Mailman 3 December 2008 - NumPy-Discussion

genloadtxt : last call
by Pierre GM 17 Dec '08

17 Dec '08

All, Here's the latest version of genloadtxt, with some recent corrections. With just a couple of tweaking, we end up with some decent speed: it's still slower than np.loadtxt, but only 15% so according to the test at the end of the package. And so, now what ? Should I put the module in numpy.lib.io ? Elsewhere ? Thx for any comment and suggestions.

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Win64 build?
by Klaus Noekel 17 Dec '08

17 Dec '08

Dear all, I would like to use numpy under Windows Vista 64-bit, but I am scared a bit by compiling a 64bit build myself. Is there an installer for Win64 somewhere, or are there any plans for one? Thanks for any advice, including directions for building myself (sigh), if that should be the only way. Klaus Noekel

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Re: [Numpy-discussion] Efficient removal of duplicates
by Daran Rife 16 Dec '08

16 Dec '08

How about a solution inspired by recipe 18.1 in the Python Cookbook, 2nd Ed: import numpy as np a = [(x0,y0), (x1,y1), ...] l = a.tolist() l.sort() unique = [x for i, x in enumerate(l) if not i or x != b[l-1]] a_unique = np.asarray(unique) Performance of this approach should be highly scalable. Daran -- Hi, I the following problem: I have a relatively long array of points [(x0,y0), (x1,y1), ...]. Apparently, I have some duplicate entries, which prevents the Delaunay triangulation algorithm from completing its task. Question, is there an efficent way, of getting rid of the duplicate entries? All I can think of involves loops. Thanks and regards, Hanno

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Mersenne twister seeds
by Michael Gilbert 16 Dec '08

16 Dec '08

According to wikipedia [1], some common Mersenne twister algorithms use a linear congruential gradient (LCG) to generate seeds. LCGs have been known to produce poor random numbers. Does numpy's Mersenne twister do this? And if so, is this potentially a problem? http://en.wikipedia.org/wiki/Linear_congruential_generator

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Concatenating Arrays to make Views
by Benjamin Haynor 15 Dec '08

15 Dec '08

Hi, I was wondering if I can concatenate 3 arrays, where the result will be a view of the original three arrays, instead of a copy of the data. For example, suppose I write the following import numpy as n a = n.array([[1,2],[3,4]]) b = n.array([[5,6],[7,8]]) c = n.array([[9,10],[11,12]]) c = n.r_[a,b] Now c = : [[1,2], [3,4], [5,6], [7,8], [9,10], [11,12]] I was hoping to get an array, such that, when I change d, a, b, and c will also change appropriately. Any ideas? - Ben bhaynor(a)hotmail.com _________________________________________________________________ Send e-mail anywhere. No map, no compass. http://windowslive.com/Explore/hotmail?ocid=TXT_TAGLM_WL_hotmail_acq_anywhe…

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Efficient removal of duplicates
by Hanno Klemm 15 Dec '08

15 Dec '08

Hi, I the following problem: I have a relatively long array of points [(x0,y0), (x1,y1), ...]. Apparently, I have some duplicate entries, which prevents the Delaunay triangulation algorithm from completing its task. Question, is there an efficent way, of getting rid of the duplicate entries? All I can think of involves loops. Thanks and regards, Hanno -- Hanno Klemm klemm(a)phys.ethz.ch

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ANNOUNCE: ETS 3.1.0 released!
by Dave Peterson 14 Dec '08

14 Dec '08

I'm pleased to announce that the Enthought Tool Suite (ETS) 3.1.0 has been tagged, released, and uploaded to PyPi[1]! Both source distributions (.tar.gz) and binary (.egg) for Windows have been built and uploaded to PyPi. You can update an existing ETS install to v3.1.0 like so: easy_install -U ETS==3.1.0 What is ETS? ------------------ The Enthought Tool Suite (ETS) is a collection of projects developed by members of the OSS community, including Enthought employees, which we use every day to construct custom scientific applications. It includes a wide variety of components, including: * an extensible application framework * application building blocks * 2-D and 3-D graphics libraries * scientific and math libraries * developer tools The cornerstone on which these tools rest is the Traits project, which provides explicit type declarations in Python; its features include initialization, validation, delegation, notification, and visualization of typed attributes. More information is available for all these packages from the Enthought Tool Suite development home page: http://code.enthought.com/projects/index.php -- Dave

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Plot directive in numpy docs
by Gael Varoquaux 13 Dec '08

13 Dec '08

Hi, What is the guideline on using the plot directive in the numpy docs? It can make some examples much easier to understand, but on the other hand, it can clutter the docstrings. In addition, I am not sure how our documentation pipeline deals with it. Cheers, Gaël

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bugfix for np.random logseries and hypergeometric are verified
by josef.pktd＠gmail.com 13 Dec '08

13 Dec '08

Hi, Now that I managed to compile numpy, I tried out the bugfixes in ticket:921 and ticket:923. For both, checking the results gives now correct results. I attached the scripts and results to the tickets. Both distributions will be tested again in scipy.stats, once I remove the skip and add the arguments that previously resulted in wrong numbers. Can someone committ these, or can I commit them myself? (This will be the maximum that I touch C, changing one word and one inequality) Josef

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numpy.random and multiprocessing
by Gael Varoquaux 12 Dec '08

12 Dec '08

Hi there, I have been using the multiprocessing module a lot to do statistical tests such as Monte Carlo or resampling, and I have just discovered something that makes me wonder if I haven't been accumulating false results. Given two files: === test.py === from test_helper import task from multiprocessing import Pool p = Pool(4) jobs = list() for i in range(4): jobs.append(p.apply_async(task, (4, ))) print [j.get() for j in jobs] p.close() p.join() === test_helper.py === import numpy as np def task(x): return np.random.random(x) ======= If I run test.py, I get: [array([ 0.35773964, 0.63945684, 0.50855196, 0.08631373]), array([ 0.35773964, 0.63945684, 0.50855196, 0.08631373]), array([ 0.35773964, 0.63945684, 0.50855196, 0.08631373]), array([ 0.65357725, 0.35649382, 0.02203999, 0.7591353 ])] In other words, the 4 processes give me the same exact results. Now I understand why this is the case: the different instances of the random number generator where created by forking from the same process, so they are exactly the very same object. This is howver a fairly bad trap. I guess other people will fall into it. The take home message is: **call 'numpy.random.seed()' when you are using multiprocessing** I wonder if we can find a way to make this more user friendly? Would be easy, in the C code, to check if the PID has changed, and if so reseed the random number generator? I can open up a ticket for this if people think this is desirable (I think so). On a side note, there are a score of functions in numpy.random with __module__ to None. It makes it inconvenient to use it with multiprocessing (for instance it forced the creation of the 'test_helper' file here). Gaël

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NumPy-Discussion December 2008