[Numpy-discussion] python-numpy debian package and f2py
Hi, I am a comaintainer of the python-scipy package in Debian and now it seems to be in quite a good shape. However, the python-numpy package is quite a mess, so as it usually goes in opensource, I got fedup and I tried to clean it. But I noticed, that f2py was moved from external package into numpy, however the versions mishmatch: The newest (deprecated) python-f2py package in Debian has a version 2.45.241+1926, so I assume this was the version of f2py, before merging with numpy. However, the f2py in numpy says when executing: Version: 2_3816 numpy Version: 1.0.3 so I assume the version of f2py in numpy is 2_3816? So has the versioning scheme of f2py changed? Another question - since both numpy and f2py is now built from the same source, doesn't f2py simply has the same version as numpy, i.e. 1.0.3? Note: I know there is a newer numpy release, but that's not the point now. I am asking because we probably will have to remove the old python-f2py package and build a new one from the sources of numpy, etc., and it will take some time until this happens (ftpmasters need to remove the old package from the archive, then the new binary package needs to go to the NEW queue for approval etc.), so I would like to make sure I understand the versioning and the future plans with numpy and f2py, before starting the transition in Debian. Actually, does it even make sense to create a python-f2py package? It seems so (to me), it's a separate program. But since you decided to merge it with numpy, what are your thoughts about it? Thanks a lot, Ondrej ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] random.hypergeometric bug
There appears to be a bug in numpy's hypergeometric random number generator. Here is an example -- if I generate 1000 hg samples with 4 draws from a space with 30 successes and 10 failures: In [39]: x = hg(30, 10, 4, 1000) I should get a mean value of: In [40]: 4*30./40 Out[40]: 3.0 But the sample mean is way to small: In [41]: mean(x) Out[41]: 0.996 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Loading a GB file into array
On Sun, Dec 02, 2007 at 05:22:49PM -0800, Martin Spacek wrote: so I run python (with Andrew Straw's package VisionEgg) as a realtime priority process in windows on a dual core computer, which lets me reliably update the video frame buffer in time for the next refresh, without having to worry about windows multitasking butting in and stealing CPU cycles for the next 15-20ms. Very interesting. Have you made measurements to see how many times you lost one of your cycles. I made these kind of measurements on Linux using the real-time clock with C and it was very interesting ( http://www.gael-varoquaux.info/computers/real-time ). I want to redo them with Python, as I except to have similar results with Python. It would be interesting to see how Windows fits in the picture (I know nothing about Windows, so I really can't make measurements on Windows). Cheers, Gaël ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] random.hypergeometric bug
Chris wrote: There appears to be a bug in numpy's hypergeometric random number generator. Here is an example -- if I generate 1000 hg samples with 4 draws from a space with 30 successes and 10 failures: In [39]: x = hg(30, 10, 4, 1000) I should get a mean value of: In [40]: 4*30./40 Out[40]: 3.0 But the sample mean is way to small: In [41]: mean(x) Out[41]: 0.996 Fixed in r4527. My original source for the algorithm was incorrect, it seems. -- 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
