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

Re: [Numpy-discussion] Right behavior

[Tim C. <tc...@op...>]

Johan Fredrik =D8hman wrote:
>=20
> The first numbers in each of your three runs are 7.98493051529 ,
> 7.98525762558 and 7.98623776436.
> They look like different numbers to me.
>=20
> First, thanks for your answer Time.
> I do agree, they are different.  But I wouldn't call it random.  I didn=
't expect
> that the small difference in the initial seed would affect the first nu=
mber with so little.
> Usually the seed numbers I have experienced other places have much more
> dramatic effect on the numbers,  if you see what I mean...

OK, you need to use Konrad Hinsen's excellent RNG module which comes
with Numeric Python:
#################################
# Python Virtual clock
import RNG

dist =3D RNG.NormalDistribution(10, 2)
rng =3D RNG.CreateGenerator(0, dist)
for i in range(1000000,10000000,1000000):
	print "Clock at time:" , i/1000000, ":", rng.ranf()
##################################

The above code gives 8.46183655136,  7.29889782477 and 5.58243682462 as
the first values in three successive runs on my system.

Hope this helps,

Tim C

>=20
> If you want the difference
> between initial values to be greater, you need to make the
> difference in your seeds greater. For example, if I run your code now, =
I
> get 8.29225027561, 8.29484963417 and 8.29744851589, but setting the see=
d
> to (1,2) gives an initial value of 5.69397783279. Remember, these are
> only pseudorandom numbers.
>=20
> Yes, they are pseudorandom and that is OK.  What I just want is some mo=
re
> initial difference between the runs without setting the seed number man=
ually.
> But know I know this is not a flaw in the RNG, but "its the way it is s=
upposed to be"
>=20
> Thanks
>=20
> --
> Johan Fredrik Ohman
>=20
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion

Re: [Numpy-discussion] Right behavior

[<jo...@oh...>]

The first numbers in each of your three runs are 7.98493051529 ,
7.98525762558 and 7.98623776436.
They look like different numbers to me.=20

First, thanks for your answer Time.=20
I do agree, they are different.  But I wouldn't call it random.  I =
didn't expect
that the small difference in the initial seed would affect the first =
number with so little.
Usually the seed numbers I have experienced other places have much more
dramatic effect on the numbers,  if you see what I mean...

If you want the difference
between initial values to be greater, you need to make the=20
difference in your seeds greater. For example, if I run your code now, I
get 8.29225027561, 8.29484963417 and 8.29744851589, but setting the seed
to (1,2) gives an initial value of 5.69397783279. Remember, these are
only pseudorandom numbers.=20

Yes, they are pseudorandom and that is OK.  What I just want is some =
more
initial difference between the runs without setting the seed number =
manually.
But know I know this is not a flaw in the RNG, but "its the way it is =
supposed to be"

Thanks

--
Johan Fredrik Ohman

Re: [Numpy-discussion] Right behavior

[Tim C. <tc...@op...>]

> Johan Fredrik =D8hman wrote:
>=20
> This code generates very non-random numbers, even when the seed value
> is reinitialized.
> Take a look at the first number in each run !
>=20

The first numbers in each of your three runs are 7.98493051529 ,
7.98525762558 and 7.98623776436.
They look like different numbers to me. If you want the difference
between initial values to be greater, you need to make the=20
difference in your seeds greater. For example, if I run your code now, I
get 8.29225027561, 8.29484963417 and 8.29744851589, but setting the seed
to (1,2) gives an initial value of 5.69397783279. Remember, these are
only pseudorandom numbers.=20

Tim C

 =20
> Is this right ?
>=20
> --
> JF=D8
>=20
>=20
> #!/usr/local/bin/python2.1
>=20
> # Python Virtual clock
> import RandomArray
>=20
> print "Seed", RandomArray.get_seed()
> for i in range(1000000,10000000,1000000):
>         print "Clock at time:" , i/1000000, ":",
> RandomArray.normal(10,2)
>=20
>=20
> [root@blekkulf /root]# ./t2.py
> Seed (101743, 1951)
> Clock at time: 1 : 7.98493051529
> Clock at time: 2 : 10.8439420462
> Clock at time: 3 : 7.59234881401
> Clock at time: 4 : 7.32021093369
> Clock at time: 5 : 10.9444898367
> Clock at time: 6 : 10.1128772199
> Clock at time: 7 : 13.1178274155
> Clock at time: 8 : 11.779414773
> Clock at time: 9 : 10.7529922128
> [root@blekkulf /root]# ./t2.py
> Seed (101743, 1953)
> Clock at time: 1 : 7.98525762558
> Clock at time: 2 : 9.38142818213
> Clock at time: 3 : 7.11979293823
> Clock at time: 4 : 10.867649436
> Clock at time: 5 : 9.62882992625
> Clock at time: 6 : 12.1940765381
> Clock at time: 7 : 6.84895467758
> Clock at time: 8 : 8.13472533226
> Clock at time: 9 : 8.15638375282
> [root@blekkulf /root]# ./t2.py
> Seed (101743, 1959)
> Clock at time: 1 : 7.98623776436
> Clock at time: 2 : 14.5040078163
> Clock at time: 3 : 11.3408681154
> Clock at time: 4 : 6.32757425308
> Clock at time: 5 : 8.94617521763
> Clock at time: 6 : 12.1802353859
> Clock at time: 7 : 12.0685124397
> Clock at time: 8 : 10.5330892205
> Clock at time: 9 : 10.9744755626

[Numpy-discussion] Right behavior

[<jo...@oh...>]

This code generates very non-random numbers, even when the seed value is =
reinitialized.
Take a look at the first number in each run !

Is this right ?

--
JF=D8


#!/usr/local/bin/python2.1

# Python Virtual clock
import RandomArray

print "Seed", RandomArray.get_seed()
for i in range(1000000,10000000,1000000):
        print "Clock at time:" , i/1000000, ":", =
RandomArray.normal(10,2)


[root@blekkulf /root]# ./t2.py=20
Seed (101743, 1951)
Clock at time: 1 : 7.98493051529
Clock at time: 2 : 10.8439420462
Clock at time: 3 : 7.59234881401
Clock at time: 4 : 7.32021093369
Clock at time: 5 : 10.9444898367
Clock at time: 6 : 10.1128772199
Clock at time: 7 : 13.1178274155
Clock at time: 8 : 11.779414773
Clock at time: 9 : 10.7529922128
[root@blekkulf /root]# ./t2.py=20
Seed (101743, 1953)
Clock at time: 1 : 7.98525762558
Clock at time: 2 : 9.38142818213
Clock at time: 3 : 7.11979293823
Clock at time: 4 : 10.867649436
Clock at time: 5 : 9.62882992625
Clock at time: 6 : 12.1940765381
Clock at time: 7 : 6.84895467758
Clock at time: 8 : 8.13472533226
Clock at time: 9 : 8.15638375282
[root@blekkulf /root]# ./t2.py=20
Seed (101743, 1959)
Clock at time: 1 : 7.98623776436
Clock at time: 2 : 14.5040078163
Clock at time: 3 : 11.3408681154
Clock at time: 4 : 6.32757425308
Clock at time: 5 : 8.94617521763
Clock at time: 6 : 12.1802353859
Clock at time: 7 : 12.0685124397
Clock at time: 8 : 10.5330892205
Clock at time: 9 : 10.9744755626

Re: [Numpy-discussion] Returning a 2-D PyArrayObject

[Konrad H. <hi...@cn...>]

Amitha P <ami...@ya...> writes:

> irrevelent values. I could successfully return single
> dimension Python array object but I am not able to
> return 2D python array object. I am attaching the
> code. Please look at it and point out the errors. 

> product = (PyArrayObject *)PyArray_FromDimsAndData(2,
> dimensions,
> PyArray_FLOAT,c);

The variable c in this call must point to a storage area which holds
the elements of the matrix, i.e. a one-dimensional float array. What
you pass in your code is a list of pointers to the rows of the matrix.

Konrad.
-- 
-------------------------------------------------------------------------------
Konrad Hinsen                            | E-Mail: hi...@cn...
Centre de Biophysique Moleculaire (CNRS) | Tel.: +33-2.38.25.56.24
Rue Charles Sadron                       | Fax:  +33-2.38.63.15.17
45071 Orleans Cedex 2                    | Deutsch/Esperanto/English/
France                                   | Nederlands/Francais
-------------------------------------------------------------------------------

[Numpy-discussion] Returning a 2-D PyArrayObject

[Amitha P <ami...@ya...>]

Hi all,
I am new to Python/Python extension programming. I am
writing a c-extension to python which takes a
3-Dimensional array object and returns the
2Dimensional array object. 
The c function returns the 2-D array and converts into
Python object, but the 2D python array object doesnot
contain the correct values, it's priting out some
irrevelent values. I could successfully return single
dimension Python array object but I am not able to
return 2D python array object. I am attaching the
code. Please look at it and point out the errors. 
Thank you very much..

-------------------------------------------------------
CMATRIXMUL16.c - This takes the array (the python 3D
array) and returns the 2D array
------------------------------------------------------
#include <stdio.h>
#include <math.h>

 float* cmatrixmul16(float *array,float *paddarr, int
n,int r,int col,float
**store)
{
 int i,j,k;
 int devices;
 int pathpoints;
 int column;
 int len;
 float sum;
 float **a,**b,**c,**d;
 float *temp;

 static float **tracematrix; 
 tracematrix = store;

 pathpoints=r;
 column = col;
 devices= n;
 len = pathpoints*2;

 temp = (float *)malloc( len* sizeof(float));
 sum =0.0;
 
 a = (float **)malloc(devices * sizeof(float));
 b = (float **)malloc(devices * sizeof(float));
 c = (float **)malloc(devices * sizeof(float));
 d = (float **)malloc(devices * sizeof(float));


 for(i=0;i<devices;i++)
 {
  a[i] = (float *)malloc(pathpoints*sizeof(float));
  b[i] = (float *)malloc(pathpoints*sizeof(float));
  c[i] = (float *)malloc(pathpoints*sizeof(float));
  d[i] = (float *)malloc(pathpoints*sizeof(float));
 }

 for(k=0;k<devices;k++){
 for(i=0;i<pathpoints;i++)
   {
    a[k][i] =
(*(array+k*pathpoints*column+i*column+0))*(1.0/sqrt(2.0));
    b[k][i] =
(*(array+k*pathpoints*column+i*column+1))*(1.0/sqrt(2.0));
    c[k][i] =
(*(array+k*pathpoints*column+i*column+2))*(1.0/sqrt(2.0));
    d[k][i] =
(*(array+k*pathpoints*column+i*column+3))*(1.0/sqrt(2.0));

    tracematrix[k][i] =
a[k][i]*a[k][i]+b[k][i]*b[k][i];
    tracematrix[k][pathpoints+i] =
c[k][i]*c[k][i]+d[k][i]*d[k][i];
   }
 fprintf(stderr,"tracematrix: %f\n",
tracematrix[k][0]);
 }
 for(i=0;i<devices;i++)
 {
  free(a[i]);
  free(b[i]);
  free(c[i]);
  free(d[i]);
  }
 free(a);
 free(b);
 free(c);
 free(d);
return (float *)tracematrix;

}
------------------------------------------------------

This is the module which calls the function
_tests16module.c
-------------------------------------------------------

#include <Python.h>
#include <arrayobject.h>
#include <math.h>
static PyObject * Py_arraytest1 (PyObject *, PyObject
*);
static char _tests17_module_doc[] ="tests11: module
documentation";
static char arraytest1__doc__[]= "mytest:function
documentation";


/********************************Python symbol table
*****************************************/
static PyMethodDef _tests17_methods[] = {

  {"arraytest1"  , (PyCFunction)Py_arraytest1 ,
METH_VARARGS,arraytest1__doc__
},
  {NULL, (PyCFunction)NULL,0,NULL }  /* terminates the
list of methods */
};
/*********************************End of Symbol table
***************************************/



void init_tests17()
{
  /* We will be using C-functions defined in the array
module.  So we
   * need to be sure that the shared library defining
these functions
   * is loaded. */
  import_array();
  (void) Py_InitModule4(
                        "_tests17",          /* module
name */
                         _tests17_methods,    /*
structure containing python
symbol info */
                         _tests17_module_doc, /*
module documentation string */
                        (PyObject *) NULL,
                        PYTHON_API_VERSION);
}
/*function to calculate the product of two arrays */


static PyObject *
Py_arraytest1(PyObject *self, PyObject *args)
{
  PyArrayObject *array, *paddarr, *product;
  char *c;
  int n,r,col,i,j,k;
  int dimensions[2];
  float **store;
  
  if (!PyArg_ParseTuple(args, "O!O!",
                           &PyArray_Type,
&array,&PyArray_Type,&paddarr))

    return NULL;
 /* The arguments are the 3-D array and the 1-Dpaddarr
*/

 n = (long) array->dimensions[0];
 r = (long) array->dimensions[1];
 col = (long) array->dimensions[2];

 store = (float **)malloc(n*sizeof(float));
 for(i=0;i<n;i++)
  store[i]=(float *)malloc(2*r*sizeof(float));

c=  (char*)cmatrixmul16((float *)array->data,(float
*)paddarr->data,n,r,col,store);
dimensions[0] = n;
dimensions[1] = 2*r;

product = (PyArrayObject *)PyArray_FromDimsAndData(2,
dimensions,
PyArray_FLOAT,c);

PyArray_Return(product);
}



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Re: [Numpy-discussion] Lazy video array?

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

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

On Wed, 27 Mar 2002 20:21:38 +0100 Magnus Lie Hetland wrote:

Magnus> By the way: Is there any documentation of the numarray C API
Magnus> anywhere yet?

I think
,----
| http://stsdas.stsci.edu/numarray/DesignOverview.html
`----
is all there is for now.  And it's probably not exactly right any
more... 

Greetings,
Jochen
- -- 
University of North Carolina                       phone: +1-919-962-4403
Department of Chemistry                            phone: +1-919-962-1579
Venable Hall CB#3290 (Kenan C148)                    fax: +1-919-843-6041
Chapel Hill, NC 27599, USA                            GnuPG key: 44BCCD8E
-----BEGIN PGP SIGNATURE-----
Version: GnuPG v1.0.6-cygwin-fcn-1 (Cygwin)
Comment: Processed by Mailcrypt and GnuPG <http://www.gnupg.org/>

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[Numpy-discussion] Lazy video array?

[Magnus L. H. <ma...@he...>]

Just something I've been thinking about for a few years (and never
gotten around to doing anything about)... How realistic would it be to
wrap a video file as a type of three-dimensional (assuming grayscale)
array object and then use e.g. numarray to manipulate it? And how easy
would it be to make this sort of thing "lazy", so that only the parts
needed for the parts you actually access (for display or whatever) are
processed?

E.g. (silly example):

>>> a = videoarray('somefile.mpg')
>>> b = sin(a) # No real computation here
>>> for frame in b:
...    displayFrame(frame) # Computation performed here...

Or something...

Or maybe I'm just bonkers ;)

By the way: Is there any documentation of the numarray C API anywhere
yet?

--
Magnus Lie Hetland                                  The Anygui Project
http://hetland.org                                  http://anygui.org

[Numpy-discussion] (numpy-discussion´Ô) µðÁöŻī¸Þ¶ó & ½ºÄ³³Ê Á¤º¸.(È«^º¸)

[¸ô¸¶½ºÅ¸ <ma...@ko...>]

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      width="45" height="45" border="0"></p></td>
        <td width="379" height="52" colspan="7"><p align="left" valign="top"><font
             color="black">허락없이 메일을 보내서 죄송합니다. &nbsp;이 메일은 
            </font>웹서핑중, <b>무작위로 추출한것이며</b> E-Mail 주소 외에, 
            다른 정보는 갖고 있지 않습니다. <font color="black">메일 수신을 
            원치 않으시면 </font><font color="red"><b><A 
      href="mailto:di...@ko...?subject=수신거부&amp;body=더이상num...@li...로 메일을 보내지 마세요..">수신거부</A></b></font><font
             
      color="black">를 해주십시요. 만약 불필요한 정보였다면 사과 드립니다 
            </font></p></td>
        <td width="53" height="52"><p>&nbsp;</p></td>
    </tr>
    <tr>
        <td width="540" height="11" colspan="10"><p>&nbsp;</p></td>
    </tr>
</table></p>
</body>

[Numpy-discussion] Re: [SciPy-dev] accuracy problem in filterdesign.py

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

On Sunday 24 March 2002 02:38 pm, you wrote:
> Hello,
>
> while writing a test driver for a minimum phase calculation routine I came
> across the following problem. It is causing asymmetriesin the output of
>

> >>> N=512
> >>> lastpoint=2*pi
> >>> w1=arange(0,lastpoint,lastpoint/N)
> >>> w2=arange(0,N)*(lastpoint/N)
> >>> lastpoint-w1[511]-w1[1]
>
> -6.3546390371982397e-014
>
> >>> lastpoint-w2[511]-w2[1]
>
> 4.0245584642661925e-016
>
> >>> w1[511]
>
> 6.2709134608765646
>
> >>> w2[511]
>
> 6.2709134608765007
>
> >>> w2[511]-w1[511]
>
> -6.3948846218409017e-014
>

I just fixed this in Numeric.

The arange in Numeric used to increment the value by the step amount.  It now 
computes the value using value = start + i*step  which fixes the problem.

Thanks for pointing this out.

f2py comparison Re: [Numpy-discussion] [ANN] Pyfort 7.0 Extending Numpy with C

[Pearu P. <pe...@ce...>]

Hi,

Nummies might be interested how to wrap C codes with f2py.

On Wed, 20 Mar 2002, Paul Dubois wrote:

> For example, if you have this C code:
> 
> double mydot(int n, double* x, double *y) {
>     int i;
>     double d;
>     d = 0.0;
>     for (i=0; i < n; ++i) d += x[i] *  y[i];
>    return d;
> }
> 
> Then you can create a Pyfort input file mymod.pyf:
>     function mydot (n, x, y)
>         integer:: n=size(x)
>         doubleprecision x(n), y(n)
>         doubleprecision mydot
>     end

Different from pyfort, f2py needs the following signature file:

 python module mymod
   interface

    function mydot (n, x, y)
      intent(c) mydot
      integer intent(c):: n=size(x)
      doubleprecision x(n), y(n)
      doubleprecision mydot
    end

   end interface
 end python module

> 
> Compile mydot.c into a library libmydot.a.
> Then:
> 
> pyfort -c cc -i -L. -l mydot mymod.pyf
> 
> builds and installs the module mymod containing function mydot,

With f2py the above is equivalent to

  f2py -c mydot.c mymod.pyf

This compiles mydot.c and builds the module mymod into the current
directory.

>  which you
> can use from Python:
> 
> import Numeric, mymod
> x=Numeric.array([1.,2.3.])
> y=Numeric.array([5., -1., -1.])
> print mymod.mydot(x,y)

Python session with f2py generated mymod:

>>> import mymod
>>> print mymod.mydot([1,2,3],[1,2,4.])
17.0


Regards,
	Pearu

[Numpy-discussion] [ANN] Pyfort-7.0b2 -- extending Numeric with C routines

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

Pyfort 7.0b2 is now available at sf.net/projects/pyfortran.

Nummies who do not use Fortran may be interested in using Pyfort; with
Michiel de Hoon's "C" option, it is now extremely easy to wrap a simple
kind of C code for processing Numeric's arrays, like this:

double ctry(int n, double* x, double* y)
{
    int i;
    double d;
    d = 0.0;
    for (i=0; i < n; ++i) {
        d += x[i] * y[i];
    }
    return d;
}

void cout(int n, double* x, double* y)
{
    int i;
    for (i = 0; i < n; ++i) {
        y[i] = 1.414159 * x[i];
    }
}

double c2(int n, int m, double x[n][m]) {
    double sum = 0.0;
    int i, j;
    for (i=0; i < n; i++) {
        for (j=0; j < m; j++) {
            sum += x[i][j];
        }
    }
    return sum;
}

and then call it from Python like this:

import testc, Numeric
x = Numeric.array([1.,2.,3.])
y = Numeric.array([6.,-1.,-1.])
z = Numeric.arange(6) *1.0
z.shape=(3,2)
print "Should be 1.0:", testc.ctry(x,y)
print "Should be sqrt(2) * [1,2,3]:", testc.cout(x)
print "Should be 15.0:", testc.c2(z)
z.shape=(2,3)
print "Should be 15.0:", testc.c2(z)

----------- notes
Somehow 7.0b1 was missing the C examples. I apparently lost the changes
I had made to the MANIFEST and did not realize my CVS commits had
failed. Bad day at the office, I guess. 

I added testc back in, and added a 2-dimensional example. I also
eliminated some warning errors in the generated code, and fixed an error
in that case. My thanks to Michiel de Hoon and J.S. Whitaker.

Re: [Numpy-discussion] Different behaviour logical_and/and

[Huaiyu Z. <hua...@ya...>]

On Thu, 21 Mar 2002, Pearu Peterson wrote:
> On 20 Mar 2002, Jochen K=FCpper wrote:
> > >>>> a
> > Aureli> array([0, 1, 0, 0, 1, 0, 1, 0, 0, 1])
> > >>>> b
> > Aureli> array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])
> >=20
> > >>>> Numeric.logical_and(a,b)
> > Aureli> array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0])
> >=20
> > This look's correct...
> >=20
> > >>>> a and b
> > Aureli> array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])
> >=20
> > ... and this suspiciously like b.
>=20
> . and also correct.  It is default behaviour of Python `and'
> operation. From Python Language Reference:
>=20
>   The expression `x and y' first evaluates `x'; if `x' is false, its va=
lue =20
>   is returned; otherwise, `y' is evaluated and the resulting value is
>   returned.=20
>=20
> So, `and' operation is "object"-operation (unless redefined to somethin=
g
> else) while logical_and is "elementwise"-operation.

There is a section in PEP-225 for elementwise/objectwise operators to=20
extend the meaning of ~ to an "elementizer", so that=20

[1, 0, 1, 0] and [0, 1, 1, 0]      =3D>  [0, 1, 1, 0]
[1, 0, 1, 0] ~and [0, 1, 1, 0]     =3D>  [0, 0, 1, 0]

There are several other places, entirely unrelated to numerical=20
computation, that elementization of an operator makes sense.

Huaiyu

Re: [Numpy-discussion] Different behaviour logical_and/and

[Pearu P. <pe...@ce...>]

On 20 Mar 2002, Jochen K=FCpper wrote:

> >>>> a
> Aureli> array([0, 1, 0, 0, 1, 0, 1, 0, 0, 1])
> >>>> b
> Aureli> array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])
>=20
>=20
> >>>> Numeric.logical_and(a,b)
> Aureli> array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0])
>=20
> This look's correct...
>=20
> >>>> a and b
> Aureli> array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])
>=20
> ... and this suspiciously like b.

=2E. and also correct. It is default behaviour of Python `and'
operation. From Python Language Reference:

  The expression `x and y' first evaluates `x'; if `x' is false, its value =
=20
  is returned; otherwise, `y' is evaluated and the resulting value is
  returned.=20

So, `and' operation is "object"-operation (unless redefined to something
else) while logical_and is "elementwise"-operation.

Pearu

Re: [Numpy-discussion] Different behaviour logical_and/and

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

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

On Wed, 20 Mar 2002 19:43:03 +0100 Aureli Soria Frisch wrote:

>>>> a
Aureli> array([0, 1, 0, 0, 1, 0, 1, 0, 0, 1])
>>>> b
Aureli> array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])


>>>> Numeric.logical_and(a,b)
Aureli> array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0])

This look's correct...

>>>> a and b
Aureli> array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])

... and this suspiciously like b.


Greetings,
Jochen
- -- 
Einigkeit und Recht und Freiheit                http://www.Jochen-Kuepper.de
    Liberté, Égalité, Fraternité                GnuPG key: 44BCCD8E
        Sex, drugs and rock-n-roll
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[Numpy-discussion] [ANN] Pyfort 7.0 Extending Numpy with C

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

A beta version of Pyfort 7.0 is available at pyfortran.sf.net. The
documentation is not yet upgraded to this version.

Pyfort 7.0 adds the ability to wrap Fortran-like C coding to extend Numpy.

Dramatically illustrating the virtue of open source software, credit for
this improvement goes to:

Michiel de Hoon
Human Genome Center
University of Tokyo

For example, if you have this C code:

double mydot(int n, double* x, double *y) {
    int i;
    double d;
    d = 0.0;
    for (i=0; i < n; ++i) d += x[i] *  y[i];
   return d;
}

Then you can create a Pyfort input file mymod.pyf:
    function mydot (n, x, y)
        integer:: n=size(x)
        doubleprecision x(n), y(n)
        doubleprecision mydot
    end

Compile mydot.c into a library libmydot.a.
Then:

pyfort -c cc -i -L. -l mydot mymod.pyf

builds and installs the module mymod containing function mydot, which you
can use from Python:

import Numeric, mymod
x=Numeric.array([1.,2.3.])
y=Numeric.array([5., -1., -1.])
print mymod.mydot(x,y)

Note that by wrapping mydot in this way, Pyfort takes care of problems like
converting  input arrays of the wrong type, such as integer; making sure
that x and y have the same length; and making sure x and y are contiguous.

I added directory testc that contains an example like this and one where an
array is output.

Mr. de Hoon explained his patch as follows.

"I have modified fortran_compiler.py to add gcc as a
compiler. This enables pyfort to be used for C code
instead of Fortran code only. To use this option, call
pyfort with the -cgcc option to specify gcc as the
compiler. In order to switch off the default TRANSPOSE
and MIRROR options, some small modifications were
needed in generator.py also. [Editor's note: both -c gcc and -c cc will
work]

Before writing this addition to pyfort, I tried to use
swig to generate the wrapper for my C code. However,
pyfort was easier to use in my case because it is
integrated with numpy. I wasn't able to get swig use
numpy arrays. In addition, I am writing extension code
both in fortran and C, so it is easier having to use
only one tool (pyfort) for both.

In a sense, it is easier to extend python with C than
with fortran because you don't have to worry about
transposing the array. I tried to be minimally
instrusive on the existing pyfort code to switch off
transposing arrays; there may be prettier ways to do
this than what I have done. With this modification, I
was able to pass one- and two-dimensional numpy arrays
from Python to C and back without problems, as well as
scalar variables with intent(in) and intent(out). I
have also used the modified Pyfort on some Fortran
routines to make sure I didn't break something in the
fortran part of Pyfort.

I haven't done an extensive test of this addition, but
I haven't found any problems with it so far. I hope
this patch will be useful to other people trying to
extend Python/numpy with C routines."

Michiel de Hoon
Human Genome Center
University of Tokyo
md...@im...

[Numpy-discussion] Different behaviour logical_and/and

[Aureli S. F. <Aur...@ip...>]

Hi all,

In the version of Numeric with MacPython2.2 the functions
"Numeric.logical_and" and "and" behave different, although up to the
on-line documentation they should behave the same:

>>> Numeric.logical_and(a,b)
array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0])
>>> a and b
array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])

for arrays:

>>> a
array([0, 1, 0, 0, 1, 0, 1, 0, 0, 1])
>>> b
array([1, 1, 0, 1, 0, 0, 1, 1, 1, 0])

or am I misunderstood something...?

Regards,
Aureli

#################################
Aureli Soria Frisch
Fraunhofer IPK
Dept. Pattern Recognition

post: Pascalstr. 8-9, 10587 Berlin, Germany
e-mail: au...@ip...
fon: +49 30 39006-150
fax: +49 30 3917517
web: http://vision.fhg.de/~aureli/web-aureli_en.html
#################################

[SciPy-dev] Re: [Numpy-discussion] adding a .M attribute to the array.

[Prabhu R. <pr...@ae...>]

hi,

I'm sorry I havent been following the discussion too closely and this
post might be completely unrelated.

>>>>> "AS" == A Schmolck <a.s...@gm...> writes:

    AS> Ideally , I'd like to see a slim core numarray, without any
    AS> frills (and more streamlined to behave like standard python
    AS> containers (e.g. indexing and type/casts behavior)) for the
    AS> python core, something more enabled and efficient for numerics
    AS> (including matrices!) as a seperate package (like the XML
    AS> example you quote). And then maybe a bigger pre-bundled
    AS> collection of (ideally rather modular) numerical libraries for
    AS> really hard-core scientific users (maybe in the spirit of
    AS> xemacs-packages and sumo-tar-balls -- no bloat if you don't
    AS> need it, plenty of features in an instant if you do).

    AS> Anyway, is there at least general agreement that there should
    AS> be some new and wonderful matrix class (plus supporting
    AS> libraries) somewhere (rather than souping up array)?

Ideally, I'd like something that also has a reasonably easy to use
interface from C/C++.  The idea is that it should be easy (and
natural) for someone to use the same library from C/C++ when
performance was desired.  This would be really nice and very useful.

prabhu

RE: [Numpy-discussion] RE: Python 2.2 seriously crippled for numerical computation?

[Tim P. <ti...@co...>]

[jo...@bo...]
> Thanks for the quick answer.  The problem is resolved.

Cool!  Glad to hear it.

> Well, changing cvs from
>   release22-maint
> to
>   r221c1
> helps.  That is, everything seems to work fine with the cvs sources
> tagged r221c1.

That shouldn't have made any difference -- r221c1 is merely a tag on the
release22-maint branch.  Now I can spend a lot of time trying to guess why
your checkout is screwed up (probably stale sticky flags, if it is), or you
can try blowing away your checkout and starting over.  I know which one gets
my vote <wink>.  CVS branches and tags are a nightmare:  when in any doubt,
kill the beast and start over.

> Then, is it really necessary to mess up the cvs tags so much? Why
> isn't it possible to have a single python-2.2 branch which one could
> follow to get all the stuff that's incorporated into that version?

That's what the release22-maint branch is supposed to be (and, AFIAK, is).

> There are huge differences between release22-maint and r221c1,

What makes you think so?  I just did

    cvs diff -r release22-maint -r r221c1

and it turned up expected differences in the handful of files that indeed
*have* changed since r221c1 was tagged, mostly in the docs and under the Mac
subdirectory:

Index: Doc/lib/libcopyreg.tex
Index: Doc/lib/libthreading.tex
Index: Lib/urllib.py
Index: Mac/_checkversion.py
Index: Mac/Build/PythonCore.mcp
Index: Mac/Distributions/(vise)/Python 2.2.vct
Index: Mac/Include/macbuildno.h
Index: Mac/Modules/macfsmodule.c
Index: Mac/Modules/macmodule.c
Index: Misc/NEWS
Index: PCbuild/BUILDno.txt

> ...
> Thanks for all your work, and thank you for the quick help again.

And thanks for checking that your problem is fixed in 221c1.  Had anyone
tried this stuff in 22a1 or 22a2 or 22a3 or 22a4 or 22b1 or 22b2 or 22c1
(yes, we actually cut 7 full prerelease distributions for 2.2!), it would
have worked in 2.2 out of the box.  Keep that in mind when 2.3a1 comes out
<wink>.

Re: [Numpy-discussion] RE: Python 2.2 seriously crippled for numerical computation?

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

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

On Tue, 19 Mar 2002 20:20:36 -0500 Tim Peters wrote:

Thanks for the quick answer.  The problem is resolved.

Tim> [jo...@bo...]

>> [1]  If it is in there, it doesn't work for me with current python cvs
>> branch release22-maint.  I still have to manually add -lieee.
>> (RedHat-7.0 with current updates.)

Tim> I don't know what "current" meant to you at the time you wrote this.

About 20:00 (8:00pm) EST today, March 19.

Tim> Tim> Michael Hudson did backport the patch into 2.2.1c1, which
Tim> Tim> was released yesterday.  So please try 2.2.1c1, and if you
Tim> Tim> still have a problem, file a bug report about it on
Tim> Tim> SourceForge.  2.2.1 final is expected in about a week.

Well, changing cvs from 
  release22-maint
to
  r221c1
helps.  That is, everything seems to work fine with the cvs sources
tagged r221c1.

Then, is it really necessary to mess up the cvs tags so much? Why
isn't it possible to have a single python-2.2 branch which one could
follow to get all the stuff that's incorporated into that version? [1]

There are huge differences between release22-maint and r221c1, it
seems from the number of patches applied when going from one to the
other.  But then some files are in the same (non-main) branch.  ???

Thanks for all your work, and thank you for the quick help again.

Greetings,
Jochen
- -- 
University of North Carolina                       phone: +1-919-962-4403
Department of Chemistry                            phone: +1-919-962-1579
Venable Hall CB#3290 (Kenan C148)                    fax: +1-919-843-6041
Chapel Hill, NC 27599, USA                            GnuPG key: 44BCCD8E
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[Numpy-discussion] RE: Python 2.2 seriously crippled for numerical computation?

[Tim P. <ti...@co...>]

[jo...@bo...]
> Ok, looking at SourceForge and google this seems to be fixed in cvs
> HEAD.  Would it be possible to put the same patch into the cvs
> python-2.2 branch, please? [1]
>
> Greetings,
> Jochen
>
> Footnotes:
> [1]  If it is in there, it doesn't work for me with current python cvs
>      branch release22-maint.  I still have to manually add -lieee.
>      (RedHat-7.0 with current updates.)

I don't know what "current" meant to you at the time you wrote this.
Michael Hudson did backport the patch into 2.2.1c1, which was released
yesterday.  So please try 2.2.1c1, and if you still have a problem, file a
bug report about it on SourceForge.  2.2.1 final is expected in about a
week.

[Numpy-discussion] Re: Python 2.2 seriously crippled for numerical computation?

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

The following message is a courtesy copy of an article
that has been posted to comp.lang.python as well.

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

On Sat, 09 Mar 2002 00:17:40 -0500 Tim Peters wrote:

Tim> I hope the bogus underflow problem is fixed in CVS Python now.
Tim> Since my platform didn't have the problem (and still doesn't, of
Tim> course), we won't know for sure until people try it and report
Tim> back.

Ok, looking at SourceForge and google this seems to be fixed in cvs
HEAD.  Would it be possible to put the same patch into the cvs
python-2.2 branch, please? [1]

Greetings,
Jochen

Footnotes: 
[1]  If it is in there, it doesn't work for me with current python cvs
     branch release22-maint.  I still have to manually add -lieee.
     (RedHat-7.0 with current updates.)
- -- 
University of North Carolina                       phone: +1-919-962-4403
Department of Chemistry                            phone: +1-919-962-1579
Venable Hall CB#3290 (Kenan C148)                    fax: +1-919-843-6041
Chapel Hill, NC 27599, USA                            GnuPG key: 44BCCD8E
-----BEGIN PGP SIGNATURE-----
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Re: [Numpy-discussion] ANN: numarray-0.3

[Todd M. <jm...@st...>]

David Ascher wrote:

>Can I suggest that the generated files not be included in the source
>tarball?  I'm making sure that numarray and Numeric will be available in
>our PPM repository, and it's confusing the build script when the
>setup.py tries to overwrite files that are under source code control.
>
>--david ascher
>
>PS: PPMs for numarray 0.3 and Numeric 21 coming up soon. =)
>
>_______________________________________________
>Numpy-discussion mailing list
>Num...@li...
>https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
Yes.  I'll tighten up the MANIFEST.in for the next release.

Todd

-- 
Todd Miller 			jm...@st...
STSCI / SSG			(410) 338 4576

Re: [Numpy-discussion] ANN: numarray-0.3

[David A. <Da...@Ac...>]

Can I suggest that the generated files not be included in the source
tarball?  I'm making sure that numarray and Numeric will be available in
our PPM repository, and it's confusing the build script when the
setup.py tries to overwrite files that are under source code control.

--david ascher

PS: PPMs for numarray 0.3 and Numeric 21 coming up soon. =)

Re: [Numpy-discussion] adding a .M attribute to the array.

[Konrad H. <hi...@cn...>]

a.s...@gm... (A.Schmolck) writes:

> > > feature aspirations and divisions of labor of numpy/numarray and scipy are
>     ^^^^^^^
> Darn, I made a confusing mistake -- this should read _future_.

Or perhaps __future__  ;-)

> I personally agree with all your above points -- if you have a look at our
> "dotblas"-patch mentioned earlier (see [1]), you will find that it aims to do

And I didn't know even about this...

> It is however inconvinient for the maintainers. Whether one should bother
> including it in this or some other way depends, among the obvious question of

There could be two teams, one maintaining a standard portable
implementation, and another one taking care of optimization add-ons.
From the user's point of view, what matters most is a single
entry-point for finding everything that is available.

> The monolithic approach is not entirely without its charms (remember
> python's "batteries included" jinggle)? Apart from convinience

Sure, but... That's the standard library. Everybody has it, in
identical form, and its consistency and portability is taken care off
by the Python development team. There can be only *one* standard
library that works like this.

I see no problem either with providing a larger integrated
distribution for specific user communities. But such distribution and
packaging strategies should be distinct from development projects. If
I can get a certain package only as part of a juge distribution that I
can't or don't want to install, then that package is effectively lost
for me. Worse, if one package comes with its personalized version of
another package (SciPy with NumPy), then I end up having to worry
about internal conflicts within my installation.

On the other hand, package interdependencies are a big problem in the
Open Source community at large, and I have personally been bitten more
than once by an incompatible change in NumPy that broke my modules.
But I don't see any other solution than better communication between
development teams.

Konrad.
-- 
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Konrad Hinsen                            | E-Mail: hi...@cn...
Centre de Biophysique Moleculaire (CNRS) | Tel.: +33-2.38.25.56.24
Rue Charles Sadron                       | Fax:  +33-2.38.63.15.17
45071 Orleans Cedex 2                    | Deutsch/Esperanto/English/
France                                   | Nederlands/Francais
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