[Numpy-discussion] David's build_with_scons branch merged!
Hello, In preparation for the upcoming NumPy 1.0.5 release, I just merged David Cournapeau's build_with_scons branch: http://projects.scipy.org/scipy/numpy/changeset/4773 The current build system using numpy.distutils is still the default. NumPy does not include numscons; this merge adds scons support to numpy.distutils, provides some scons scripts, and modifies the configuration of numpy/core. David has extensively tested these changes and I did a very quick sanity check to make sure I didn't completely break everything. Obviously, we will need to push back the 1.0.5 release date again to ensure that there is sufficient testing. So please test these changes and let us know if you have any problems (or successes). David has been putting in a considerable effort over the last several months in developing numscons. If you are interested in the advantages to Davids approach, please read the description here: http://projects.scipy.org/scipy/numpy/wiki/NumpyScons Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] bug report ?
Hi, What type is pos-dimensions in your case ? It may be long (64bits long) instead of the expected int (32bits) or something like that ? Matthieu 2008/2/8, Yves Revaz [EMAIL PROTECTED]: Dear list, I'm using old numarray C api with numpy. It seems that there is a bug when using the PyArray_FromDims function. for example, if I define : acc = (PyArrayObject *) PyArray_FromDims(pos-nd,pos-dimensions,pos-descr-type_num); where pos is PyArrayObject *pos; (3x3 array) when using return PyArray_Return(acc); I get array([], shape=(3, 0), dtype=float32) It is possible to make everything works if I use the following lines instead : int ld[2]; ld[0]=pos-dimensions[0]; ld[1]=pos-dimensions[1]; acc = (PyArrayObject *) PyArray_FromDims(pos-nd,ld,pos-descr-type_num); So, the problem comes from the pos-dimensions. Is it a known bug ? (I'm working on a linux 64bits machine.) Cheers, yves ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion -- French PhD student Website : http://matthieu-brucher.developpez.com/ Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn : http://www.linkedin.com/in/matthieubrucher ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug report ?
Dear list, I'm using old numarray C api with numpy. It seems that there is a bug when using the PyArray_FromDims function. for example, if I define : acc = (PyArrayObject *) PyArray_FromDims(pos-nd,pos-dimensions,pos-descr-type_num); where pos is PyArrayObject *pos; (3x3 array) when using return PyArray_Return(acc); I get array([], shape=(3, 0), dtype=float32) It is possible to make everything works if I use the following lines instead : int ld[2]; ld[0]=pos-dimensions[0]; ld[1]=pos-dimensions[1]; acc = (PyArrayObject *) PyArray_FromDims(pos-nd,ld,pos-descr-type_num); So, the problem comes from the pos-dimensions. Is it a known bug ? (I'm working on a linux 64bits machine.) Cheers, yves ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] C-api to slicing?
Robert Kern wrote:
Neal Becker wrote:
Is there a C-api to array slicing?
PyObject_GetItem(), PySlice_New(), and friends, for the most part.
I tried PySequence_GetItem on my array, and it seems the refcount isn't
working.
inline object test_slice3 (object const in_obj, int r) {
if (!PyArray_Check (in_obj.ptr())) {
throw std::runtime_error (test_slice3: input must be numpy::array);
}
PyArrayObject* ao = (PyArrayObject*)(in_obj.ptr());
PyArrayObject* ro = (PyArrayObject*) PySequence_GetItem ((PyObject*)ao,
r);
return object (handlePyArrayObject (ro));
}
In the above, object is a boost::python::object, which is just a wrapper
around a PyArrayObject*. I extract the PyArrayObject*.
Now test:
b = array ((2,3))
print sys.getrefcount (b)
c = test_slice3(a,0)
print sys.getrefcount (b), sys.getrefcount(c)
This prints:
2
2 2
Here's what it should do:
print sys.getrefcount (b)
c = b[0]
print sys.getrefcount (b), sys.getrefcount(c)
2
3 2
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Re: [Numpy-discussion] String sort
On Feb 8, 2008 5:29 AM, Francesc Altet [EMAIL PROTECTED] wrote: Hi, I'm a bit confused that the sort method of a string character doesn't allow a mergesort: s = numpy.empty(10, S10) s.sort(kind=merge) TypeError: desired sort not supported for this type I think it's an error parsing the keyword. In fact, I thought I fixed that, but maybe I was waiting till I added the other methods. However, by looking at the numpy sources, it seems that the only implemented method for sorting array strings is merge (I presume because it is stable). So, perhaps the message above should be fixed. Also, in the context of my work in indexing, and because of the slowness of the current implementation in NumPy, I've ended with an implementation of the quicksort method for 1-D array strings. For moderately large arrays, it is about 2.5x-3x faster than the (supposedly) mergesort version in NumPy, not only due to the quicksort, but also because I've implemented a couple of macros for efficient string swapping and copy. If this is of interest for NumPy developers, tell me and I will provide the code. I have some code for this too and was going to merge it. Send yours along and I'll get to it this weekend. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy-discussion Digest, Vol 17, Issue 15
Thanks I been trying to compile a code that uses random,pylab and numpy with
py2exe
the code of setup.py(compiles mycode.py into mycode.exe) follows
#Start here
from distutils.core import setup
import py2exe
import pylab
import numpy
import glob
import scipy
import random
import os
setup( console=['mycode.py'],options={'py2exe':
{skip_archive:1,'packages':['matplotlib','pytz']',}},data_files=[
matplotlib.get_py2exe_datafiles()])
#End here
It works well for codes that uses pylab only. But It i add more modules i
get trouble
Is there any good books on how to use py2exe?
On 2/7/08, [EMAIL PROTECTED]
[EMAIL PROTECTED] wrote:
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When replying, please edit your Subject line so it is more specific
than Re: Contents of Numpy-discussion digest...
Today's Topics:
1. f2py compiled module not found by python (Chris)
2. Re: f2py compiled module not found by python (Pearu Peterson)
3. Re: Bug in numpy all() function (Robert Kern)
4. Re: f2py compiled module not found by python (Chris)
5. Re: random enhancement (Robert Kern)
6. Re: Bug in numpy all() function (Anne Archibald)
7. Re: Bug in numpy all() function (Robert Kern)
8. Re: Numpy-discussion Digest, Vol 17, Issue 13 (Steven H. Rogers)
9. isn't it a bug? (matrix multiplication) (dmitrey)
10. Re: isn't it a bug? (matrix multiplication) (Alan G Isaac)
--
Message: 1
Date: Wed, 6 Feb 2008 18:35:35 + (UTC)
From: Chris [EMAIL PROTECTED]
Subject: [Numpy-discussion] f2py compiled module not found by python
To: numpy-discussion@scipy.org
Message-ID: [EMAIL PROTECTED]
Content-Type: text/plain; charset=utf-8
Hello,
I'm trying to build a package on Linux (Ubuntu) that contains a fortran
module, built using f2py. However, despite the module building and
installing without error, python cannot seem to see it (see log below).
This works fine on Windows and Mac; the problem only seems to
happen on Linux:
In [1]: import PyMC
---
exceptions.ImportError Traceback (most
recent call last)
/home/tianhuil/?ipython console
/usr/lib/python2.4/site-packages/PyMC/__init__.py
/home/tianhuil/?string
/usr/lib/python2.4/site-packages/PyMC/MCMC.py
ImportError: No module named flib
/usr/lib/python2.4/site-packages/PyMC/MCMC.py
Notice that the module exists in the site-packages
directory:
tianhuil at tianhuil:/usr/lib/python2.4/site-packages/PyMC$ ll
total 432
drwxr-xr-x 2 root root 4096 2008-02-03 17:24 Backends
-rwxrwx--- 1 root root 195890 2008-02-03 17:24 flib.so
-rwxrwx--- 1 root root259 2008-02-03 17:14 __init__.py
-rw-r--r-- 1 root root473 2008-02-03 17:24 __init__.pyc
-rwxrwx--- 1 root root 10250 2008-02-03 17:14 Matplot.py
-rw-r--r-- 1 root root 7516 2008-02-03 17:24 Matplot.pyc
-rwxrwx--- 1 root root 98274 2008-02-03 17:14 MCMC.py
-rw-r--r-- 1 root root 79039 2008-02-03 17:24 MCMC.pyc
drwxr-xr-x 2 root root 4096 2008-02-03 17:24 Tests
-rwxrwx--- 1 root root 6631 2008-02-03 17:14 TimeSeries.py
-rw-r--r-- 1 root root 5043 2008-02-03 17:24 TimeSeries.pyc
--
Message: 2
Date: Wed, 6 Feb 2008 20:58:07 +0200 (EET)
From: Pearu Peterson [EMAIL PROTECTED]
Subject: Re: [Numpy-discussion] f2py compiled module not found by
python
To: Discussion of Numerical Python numpy-discussion@scipy.org
Message-ID: [EMAIL PROTECTED]
Content-Type: text/plain;charset=iso-8859-1
On Wed, February 6, 2008 8:35 pm, Chris wrote:
Hello,
I'm trying to build a package on Linux (Ubuntu) that contains a fortran
module, built using f2py. However, despite the module building and
installing without error, python cannot seem to see it (see log below).
This works fine on Windows and Mac; the problem only seems to
happen on Linux:
Can you import flib module directly? That is, what happens if you
execute
cd .../PyMC
PYTHONPATH=. python -c 'import flib'
?
Pearu
--
Message: 3
Date: Wed, 06 Feb 2008 14:03:20 -0600
From: Robert Kern [EMAIL PROTECTED]
Subject: Re: [Numpy-discussion] Bug in numpy all() function
To: Discussion of Numerical Python numpy-discussion@scipy.org
Message-ID: [EMAIL PROTECTED]
Content-Type: text/plain; charset=UTF-8; format=flowed
Dan Goodman wrote:
Hi all,
I think this is a bug (I'm running Numpy 1.0.3.1):
from numpy import *
def f(x): return False
all(f(x) for x in range(10))
True
I guess the all function doesn't know about generators?
Yup. It works on
[Numpy-discussion] CVXOPT and OpenOffice
Hi, I'm trying to use the CVXOPT extension for OpenOffice under Windows, but I got this error: CVXOPT might not be installed. On the CVXOPT plugin for OpenOffice.org Users's Guide I have seen the warning The installation of CVXOPT must be in a location known to the OpenOffice.org spreadsheet. On a Linux system this corresponds to a regular system-wide installation. For other platforms installation may vary. So what is the right location to install CVXOPT on Windows in order to make OpenOffice found it? Thanks in advance. Guilherme Flach ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] David's build_with_scons branch merged!
Jarrod and David, I am reporting a success on FC8, Xeon. Some tests don't pass, but I don't believe it is related to the build process. Well done, David 2008/2/8, Jarrod Millman [EMAIL PROTECTED]: Hello, In preparation for the upcoming NumPy 1.0.5 release, I just merged David Cournapeau's build_with_scons branch: http://projects.scipy.org/scipy/numpy/changeset/4773 The current build system using numpy.distutils is still the default. NumPy does not include numscons; this merge adds scons support to numpy.distutils, provides some scons scripts, and modifies the configuration of numpy/core. David has extensively tested these changes and I did a very quick sanity check to make sure I didn't completely break everything. Obviously, we will need to push back the 1.0.5 release date again to ensure that there is sufficient testing. So please test these changes and let us know if you have any problems (or successes). David has been putting in a considerable effort over the last several months in developing numscons. If you are interested in the advantages to Davids approach, please read the description here: http://projects.scipy.org/scipy/numpy/wiki/NumpyScons Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/ ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] String sort
A Friday 08 February 2008, Charles R Harris escrigué:
Also, in the context of my work in indexing, and because of the
slowness of the current implementation in NumPy, I've ended with an
implementation of the quicksort method for 1-D array strings. For
moderately large arrays, it is about 2.5x-3x faster than the
(supposedly) mergesort version in NumPy, not only due to the
quicksort, but also because I've implemented a couple of macros for
efficient string swapping and copy. If this is of interest for
NumPy developers, tell me and I will provide the code.
I have some code for this too and was going to merge it. Send yours
along and I'll get to it this weekend.
Ok, great. I'm attaching it. Tell me if you need some clarification on
the code.
Cheers,
--
0,0 Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
-
/* This is a quick sort implementation for numpy strings, mainly for
testing purposes, but could be useful in the future. It happens to
be between 2.5x and 3x (for long enough arrays) than the string
sort in NumPy (based in merge sort).
Francesc Altet 2008-02-07
*/
#define sSWAP(a,b) { \
for (i=0; iss; i++) { \
((char *)SWAP_temp)[i] = ((char *)(b))[i]; \
((char *)b)[i] = ((char *)(a))[i]; \
((char *)a)[i] = ((char *)(SWAP_temp))[i]; \
}\
}
#define opt_memcpy(a,b,n) {\
for (i=0; i(n); i++) {\
((char *)a)[i] = ((char *)(b))[i]; \
} \
}
/* opt_strncmp is an optimized version of strncmp, mainly for easy the
inlining by the compiler. I'm not sure whether the inline would
create problems with other compilers than GCC, but it can safely
removed and let the compiler decide if it should do the inlining or
not. In any case, the inlining can improve the global performance
by a 20% or more.
This is only valid for regular strings, but adding support for UCS4
should be a matter of replacing 'char' by 'PyArray_UCS4', I think.
*/
static inline int opt_strncmp(char *a, char *b, int n) {
int i;
for (i=0; in; i++) {
if (a[i] b[i]) return i;
if (a[i] b[i]) return -i;
}
return 0;
}
/* start: the address where the data for 1-D string array starts
ss: the size of the string elements.
num: the number of elements
*/
int quicksort_string(char *start, int ss, intp num)
{
char *pl = start;
char *pr = start + (num - 1) * ss;
char *vp;
char *SWAP_temp;
char *stack[PYA_QS_STACK], **sptr = stack;
char *pm, *pi, *pj, *pt;
int i;
vp = malloc(ss);
SWAP_temp = malloc(ss);
for(;;) {
while (((pr - pl)/ss) SMALL_QUICKSORT) {
/* quicksort partition */
pm = pl + (((pr-pl)/ss) 1)*ss;
if (opt_strncmp(pm, pl, ss) 0) { sSWAP(pm, pl); }
if (opt_strncmp(pr, pm, ss) 0) { sSWAP(pr, pm); }
if (opt_strncmp(pm, pl, ss) 0) { sSWAP(pm, pl); }
opt_memcpy(vp, pm, ss);
pi = pl;
pj = pr - ss;
sSWAP(pm, pj);
for(;;) {
do { pi += ss; } while (opt_strncmp(pi, vp, ss) 0);
do { pj -= ss; } while (opt_strncmp(vp, pj, ss) 0);
if (pi = pj) break;
sSWAP(pi, pj);
}
sSWAP(pi, pr-ss);
/* push largest partition on stack */
if (pi - pl pr - pi) {
*sptr++ = pi + ss;
*sptr++ = pr;
pr = pi - ss;
}else{
*sptr++ = pl;
*sptr++ = pi - ss;
pl = pi + ss;
}
}
/* insertion sort */
for(pi = pl + ss; pi = pr; pi += ss) {
opt_memcpy(vp, pi, ss);
for(pj = pi, pt = pi - ss; pj pl opt_strncmp(vp, pt, ss) 0;) {
opt_memcpy(pj, pt, ss);
pj -= ss; pt -= ss;
}
opt_memcpy(pj, vp, ss);
}
if (sptr == stack) break;
pr = *(--sptr);
pl = *(--sptr);
}
free(vp);
free(SWAP_temp);
return 0;
}
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[Numpy-discussion] bayPIGgies meets Thursday, 2/21: Guido van Rossum on Python 3.0
* SPECIAL NOTE: because Valentine's Day is on the second * Thursday of February (2/14) bayPIGgies has moved our * meeting to the third Thursday of the month, 2/21. bayPIGgies meeting Thursday 2/21: Guido van Rossum on Python 3.0 by Guido van Rossum Guido previews his keynote about Python 3000 at PyCon next month. Hear all about what Python 3000 means for your code, what tools will be available to help you in the transition, and how to be prepared for the next millennium. Location: Google Campus in Mountain View, CA Building 40, the Kiev room (first floor) bayPIGgies meeting information: http://baypiggies.net/new/plone * Please sign up in advance to have your google access badge ready: http://wiki.python.org/moin/BayPiggiesGoogleMeetings (no later than close of business on Wednesday.) Agenda- . 7:30 PM ... General hubbub, inventory end-of-meeting announcements, any first-minute announcements. . 7:35 PM to 8:45 PM The Talk (may extend a bit late) . 8:45 PM to 9:00 PM or After The Talk Mapping and Random Access Mapping is a rapid-fire audience announcement of topics the announcers are interested in. Random Access follows immediately to allow follow up individually on the announcements and other topics of interest. . The March Meeting TBD ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy-discussion Digest, Vol 17, Issue 15
Matthew, please do not reply to the digests. Think of them as read-only. If you want to start a new thread, send your mail, with a descriptive Subject line, to numpy-discussion@scipy.org . If you want to reply to individual messages, please turn digest delivery *off* and receive and respond to messages normally. Thank you. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] String sort
A Friday 08 February 2008, Francesc Altet escrigué:
A Friday 08 February 2008, Charles R Harris escrigué:
Also, in the context of my work in indexing, and because of the
slowness of the current implementation in NumPy, I've ended with
an implementation of the quicksort method for 1-D array strings.
For moderately large arrays, it is about 2.5x-3x faster than the
(supposedly) mergesort version in NumPy, not only due to the
quicksort, but also because I've implemented a couple of macros
for efficient string swapping and copy. If this is of interest
for NumPy developers, tell me and I will provide the code.
I have some code for this too and was going to merge it. Send yours
along and I'll get to it this weekend.
Ok, great. I'm attaching it. Tell me if you need some clarification
on the code.
Ops. I've introduced a last-minute problem in my code. To fix this,
just replace the flawed opt_strncmp() that I sent before by:
static int inline opt_strncmp(char *a, char *b, int n) {
int i;
for (i=0; in; i++) {
if (a[i] b[i]) return i+1;
if (a[i] b[i]) return -(i+1);
/* Another way, but seems equivalent in speed, at least here */
/* if (a[i] != b[i]) */
/* return (((unsigned char *)a)[i] - ((unsigned char *)b)[i]); */
}
return 0;
}
Apparently, this version works just fine.
Cheers,
--
0,0 Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
-
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Re: [Numpy-discussion] PySequence_GetItem on numpy array doesn't inc refcount?
Neal Becker wrote: It seems that calling PySequence_GetItem on a PyArrayObject does not inc refcount on the original object? That is surprising. Then, I suppose my code is supposed to do that itself? Not sure what I was doing wrong before, but seems to be working as expected now, sorry for the noise. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] String sort
On Feb 8, 2008 8:58 AM, Francesc Altet [EMAIL PROTECTED] wrote: A Friday 08 February 2008, Charles R Harris escrigué: Also, in the context of my work in indexing, and because of the slowness of the current implementation in NumPy, I've ended with an implementation of the quicksort method for 1-D array strings. For moderately large arrays, it is about 2.5x-3x faster than the (supposedly) mergesort version in NumPy, not only due to the quicksort, but also because I've implemented a couple of macros for efficient string swapping and copy. If this is of interest for NumPy developers, tell me and I will provide the code. I have some code for this too and was going to merge it. Send yours along and I'll get to it this weekend. Ok, great. I'm attaching it. Tell me if you need some clarification on the code. I ran a few timing tests. On my machine strncmp is about 100x faster than opt_strncmp, but sSWAP (with some fixes), is about 10x faster then using the memcpy in a recent compiler. Does this match with your experience. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] String sort
On Feb 8, 2008 10:31 AM, Francesc Altet [EMAIL PROTECTED] wrote:
A Friday 08 February 2008, Francesc Altet escrigué:
A Friday 08 February 2008, Charles R Harris escrigué:
Also, in the context of my work in indexing, and because of the
slowness of the current implementation in NumPy, I've ended with
an implementation of the quicksort method for 1-D array strings.
For moderately large arrays, it is about 2.5x-3x faster than the
(supposedly) mergesort version in NumPy, not only due to the
quicksort, but also because I've implemented a couple of macros
for efficient string swapping and copy. If this is of interest
for NumPy developers, tell me and I will provide the code.
I have some code for this too and was going to merge it. Send yours
along and I'll get to it this weekend.
Ok, great. I'm attaching it. Tell me if you need some clarification
on the code.
Ops. I've introduced a last-minute problem in my code. To fix this,
just replace the flawed opt_strncmp() that I sent before by:
static int inline opt_strncmp(char *a, char *b, int n) {
int i;
for (i=0; in; i++) {
if (a[i] b[i]) return i+1;
if (a[i] b[i]) return -(i+1);
/* Another way, but seems equivalent in speed, at least here */
/* if (a[i] != b[i]) */
/* return (((unsigned char *)a)[i] - ((unsigned char *)b)[i]); */
}
return 0;
}
Apparently, this version works just fine.
Did you find this significantly faster than strncmp? There is also a unicode
compare, do you have thoughts about that?
Chuck
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Re: [Numpy-discussion] segfault problem with numpy and pickle
I have added a valgrind report to bug 551. The report indicates a problem with uninitialized values. The segfault does seem to be related to certain configurations of atlas. I can confirm that I had this same problem occurs with the Ubuntu 7.04 installed scipy with SSE2 optimized ATLAS. The valgrind output is from a run where the code did not crash but valgrind still detected many errors. On Jan 26, 2008, at 3:01 PM, David Bolme wrote: I think you are right. This does seem to be the same bug as 551. I will try a non optimized ATLAS to see if that helps. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] PySequence_GetItem on numpy array doesn't inc refcount?
It seems that calling PySequence_GetItem on a PyArrayObject does not inc refcount on the original object? That is surprising. Then, I suppose my code is supposed to do that itself? ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] String sort
Hi, I'm a bit confused that the sort method of a string character doesn't allow a mergesort: s = numpy.empty(10, S10) s.sort(kind=merge) TypeError: desired sort not supported for this type However, by looking at the numpy sources, it seems that the only implemented method for sorting array strings is merge (I presume because it is stable). So, perhaps the message above should be fixed. Also, in the context of my work in indexing, and because of the slowness of the current implementation in NumPy, I've ended with an implementation of the quicksort method for 1-D array strings. For moderately large arrays, it is about 2.5x-3x faster than the (supposedly) mergesort version in NumPy, not only due to the quicksort, but also because I've implemented a couple of macros for efficient string swapping and copy. If this is of interest for NumPy developers, tell me and I will provide the code. Cheers, -- 0,0 Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data - ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] svn 4777 distutils fails to find Intel MKL
This is the 3rd time I have reported this problem and a fix.
-rex
- Forwarded message from rex [EMAIL PROTECTED] -
Date: Fri, 9 Nov 2007 11:16:17 -0800
From: rex [EMAIL PROTECTED]
To: Discussion of Numerical Python numpy-discussion@scipy.org
Subject: NumPy 1.04, MKL 10.0, Intel 10.1 icc ifort
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Build was successful after a change in distutils.
Core 2 Duo, Debian Etch-32, Python 2.5, icc 10.1, ifort 10.1, mkl
10.0. MKL the compilers were installed to their default
locations: /opt/intel/mkl/, /opt/intel/cc/, /opt/intel/fc/
Installation will not interfere with earlier versions.
NumPy site.cfg for MKL 10.0
==
[DEFAULT]
library_dirs=/opt/intel/mkl/10.0.011/lib/32
include_dirs=/opt/intel/mkl/10.0.011/include
[mkl]
libraries=mkl,guide
[lapack_info]
libraries=mkl_lapack,mkl,guide
==
Changes in distutils:
=
In MKL 9.1 10.0, mkl_lapack32 mkl_lapack64 no longer
exist. They were replaced by /32/mkl_lapack and
/64/mkl_lapack. As a result,
numpy-1.04/numpy/distutils/system_info.py
needs to be changed:
#lapack_libs = self.get_libs('lapack_libs',['mkl_lapack32','mkl_lapack64'])
lapack_libs = self.get_libs('lapack_libs',['mkl_lapack'])
If the above change is not made numpy builds, but without using MKL.
In numpy-1.04/numpy/distutils/ccompiler.py change:
#cc_exe = 'icc'#works, but is suboptimal
cc_exe = 'icc -msse3 -fast'#adjust to suit your cpu
=
Running the command below (all one line) from the numpy-1.04 directory
python setup.py config --compiler=intel build_clib
--compiler=intel build_ext --compiler=intel install
inst.log
Gave this inst.log:
=
F2PY Version 2_4412
blas_opt_info:
blas_mkl_info:
FOUND:
libraries = ['mkl', 'pthread', 'mkl', 'guide']
library_dirs = ['/opt/intel/mkl/10.0.011/lib/32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['/opt/intel/mkl/10.0.011/include']
FOUND:
libraries = ['mkl', 'pthread', 'mkl', 'guide']
library_dirs = ['/opt/intel/mkl/10.0.011/lib/32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['/opt/intel/mkl/10.0.011/include']
lapack_opt_info:
lapack_mkl_info:
mkl_info:
FOUND:
libraries = ['mkl', 'pthread', 'mkl', 'guide']
library_dirs = ['/opt/intel/mkl/10.0.011/lib/32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['/opt/intel/mkl/10.0.011/include']
FOUND:
libraries = ['mkl_lapack', 'mkl', 'pthread', 'mkl', 'guide', 'mkl',
'guide']
library_dirs = ['/opt/intel/mkl/10.0.011/lib/32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['/opt/intel/mkl/10.0.011/include']
FOUND:
libraries = ['mkl_lapack', 'mkl', 'pthread', 'mkl', 'guide', 'mkl',
'guide']
library_dirs = ['/opt/intel/mkl/10.0.011/lib/32']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['/opt/intel/mkl/10.0.011/include']
running config
running build_clib
running build_ext
running build_src
building py_modules sources
creating build
[...]
===
For reference, here are the respective lib/32 files for MKL 9.1
and 10.0:
c2d0:/opt/intel/mkl/9.1/lib/32# ls
libguide.a libmkl_gfortran.a libmkl_ias.so
libmkl_p3.so libmkl_p4.so libmkl_vml_def.so
libmkl_vml_p4p.so libguide.so libmkl_gfortran.so
libmkl_lapack.alibmkl_p4m.so libmkl.so
libmkl_vml_p3.so libmkl_vml_p4.so libmkl_def.so
libmkl_ia32.a libmkl_lapack.so libmkl_p4p.so
libmkl_solver.alibmkl_vml_p4m.so libvml.so
c2d0:/opt/intel/mkl/10.0.011/lib/32# ls
libguide.a libmkl_cdft_core.alibmkl_intel.a
libmkl_p4.so libmkl_vml_ia.so libguide.so
libmkl_core.a libmkl_intel.so libmkl_scalapack.a
libmkl_vml_p3.so libiomp5.alibmkl_core.so
libmkl_intel_thread.a libmkl_scalapack_core.a libmkl_vml_p4m2.so
libiomp5.solibmkl_def.so libmkl_intel_thread.so
libmkl_sequential.alibmkl_vml_p4m.s olibmkl_blacs.a
libmkl_gf.alibmkl_lapack.a libmkl_sequential.so
libmkl_vml_p4p.so libmkl_blacs_intelmpi20.a libmkl_gf.so
libmkl_lapack.so libmkl.so libmkl_vml_p4.so
libmkl_blacs_intelmpi.alibmkl_gnu_thread.a libmkl_p3.so
libmkl_solver.alibmkl_blacs_openmpi.a libmkl_gnu_thread.so
libmkl_p4m.so libmkl_solver_sequential.a libmkl_cdft.a
libmkl_ia32.a libmkl_p4p.so libmkl_vml_def.so
Re: [Numpy-discussion] py2exe issues (was Numpy-discussion Digest, Vol 17, Issue 15)
matthew yeomans wrote:
Thanks I been trying to compile a code that uses random,pylab and
numpy with py2exe
the code of setup.py(compiles mycode.py into mycode.exe) follows
#Start here
from distutils.core import setup
import py2exe
import pylab
import numpy
import glob
import scipy
import random
import os
setup( console=['mycode.py'],options={'py2exe':
{skip_archive:1,'packages':['matplotlib','pytz']',}},data_files=[matplotlib.get_py2exe_datafiles()])
#End here
It works well for codes that uses pylab only. But It i add more
modules i get trouble
Is there any good books on how to use py2exe?
Matthew:
I've only used py2exe with a couple of imports (numpy and os). You're
importing an awful lot. Slimming this down to only import the things
you really need from each module may help, e.g.
from numpy import foo
from random import bar
etc.
# Steve
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[Numpy-discussion] svn 4777 fails to build with icc
After doing the necessary fix for distutils, svn4774 builds with gcc, but trying to build with icc with: python setup.py config --compiler=intel build_clib --compiler=intel build_ext --compiler=intel install inst.log fails with: Running from numpy source directory. /usr/local/src/numpy4777/numpy/distutils/system_info.py:1341: UserWarning: Atlas (http://math-atlas.sourceforge.net/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [atlas]) or by setting the ATLAS environment variable. warnings.warn(AtlasNotFoundError.__doc__) /usr/local/src/numpy4777/numpy/distutils/system_info.py:1350: UserWarning: Blas (http://www.netlib.org/blas/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [blas]) or by setting the BLAS environment variable. warnings.warn(BlasNotFoundError.__doc__) /usr/local/src/numpy4777/numpy/distutils/system_info.py:1353: UserWarning: Blas (http://www.netlib.org/blas/) sources not found. Directories to search for the sources can be specified in the numpy/distutils/site.cfg file (section [blas_src]) or by setting the BLAS_SRC environment variable. warnings.warn(BlasSrcNotFoundError.__doc__) /usr/local/src/numpy4777/numpy/distutils/system_info.py:1248: UserWarning: Atlas (http://math-atlas.sourceforge.net/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [atlas]) or by setting the ATLAS environment variable. warnings.warn(AtlasNotFoundError.__doc__) /usr/local/src/numpy4777/numpy/distutils/system_info.py:1259: UserWarning: Lapack (http://www.netlib.org/lapack/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [lapack]) or by setting the LAPACK environment variable. warnings.warn(LapackNotFoundError.__doc__) /usr/local/src/numpy4777/numpy/distutils/system_info.py:1262: UserWarning: Lapack (http://www.netlib.org/lapack/) sources not found. Directories to search for the sources can be specified in the numpy/distutils/site.cfg file (section [lapack_src]) or by setting the LAPACK_SRC environment variable. warnings.warn(LapackSrcNotFoundError.__doc__) site.cfg is: [DEFAULT] library_dirs=/opt/intel/mkl/10.0.1.014/lib/32 include_dirs=/opt/intel/mkl/10.0.1.014/include [mkl] library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/ lapack_libs = mkl_lapack [lapack_src] libraries=mkl_lapack,mkl,guide OS is Debian Etch 4.0 I've built hundreds of programs from source over the years, including many versions of Numpy and Scipy. NOTHING has ever been as frustrating as these two. I often spend days trying to build a new release. Sometimes it works. More often, it does not, especially Scipy. I even switched from SUSE to Debian in the hope that it would help. It did not. I suggest that efforts to produce a product that ordinary 150 IQ mortals can install rather than add new features would grow the user population faster. For example, distutils has been broken for months for Intel MKL. I've posted 3x about it now. -rex -- I pray for a soroban. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
