Re: [Numpy-discussion] ANN: NumPy 1.2.0
> BTW, I can confirm that the latest official MKL does not work with > numpy, as it is explained on the Intel forum > (http://software.intel.com/en-us/forums/intel-math-kernel-library/topic/60460). > I get the i_free not defined issue. For those who run into this issue, you have to use MKL 10.0.2 which does nto have the issue. Matthieu -- Information System Engineer, Ph.D. 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
Re: [Numpy-discussion] how to tell if a point is inside a polygon
2008/10/16 Rob Hetland <[EMAIL PROTECTED]>: > > On Oct 14, 2008, at 12:56 AM, Stéfan van der Walt wrote: > >> Here is an implementation in Python, ctypes and in weave: >> >> http://mentat.za.net/source/pnpoly.tar.bz2 >> >> Regards >> Stéfan > > This question gets asked about once a month on the mailing list. > Perhaps pnpoly could find a permanent home in scipy? (or somewhere?) > Obviously, many would find it useful. Maybe scipy.spatial could accommodate it? Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Request for Python 2.6 win32 package
Please add numpy 1.2.0 win32 package for python 2.6 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for Python 2.6 win32 package
Adam Foster wrote: > Please add numpy 1.2.0 win32 package for python 2.6 Hi, numpy 1.2 is not buildable with python 2.6. You will have to wait for a later version, most probably 1.3, cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Request for Python 2.6 win32 package
numpy 1.2 is not buildable with python 2.6. You will have to wait > for a later version, most probably 1.3, > Ok thanks David, guess I will have to wait till I can leverage the new IEEE 754 support in python 2.6 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] choose() broadcasting
* Travis E. Oliphant <[EMAIL PROTECTED]> [081003 22:20]:
> Roman Bertle wrote:
> > Hello,
> >
> > I have found something I call a bug in the numpy choose() method and
> > wanted to report it in trac.
> >
> Thanks for your report. I'm not sure why you are having trouble with
> Trac, but I've created a ticket for this problem.
Hello,
trac works for me know. And thank you for fixing the bug, the svn numpy
version works now for me. But there remains an issue I want to report.
choose is much slower in numpy than in numarray, and even more if an
output array is specified, as these tests show:
import timeit
setups = {
'numarray': """
import numarray as N
n1, n2 = 4, 100
a1 = N.arange(n1*n2, type='Float64', shape=(n1,n2))
a2 = -N.arange(n1*n2, type='Float64', shape=(n1,n2))
a3 = -N.arange(n1*n2, type='Float64', shape=(n1,n2))
b1 = N.arange(n2, type='Float64')
b2 = -N.arange(n2, type='Float64')
b3 = -N.arange(n2, type='Float64')
c = N.remainder(N.arange(n2, type='Int32'),2)
""",
'numpy': """
import numpy as N
n1, n2 = 4, 100
a1 = N.arange(n1*n2, dtype='Float64').reshape((n1,n2))
a2 = -N.arange(n1*n2, dtype='Float64').reshape((n1,n2))
a3 = -N.arange(n1*n2, dtype='Float64').reshape((n1,n2))
b1 = N.arange(n2, dtype='Float64')
b2 = -N.arange(n2, dtype='Float64')
b3 = -N.arange(n2, dtype='Float64')
c = N.remainder(N.arange(n2, dtype='Int32'),2)
"""
}
stmta = "N.choose(c, (a1, a2))"
stmtao = "N.choose(c, (a1, a2), a3)"
stmtb = "N.choose(c, (b1, b2))"
stmtbo = "N.choose(c, (b1, b2), b3)"
timeit.Timer(setup=setups['numarray'], stmt=stmta).repeat(3,100)
[3.3187780380249023, 3.2966721057891846, 3.3234250545501709]
timeit.Timer(setup=setups['numpy'], stmt=stmta).repeat(3,100)
[14.842453002929688, 14.833296060562134, 14.836632966995239]
timeit.Timer(setup=setups['numarray'], stmt=stmtao).repeat(3,100)
[3.1973719596862793, 3.2031948566436768, 3.2093629837036133]
timeit.Timer(setup=setups['numpy'], stmt=stmtao).repeat(3,100)
[17.546916007995605, 17.548220157623291, 17.536314010620117]
timeit.Timer(setup=setups['numarray'], stmt=stmtb).repeat(3,100)
[0.6694338321685791, 0.66939401626586914, 0.67307686805725098]
timeit.Timer(setup=setups['numpy'], stmt=stmtb).repeat(3,100)
[3.7615809440612793, 3.7627589702606201, 3.7547731399536133]
timeit.Timer(setup=setups['numarray'], stmt=stmtbo).repeat(3,100)
[0.67037606239318848, 0.67186903953552246, 0.66994881629943848]
timeit.Timer(setup=setups['numpy'], stmt=stmtbo).repeat(3,100)
[4.4750981330871582, 4.4650890827178955, 4.4679431915283203]
Best Regards,
Roman
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how to tell if a point is inside a polygon
On Thu, Oct 16, 2008 at 2:28 PM, Rob Hetland <[EMAIL PROTECTED]> wrote: > I did not know that very useful thing. But now I do. This is solid > proof that lurking on the mailing lists makes you smarter. and that our documentation effort still has a long way to go ! FAQ added at http://matplotlib.sourceforge.net/faq/howto_faq.html?#how-do-i-test-whether-a-point-is-inside-a-polygon though I am having trouble getting the module functions pnpoly and points_inside_poly to show up in the sphinx automodule documentation for nxutils. These functions are defined in extension code and I have a post in to the sphinx mailing list http://groups.google.com/group/sphinx-dev/t/7ad1631d3117e4eb but if anyone on this list has seen problems with automodule and extension code functions, and knows how to fix them, let me know. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Weird clipping when astype(int) used on large numbers
I ran into this weird behavior with astype(int) In [57]: a = np.array(1E13) In [58]: a.astype(int) Out[58]: array(-2147483648) I understand why large numbers need to be clipped when converting to int (although I would have expected some sort of warning), but I'm puzzled by the negative value. Shouldn't the above code clip the value to the max positive int (2147483647)... and maybe issue a warning? Thanks, -Tony P.S. In case this is a problem with my install: OS X 10.5.5 Python 2.5.1 Numpy 1.2.0 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Weird clipping when astype(int) used on large numbers
Hi, This a usual thing in integers conversions. If you transform an integer like 0x from 16 bits to 8bits, you get 0x, thus a negative number. As there are no processor instructions that do saturations (DSP instructions), the behavior is to be expected. Matthieu 2008/10/17 Tony S Yu <[EMAIL PROTECTED]>: > I ran into this weird behavior with astype(int) > > In [57]: a = np.array(1E13) > > In [58]: a.astype(int) > > Out[58]: array(-2147483648) > > I understand why large numbers need to be clipped when converting to > int (although I would have expected some sort of warning), but I'm > puzzled by the negative value. Shouldn't the above code clip the value > to the max positive int (2147483647)... and maybe issue a warning? > > Thanks, > -Tony > > P.S. In case this is a problem with my install: > OS X 10.5.5 > Python 2.5.1 > Numpy 1.2.0 > ___ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion > -- Information System Engineer, Ph.D. 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
Re: [Numpy-discussion] Weird clipping when astype(int) used on large numbers
On Fri, Oct 17, 2008 at 1:27 PM, Tony S Yu <[EMAIL PROTECTED]> wrote: > I ran into this weird behavior with astype(int) > > In [57]: a = np.array(1E13) > > In [58]: a.astype(int) > > Out[58]: array(-2147483648) > > I understand why large numbers need to be clipped when converting to > int (although I would have expected some sort of warning), but I'm > puzzled by the negative value. Shouldn't the above code clip the value > to the max positive int (2147483647)... and maybe issue a warning? > Try more precision: In [2]: a = np.array(1e13) In [3]: a.astype(np.int64) Out[3]: array(10) Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] choose() broadcasting
Roman Bertle wrote: > * Travis E. Oliphant <[EMAIL PROTECTED]> [081003 22:20]: > >> Roman Bertle wrote: >> >>> Hello, >>> >>> I have found something I call a bug in the numpy choose() method and >>> wanted to report it in trac. >>> >>> >> Thanks for your report. I'm not sure why you are having trouble with >> Trac, but I've created a ticket for this problem. >> > > Hello, > > trac works for me know. And thank you for fixing the bug, the svn numpy > version works now for me. But there remains an issue I want to report. > choose is much slower in numpy than in numarray, and even more if an > output array is specified, as these tests show: > Thanks for the report. You should add another ticket for this case. I suspect it might be a result of the extra copies that are done in the PyArray_Choose routine because the algorithm assumes contiguous arrays. It deserves a look. It probably wouldn't be too difficult to avoid the copy. -Travis ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] ANNOUNCE: EPD with Py2.5 version 4.0.30002 RC2 available for testing
Hello, We've recently posted the RC2 build of EPD (the Enthought Python Distribution) with Python 2.5 version 4.0.30002 to the EPD website. You may download the RC from here: http://www.enthought.com/products/epdbeta.php You can check out the release notes here: https://svn.enthought.com/epd/wiki/Python2.5.2/4.0.300/RC2 Please help us test it out and provide feedback on the EPD Trac instance: https://svn.enthought.com/epd or via e-mail to [EMAIL PROTECTED] If everything goes well, we are planning a final release for this coming Tuesday, October 21st. About EPD - The Enthought Python Distribution (EPD) is a "kitchen-sink-included" distribution of the Python™ Programming Language, including over 60 additional tools and libraries. The EPD bundle includes NumPy, SciPy, IPython, 2D and 3D visualization, database adapters, and a lot of other tools right out of the box. http://www.enthought.com/products/epd.php It is currently available as a single-click installer for Windows XP (x86), Mac OS X (a universal binary for OS X 10.4 and above), and RedHat 3 and 4 (x86 and amd64). EPD is free for academic use. An annual subscription and installation support are available for individual commercial use. An enterprise subscription with support for particular deployment environments is also available for commercial purchase. -- Dave ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
