Mailman 3 March 2023 - SciPy-User

ANN: pvlib-0.9.5 released
by Dr. Mark Alexander Mikofski PhD April 3, 2023

April 3, 2023

Dear Pythonistas and solar power enthusiasts, The maintainers are happy to announce a new release of pvlib python: software for simulating performance of photovoltaic solar energy systems. *v0.9.5 Highlights:* * The infinite_sheds model now has options to use the hay-davies transposition model and faster vectorized calculations. * New models for irradiance decomposition (boland) and relative airmass (gueymard2003). * Model extensions for multiple strings in pvlib.snow.loss_townsend and AR coating in pvlib.iam.physical. * Updated the parameters database for the ADR inverter model. * Various other bug fixes and testing updates. For the full list of what's new, see the documentation: https://pvlib-python.readthedocs.io/en/stable/whatsnew.html *Releases are available from PyPI and the conda-forge channel:* * https://pypi.org/project/pvlib/ * https://anaconda.org/conda-forge/pvlib and https://anaconda.org/conda-forge/pvlib-python NOTE: new pvlib releases are no longer uploaded to the "pvlib" conda channel. Please install from PyPI or the conda-forge channel instead. *Read the Documentation:* * https://pvlib-python.readthedocs.io/en/stable/index.html *Report issues & contribute:* * https://github.com/pvlib/pvlib-python *Community discussion & support:* * https://groups.google.com/g/pvlib-python * https://github.com/pvlib/pvlib-python/discussions *Thank you for using pvlib python!* -- Mark Mikofski, PhD (2005) *Fiat Lux*

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Re: Textbook Recommendations
by Kevin Dunn April 3, 2023

April 3, 2023

(Sent with wrong subject earlier due to user error. Resending. Apologies for the noise) -- I am a bit biased of course, but my online book could also meet your needs. Process Improvement using Data. https://learnche.org/pid/ Chapter 4 is about Regression, chapter 2 on fundamental stats, and chapter 5 on designed experiments (including a full online course on Coursera). It's been a free, Creative Commons licensed resource since 2010, and used by quite a number of industrial companies and universities daily. I'm working on converting the R code to Python, so both are available side by side. The repo is available on GitHub if anyone is interested in helping speed that up ;) https://github.com/kgdunn/pid-book Kevin

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How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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NumPy 1.23.2 release
by Charles R Harris March 22, 2023

March 22, 2023

Hi All, On behalf of the NumPy team, I am pleased to announce the release of NumPy 1.23.2. NumPy 1.23.2 is a maintenance release that fixes bugs discovered after the 1.23.1 release. Notable features are: - Typing changes needed for Python 3.11 - Wheels for Python 3.11.0rc1 The Python versions supported for this release are 3.8-3.11. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.23.2>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.23.2>. *Contributors* A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alexander Grund + - Bas van Beek - Charles Harris - Jon Cusick + - Matti Picus - Michael Osthege + - Pal Barta + - Ross Barnowski - Sebastian Berg *Pull requests merged* A total of 15 pull requests were merged for this release. - #22030: ENH: Add ``__array_ufunc__`` typing support to the ``nin=1`` ufuncs - #22031: MAINT, TYP: Fix ``np.angle`` dtype-overloads - #22032: MAINT: Do not let ``_GenericAlias`` wrap the underlying classes'... - #22033: TYP,MAINT: Allow ``einsum`` subscripts to be passed via integer... - #22034: MAINT,TYP: Add object-overloads for the ``np.generic`` rich comparisons - #22035: MAINT,TYP: Allow the ``squeeze`` and ``transpose`` method to... - #22036: BUG: Fix subarray to object cast ownership details - #22037: BUG: Use ``Popen`` to silently invoke f77 -v - #22038: BUG: Avoid errors on NULL during deepcopy - #22039: DOC: Add versionchanged for converter callable behavior. - #22057: MAINT: Quiet the anaconda uploads. - #22078: ENH: reorder includes for testing on top of system installations... - #22106: TST: fix test_linear_interpolation_formula_symmetric - #22107: BUG: Fix skip condition for test_loss_of_precision[complex256] - #22115: BLD: Build python3.11.0rc1 wheels. Cheers, Charles Harris

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15ᵗʰ Advanced Scientific Programming in Python in Heraklion, Crete, Greece, 27 August – 3 September,2023
by Tiziano Zito March 17, 2023

March 17, 2023

ASPP2023: 15ᵗʰ Advanced Scientific Programming in Python ======================================================== a Summer School https://aspp.school 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 interactive and allow students to acquire direct hands-on experience with the topics. 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. Python is the standard tool for the programming scientist due to clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization. This school is targeted at PhD students, postdocs and more senior researchers from all areas of science. Competence in Python or in another language such as Java, JavaScript, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and git or another version control system is assumed. Participants without any prior experience with Python or git should work through the proposed introductory material before the course. We care for diversity and inclusion, and strive for a welcoming atmosphere to programming scientists of all levels. In particular, we have focused on recruiting an international and gender-balanced pool of students. Date & Location =============== 27 August – 3 September, 2023. Heraklion, Crete, Greece Application =========== You can apply online: https://aspp.school Application deadline: 23:59 UTC, Monday 1 May, 2023. There will be no deadline extension, so be sure to apply on time. Invitations and notification of rejection will be sent by Sunday 28 May, 2023. Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program ======= • Large-scale collaborative scientific code development with git and GitHub • Best practices in data visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Scientific programming patterns in Python • Writing parallel applications in Python • Profiling and speeding up scientific code • Programming in teams Faculty ======= • Aitor Morales-Gregorio, Theoretical Neuroanatomy, Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich Germany • Guillermo Aguilar, Department of Computational Psychology, Technische Universität Berlin Germany • Jakob Jordan, Department of Physiology, University of Bern Switzerland • Lisa Schwetlick, Experimental and Biological Psychology, Universität Potsdam Germany • Pamela Hathway, Orange Business, Berlin/Nürnberg Germany • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, innoCampus, Technische Universität Berlin Germany • Verjinia Metodieva, NeuroCure, Charité – Universitätsmedizin Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers ========== Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, innoCampus, Technische Universität Berlin Organization team from IMBB/FORTH: • Athanasia Papoutsi, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Greece • Emmanouil Froudarakis, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Sponsors ======== We are able to hold this year's ASPP school thanks to the financial support of the Tübingen AI Center. The Institute of Molecular Biology & Biotechnology of the Foundation for Research and Technology – Hellas is hosting us in Heraklion and is taking care of the local organization. We also explicitly thank Prof. Felix Wichmann at the Neural Information Processing Group, Eberhard Karls Universität Tübingen, Germany, for his invaluable help. Website: https://aspp.school Contact: info(a)aspp.school

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Compiler optimizations and other performance hacks for HiGHS
by Søren Fuglede Jørgensen March 14, 2023

March 14, 2023

I have a workflow where I'm solving a bunch of (non-integral) LPs, and `scipy.optimize._highs._highs_wrapper` is the main bottleneck, and I was looking for ways to squeeze out as much performance of that as I could. For instance, I was looking around for stuff like "-O3" in the `meson.build`, `CMakeLists.txt`, `setup.py` but didn't find anything, so I was wondering if the compilation uses performance optimizations, or if it even matters. Or, more generally: Are there any tricks, hacks, optimizations that one should be aware of to get as much as possible out of HiGHS? I only care about LPs and stuff that works on x86 and/or ARM64. Thanks!

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