribution is in its infancy, and most likely will suffer through a
number of growing pains. We're hoping that many of you who might have been
looking for an easier way to get many of your favorite (though sometimes
difficult to install) Python packages in one convenient distro will give it
a try.

We're looking forward to hearing what you think, both here on the mailing
lists and through the bug tracker we've set up to track any suggestions or
problems you might have (the bug tracker link is on the web page referenced
above).

Regards,
the Python guys at Enthought




From nwagner at mecha.uni-stuttgart.de  Mon Jun 16 08:30:45 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Mon, 16 Jun 2003 14:30:45 +0200
Subject: [SciPy-user] Gaussian elimination with complete pivoting
Message-ID: <3EEDB875.B768E57F@mecha.uni-stuttgart.de>

Hi all,

I am looking for a routine which performs 
Gaussian elimination with complete pivoting.

Has anyone written such a routine ?

Thanks in advance.

                     Nils



From nwagner at mecha.uni-stuttgart.de  Mon Jun 16 11:06:21 2003
From: nwagner at mecha.uni-stuttgart.de (Nils Wagner)
Date: Mon, 16 Jun 2003 17:06:21 +0200
Subject: [SciPy-user] Matlab function eigs(A,B) in scipy
Message-ID: <3EEDDCED.874DB5C5@mecha.uni-stuttgart.de>

Hi all,

I wonder if the matlab function eigs will be available in scipy in the
near future.

Any suggestion ?

Nils

>>help eigs

 EIGS  Find a few eigenvalues and eigenvectors of a matrix using ARPACK.
    D = EIGS(A) returns a vector of A's 6 largest magnitude eigenvalues.
    A must be square and should be large and sparse.
 
    [V,D] = EIGS(A) returns a diagonal matrix D of A's 6 largest
magnitude
    eigenvalues and a matrix V whose columns are the corresponding
eigenvectors.
 
    [V,D,FLAG] = EIGS(A) also returns a convergence flag.  If FLAG is 0
    then all the eigenvalues converged; otherwise not all converged.
 
    EIGS(A,B) solves the generalized eigenvalue problem A*V == B*V*D.  B
must
    be symmetric (or Hermitian) positive definite and the same size as
A.
    EIGS(A,[],...) indicates the standard eigenvalue problem A*V == V*D.
 
    EIGS(A,K) and EIGS(A,B,K) return the K largest magnitude
eigenvalues.
 
    EIGS(A,K,SIGMA) and EIGS(A,B,K,SIGMA) return K eigenvalues based on
SIGMA:
       'LM' or 'SM' - Largest or Smallest Magnitude
    For real symmetric problems, SIGMA may also be:
       'LA' or 'SA' - Largest or Smallest Algebraic
       'BE' - Both Ends, one more from high end if K is odd
    For nonsymmetric and complex problems, SIGMA may also be:
       'LR' or 'SR' - Largest or Smallest Real part
       'LI' or 'SI' - Largest or Smallest Imaginary part
    If SIGMA is a real or complex scalar including 0, EIGS finds the
eigenvalues
    closest to SIGMA.  For scalar SIGMA, and also when SIGMA = 'SM'
which uses
    the same algorithm as SIGMA = 0, B need only be symmetric (or
Hermitian)
    positive semi-definite since it is not Cholesky factored as in the
other cases.
 
    EIGS(A,K,SIGMA,OPTS) and EIGS(A,B,K,SIGMA,OPTS) specify options:
    OPTS.issym: symmetry of A or A-SIGMA*B represented by AFUN [{0} | 1]
    OPTS.isreal: complexity of A or A-SIGMA*B represented by AFUN [0 |
{1}]
    OPTS.tol: convergence: Ritz estimate residual <= tol*NORM(A) [scalar
| {eps}]
    OPTS.maxit: maximum number of iterations [integer | {300}]
    OPTS.p: number of Lanczos vectors: K+1<p<=N [integer | {2K}]
    OPTS.v0: starting vector [N-by-1 vector | {randomly generated by
ARPACK}]
    OPTS.disp: diagnostic information display level [0 | {1} | 2]
    OPTS.cholB: B is actually its Cholesky factor CHOL(B) [{0} | 1]
    OPTS.permB: sparse B is actually CHOL(B(permB,permB)) [permB |
{1:N}]
 
    EIGS(AFUN,N) accepts the function AFUN instead of the matrix A.
    Y = AFUN(X) should return
       A*X            if SIGMA is not specified, or is a string other
than 'SM'
       A\X            if SIGMA is 0 or 'SM'
       (A-SIGMA*I)\X  if SIGMA is a nonzero scalar (standard eigenvalue
problem)
       (A-SIGMA*B)\X  if SIGMA is a nonzero scalar (generalized
eigenvalue problem)
    N is the size of A. The matrix A, A-SIGMA*I or A-SIGMA*B represented
by AFUN is
    assumed to be real and nonsymmetric unless specified otherwise by
OPTS.isreal
    and OPTS.issym. In all these EIGS syntaxes, EIGS(A,...) may be
replaced by
    EIGS(AFUN,N,...).
 
    EIGS(AFUN,N,K,SIGMA,OPTS,P1,P2,...) and
EIGS(AFUN,N,B,K,SIGMA,OPTS,P1,P2,...)
    provide for additional arguments which are passed to
AFUN(X,P1,P2,...).
 
    Examples:
       A = delsq(numgrid('C',15));  d1 = eigs(A,5,'SM');
    Equivalently, if dnRk is the following one-line function:
       function y = dnRk(x,R,k)
       y = (delsq(numgrid(R,k))) \ x;
    then pass dnRk's additional arguments, 'C' and 15, to EIGS:
       n = size(A,1);  opts.issym = 1;  d2 =
eigs(@dnRk,n,5,'SM',opts,'C',15);
 
    See also EIG, SVDS, ARPACKC.

>>



From chris at fonnesbeck.org  Mon Jun 16 20:21:15 2003
From: chris at fonnesbeck.org (Christopher Fonnesbeck)
Date: Mon, 16 Jun 2003 20:21:15 -0400
Subject: [SciPy-user] OSX and gnuplot
Message-ID: <9BD8F306-A059-11D7-9D1D-000A956FDAC0@fonnesbeck.org>

Well, my lone regret about moving to OSX from Linux is that I cant get 
scipy working as easily. All things considered, thats not too bad! 
Anyhow, I think most stuff works now, except that I get a broken pipe 
every time I use gnuplot through scipy.gplt. As this is my sole source 
of graphical output for my extensive scipy-based models, I am anxious 
to get this working. I have tried both flavours of gnuplot on Mac, but 
neither work. Should I simply abandon gplt and move to Chaco, or have 
others gotten it to work.

Thanks for the advice,
cjf



From oliphant.travis at ieee.org  Mon Jun 16 21:34:14 2003
From: oliphant.travis at ieee.org (Travis Oliphant)
Date: Mon, 16 Jun 2003 19:34:14 -0600
Subject: [SciPy-user] OSX and gnuplot
References: <9BD8F306-A059-11D7-9D1D-000A956FDAC0@fonnesbeck.org>
Message-ID: <3EEE7016.2030806@ieee.org>


Christopher Fonnesbeck wrote:
> Well, my lone regret about moving to OSX from Linux is that I cant get 
> scipy working as easily. All things considered, thats not too bad! 
> Anyhow, I think most stuff works now, except that I get a broken pipe 
> every time I use gnuplot through scipy.gplt. As this is my sole source 
> of graphical output for my extensive scipy-based models, I am anxious to 
> get this working. I have tried both flavours of gnuplot on Mac, but 
> neither work. Should I simply abandon gplt and move to Chaco, or have 
> others gotten it to work.
> 

In theory xplt should work under Mac OSX but I've never tried it nor 
heard reports of success.

Chaco is moving along quite well, though.


-Travis O.




From ariciputi at pito.com  Tue Jun 17 09:35:56 2003
From: ariciputi at pito.com (Andrea Riciputi)
Date: Tue, 17 Jun 2003 15:35:56 +0200
Subject: [SciPy-user] OSX and gnuplot
In-Reply-To: <9BD8F306-A059-11D7-9D1D-000A956FDAC0@fonnesbeck.org>
Message-ID: <9F92E538-A0C8-11D7-9FF2-000393933E4E@pito.com>

On Tuesday, Jun 17, 2003, at 02:21 Europe/Rome, Christopher Fonnesbeck 
wrote:

> Well, my lone regret about moving to OSX from Linux is that I cant get 
> scipy working as easily. All things considered, thats not too bad! 
> Anyhow, I think most stuff works now, except that I get a broken pipe 
> every time I use gnuplot through scipy.gplt. As this is my sole source 
> of graphical output for my extensive scipy-based models, I am anxious 
> to get this working. I have tried both flavours of gnuplot on Mac, but 
> neither work. Should I simply abandon gplt and move to Chaco, or have 
> others gotten it to work.
>
> Thanks for the advice,
> cjf

I've not tried it yet, but here it is what you can read on Fink's 
distribution of SciPy:

> scipy-py22-20030415-1: Scientific tools for Python
>   SciPy supplements the popular Numeric module, gathering a variety
>   of high level science and engineering modules together as a
>   single package. Within SciPy are modules for graphics and plotting,
>   optimization, integration, special functions, signal and image
>   processing, genetic algorithms, ODE solvers, and others.
>  .
>   plt module not available (no wxpython-wxgtk available).
>   xplt and gplt are OK.
>  .
>  Web site: http://www.scipy.org/
>  .
>  Maintainer: Jeffrey Whitaker <jsw at cdc.noaa.gov>

You can read more about Fink project at 
<http://fink.sourceforge.net/index.php>

Hope this help,
	Andrea.

---
Andrea Riciputi

"Science is like sex: sometimes something useful comes out,
   but that is not the reason we are doing it" -- (Richard Feynman)


---
Andrea Riciputi

"Science is like sex: sometimes something useful comes out,
   but that is not the reason we are doing it" -- (Richard Feynman)



From wagner.nils at vdi.de  Tue Jun 17 14:57:48 2003
From: wagner.nils at vdi.de (Nils Wagner)
Date: Tue, 17 Jun 2003 20:57:48 +0200
Subject: [SciPy-user] sparse function
Message-ID: <20030617201206.18A593EB0C@www.scipy.com>

Hi all, 
 
Is there a function in scipy which converts a full matrix to sparse 
form by squeezing out any zero elements ?  
 
How do I use it ? 
 
Nils 
 
  



From turner at lanl.gov  Tue Jun 17 21:07:31 2003
From: turner at lanl.gov (John A. Turner)
Date: Tue, 17 Jun 2003 19:07:31 -0600
Subject: [SciPy-user] compatible license(s)
Message-ID: <3EEFBB53.1050103@lanl.gov>

quick question - of the gazillion open-source licenses:

   http://www.opensource.org/licenses/

which are compatible with scipy?  are some "more compatible"
than others?  any outright incompatible?

thanks,

-John A. Turner
  Los Alamos National Lab
  Advanced Scientific Simulation, CCS-2



From jdhunter at ace.bsd.uchicago.edu  Wed Jun 18 10:20:03 2003
From: jdhunter at ace.bsd.uchicago.edu (John Hunter)
Date: Wed, 18 Jun 2003 09:20:03 -0500
Subject: [SciPy-user] compatible license(s)
In-Reply-To: <3EEFBB53.1050103@lanl.gov> ("John A. Turner"'s message of
 "Tue, 17 Jun 2003 19:07:31 -0600")
References: <3EEFBB53.1050103@lanl.gov>
Message-ID: <m2wufjjwmk.fsf@mother.paradise.lost>

>>>>> "John" == John A Turner <turner at lanl.gov> writes:

    John> quick question - of the gazillion open-source licenses:
    John> http://www.opensource.org/licenses/

    John> which are compatible with scipy?  are some "more compatible"
    John> than others?  any outright incompatible?

The question of licenses came up recently in the thread "constrained
optimization", and the answers

  Travis Oliphant:

    Libraries in SciPy need an unrestrictive open source license
    (something like the Python license itself or the BSD license)

  Eric Jones:

    free for academic/commercial, no restrictions that additions must be
    contributed back to package

Here is a link to the license itself:

  http://scipy.net/cgi-bin/viewcvsx.cgi/*checkout*/scipy/LICENSE.txt?rev=1.2

This is not a direct answer to your question, but may give you enough
information to answer it.

John Hunter



From eric at enthought.com  Wed Jun 18 12:20:14 2003
From: eric at enthought.com (eric jones)
Date: Wed, 18 Jun 2003 11:20:14 -0500
Subject: [SciPy-user] compatible license(s)
In-Reply-To: <m2wufjjwmk.fsf@mother.paradise.lost>
Message-ID: <006601c335b5$831107f0$8901a8c0@ERICDESKTOP>

Yep.  I'd say that about covers it.

eric

----------------------------------------------
eric jones                    515 Congress Ave
www.enthought.com             Suite 1614
512 536-1057                  Austin, Tx 78701 


> -----Original Message-----
> From: scipy-user-admin at scipy.net [mailto:scipy-user-admin at scipy.net]
On
> Behalf Of John Hunter
> Sent: Wednesday, June 18, 2003 8:20 AM
> To: scipy-user at scipy.net
> Subject: Re: [SciPy-user] compatible license(s)
> 
> >>>>> "John" == John A Turner <turner at lanl.gov> writes:
> 
>     John> quick question - of the gazillion open-source licenses:
>     John> http://www.opensource.org/licenses/
> 
>     John> which are compatible with scipy?  are some "more compatible"
>     John> than others?  any outright incompatible?
> 
> The question of licenses came up recently in the thread "constrained
> optimization", and the answers
> 
>   Travis Oliphant:
> 
>     Libraries in SciPy need an unrestrictive open source license
>     (something like the Python license itself or the BSD license)
> 
>   Eric Jones:
> 
>     free for academic/commercial, no restrictions that additions must
be
>     contributed back to package
> 
> Here is a link to the license itself:
> 
>   http://scipy.net/cgi-
> bin/viewcvsx.cgi/*checkout*/scipy/LICENSE.txt?rev=1.2
> 
> This is not a direct answer to your question, but may give you enough
> information to answer it.
> 
> John Hunter
> 
> _______________________________________________
> SciPy-user mailing list
> SciPy-user at scipy.net
> http://www.scipy.net/mailman/listinfo/scipy-user




From turner at lanl.gov  Wed Jun 18 12:49:22 2003
From: turner at lanl.gov (John A. Turner)
Date: Wed, 18 Jun 2003 10:49:22 -0600
Subject: [SciPy-user] compatible license(s)
In-Reply-To: <006601c335b5$831107f0$8901a8c0@ERICDESKTOP>
References: <006601c335b5$831107f0$8901a8c0@ERICDESKTOP>
Message-ID: <3EF09812.9070305@lanl.gov>

eric jones wrote:
> Yep.  I'd say that about covers it.

sorry to need further explanation...

so in addition to the Python and BSD licenses, Artisic (Perl)
would be ok as well, but GPL would not, right?

how about Mozilla (MPL 1.1):

http://www.opensource.org/licenses/mozilla1.1.php

I'm wondering specifically about sections 3.1-3.3 and 3.7

thanks...

-John Turner
  LANL / CCS-2



From Smconnery at aol.com  Wed Jun 18 20:25:07 2003
From: Smconnery at aol.com (Smconnery at aol.com)
Date: Wed, 18 Jun 2003 20:25:07 EDT
Subject: [SciPy-user] new subscription
Message-ID: <168.203f5bcd.2c225ce3@aol.com>

     I would a membership to flylady, however I am unable to get thru to the 
web sites I have been trying. Can you help me?
     My name is Shirley Connery and my e-mail address is: smconnery at aol.com   
 

                                Thanks, Shirley



From oladarepeters at indiatimes.com  Thu Jun 19 14:19:15 2003
From: oladarepeters at indiatimes.com (DR.OLADARE PETERS)
Date: Thu, 19 Jun 2003 11:19:15 -0700
Subject: [SciPy-user] Your Assistance Needed
Message-ID: <20030619193406.4CE9B3EB09@www.scipy.com>

FROM THE DESK OF DR.OLADARE PETERS 
MANAGER 11 BUDGET AND PLANNING 
TEL:234-8033-486079
EMAIL:oladarepeters at indiatimes.com 

THE MANAGING DIRECTOR/CEO WE ARE SENDIND THIS LETTER TO YOU BASED ON THE INFORMATION GARTHERED FROM THE FOREIGN TRADE OF THE NIGERIAN CHAMBERS OF COMMERCE AND INDUSTRY.

WE BELIEVE THAT YOU WOULD BE IN THE POSITION TO HELP US IN OUR BID TO TRANSFER THE SUM OF TWENTY MILLION DOLLARS ($20mUSD)INTO A FORIEGN ACCOUNT. WE ARE MEMBERS OF THE SPECIAL COMMITTEE OF BUGET AND PLANING OF THE MINISTRY OF PETROLEUM,THIS COMMITTEE IS SPECIALY CONCERNED WITH CONTRACT APPRAISALS AND APROVAL OF CONTRACT IN ORDER OF PRIORITIES AS REGARDS CAPITAL PROJECTS OF THE FEDERAL GOVERNMENT OF NIGERIA.WITH OUR POSITION WE HAVE CAREFULLY SECURED THE SUM ($20.mUSD)THIS AMOUNT WAS ACCUMULATED THROUGH UNDELARED WINDFALL FROM THE SALE OF CRUDE OIL DURING THE GOLF WAR. WHAT WE NEED FROM YOU IS TO PROVIDE A SAFE ACCOUNT WERE THIS FUND CAN BE TRANSFERRED SINCE GOVERNMENT OFFICIALS ARE NOT ALLOWED BY OUR LAWS TO OPERATE FORIEGN ACCOUNT. 

IT MAY INTEREST YOU TO KNOW THAT SEVERAL YEARS AGO A SIMILAR CONTRACT WAS CARRIED OUT WITH ONE MR PATRICK MILLER THE PRESIDENT OF CRAINE INTERNATION TRADING COMPANY AT NUMBER 135,EAST 57TH STREET 28TH FLOOR NEW YORK.WITH TELEPHONE NUMBER 212 308 7788 AND TELEX NUMBER 6731689 AFTER AGREEMENT BETWEEN BOTH PARTNER WHICH HE WAS TO TAKE 5% THE MONEY WAS DULY TRANSFERRED INTO HIS ACCOUNT TO BE DISAPPOINTED ON OUR ARRIVAL IN NEW YORK AS WE WERE RELIABLY TOLD THAT MR PATRICE MILLER WAS NO LONGER ON THAT ADDRESS AND HIS TELEPHONE/FAX NUMBER WAS REALLOCATED TO SOMEBODY ELSE THAT IS HOW WE LOST THE SUM OF US$27.5M TO HIM. 

THIS TIME AROUND WE NEED A MORE RELIABLE AND TRUST WORTHY PERSON OR A REPUTABLE COMPANY TO DO BUSINESS WITH HENCE THIS LETTER TO YOU,SO IF YOU CAN PROVE YOURSELF TO BE TRUSTED AND INTERESTED IN THIS DEAL THEN WE ARE PREPARED TO DO BUSINESS WITH YOU.WHAT WE WANT FROM YOU IS THE ASSURANCE THAT YOU WILL LET US HAVE OUR OWN SHARE WHEN THIS FUND ($20 MUSD) FINALLY GETS INTO YOUR ACCOUNT. IF THIS SATISFIES YOU PLEASE EMAIL OR CALL ME ON YOUR RESPONSE SO THAT WE CAN ADVICE YOU ON THE MODALITIES OF THIS TRANSACTION.

ALL MODALITIES OF THE TRANSACTION HAS BEEN WORKED OUT AND ONCE STARTED WILL NOT TAKE MORE THAN 14 WORKING DAYS WITH THE ABSOLUTE SUPPORT OF ALL CONCERNED.THIS TRANSACTION IS 100% RISK FREE. PLEASE TREAT AS URGENT AND CONFIDENTIAL. 

YOURS FAITH FULLY 

DR.OLADARE PETERS 




From camartin at snet.net  Tue Jun 24 23:00:01 2003
From: camartin at snet.net (Cliff Martin)
Date: Tue, 24 Jun 2003 23:00:01 -0400
Subject: [SciPy-user] How to use CVS etc.
Message-ID: <3EF91031.8080003@snet.net>

In posts around June 4 I saw that the io.read_array that didn't read 
negative numbers was fixed and 'in CVS'. I'm currently using SciPy .2 
under Windows 2000. Is there some directions of how to get the fix into 
my setup. Sorry for being so stupid about this.  If I was in Linux I'd 
just download the new source and recompile etc. but I'm not sure what 
steps I've got to take in Windows.

Cliff Martin



From DPGrote at lbl.gov  Wed Jun 25 19:58:45 2003
From: DPGrote at lbl.gov (David Grote)
Date: Wed, 25 Jun 2003 16:58:45 -0700
Subject: [SciPy-user] scipy_distutils: fortran without f2py
Message-ID: <3EFA3735.6060104@lbl.gov>

I need help with scipy_distutils. I can't figure out how to get it to do 
what I need. I am trying to find a nice package which will help build 
python extensions, some of which is written in fortran, and 
scipy_distutils looks good. For various reasons, I create my own 
wrappers and so I don't need the wrapper to be generated by f2py. 
However, with everything I have tried with scipy_distutils, it either 
insists on using f2py, or does nothing. Does anyone have an example 
setup.py that I could see which would help, something that doesn't use 
f2py? F2py is a great piece of software but there are some things I need 
that it doesn't do (like wrap fortran derived types).
  Any help would be much appreciated!
    Thanks!
       Dave Grote



From pearu at scipy.org  Thu Jun 26 18:14:15 2003
From: pearu at scipy.org (Pearu Peterson)
Date: Thu, 26 Jun 2003 17:14:15 -0500 (CDT)
Subject: [SciPy-user] scipy_distutils: fortran without f2py
In-Reply-To: <3EFA3735.6060104@lbl.gov>
Message-ID: <Pine.LNX.4.44.0306261641370.727-100000@scipy.org>


On Wed, 25 Jun 2003, David Grote wrote:

> I need help with scipy_distutils. I can't figure out how to get it to do 
> what I need. I am trying to find a nice package which will help build 
> python extensions, some of which is written in fortran, and 
> scipy_distutils looks good. For various reasons, I create my own 
> wrappers and so I don't need the wrapper to be generated by f2py. 
> However, with everything I have tried with scipy_distutils, it either 
> insists on using f2py, or does nothing. Does anyone have an example 
> setup.py that I could see which would help, something that doesn't use 
> f2py? F2py is a great piece of software but there are some things I need 
> that it doesn't do (like wrap fortran derived types).

Here follows an example setup.py file that does not call f2py but
compiles also fortran sources:

####################

from scipy_distutils.core import Extension
ext1 = Extension(name = 'foo',
                 sources = ['foomodule.c']
                 libraries = ['foo_fort'])
if __name__ == "__main__":
    from scipy_distutils.core import setup
    setup(name = 'nof2py',
          ext_modules = [ext1],
          fortran_libraries = [('foo_fort',{'sources':['foo.f']})],
          )
####################

HTH,
	Pearu



From DPGrote at lbl.gov  Thu Jun 26 19:55:22 2003
From: DPGrote at lbl.gov (David Grote)
Date: Thu, 26 Jun 2003 16:55:22 -0700
Subject: [SciPy-user] scipy_distutils: fortran without f2py
Message-ID: <3EFB87EA.9090408@lbl.gov>

An HTML attachment was scrubbed...
URL: <http://mail.scipy.org/pipermail/scipy-user/attachments/20030626/8ac9f082/attachment.html>

From ggerber at sun.ac.za  Fri Jun 27 08:24:22 2003
From: ggerber at sun.ac.za (Gerber G <ggerber@sun.ac.za>)
Date: Fri, 27 Jun 2003 14:24:22 +0200
Subject: [SciPy-user] Debian packages for scipy?
Message-ID: <80B1B9445E130349B4032DDD638848D60904EC@stbevs02r.stb.sun.ac.za>

Hi,
 
If I am not mistaken, there was a previous post about someone having made debian packages of scipy(during 2003). The scipy website, however only has .deb packages of cvs 07/24/2002. Where can I find the latest debs?
 
regards,
George Gerber



From chris at fonnesbeck.org  Fri Jun 27 11:13:51 2003
From: chris at fonnesbeck.org (Christopher Fonnesbeck)
Date: Fri, 27 Jun 2003 11:13:51 -0400
Subject: [SciPy-user] weave failures on OSX
Message-ID: <F5A13197-A8B1-11D7-A25E-000A956FDAC0@fonnesbeck.org>

I have some inline statements that work wonderfully under Linux, but on  
OSX (python2.3 using recent CVS weave), they die the death. Note that  
weave.test() on my machine runs cleanly. Any clues would be most  
appreciated. Here is the code and the resultant errors:

code = '''
         #line 610 "ReinforcementLearning.py"

         PyObject *key, *qval, *traceval = NULL;
         Py::List keys = qfunction.keys();
         for (int i=0; i<keys.length(); i++) {
             key = PyList_GET_ITEM(keys.ptr(),i);

             /* Grab and convert current Q-value */
             qval = PyDict_GetItem(qfunction.ptr(),key);
             double qval_raw = py_to_float(qval,"qval_raw");

             /* Grab and convert current elegibility value */
             traceval = PyDict_GetItem(etrace.ptr(),key);
             double traceval_raw = py_to_float(traceval,"traceval_raw");

             /* Calculate new Q-value */
             qval_raw += alpha * delta * traceval_raw;

             /* Return new Q-value to Q-function */
             PyObject* qval_new = PyFloat_FromDouble(qval_raw);
             PyDict_SetItem( qfunction.ptr(),
               key, qval_new );

             /* Cleanup */
             Py_DECREF(qval_new);

             /* Only update nonzero e-trace values */
             if (traceval_raw>0.0) {

                 /* Only interested in sizeable values */
                 if (traceval_raw>=tolerance) {

                     /* Calculate new elegibility value */
                     traceval_raw = gamma * lamda * traceval_raw;

                     /* Return new elegibility value to trace */
                     PyObject* traceval_new =  
PyFloat_FromDouble(traceval_raw);
                     PyDict_SetItem( etrace.ptr(),
                       key, traceval_new );

                     /* Cleanup */
                     Py_DECREF(traceval_new);
                 }
                 /* Set very small values to zero */
                 else PyDict_SetItem( etrace.ptr(), key,
                   PyFloat_FromDouble(0.0) );
             }
        }
         '''

         'Call to weave'
         weave.inline(code,
                      ['qfunction',
                       'alpha','delta','etrace',
                       'gamma','lamda','tolerance'])


This results in:

ReinforcementLearning.py: In function `PyObject*  
compiled_func(PyObject*,
    PyObject*)':
ReinforcementLearning.py:612: `Py' undeclared (first use this function)
ReinforcementLearning.py:612: (Each undeclared identifier is reported  
only once
    for each function it appears in.)
ReinforcementLearning.py:612: parse error before `::' token
ReinforcementLearning.py:613: `keys' undeclared (first use this  
function)
ReinforcementLearning.py:617: no matching function for call to  
`py::dict::ptr()
    '
ReinforcementLearning.py:621: no matching function for call to  
`py::dict::ptr()
    '
ReinforcementLearning.py:629: no matching function for call to  
`py::dict::ptr()
    '
ReinforcementLearning.py:646: no matching function for call to  
`py::dict::ptr()
    '
ReinforcementLearning.py:653: no matching function for call to  
`py::dict::ptr()
    '
Traceback (most recent call last):
   File "ReinforcementLearning.py", line 931, in __call__
     s==maxsteps-1)
   File "ReinforcementLearning.py", line 678, in update_qfunction
     ['qfunction',
   File "/Users/chris/Development/python/weave/inline_tools.py", line  
335, in inline
     auto_downcast = auto_downcast,
   File "/Users/chris/Development/python/weave/inline_tools.py", line  
439, in compile_function
     verbose=verbose, **kw)
   File "/Users/chris/Development/python/weave/ext_tools.py", line 340,  
in compile
     verbose = verbose, **kw)
   File "/Users/chris/Development/python/weave/build_tools.py", line  
272, in build_extension
     setup(name = module_name, ext_modules = [ext],verbose=verb)
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/site- 
packages/scipy_distutils/core.py", line 42, in setup
     return old_setup(**new_attr)
   File  
"/Library/Frameworks/Python.framework/Versions/2.3/lib/python2.3/ 
distutils/core.py", line 166, in setup
     raise SystemExit, "error: " + str(msg)
CompileError: error: command 'gcc' failed with exit status 1
  



From eric at enthought.com  Fri Jun 27 18:08:02 2003
From: eric at enthought.com (eric jones)
Date: Fri, 27 Jun 2003 17:08:02 -0500
Subject: [SciPy-user] [ANN] SciPy '03 -- The 2nd Annual Python for Scientific Computing
 Workshop
Message-ID: <015701c33cf8$96be3670$8901a8c0@ERICDESKTOP>

Hey folks,

I've been postponing this announcement because the registration page
isn't active yet.  It's getting late though, and I thought I'd at least
let you know SciPy '03 is happening.  I'll repost when registration is
open.

Thanks,
Eric

-------------------------------------------------------
SciPy '03
The 2nd Annual Python for Scientific Computing Workshop
-------------------------------------------------------

CalTech, Pasadena, CA
September 11-12, 2003

http://www.scipy.org/site_content/scipy03

This workshop provides a unique opportunity to learn and affect what is
happening in the realm of scientific computing with Python. Attendees
will have the opportunity to review the available tools and how they
apply to specific problems. By providing a forum for developers to share
their Python expertise with the wider industrial, academic, and research
communities, this workshop will foster collaboration and facilitate the
sharing of software components, techniques and a vision for high level
language use in scientific computing.

The cost of the workshop is $100.00 and includes 2 breakfasts and 2
lunches on Sept. 11th and 12th, one dinner on Sept. 11th, and snacks
during breaks.  Online registration is not available yet, but will be
soon.

We would like to have a wide variety of presenters this year.  If you
have a paper you would like to present, please contact
eric at enthought.com.

Discussion about the conference may be directed to the SciPy-user
mailing list:

Mailing list page:     http://www.scipy.org/MailList
Mailinbg list address: scipy-user at scipy.org

Please forward this announcement to anyone/list that might be
interested.


-------------
Co-Hosted By:
-------------

The National Biomedical Computation Resource (NBCR, SDSC, San Diego, CA)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
http://nbcr.sdsc.edu
The mission of the National Biomedical Computation Resource at the San
Diego
Supercomputer Center is to conduct, catalyze, and enable biomedical
research by harnessing advanced computational technology.


The Center for Advanced Computing Research (CACR, CalTech, Pasadena, CA)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
http://nbcr.sdsc.edu
CACR is dedicated to the pursuit of excellence in the field of
high-performance computing, communication, and data engineering. Major
activities include carrying out large-scale scientific and engineering
applications on parallel supercomputers and coordinating collaborative
research projects on high-speed network technologies, distributed
computing and database methodologies, and related topics. Our goal is to
help further the state of the art in scientific computing.


Enthought, Inc. (Austin, TX)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
http://enthought.com
Enthought, Inc. provides scientific and business computing solutions
through software development, consulting and training.


----------------------------------------------
eric jones                    515 Congress Ave
www.enthought.com             Suite 1614
512 536-1057                  Austin, Tx 78701 






From Ralf_Ahlbrink at web.de  Sat Jun 28 10:06:08 2003
From: Ralf_Ahlbrink at web.de (Ralf Ahlbrink)
Date: Sat, 28 Jun 2003 16:06:08 +0200
Subject: [SciPy-user] Debian packages for scipy?
In-Reply-To: <80B1B9445E130349B4032DDD638848D60904EC@stbevs02r.stb.sun.ac.za>
References: <80B1B9445E130349B4032DDD638848D60904EC@stbevs02r.stb.sun.ac.za>
Message-ID: <200306281606.08644.Ralf_Ahlbrink@web.de>

Am Freitag 27 Juni 2003 14:24 schrieb Gerber G <ggerber at sun.ac.za>:
> Hi,
>
> If I am not mistaken, there was a previous post about someone having made
> debian packages of scipy(during 2003). The scipy website, however only has
> .deb packages of cvs 07/24/2002. Where can I find the latest debs?

http://www.scipy.org/Members/RalfA/packages/Debian-woody-DEB/



From fperez at colorado.edu  Mon Jun 30 12:04:23 2003
From: fperez at colorado.edu (Fernando Perez)
Date: Mon, 30 Jun 2003 10:04:23 -0600
Subject: [SciPy-user] weave failures on OSX
In-Reply-To: <F5A13197-A8B1-11D7-A25E-000A956FDAC0@fonnesbeck.org>
References: <F5A13197-A8B1-11D7-A25E-000A956FDAC0@fonnesbeck.org>
Message-ID: <3F005F87.2020105@colorado.edu>

Christopher Fonnesbeck wrote:
> I have some inline statements that work wonderfully under Linux, but on  
> OSX (python2.3 using recent CVS weave), they die the death. Note that  
> weave.test() on my machine runs cleanly. Any clues would be most  
> appreciated. Here is the code and the resultant errors:
> 
> code = '''
>          #line 610 "ReinforcementLearning.py"
> 
>          PyObject *key, *qval, *traceval = NULL;
>          Py::List keys = qfunction.keys();

[snip]

> ReinforcementLearning.py: In function `PyObject*  
> compiled_func(PyObject*,
>     PyObject*)':
> ReinforcementLearning.py:612: `Py' undeclared (first use this function)

try using py::list instead of Py::List.  I think something changed at some 
point, because I had to update similar code myself.

hth.

best,

f



From camartin at snet.net  Mon Jun 30 21:36:35 2003
From: camartin at snet.net (Cliff Martin)
Date: Mon, 30 Jun 2003 21:36:35 -0400
Subject: [SciPy-user] read_array
Message-ID: <3F00E5A3.40005@snet.net>

I'm trying to read a large ASCII file (each line has a carraige return) 
with elements separated by spaces(it can be variable due to large number 
differences). My code is below and the error message from the code 
follows. By the way the size of the array is 223 by 221.  This read 
works quite well with a smaller file.  What is the problem?

A = scipy.io.read_array('c:/transfer/av56tst1.int')
Traceback (most recent call last):
  File "<interactive input>", line 1, in ?
  File "C:\Python22\Lib\site-packages\scipy\io\array_import.py", line 
369, in read_array
    outarr[k][row] = vals[k]
ValueError: matrices are not aligned for copy

I'm running on a Windows 2000 platform.  Thanks for any help.

Cliff Martin



From manueloko1000 at netscape.net  Sat Jun  7 09:09:04 2003
From: manueloko1000 at netscape.net (MANUEL OKO)
Date: Sat, 07 Jun 2003 15:09:04 +0200
Subject: [SciPy-user] STRICTLY CONFIDENTIAL
Message-ID: <20030706143024.D4C903EB09@www.scipy.com>

ATTN:SIR/MADAN      
               STRICTLY CONFIDENTIAL.
I am pleased to introduce myself  to you.My name is Manuel Oko
a native of South Africa and a senior employee of mines and natural 
resources department currently on a trainning course in Holland for few months.
I  am writing this letter to request your assistance in order to redeem an investment with the South African mining Corporation.The said investment, now valued at  ($15.5 million dollars ) Fifteen  million,five hundred thousand dollars only was purchased by Lucio Harper and contracted out to the South  African Mining Corporation in 1977 now recognised as mines and natural resources department.This redeemable investment interest,has now matured since March last year.
Since MARCH last year, several attempts have been made to contact Lucio Harper without success and there is no way to contact any of his close relatives in whose favour the investment cash value can be paid.                                                                                      
Since we have access to all Lucio Harper's information,we can claim this money with the help of my partners with the South African Mines and natural resources department.All we have to do is to file claim using you as Lucio Harper's relative.
I will like to assure you that there is absolutely nothing to worry about,because it is perfectly safe with no risk involved.Please ensure to keep this matter strictly confidential.My partner will file a claim for this money on your behalf from the SouthAfrican mining Corporation.When the claim is approved,you as the beneficiary 
will be paid (25%) of the total amouth.
Since this money can be paid directly into any bank account of your 
choice,you have responsibility to ensure that my partner and Ireceive(70%of 
the total amouth.While the balance (5%) will be set aside for any unforseen expenses in the cause of transfering this money.
I will appreciate if you can give your assurance and guarantee that 
our share will be well secured.Please for the sake of confidentiality,reach 
me on my e-mail address: manueloko1000 at netscape.net Please let me know if this proposal is acceptable to you. Kindly reach me immediately 
with any of the stated contact addresses so that better clearifications 
relating to the transaction will be explained to you.

Truly yours,
Manuel  oko