Mailman 3 June 2019 - SciPy-User

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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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn July 28, 2022

July 28, 2022

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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NumPy 1.17.0rc1 released
by Charles R Harris July 26, 2019

July 26, 2019

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.17.0rc1. The 1.17 release contains a number of new features that should substantially improve its performance and usefulness. The Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped. Python 3.8b1 should work with the released source packages, but there are no guarantees about future releases. Highlights of this release are: - A new extensible random module along with four selectable random numbe5 generators and improved seeding designed for use in parallel processes has been added. The currently available bit generators are MT19937, PCG64, Philox, and SFC64. - NumPy's FFT implementation was changed from fftpack to pocketfft, resulting in faster, more accurate transforms and better handling of datasets of prime length. - New radix sort and timsort sorting methods. It is currently not possible to choose which will be used, but they are hardwired to the datatype and used when either ``stable`` or ``mergesort`` is passed as the method. - Overriding numpy functions is now possible by default Downstream developers should use Cython >= 0.29.10 for Python 3.8 support and OpenBLAS >= 3.7 (not currently out) to avoid problems on the Skylake architecture. The NumPy wheels on PyPI are built from the OpenBLAS development branch in order to avoid those problems. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.17.0rc1/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.17.0rc1>. *Contributors* A total of 142 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aaron Voelker + - Abdur Rehman + - Abdur-Rahmaan Janhangeer + - Abhinav Sagar + - Adam J. Stewart + - Adam Orr + - Albert Thomas + - Alex Watt + - Alexander Blinne + - Alexander Shadchin - Allan Haldane - Ander Ustarroz + - Andras Deak - Andreas Schwab - Andrew Naguib + - Andy Scholand + - Ankit Shukla + - Anthony Sottile - Antoine Pitrou - Antony Lee - Arcesio Castaneda Medina + - Assem + - Bernardt Duvenhage + - Bharat Raghunathan + - Bharat123rox + - Bran + - Bruce Merry + - Charles Harris - Chirag Nighut + - Christoph Gohlke - Christopher Whelan + - Chuanzhu Xu + - Daniel Hrisca - Daniel Lawrence + - Debsankha Manik + - Dennis Zollo + - Dieter Werthmüller + - Dominic Jack + - EelcoPeacs + - Eric Larson - Eric Wieser - Fabrice Fontaine + - Gary Gurlaskie + - Gregory Lee + - Gregory R. Lee - Hameer Abbasi - Haoyu Sun + - He Jia + - Hunter Damron + - Ian Sanders + - Ilja + - Isaac Virshup + - Isaiah Norton + - Jaime Fernandez - Jakub Wilk - Jan S. (Milania1) + - Jarrod Millman - Javier Dehesa + - Jeremy Lay + - Jim Turner + - Jingbei Li + - Joachim Hereth + - John Belmonte + - John Kirkham - John Law + - Jonas Jensen - Joseph Fox-Rabinovitz - Joseph Martinot-Lagarde - Josh Wilson - Juan Luis Cano Rodríguez - Julian Taylor - Jérémie du Boisberranger + - Kai Striega + - Katharine Hyatt + - Kevin Sheppard - Kexuan Sun - Kiko Correoso + - Kriti Singh + - Lars Grueter + - Maksim Shabunin + - Manvi07 + - Mark Harfouche - Marten van Kerkwijk - Martin Reinecke + - Matthew Brett - Matthias Bussonnier - Matti Picus - Michel Fruchart + - Mike Lui + - Mike Taves + - Min ho Kim + - Mircea Akos Bruma - Nick Minkyu Lee - Nick Papior - Nick R. Papior + - Nicola Soranzo + - Nimish Telang + - OBATA Akio + - Oleksandr Pavlyk - Ori Broda + - Paul Ivanov - Pauli Virtanen - Peter Andreas Entschev + - Peter Bell + - Pierre de Buyl - Piyush Jaipuriayar + - Prithvi MK + - Raghuveer Devulapalli + - Ralf Gommers - Richard Harris + - Rishabh Chakrabarti + - Riya Sharma + - Robert Kern - Roman Yurchak - Ryan Levy + - Sebastian Berg - Sergei Lebedev + - Shekhar Prasad Rajak + - Stefan van der Walt - Stephan Hoyer - SuryaChand P + - Søren Rasmussen + - Thibault Hallouin + - Thomas A Caswell - Tobias Uelwer + - Tony LaTorre + - Toshiki Kataoka - Tyler Moncur + - Tyler Reddy - Valentin Haenel - Vrinda Narayan + - Warren Weckesser - Weitang Li - Wojtek Ruszczewski - Yu Feng - Yu Kobayashi + - Yury Kirienko + - @aashuli + - @euronion + - @luzpaz - @parul + - @spacescientist + Cheers, Charles Harris

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specify time steps for ode solvers?
by Max Aifer June 25, 2019

June 25, 2019

Is there a way to force ode solvers like scipy.integrate.RK45 to use specific steps? I know that the evaluation points can be specified for output, but I want to control the actual timesteps used for integration. Thanks, Max Aifer

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[JOB] Full-time opportunity – Software engineer for open source project
by Umberto Lupo June 24, 2019

June 24, 2019

Who we are L2F is a start-up based on the EPFL Innovation Park (Lausanne, Switzerland). We are currently working at the frontier of machine learning and topological data analysis, in collaboration with several academic partners. Our Mission We are developing an open source Python library implementing innovative topological data analysis algorithms which are being designed by our team of full-time research scientists and post-doctoral researchers. The library shall be user-friendly, well documented, high-performance and well integrated with state-of-the-art machine learning libraries (such as NumPy/SciPy, scikit-learn and Keras or other popular deep learning frameworks). We are offering a full-time job in our company to help us develop this library. The candidate will work in the L2F research team. Profile description We are looking for a computer scientist matching these characteristics: * 2+ years of experience in software engineering. * Skilled with Python and C++ (in particular, at ease wrapping C++ code for Python). * Aware of how open source communities work. Better if he/she contributed in open-source collaborations, such as scikit-learn. * At ease writing specifications, developer documentation and good user documentation. * Fluent with continuous integration, Git and common developer tools. * Skilled in testing architectures (unit tests, integration tests, etc.). How to apply Applicants can write an e-mail to Dr. Matteo Caorsi (m.caorsi(a)l2f.ch<mailto:m.caorsi@l2f.ch>) attaching their CV and a short letter detailing their relevant experience and motivation. Starting date This position is available for immediate start for the right candidate.

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SciPy paper
by Andrew Nelson June 20, 2019

June 20, 2019

A paper about SciPy 1.0 is in preparation. We appreciate all contributions made to SciPy, and we want to credit in the paper everyone who has made a substantial contribution. The definition of 'substantial' is "one new feature, significant improvement (e.g. performance or accuracy) to an existing feature, or multiple smaller contributions". We have tried to contact those with GitHub accounts (via the associated email address) who have made at least one commit, issue, pull request, review, or comment at the SciPy main repository prior to the release of SciPy 1.0. However, we realise that this may not have reached contributors whose associated GitHub email address is no longer reachable. If you believe that you have made substantial contributions to the project we ask that you fill out the following (short) form *by Friday, June 28,* if you wish to be considered for inclusion on the author list. SciPy 1.0 Paper Author Form ( https://docs.google.com/forms/d/e/1FAIpQLScApdlW3PvJBhdXvSzzpJ54oAxUnZTNqD8… ) By submitting the form, you will certify that you have reviewed and agree to the submission of the manuscript ( https://github.com/scipy/scipy-articles/blob/master/scipy-1.0/paper.pdf), and that if selected as an author you will read the final version and take appropriate action if you disagree with any content. The coordination committee has determined that the author order will be 'The SciPy Developers' as first author; maintainers, paper writers, and other key contributors in order of contribution level; and all other authors in alphabetical order. The Author Contributions Statement at the end of the paper will document how the author order was determined. Final decisions about author order will be made by the paper coordination committee with a deciding vote, if needed, provided by the Chair of the SciPy Steering Council, Ralf Gommers. Once authorship decisions are made, those who submitted the form linked above will receive an email with their status and a link to a final version of the paper, which we intend to submit to Scientific Reports <https://www.nature.com/srep/>. Please post an issue on the scipy-articles GitHub repository ( https://github.com/scipy/scipy-articles) if you have any questions or comments so that a member of the coordination committee can respond. Thanks to everyone for your contributions to SciPy!

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ANN: Astropy v3.2 released
by Erik Tollerud June 13, 2019

June 13, 2019

Dear colleagues, We are very happy to announce the v3.2 release of the Astropy package, a core Python package for Astronomy: http://www.astropy.org Astropy is a community-driven Python package intended to contain much of the core functionality and common tools needed for astronomy and astrophysics. It is part of the Astropy Project, which aims to foster an ecosystem of interoperable astronomy packages for Python. New and improved major functionality in this release includes: * New Sub-package for Time Series * New SI/CODATA 2018 Constants * Additions and changes to Ecliptic Transformations * Table performance improvements and change in metadata handling * Table I/O integration of pandas I/O functions for ASCII tables * Improved help on Table read() and write() methods In addition, hundreds of smaller improvements and fixes have been made. An overview of the changes is provided at: http://docs.astropy.org/en/stable/whatsnew/3.2.html Note that the Astropy 3.x series only supports Python 3. Python 2 users can continue to use the 2.x (LTS) series (but without new features). Instructions for installing Astropy are provided on our website, and extensive documentation can be found at: http://docs.astropy.org If you make use of the Anaconda Python Distribution, you can update to Astropy v3.2 with: conda update astropy Whereas if you usually use pip, you can do: pip install astropy --upgrade Please report any issues, or request new features via our GitHub repository: https://github.com/astropy/astropy/issues Over 300 developers have contributed code to Astropy so far, and you can find out more about the team behind Astropy here: http://www.astropy.org/team.html As a reminder, Astropy v2.0 (our long term support release) will continue to be supported with bug fixes (but no new features) until the end of 2019, so if you need to use Astropy in a very stable environment, you may want to consider staying on the v2.0.x set of releases (for which we have recently released v2.0.13). If you use Astropy directly for your work, or as a dependency to another package, please remember to acknowledgment it by citing the appropriate Astropy paper. For the most up-to-date suggestions, see the acknowledgement page, but as of this release the recommendation is: This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2018). where (Astropy Collaboration, 2018) is a reference to https://doi.org/10.3847/1538-3881/aabc4f Special thanks to the coordinator for this release: Brigitta Sipocz. We hope that you enjoy using Astropy as much as we enjoyed developing it! Erik Tollerud, Tom Robitaille, Kelle Cruz, and Tom Aldcroft on behalf of The Astropy Collaboration

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Need for binary fixed point module?
by smlgit June 8, 2019

June 8, 2019

Hi, I have been designing for FPGAs and instead of using Matlab for simulation, I decided to use python. Matlab provides a fixed point feature that is useful for simulating finite word length and rounding effects. I wrote my own fixed point library (as a c extension) for python because the existing couple of python modules were incomplete and slow. I'm interested in knowing if there are many scipy users that have a need for a fixed point (binary, not decimal) library or if there are many users that just use python to aid their FPGA or ASIC work? If you are interested, my library is at https://github.com/smlgit/fpbinary . sml.

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