Mailman 3 July 2010 - SciPy-Dev

New Wiki page
by David Goldsmith 20 Jul '10

20 Jul '10

Hi, all! As per a suggestion, I've added a "Who's Who"<http://docs.scipy.org/numpy/Who%27s%20Who>of "permission granters, etc." for our Wikiland to the doc wiki. If you have any manner of administrative capability on the Wiki, please visit the page and, if you please, edit the table to include yourself and your capabilities. Thanks! DG

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small patch to scipy.ndimage.measurements
by Thouis (Ray) Jones 19 Jul '10

19 Jul '10

Lee Kamentsky noticed that ndimage.measurements has a too strict test in _stats() and _select() that pushes it off the fast path when the label matrix is a uint instead of an int. I've filed a patch with ticket 1242 (which I can't get to right now, for some reason). I include the patch below, as well: Index: measurements.py =================================================================== --- measurements.py (revision 6630) +++ measurements.py (working copy) @@ -298,7 +298,7 @@ # remap labels to unique integers if necessary, or if the largest # label is larger than the number of values. - if ((not numpy.issubdtype(labels.dtype, numpy.int)) or + if ((not numpy.issubdtype(labels.dtype, (numpy.int, numpy.uint))) or (labels.min() < 0) or (labels.max() > labels.size)): unique_labels, new_labels = numpy.unique1d(labels, return_inverse=True) @@ -465,7 +465,7 @@ # remap labels to unique integers if necessary, or if the largest # label is larger than the number of values. - if ((not numpy.issubdtype(labels.dtype, numpy.int)) or + if ((not numpy.issubdtype(labels.dtype, (numpy.int, numpy.uint))) or (labels.min() < 0) or (labels.max() > labels.size)): # remap labels, and indexes unique_labels, labels = numpy.unique1d(labels, return_inverse=True)

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Introducing myself...
by Hamman, RA, Mr <14772361＠sun.ac.za> 19 Jul '10

19 Jul '10

Hi all, I thought I'd introduce myself. I'm a mechanical engineering master's student at Stellenbosch University, in South Africa. I work in the optimization world and am interested in multi-physics problems and the use of nonlinear approximations in place of complex engineering analyses. I use scipy all the time for my studies and I'm interested in getting involved and helping out were I can. I thought I'd lurk a bit, and then jump in. I look forward to learning from all of you and contributing where I can. Regards, Richard

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Required Python Version
by Charles R Harris 19 Jul '10

19 Jul '10

Hi All, Is there a good reason we shouldn't raise the required python version up to 2.5? It was released on September 19th 2006 and I think enough time has gone by to consider moving the version number up. Doing so will give us access to the "n" format letter for PyArg_ParseTuple*, allow use of the with statement, and provide the new absolute/relative imports with the "from __future__ import absolute_import" statement. Chuck

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ANN: scipy 0.8.0 release candidate 3
by Ralf Gommers 19 Jul '10

19 Jul '10

I'm pleased to announce the availability of the third release candidate of SciPy 0.8.0. The only changes compared to rc2 are a fix for a regression in interpolate.Rbf and some fixes for failures on 64-bit Windows. If no more problems are reported, the final release will be available in one week. SciPy is a package of tools for science and engineering for Python. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more. This release candidate release comes one and a half year after the 0.7.0 release and contains many new features, numerous bug-fixes, improved test coverage, and better documentation. Please note that SciPy 0.8.0rc3 requires Python 2.4-2.6 and NumPy 1.4.1 or greater. For more information, please see the release notes: http://sourceforge.net/projects/scipy/files/scipy/0.8.0rc3/NOTES.txt/view<http://sourceforge.net/projects/scipy/files/scipy/0.8.0rc1/NOTES.txt/view> You can download the release from here: https://sourceforge.net/projects/scipy/ Python 2.5/2.6 binaries for Windows and OS X are available, as well as source tarballs for other platforms and the documentation in pdf form. Thank you to everybody who contributed to this release. Enjoy, Ralf

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f2py: questions on array arguments
by Ramon Crehuet 19 Jul '10

19 Jul '10

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signal.freqs/freqz plot keyword
by Ralf Gommers 18 Jul '10

18 Jul '10

freqs/freqz have a keyword plot=None, which if True does this: if not plot is None: plot(w, h) while plot is not even defined. This keyword should be removed or the implementation should be something like: if plot: try: import matplotlib.pyplot as plt plt.plot(w, h) plt.show() except ImportError: warnings.warn("`plot` is True, but can't import matplotlib.pyplot.") Removal makes more sense I think, since scipy does not depend on matplotlib. And why provide a plot keyword in these functions and not in many others where it would also make sense? It's ticket http://projects.scipy.org/scipy/ticket/896 Cheers, Ralf

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PyDocWeb "the doc editor" and github transition
by Vincent Davis 17 Jul '10

17 Jul '10

I have not seen anything about the move to github and how this effects pydocweb or did I just miss it. I am willing to adapt pydocweb to work with github but not sure what the desired integration would be. This should not be to difficult but should/needs to be part of the plan and documented. My initial thought is to create a branch for the doc editor. My only thought is to allow the doc editor (and those administering it) to maintain a github repo/branch and then allow those that are in control of the numpy/scipy source to merge/pull the docs with the doc repo/branch. (tell me what to do and I will work on it) Vincent

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replacing integrate.cumtrapz with numpy.trapz
by Ralf Gommers 17 Jul '10

17 Jul '10

integrate.cumtrapz and numpy.trapz do exactly the same thing and the code is very similar but not identical, as pointed out in http://projects.scipy.org/scipy/ticket/720. Assuming numpy.trapz is not going anywhere, can we replace the scipy version with it? For a small bonus, the numpy version is about 10% faster (tested for several array shapes): >>> a = np.arange(1e4).reshape(500, 20) >>> %timeit np.trapz(a, axis=1) 10000 loops, best of 3: 182 us per loop >>> %timeit sp.integrate.cumtrapz(a, axis=1) 1000 loops, best of 3: 209 us per loop Cheers, Ralf

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(no subject)
by Brian Hawthorne 17 Jul '10

17 Jul '10

http://sites.google.com/site/fdgy754g/ljie4j

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