Mailman 3 - SciPy-User

NumPy 1.16.0rc2 released
by Charles R Harris 04 Jan '19

04 Jan '19

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.16.0rc2. This is the last NumPy release to support Python 2.7 and will be maintained as a long term release with bug fixes until 2020. This release has seen a lot of refactoring and features many bug fixes, improved code organization, and better cross platform compatibility. Not all of these improvements will be visible to users, but they should help make maintenance easier going forward. Highlights are - Experimental support for overriding numpy functions in downstream projects. - The matmul function is now a ufunc and can be overridden using __array_ufunc__. - Improved support for the ARM and POWER architectures. - Improved support for AIX and PyPy. - Improved interoperation with ctypes. - Improved support for PEP 3118. The supported Python versions are 2.7 and 3.5-3.7, support for 3.4 has been dropped. The wheels on PyPI are linked with OpenBLAS v0.3.4+, which should fix the known threading issues found in previous OpenBLAS versions. Downstream developers building this release should use Cython >= 0.29.2 and, if linking OpenBLAS, OpenBLAS > v0.3.4. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.0rc2/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.0rc2>. *Contributors* A total of 112 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Alan Fontenot + * Allan Haldane * Alon Hershenhorn + * Alyssa Quek + * Andreas Nussbaumer + * Anner + * Anthony Sottile + * Antony Lee * Ayappan P + * Bas van Schaik + * C.A.M. Gerlach + * Charles Harris * Chris Billington * Christian Clauss * Christoph Gohlke * Christopher Pezley + * Daniel B Allan + * Daniel Smith * Dawid Zych + * Derek Kim + * Dima Pasechnik + * Edgar Giovanni Lepe + * Elena Mokeeva + * Elliott Sales de Andrade + * Emil Hessman + * Eric Schles + * Eric Wieser * Giulio Benetti + * Guillaume Gautier + * Guo Ci * Heath Henley + * Isuru Fernando + * J. Lewis Muir + * Jack Vreeken + * Jaime Fernandez * James Bourbeau * Jeff VanOss * Jeffrey Yancey + * Jeremy Chen + * Jeremy Manning + * Jeroen Demeyer * John Darbyshire + * John Kirkham * John Zwinck * Jonas Jensen + * Joscha Reimer + * Juan Azcarreta + * Julian Taylor * Kevin Sheppard * Krzysztof Chomski + * Kyle Sunden * Lars Grüter * Lilian Besson + * MSeifert04 * Mark Harfouche * Marten van Kerkwijk * Martin Thoma * Matt Harrigan + * Matthew Bowden + * Matthew Brett * Matthias Bussonnier * Matti Picus * Max Aifer + * Michael Hirsch, Ph.D + * Michael James Jamie Schnaitter + * MichaelSaah + * Mike Toews * Minkyu Lee + * Mircea Akos Bruma + * Mircea-Akos Brumă + * Moshe Looks + * Muhammad Kasim + * Nathaniel J. Smith * Nikita Titov + * Paul Müller + * Paul van Mulbregt * Pauli Virtanen * Pierre Glaser + * Pim de Haan * Ralf Gommers * Robert Kern * Robin Aggleton + * Rohit Pandey + * Roman Yurchak + * Ryan Soklaski * Sebastian Berg * Sho Nakamura + * Simon Gibbons * Stan Seibert + * Stefan Otte * Stefan van der Walt * Stephan Hoyer * Stuart Archibald * Taylor Smith + * Tim Felgentreff + * Tim Swast + * Tim Teichmann + * Toshiki Kataoka * Travis Oliphant * Tyler Reddy * Uddeshya Singh + * Warren Weckesser * Weitang Li + * Wenjamin Petrenko + * William D. Irons * Yannick Jadoul + * Yaroslav Halchenko * Yug Khanna + * Yuji Kanagawa + * Yukun Guo + * ankokumoyashi + * lerbuke + Cheers, Charles Harris

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ANN: SfePy 2018.4
by Robert Cimrman 26 Dec '18

26 Dec '18

I am pleased to announce release 2018.4 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 (limited support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------- - better support for eigenvalue problems - improved MUMPS solver interface - support for logging and plotting of complex values For full release notes see [1]. Cheers, Robert Cimrman [1] http://docs.sfepy.org/doc/release_notes.html#id1 --- Contributors to this release in alphabetical order: Robert Cimrman Vladimir Lukes Matyas Novak Jan Heczko Lubos Kejzlar

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Are the scores normally distributed?
by Bakary N'tji Diallo 21 Dec '18

21 Dec '18

Dear all, Hope you are doing very well. I am trying to apply a statistical normalization which require the values to be normally distributed. I have prepared a short notebook with all details. https://nbviewer.jupyter.org/github/diallobakary4/bioinformatics/blob/maste… It will be great if someone can help me out. Thanks Best regards -- Bakary N’tji DIALLO PhD Student (Bioinformatics) <http://linkedin.com/in/bakarydiallo>, Research Unit in Bioinformatics (RUBi) <https://rubi.ru.ac.za/> Mail: diallobakary4(a)gmail.com | Skype: diallobakary4 Tel: +27798233845 | +223 74 56 57 22 | +223 97 39 77 14

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NumPy 1.16.0rc1 released
by Charles R Harris 20 Dec '18

20 Dec '18

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.16.0rc1. This is the last NumPy release to support Python 2.7 and will be maintained as a long term release with bug fixes until 2020. This release has seen a lot of refactoring and features many bug fixes, improved code organization, and better cross platform compatibility. Not all of these improvements will be visible to users, but they should help make maintenance easier going forward. Highlights are - Experimental support for overriding numpy functions in downstream projects. - The matmul function is now a ufunc and can be overridden using __array_ufunc__. - Improved support for the ARM and POWER architectures. - Improved support for AIX and PyPy. - Improved interoperation with ctypes. - Improved support for PEP 3118. The supported Python versions are 2.7 and 3.5-3.7, support for 3.4 has been dropped. The wheels on PyPI are linked with OpenBLAS v0.3.4+, which should fix the known threading issues found in previous OpenBLAS versions. Downstream developers building this release should use Cython >= 0.29 and, if linking OpenBLAS, OpenBLAS > v0.3.4. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.0rc1/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.0rc1>. *Contributors* A total of 111 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alan Fontenot + - Allan Haldane - Alon Hershenhorn + - Alyssa Quek + - Andreas Nussbaumer + - Anner + - Anthony Sottile + - Antony Lee - Ayappan P + - Bas van Schaik + - C.A.M. Gerlach + - Charles Harris - Chris Billington - Christian Clauss - Christoph Gohlke - Christopher Pezley + - Daniel B Allan + - Daniel Smith - Dawid Zych + - Derek Kim + - Dima Pasechnik + - Edgar Giovanni Lepe + - Elena Mokeeva + - Elliott Sales de Andrade + - Emil Hessman + - Eric Schles + - Eric Wieser - Giulio Benetti + - Guillaume Gautier + - Guo Ci - Heath Henley + - Isuru Fernando + - J. Lewis Muir + - Jack Vreeken + - Jaime Fernandez - James Bourbeau - Jeff VanOss - Jeffrey Yancey + - Jeremy Chen + - Jeremy Manning + - Jeroen Demeyer - John Darbyshire + - John Zwinck - Jonas Jensen + - Joscha Reimer + - Juan Azcarreta + - Julian Taylor - Kevin Sheppard - Krzysztof Chomski + - Kyle Sunden - Lars Grüter - Lilian Besson + - MSeifert04 - Mark Harfouche - Marten van Kerkwijk - Martin Thoma - Matt Harrigan + - Matthew Bowden + - Matthew Brett - Matthias Bussonnier - Matti Picus - Max Aifer + - Michael Hirsch, Ph.D + - Michael James Jamie Schnaitter + - MichaelSaah + - Mike Toews - Minkyu Lee + - Mircea Akos Bruma + - Mircea-Akos Brumă + - Moshe Looks + - Muhammad Kasim + - Nathaniel J. Smith - Nikita Titov + - Paul Müller + - Paul van Mulbregt - Pauli Virtanen - Pierre Glaser + - Pim de Haan - Ralf Gommers - Robert Kern - Robin Aggleton + - Rohit Pandey + - Roman Yurchak + - Ryan Soklaski - Sebastian Berg - Sho Nakamura + - Simon Gibbons - Stan Seibert + - Stefan Otte - Stefan van der Walt - Stephan Hoyer - Stuart Archibald - Taylor Smith + - Tim Felgentreff + - Tim Swast + - Tim Teichmann + - Toshiki Kataoka - Travis Oliphant - Tyler Reddy - Uddeshya Singh + - Warren Weckesser - Weitang Li + - Wenjamin Petrenko + - William D. Irons - Yannick Jadoul + - Yaroslav Halchenko - Yug Khanna + - Yuji Kanagawa + - Yukun Guo + - lerbuke + - @ankokumoyashi + Cheers, Charles Harris

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Conversion to graph adjacency list
by elastica＠laposte.net 08 Dec '18

08 Dec '18

Hi, Scipy library provides C-implementation for some classical graph algorithms like Kruskal or connected component finding. Nevertheless, there is an efficiency problem: sparse graphs are usually given by list of edges or adjacency list (and not adjacency matrix). And in order to run the Scipy routines, we have to convert our graphs to the compressed SCR format (dense array is not well suited for graph with a lot of vertices, say more than 20,000). And sometimes, after execution we have to convert back. This causes the routine spending most of the execution time in conversion and retro-conversion tasks. So my question is: does Scipy provide a C-routine for converting and back converting to edge/adjacency list format?

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slides for python3 in science advocacy
by Gerrit Holl 07 Dec '18

07 Dec '18

Hi, does anyone have a presentation format (bibtex or pdf or otherwise) advocating why scientists should care about Python 3? A bit like https://python-3-for-scientists.readthedocs.io/en/latest/python3_features.h… but in a presentation form. I find that frighteningly many colleagues are still using Python 2 even when they write new code from scratch. I could use pandoc to convert python-3-for-scientists to tex and then manually turn it into a collection of beamer frames, but maybe someone has already done something similar? regards, Gerrit.

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Confusion matrix statistical analysis
by Sepand Haghighi 07 Dec '18

07 Dec '18

Dear All, Here I want to introduce an open source Python library which named PyCM. PyCM is a machine learning library providing statistical analysis of confusion matrix through a large variety of parameters such as AUC, Confusion Entropy, information theory related parameters, and etc. This developing library can be used in order to evaluate the performance of different machine learning algorithms by offering different evaluation parameters on their resulted confusion matrix. PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. Do not hesitate to contact us about this library and help us to develop it by your valuable suggestions.You can find us on https://github.com/sepandhaghighi/pycm

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Need some advice on integration
by Joan Quintana 30 Nov '18

30 Nov '18

I'm new on scipy (and new with numerical methods), so excuse me if you found trivial my question, but I spent time searching for an answer. Well, the problem: I want to integrate this equation: dv = [K/(m-dm)] dm where where d means differential (of course). dm is in the numerator and denominator. -Is there chance to solve this integral analytically? -how can I use scipy to integrate this equation (knowh the initial values)? TIA

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Is there an equivalant for Griddata in Obj-c or Java?
by BOUKRAA, Kacem 30 Nov '18

30 Nov '18

Hello pals! I really need your help! So i have a set of data for example: X Y Z 1 3 7 2 5 8 1 4 9 3 6 10 I would like to interpolate Z for X=2.5 and Y=3.5. I know i can use interp2.griddata from Scipy in Python or ScatteredInterpolant in Matlab like this: z = griddata( [1 2 1 3], [3 5 4 6], [7 8 9 10], 2.5, 3.5, 'nearest' ) or S = scatteredInterpolant(x,y,z,d); My question Is if there is a way i could use in Swift/Objective-c or Android Java or any other compatible language to be able to do the same thing on iOS or Android platforms? Thanks in advance. -- [image: Image] *Kacem BOUKRAA**Computer Science Engineer (ESI Ex ini)*

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webinar and roadmap discussion
by Ralf Gommers 29 Nov '18

29 Nov '18

Hi all, Here's two things that may be of interest. SciPy will be featured on an upcoming episode of Open Source Directions hosted by Quansight on Friday November 30th (tomorrow) at 9am Pacific time / noon Eastern time. See [1] for registration link. We're updating the SciPy roadmap in https://github.com/scipy/scipy/pull/9537. A first version of a "roadmap brochure" is now available at [2] - it's mainly aimed at people who want to contribute to SciPy (either financially or with development resources); contents match what is in gh-9537. An up to date version will be maintained at [3]. Cheers, Ralf [1] https://app.livestorm.co/quansight/ [2] https://docs.wixstatic.com/ugd/cccccf_0061fb73823343048c01a4a8f2c34dfd.pdf [3] https://www.quansight.com/projects

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