Mailman 3 April 2015 - NumPy-Discussion

SIAM meeting in Snowbird anyone?
by Sebastian Berg April 9, 2015

April 9, 2015

Hey, since I am not in the US often and the SIAM conference is pretty large, I was wondering if some more of our community will be at the SIAM conference in Snowbird around May 17th-21st and would like to meet up then. - Sebastian

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FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
by Andreas Hilboll April 8, 2015

April 8, 2015

Hi all, I'm commonly using function signatures like def myfunc(a, b, c=None): if c is None: # do something ... ... where c is an optional array argument. For some time now, I'm getting a FutureWarning: comparison to `None` will result in an elementwise object comparison in the future from the "c is None" comparison. I'm wondering what would be the best way to do this check in a future-proof way? Best, -- Andreas.

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Behavior of np.random.multivariate_normal with bad covariance matrices
by Blake Griffith April 7, 2015

April 7, 2015

I have an open PR which lets users control the checks on the input covariance matrix. The matrix is required to be symmetric and positve semi-definite (PSD). The current behavior is that NumPy raises a warning if the matrix is not PSD, and does not even check for symmetry. I added a symmetry check, which raises a warning when the input is not symmetric. And added two keyword args which users can use to turn off the checks/warnings when the matrix is ill formed. So this would only cause another new warning to be raised in existing code. This is needed because sometimes the covariance matrix is only *almost* symmetric or PSD due to roundoff error. Thoughts? PR: https://github.com/numpy/numpy/pull/5726

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Research position in the Brazilian Research Institute for Science and Neurotechnology - BRAINN
by Thiago Franco Moraes April 7, 2015

April 7, 2015

Research position in the Brazilian Research Institute for Science and Neurotechnology – BRAINN Postdoc researcher to work with software development for medical imaging The Brazilian Research Institute for Neuroscience and Neurotechnology (BRAINN) (www.brainn.org.br) focuses on the investigation of basic mechanisms leading to epilepsy and stroke, and the injury mechanisms that follow disease onset and progression. This research has important applications related to prevention, diagnosis, treatment and rehabilitation and will serve as a model for better understanding normal and abnormal brain function. The BRAINN Institute is composed of 10 institutions from Brazil and abroad and hosted by State University of Campinas (UNICAMP). Among the associated institutions is Renato Archer Information Technology Center (CTI) that has a specialized team in open-source software development for medical imaging (www.cti.gov.br/invesalius) and 3D printing applications for healthcare. CTI is located close the UNICAMP in the city of Campinas, State of São Paulo in a very technological region of Brazil and is looking for a postdoc researcher to work with software development for medical imaging related to the imaging analysis, diagnosis and treatment of brain diseases. The postdoc position is for two years with the possibility of being renovated for more two years. Education - PhD in computer science, computer engineering, mathematics, physics or related. Requirements - Digital image processing (Medical imaging) - Computer graphics (basic) Benefits 6.143,40 Reais per month free of taxes (about US$ 2.000,00); 15% technical reserve for conferences participation and specific materials acquisition; Interested Send curriculum to: jorge.silva(a)cti.gov.br with subject “Postdoc position” Applications reviews will begin April 30, 2015 and continue until the position is filled.

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Multidimensional Indexing
by Nicholas Devenish April 7, 2015

April 7, 2015

With the indexing example from the documentation: y = np.arange(35).reshape(5,7) Why does selecting an item from explicitly every row work as I’d expect: >>> y[np.array([0,1,2,3,4]),np.array([0,0,0,0,0])] array([ 0, 7, 14, 21, 28]) But doing so from a full slice (which, I would naively expect to mean “Every Row”) has some…other… behaviour: >>> y[:,np.array([0,0,0,0,0])] array([[ 0, 0, 0, 0, 0], [ 7, 7, 7, 7, 7], [14, 14, 14, 14, 14], [21, 21, 21, 21, 21], [28, 28, 28, 28, 28]]) What is going on in this example, and how do I get what I expect? By explicitly passing in an extra array with value===index? What is the rationale for this difference in behaviour? Thanks, Nick

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OS X wheels: speed versus multiprocessing
by Nathaniel Smith April 7, 2015

April 7, 2015

Hi all, Starting with 1.9.1, the official numpy OS X wheels (the ones you get by doing "pip install numpy") have been built to use Apple's Accelerate library for linear algebra. This is fast, but it breaks multiprocessing in obscure ways (e.g. see this user report: https://github.com/numpy/numpy/issues/5752). Unfortunately, there is no obvious best solution to what linear algebra package to use, so we have to make a decision as to which set of compromises we prefer. Options: Accelerate: fast, but breaks multiprocessing as above. OpenBLAS: fast, but Julian raised concerns about its trustworthiness last year ( http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069659.html). Possibly things have improved since then (I get the impression that they've gotten some additional developer attention from the Julia community), but I don't know. Atlas: slower (faster than reference blas but definitely slower than fancy options like the above), but solid. My feeling is that for wheels in particular it's more important that everything "just work" than that we get the absolute fastest speeds. And this is especially true for the multiprocessing issue, given that it's a widely used part of the stdlib, the failures are really obscure/confusing, and there is no workaround for python 2 which is still where a majority of our users still are. So I'd vote for using either atlas or OpenBLAS. (And would defer to Julian and Matthew about which to choose between these.) Any opinions, objections? -n

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1.10 release again.
by Charles R Harris April 7, 2015

April 7, 2015

Hi All, I'd like to mark current PR's for inclusion in 1.10. If there is something that you want to have in the release, please mention it here by PR #.I think new enhancement PR's should be considered for 1.11 rather than 1.10, but bug fixes will go in. There is some flexibility, of course, as there are always last minute items that come up when release contents are begin decided. Chuck

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Re: [Numpy-discussion] IDE's for numpy development?
by Suzen, Mehmet April 6, 2015

April 6, 2015

Hi Chuck, Spider is good. If you are coming from Matlab world. http://spyder-ide.blogspot.co.uk/ I don't think it supports C. But Maybe you are after Eclipse. Best, -m

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Advanced indexing: "fancy" vs. orthogonal
by R Hattersley April 5, 2015

April 5, 2015

There are two different interpretations in common use of how to handle multi-valued (array/sequence) indexes. The numpy style is to consider all multi-valued indices together which allows arbitrary points to be extracted. The orthogonal style (e.g. as provided by netcdf4-python) is to consider each multi-valued index independently. For example: >>> type(v)<type 'netCDF4.Variable'>>>> v.shape (240, 37, 49)>>> v[(0, 1), (0, 2, 3)].shape (2, 3, 49)>>> np.array(v)[(0, 1), (0, 2, 3)].shape Traceback (most recent call last): File "<stdin>", line 1, in <module>IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (2,) (3,) In a netcdf4-python GitHub issue <https://github.com/Unidata/netcdf4-python/issues/385> the authors of various orthogonal indexing packages have been discussing how to distinguish the two behaviours and have currently settled on a boolean __orthogonal_indexing__ attribute. 1. Is there any appetite for adding that attribute (with the value `False`) to ndarray? 2. As suggested by shoyer <https://github.com/Unidata/netcdf4-python/issues/385#issuecomment-87775034>, is there any appetite for adding an alternative indexer to ndarray where __orthogonal_indexing__ = True? For example: myarray.ix_[(0,1), (0, 2, 3)] Richard

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NPY_SEPARATE_COMPILATION and RELAXED_STRIDES_CHECKING
by Charles R Harris April 5, 2015

April 5, 2015

Hi All, Just to raise the question if these two options should be removed at some point? The current default value for both is 0, so we have separate compilation and relaxed strides checking by default. Chuck

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