Mailman 3 February 2016 - NumPy-Discussion

ANN: Stop plotting your data -- HoloViews 1.4 released!
by James A. Bednar Feb. 24, 2016

Feb. 24, 2016

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 … [View More]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. [View Less]

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Numpy 1.11.0rc1 released.
by Charles R Harris Feb. 24, 2016

Feb. 24, 2016

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 Feb. 24, 2016

Feb. 24, 2016

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 … [View More]

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ENH: `scale` parameter for `sinc`
by Joseph Fox-Rabinovitz Feb. 23, 2016

Feb. 23, 2016

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 … [View More]

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How to check for memory leaks?
by Charles R Harris Feb. 23, 2016

Feb. 23, 2016

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 Feb. 23, 2016

Feb. 23, 2016

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 (… [View More]

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DyND 0.7.1 Release
by Irwin Zaid Feb. 21, 2016

Feb. 21, 2016

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 … [View More]

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PyData Madrid
by Jaime Fernández del Río Feb. 20, 2016

Feb. 20, 2016

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" … [View More]

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[Suggestion] Labelled Array
by Sérgio Feb. 19, 2016

Feb. 19, 2016

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 … [View More]

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Re: [Numpy-discussion] proposal: new logspace without the log in the argument
by Joseph Fox-Rabinovitz Feb. 19, 2016

Feb. 19, 2016

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 … [View More]

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