Mailman 3 - SciPy-User

[ANN] 12ᵗʰ Advanced Scientific Programming in Python in Camerino, Italy, 2—7 September, 2019
by Tiziano Zito 28 Feb '19

28 Feb '19

12ᵗʰ Advanced Scientific Programming in Python ============================================== a Summer School by the G-Node and the University of Camerino https://python.g-node.org Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. Date & Location =============== 2–7 September, 2019. Camerino, Italy. Application =========== You can apply online: https://python.g-node.org/wiki/applications Application deadline: 23:59 UTC, 26 May, 2019. There will be no deadline extension, so be sure to apply on time. Be sure to read the FAQ before applying: https://python.g-node.org/wiki/faq Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program ======= • Version control with git and how to contribute to open source projects with GitHub • Tidy data analysis and visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Advanced scientific Python: context managers and generators • Writing parallel applications in Python • Profiling and speeding up scientific code with Cython and numba • Programming in teams Faculty ======= • Caterina Buizza, Personal Robotics Lab, Imperial College London, UK • Jenni Rinker, Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark • Juan Nunez-Iglesias, Bioimage Analysis Research Fellow, Monash University, Australia • Nelle Varoquaux, Department of Statistics, UC Berkeley, CA, USA • Pamela Hathway, Neural Reckoning, Imperial College London, UK • Pietro Berkes, NAGRA Kudelski, Lausanne, Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Stéfan van der Walt, Berkeley Institute for Data Science, UC Berkeley, CA, USA • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin, Germany Organizers ========== For the German Neuroinformatics Node of the INCF (G-Node), Germany: • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin, Germany • Caterina Buizza, Personal Robotics Lab, Imperial College London, UK • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw, Poland • Jakob Jordan, Department of Physiology, University of Bern, Switzerland For the University of Camerino, Italy: • Barbara Re, Computer Science Division, School of Science and Technology, University of Camerino Italy Website: https://python.g-node.org Contact: python-info(a)g-node.org

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NumPy 1.16.2 released.
by Charles R Harris 26 Feb '19

26 Feb '19

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.16.2. This is a quick release fixing several problems encountered on Windows. The Python versions supported are 2.7 and 3.5-3.7. The Windows problems addressed are: - DLL load problems for NumPy wheels on Windows, - distutils command line parsing on Windows. There is also a regression fix correcting signed zeros produced by divmod, see the release notes for details. Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.4. If you are installing using pip, you may encounter a problem with older installed versions of NumPy that pip did not delete becoming mixed with the current version, resulting in an ``ImportError``. That problem is particularly common on Debian derived distributions due to a modified pip. The fix is to make sure all previous NumPy versions installed by pip have been removed. See #12736 <https://github.com/numpy/numpy/issues/12736> for discussion of the issue. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.2/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.2>. *Contributors* A total of 5 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Charles Harris * Eric Wieser * Matti Picus * Tyler Reddy * Tony LaTorre + *Pull requests merged* A total of 7 pull requests were merged for this release. * #12909: TST: fix vmImage dispatch in Azure * #12923: MAINT: remove complicated test of multiarray import failure mode * #13020: BUG: fix signed zero behavior in npy_divmod * #13026: MAINT: Add functions to parse shell-strings in the platform-native... * #13028: BUG: Fix regression in parsing of F90 and F77 environment variables * #13038: BUG: parse shell escaping in extra_compile_args and extra_link_args * #13041: BLD: Windows absolute path DLL loading Cheers, Charles Harris

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Why slicing Pandas column and then subtract gives NaN?
by C W 15 Feb '19

15 Feb '19

Dear list, I have the following to Pandas Series: a, b. I want to slice and then subtract. Like this: a[1:4] - b[0:3]. Why does it give me NaN? But it works in Numpy. Example 1: did not work >>>a = pd.Series([85, 86, 87, 86]) >>>b = pd.Series([15, 72, 2, 3]) >>> a[1:4]-b[0:3] 0 NaN 1 14.0 2 85.0 3 NaN >>> type(a[1:4]) <class 'pandas.core.series.Series'> Example 2: worked If I use values() method, it's converted to a Numpy object. And it works! >>> a.values[1:4]-b.values[0:3] array([71, 15, 84]) >>> type(a.values[1:4]) <class 'numpy.ndarray'> What's the reason that Pandas in example 1 did not work? Isn't Numpy built on top of Pandas? So, why is everything ok in Numpy, but not in Pandas? Thanks in advance!

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SciPy 2019 Conference - 10 days left for submissions, registration now open
by Paul Ivanov 05 Feb '19

05 Feb '19

SciPy 2019, the 18th annual Scientific Computing with Python conference, will be held July 8-14, 2019 in Austin, Texas. The annual SciPy Conference brings together over 800 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The call for abstracts for SciPy 2019 for talks, posters and tutorials is now open. The original deadline for submissions has been extended and the new deadline is February 15, 2019. Conference Website: https://www.scipy2019.scipy.org/ Submission Website: https://easychair.org/conferences/?conf=scipy2019 *Talks and Posters (July 10-12, 2019)* In addition to the general track, this year will have specialized tracks focused on: - Data Driven Discoveries (including Machine Learning and Data Science) - Open Source Communities (Sustainability) *Mini Symposia* - Science Communication through Visualization - Neuroscience and Cognitive Science - Image Processing - Earth, Ocean, Geo and Atmospheric Science There will also be a SciPy Tools Plenary Session each day with 2 to 5 minute updates on tools and libraries. *Tutorials (July 8-9, 2019)* Tutorials should be focused on covering a well-defined topic in a hands-on manner. We are looking for useful techniques or packages, helping new or advanced Python programmers develop better or faster scientific applications. We encourage submissions to be designed to allow at least 50% of the time for hands-on exercises even if this means the subject matter needs to be limited. Tutorials will be 4 hours in duration. In your tutorial application, you can indicate what prerequisite skills and knowledge will be needed for your tutorial, and the approximate expected level of knowledge of your students (i.e., beginner, intermediate, advanced). Instructors of accepted tutorials will receive a stipend. -- _ / \ A* \^ - ,./ _.`\\ / \ / ,--.S \/ \ / `"~,_ \ \ __o ? _ \<,_ /:\ --(_)/-(_)----.../ | \ --------------.......J Paul Ivanov http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 -- _ / \ A* \^ - ,./ _.`\\ / \ / ,--.S \/ \ / `"~,_ \ \ __o ? _ \<,_ /:\ --(_)/-(_)----.../ | \ --------------.......J Paul Ivanov http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7

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Re: [SciPy-User] A priority of constrains for scipy.optimize.minimize
by federico vaggi 05 Feb '19

05 Feb '19

Change your loss function to a penalized form. IE: Instead of minimizing L(x) s.t. f_1(x) (necessary constraint) f_2(x) (nice but not necessary constraint) .... Do this instead: Minimize L(x) + \lambda * f_2(x) s.t. f_1(x) where \lambda can be a hyper parameter you can tune to trade off how important constraint f_2 is relative to loss function quality. Alternatively: if you are doing minimization on a probability simplex, you can probably re-parametrize your problem so the only viable solution automatically satisfies those probabilities. The most common way is to run your output through a softmax ( https://en.wikipedia.org/wiki/Softmax_function) On Tue, Feb 5, 2019 at 9:06 AM <scipy-user-request(a)python.org> wrote: > Send SciPy-User mailing list submissions to > scipy-user(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scipy-user > or, via email, send a message with subject or body 'help' to > scipy-user-request(a)python.org > > You can reach the person managing the list at > scipy-user-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of SciPy-User digest..." > > > Today's Topics: > > 1. A priority of constrains for scipy.optimize.minimize > (Jan Hendrik Berlin) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 5 Feb 2019 02:07:35 +0100 > From: Jan Hendrik Berlin <jan-hendrik.berlin(a)cs.uni-dortmund.de> > To: SciPy-User <scipy-user(a)python.org> > Subject: [SciPy-User] A priority of constrains for > scipy.optimize.minimize > Message-ID: <3953ef2d-0660-8fe6-7e04-45b8f2c2735a(a)cs.uni-dortmund.de> > Content-Type: text/plain; charset=utf-8; format=flowed > > Hi, > > I am solving a problem with some constrains. At first there is a > constraint, that the sum of the percentages must be 1. The single > percentage could be in the range from 0,0 to 1. And this is the main > constraint. On the other side there are some constrains belonging to > stuff of the calculation. It is possible, that this constrains are to > strong and the solver can't get a solution. In this Case I want to have > an option to get a solution respecting the first constraint. > > Has anybody an idea of a solution? I think there is no option for a > priority of the constrains. > > kind regards > Jan Hendrik Berlin > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > SciPy-User mailing list > SciPy-User(a)python.org > https://mail.python.org/mailman/listinfo/scipy-user > > > ------------------------------ > > End of SciPy-User Digest, Vol 186, Issue 2 > ****************************************** >

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A priority of constrains for scipy.optimize.minimize
by Jan Hendrik Berlin 04 Feb '19

04 Feb '19

Hi, I am solving a problem with some constrains. At first there is a constraint, that the sum of the percentages must be 1. The single percentage could be in the range from 0,0 to 1. And this is the main constraint. On the other side there are some constrains belonging to stuff of the calculation. It is possible, that this constrains are to strong and the solver can't get a solution. In this Case I want to have an option to get a solution respecting the first constraint. Has anybody an idea of a solution? I think there is no option for a priority of the constrains. kind regards Jan Hendrik Berlin

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NumPy 1.16.1 release
by Charles R Harris 31 Jan '19

31 Jan '19

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.16.1. This release fixes bugs reported against the 1.16.0 release, and also backports several enhancements from master that seem appropriate for a release series that is the last to support Python 2.7. The supported Python versions are 2.7 and 3.5-3.7. 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 using OpenBLAS, OpenBLAS > v0.3.4. If you are installing using pip, you may encounter a problem with older installed versions of NumPy that pip did not delete becoming mixed with the current version, resulting in an *ImportError*. That problem is particularly common on Debian derived distributions due to a modified pip. The fix is to make sure all previous NumPy versions installed by pip have been removed. See #12736 <https://github.com/numpy/numpy/issues/12736> for discussion of the issue. Note that previously this problem resulted in an *AttributeError*. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.1/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.1>. *Enhancements* * #12767: ENH: add mm->q floordiv * #12768: ENH: port np.core.overrides to C for speed * #12769: ENH: Add np.ctypeslib.as_ctypes_type(dtype), improve `np.ctypeslib.as_ctypes` * #12773: ENH: add "max difference" messages to np.testing.assert_array_equal... * #12820: ENH: Add mm->qm divmod * #12890: ENH: add _dtype_ctype to namespace for freeze analysis *Contributors* A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Antoine Pitrou * Arcesio Castaneda Medina + * Charles Harris * Chris Markiewicz + * Christoph Gohlke * Christopher J. Markiewicz + * Daniel Hrisca + * EelcoPeacs + * Eric Wieser * Kevin Sheppard * Matti Picus * OBATA Akio + * Ralf Gommers * Sebastian Berg * Stephan Hoyer * Tyler Reddy Cheers, Charles Harris

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[ANN] pvlib-python v0.6.1: predicting power from PV (renweable solar energy)
by Mark Alexander Mikofski 31 Jan '19

31 Jan '19

pvlib-0.6.1 is now available PyPI: https://pypi.org/project/pvlib/ Read the Docs: https://pvlib-python.readthedocs.io/en/latest/ GitHub: https://github.com/pvlib/pvlib-python -- Mark Mikofski, PhD (2005) *Fiat Lux*

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NumPy 1.16.0 released.
by Charles R Harris 13 Jan '19

13 Jan '19

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.16.0. 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, POWER, and SPARC 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/1.16.0/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.0>. *Contributors* A total of 113 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 Larson * 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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multidimensional interpolation
by Neal Becker 09 Jan '19

09 Jan '19

What might be a good scipy method to perform interpolate of samples which map from R^N -> R, IOW from N-dim real input to scalar real outputs?

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