Mailman 3 February 2016 - NumPy-Discussion

ANN: Stop plotting your data -- HoloViews 1.4 released!
by James A. Bednar 25 Feb '16

25 Feb '16

We are pleased to announce the fifth public release of HoloViews, a Python package for exploring and visualizing numerical data: http://holoviews.org HoloViews provides composable, sliceable, declarative data structures for building even complex visualizations easily. Instead of you having to explicitly and laboriously plot your data, HoloViews lets you simply annotate your data so that any part of it visualizes itself automatically. You can now work with large datasets as easily as you work with simple datatypes at the Python prompt. The new version can be installed using conda: conda install -c ioam holoviews Release 1.4 introduces major new features, incorporating over 1700 new commits and closing 142 issues: - Now supports both Bokeh (bokeh.pydata.org) and matplotlib backends, with Bokeh providing extensive interactive features such as panning and zooming linked axes, and customizable callbacks - DynamicMap: Allows exploring live streams from ongoing data collection or simulation, or parameter spaces too large to fit into your computer's or your browser's memory, from within a Jupyter notebook - Columnar data support: Underlying data storage can now be in Pandas dataframes, NumPy arrays, or Python dictionaries, allowing you to define HoloViews objects without copying or reformatting your data - New Element types: Area (area under or between curves), Spikes (sequence of lines, e.g. spectra, neural spikes, or rug plots), BoxWhisker (summary of a distribution), QuadMesh (nonuniform rasters), Trisurface (Delaunay-triangulated surface plots) - New Container type: GridMatrix (grid of heterogenous Elements) - Improved layout handling, with better support for varying aspect ratios and plot sizes - Improved help system, including recursively listing and searching the help for all the components of a composite object - Improved Jupyter/IPython notebook support, including improved export using nbconvert, and standalone HTML output that supports dynamic widgets even without a Python server - Significant performance improvements for large or highly nested data And of course we have fixed a number of bugs found by our very dedicated users; please keep filing Github issues if you find any! For the full list of changes, see: https://github.com/ioam/holoviews/releases HoloViews is now supported by Continuum Analytics, and is being used in a wide range of scientific and industrial projects. HoloViews remains freely available under a BSD license, is Python 2 and 3 compatible, and has minimal external dependencies, making it easy to integrate into your workflow. Try out the extensive tutorials at holoviews.org today! Jean-Luc R. Stevens Philipp Rudiger James A. Bednar Continuum Analytics, Inc., Austin, TX, USA School of Informatics, The University of Edinburgh, UK -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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Numpy 1.11.0rc1 released.
by Charles R Harris 24 Feb '16

24 Feb '16

Hi All, I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the issues discovered in 1.11.0b3 have been dealt with and this release can go on to become the official release. Source files and documentation can be found on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc1/>, while source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be installed from Pypi. Please test thoroughly. Chuck

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ANN: SfePy 2016.1
by Robert Cimrman 24 Feb '16

24 Feb '16

I am pleased to announce release 2016.1 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (preliminary support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: http://groups.google.com/group/sfepy-devel Git (source) repository, issue tracker, wiki: http://github.com/sfepy Highlights of this release -------------------------- - major simplification of finite element field code - automatic checking of shapes of term arguments - improved mesh parametrization code and documentation - support for fieldsplit preconditioners of PETSc For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Best regards, Robert Cimrman on behalf of the SfePy development team --- Contributors to this release in alphabetical order: Robert Cimrman Vladimir Lukes

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ENH: `scale` parameter for `sinc`
by Joseph Fox-Rabinovitz 23 Feb '16

23 Feb '16

I have created PR #7322 (https://github.com/numpy/numpy/pull/7322) to add a scale parameter to `sinc`. What this allows is to compute `sinc` as `sin(x)/x` or really `sin(n*x)/(n*x)` for arbitrary `n` instead of just `sin(pi*x)/(pi*x)` as is being done now. The parameter accepts two string arguments in addition to the actual scale value: 'normalized' and 'unnormalized'. 'normalized' is the default since that is the existing functionality. 'unnormalized' is equivalent to a `scale` of 1.0. The parameter also supports broadcasting against the input array. Regards, -Joe P.S. I would like to turn `sinc` into a `ufunc` at some point if the community approves. It would make the computation much cleaner (e.g., in-place `where`) and faster. It would also complement the existing trig functions nicely. The only question I have is whether or not it is possible to pass in optional parameters to ufuncs beyond the ones listed in http://docs.scipy.org/doc/numpy-1.10.0/reference/ufuncs.html#optional-keywo…

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How to check for memory leaks?
by Charles R Harris 23 Feb '16

23 Feb '16

Hi All, I'm suspecting a possible memory leak in 1.11.x, what is the best way to check for that? Chuck

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reshaping empty array bug?
by Benjamin Root 23 Feb '16

23 Feb '16

Not exactly sure if this should be a bug or not. This came up in a fairly general function of mine to process satellite data. Unexpectedly, one of the satellite files had no scans in it, triggering an exception when I tried to reshape the data from it. >>> import numpy as np >>> a = np.zeros((0, 5*64)) >>> a.shape (0, 320) >>> a.shape = (0, 5, 64) >>> a.shape (0, 5, 64) >>> a.shape = (0, 5*64) >>> a.shape = (0, 5, -1) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: total size of new array must be unchanged So, if I know all of the dimensions, I can reshape just fine. But if I wanted to use the nifty -1 semantic, it completely falls apart. I can see arguments going either way for whether this is a bug or not. Thoughts? Ben Root

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DyND 0.7.1 Release
by Irwin Zaid 22 Feb '16

22 Feb '16

Hello everyone, I'm pleased to announce the latest 0.7.1 release of DyND. The release notes are at https://github.com/libdynd/libdynd/blob/master/docs/release_notes.txt . Over the last 6 months, DyND has really matured a lot and many features that were "experimental" before are quite usable at the moment. At the same time, we still have bugs and issues to sort out, so I wouldn't claim DyND has reached stability just yet. Nevertheless, I'd encourage early adopters to try it out. You can install it easily using conda, via "conda install -c dynd/channel/dev dynd-python". Presently, the core DyND team consists of myself, Mark Wiebe, and Ian Henriksen, alongside several other contributors. Our focus is almost entirely on gaps in stability and usability -- the novel features in DyND that people find attractive (including missing values, ragged arrays, variable-sized strings, dynamic custom types, and overloadable callables, among others) are functioning pretty well now. NumPy compatibility and interoperability is very important to us, and is something we are constantly improving. We would eventually like to have an optional NumPy-like API that is fully consistent with what a NumPy user would expect, but we're not there just yet. The DyND team would be happy to answer any questions people have about DyND, like "what is working and what is not" or "what do we still need to do to hit DyND 1.0". All the best, Irwin

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PyData Madrid
by Jaime Fernández del Río 20 Feb '16

20 Feb '16

Hi all, I just found out there is a PyData Madrid happening in early April, and it would feel wrong not to go, it being my hometown and all. Aside from the usual "Who else is going? We should meet!" I was also thinking of submitting a proposal for a talk. My idea was to put something together on "The future of NumPy indexing" and use it as an opportunity to raise awareness and hopefully gather feedback from users on the proposed changes, in sort of a "if the mountain won't come to Muhammad" type of thing. Thoughts? Comments? Anyone else going or thinking about going? Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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[Suggestion] Labelled Array
by Sérgio 19 Feb '16

19 Feb '16

Hello, This is my first e-mail, I will try to make the idea simple. Similar to masked array it would be interesting to use a label array to guide operations. Ex.: >>> x labelled_array(data = [[0 1 2] [3 4 5] [6 7 8]], label = [[0 1 2] [0 1 2] [0 1 2]]) >>> sum(x) array([9, 12, 15]) The operations would create a new axis for label indexing. You could think of it as a collection of masks, one for each label. I don't know a way to make something like this efficiently without a loop. Just wondering... Sérgio.

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Re: [Numpy-discussion] proposal: new logspace without the log in the argument
by Joseph Fox-Rabinovitz 19 Feb '16

19 Feb '16

If the author is willing, I'd say both functions are useful. The "geom" prefix is very fitting. - Joe ------ Original message------From: Robert KernDate: Fri, Feb 19, 2016 08:00To: Discussion of Numerical Python;Subject:Re: [Numpy-discussion] proposal: new logspace without the log in the argumentOn Fri, Feb 19, 2016 at 12:10 PM, Andrew Nelson <andyfaff(a)gmail.com> wrote: > > With respect to geomspace proposals: instead of specifying start and end values and the number of points I'd like to have an option where I can set the start and end points and the ratio. The function would then work out the correct number of points to get closest to the end value. > > E.g. geomspace(start=1, finish=2, ratio=1.03) > > The first entries would be 1.0, 1.03, 1*1.03**2, etc. > > I have a requirement for the correct ratio between the points, and it's a right bind having to calculate the exact number of points needed. At the risk of extending the twisty little maze of names, all alike, I would probably call a function with this signature geomrange() instead. It is more akin to arange(start, stop, step) than linspace(start, stop, num_steps). -- Robert Kern

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