Mailman 3 February 2010 - SciPy-Dev

2-review system on doc wiki
by Joe Harrington 14 Feb '10

14 Feb '10

Chuck Harris recently posted on the numpy-discussion list a request for numpy 2.0 requirements. I suggested that we include reviewed docs in 2.0. The problem is that the docstring review is stuck. We need to implement both a technical and a presentation review, but we currently lack labor to do the job. Pauli Virtanen has been busy with other commitments, so there has been little progress on doc wiki changes. So, this is also a call for a Django programmer who can add a second review capability to the doc wiki. If nobody steps forward, then we'll have to abandon the idea. I think this would be a shame, because there are many docstrings that are technically complete but impenetrable, and others that are well presented but technically incomplete. It won't be hard to fix these, but we need a system to find them. Any takers? Ideally, it should be someone who has written docs in our system and who has Django experience, but likely Django or similar experience is more important for this. Thanks, --jh-- Prof. Joseph Harrington Planetary Sciences Group Department of Physics MAP 414 4000 Central Florida Blvd. University of Central Florida Orlando, FL 32816-2385 jh(a)physics.ucf.edu planets.ucf.edu

5 5

Buildbots showing red
by Charles R Harris 14 Feb '10

14 Feb '10

*All* the buildbots are showing errors. Here are some: ====================================================================== ERROR: test_view_to_flexible_dtype (test_core.TestMaskedView) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/numpybb/Buildbot/numpy/b13/numpy-install/lib/python2.4/site-packages/numpy/ma/tests/test_core.py", line 3333, in test_view_to_flexible_dtype test = a[0].view([('A', float), ('B', float)]) File "../numpy-install/lib/python2.4/site-packages/numpy/ma/core.py", line 2866, in view File "../numpy-install/lib/python2.4/site-packages/numpy/ma/core.py", line 2786, in __array_finalize__ TypeError: attribute 'shape' of 'numpy.generic' objects is not writable ====================================================================== ERROR: test_view_to_subdtype (test_core.TestMaskedView) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/numpybb/Buildbot/numpy/b13/numpy-install/lib/python2.4/site-packages/numpy/ma/tests/test_core.py", line 3354, in test_view_to_subdtype test = a[0].view((float, 2)) File "../numpy-install/lib/python2.4/site-packages/numpy/ma/core.py", line 2866, in view File "../numpy-install/lib/python2.4/site-packages/numpy/ma/core.py", line 2786, in __array_finalize__ TypeError: attribute 'shape' of 'numpy.generic' objects is not writable ====================================================================== FAIL: test_buffer_hashlib (test_regression.TestRegression) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/numpybb/Buildbot/numpy/b13/numpy-install/lib/python2.4/site-packages/numpy/core/tests/test_regression.py", line 1255, in test_buffer_hashlib assert_equal(md5(x).hexdigest(), '2a1dd1e1e59d0a384c26951e316cd7e6') File "../numpy-install/lib/python2.4/site-packages/numpy/testing/utils.py", line 305, in assert_equal AssertionError: Items are not equal: ACTUAL: '1264d4a9f74dc462700fd163e3ff09a6' DESIRED: '2a1dd1e1e59d0a384c26951e316cd7e6' Chuck

1 0

Not Re: Removing datetime support. :-)
by David Goldsmith 11 Feb '10

11 Feb '10

In the doc Wiki, how does one make a three line, a.k.a. "identically" equal to sign? Thanks! DG

3 2

Web space for docstring-linked PDFs?
by Robert Kern 11 Feb '10

11 Feb '10

I would like to make the PDFs of the ODRPACK User's Guide available somewhere on (docs.)scipy.org in order to provide a stable URL for them. The URL currently in the scipy.odr docstring is now broken. I currently have them on my own web space, but it would be nice to have them available somewhere stable on scipy.org (although not as a wiki attachment). I am now getting an email every month or so telling me that the link is broken. Is there a reasonable place in the docs.scipy.org infrastructure where I can place these static files such that they will be backed up and eventually migrated along with the rest of the infrastructure as it evolves? -- 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

3 4

Re: [SciPy-dev] SciPy-Dev Digest, Vol 76, Issue 14
by Robert Layton 10 Feb '10

10 Feb '10

>2010/2/10 St?fan van der Walt <stefan(a)sun.ac.za>: >> Hi Robert >> >> On 10 February 2010 06:38, Robert Layton <robertlayton(a)gmail.com> wrote: >>> I submitted a fix for ticket #467 a while ago, which is quite a simple fix. >>> >>> As scipy's mean and std functions are now passing through to numpy, there is >>> little reason to test them as part of scipy (the appropriate tests should be >>> in numpy). >>> Even if its decided that the tests should be retained, theres a patch for >>> that (r) as well. >> >> Sorry for not paying attention to this earlier. ?I think we should >> remove tests that only validate numpy's behaviour, so your >> `without_numpy.patch' looks good. ?Unfortunately, it doesn't apply >> cleanly; would you have a chance to look at it again? > >this was sitting in my drafts folder since November > >''' >Sorry, for not replying earlier, I have seen your patches before but I >don't know >what I would prefer. > >I agree that numpy functions should be tested in numpy. On the other hand, >the stats tests already include additional test matrices, that can be used >to check the precision of the numpy functions. And I would like to > >(As an example, numpy random is mostly tested in scipy.stats since there >pdf, pmf and cdf of the distributions are available.) >'''' > >The point for stats is that I didn't find any precision test in the >numpy test suite for mean, var and so on. > >When I wrote the anova tests using the NIST reference cases, >numpy.mean did pretty badly for the badly scaled test cases. I never >checked the NIST test cases specifically for mean, var, ... > >I still don't know where precision tests should be, but the outcome >would be very useful. In ANOVA I ended up calculating the mean twice >(if the dataset is badly scaled) to pass the NIST test. > >Josef >(google is not very informative about donkeys and piles of hay) > I have updated the patch and it now applys to the current SVN version. As I stated in the ticket, there is a problem with my build environment with a different test (test_ltisys.TestSS2TF.test_basic(0, 3, 3) ... ** On entry to DGEEV parameter number 5 had an illegal value ) so I can't verify that the test works, but it does build and install. Thanks Robert

1 0

Ticket #467 pending decision for 3 months
by Robert Layton 10 Feb '10

10 Feb '10

I submitted a fix for ticket #467 a while ago, which is quite a simple fix. As scipy's mean and std functions are now passing through to numpy, there is little reason to test them as part of scipy (the appropriate tests should be in numpy). Even if its decided that the tests should be retained, theres a patch for that (r) as well. Thoughts?

3 3

Why does orth use svd instead of QR ?
by David Cournapeau 08 Feb '10

08 Feb '10

Hi, I wanted to know if there was a rationale for using svd to orthonormalize the columns of a matrix (in scipy.linalg). QR-based methods are likely to be much faster, and I thought this was the standard, numerically-stable method to orthonormalize a basis ? If the reason is to deal with rank-deficient matrices, maybe we could add an option to choose between them ? cheers, David

5 17

Comments on API for Matlab's eigs equivalent (computing a few eigenvalues only)
by David Cournapeau 07 Feb '10

07 Feb '10

Hi, I have played a bit with adding support for computing a few eigenvalues of full symmetric matrices. I would like some comments on the current API: import numpy as np from scipy.linalg import eigs x = np.random.randn(10, 10) x = np.dot(x.T, x) # Retrieve the 3 biggest eigenvalues eigs(x, 3)[0] # Retrieve the 3 smallest eigenvalues eigs(x, -3)[0] # Retrieve the 3 biggest eigenvalues eigs(x, [0, 3], mode="index")[0] # Retrieve the 2nd and 3rd biggest eigs(x, [1, 3], mode="index")[0] # Retrieve all the eigenvalues in the range [1.5, 3.5[ eigs(x, [1.5, 3.5], mode="range")[0] One thing which does not feel right is that that the range in the "index" mode is exactly inverted compared to the output (i.e. if you ask for the range [0, 3], you get the last three items from what you would get if you asked for the full range [0, 10]), but this is because I kept compatibility with Octave (always showing from biggest to smallest). It would be easy to always do the contrary (from smallest to biggest) - besides consistency, it has the advantages that it is the actual order you get back from the underlying LAPACK function. Also, I needed to modify the f2py file for the related LAPACK functions, effectively changing their API in a backward incompatible way. Are the low-level f2py wrappers considered public API ? cheers, David

10 18

Don't you mind me to put a minor fix into some scipy.optimize docstrings?
by Dmitrey 07 Feb '10

07 Feb '10

Hello, I've been informed scipy.optimize docstrings still point to scikits.openopt (thus google search yields deprecated webpages), so if you don't mind I'll point it to mere openopt. Regards, D.

1 0

WOT: Experiences running 64 bit Vista on a 32 bit machine
by David Goldsmith 05 Feb '10

05 Feb '10

Please forgive the widely OT request for input: I'm thinking about trying to run 64 bit Vista on my 32 bit machine (Pentium Dual-Core T4300 @ 2x2.10Ghz); following http://windows.microsoft.com/en-US/windows-vista/32-bit-and-64-bit-Windows-…... I've confirmed that I'm "64 bit capable." Has anyone reading this far ;-) had a particularly positive/negative experience doing this? Also, how do I determine the 64 bit linux capability of my hardware? Thanks! DG

3 4