Organizers: Nicolas Fontaine, Nokia Bell Labs, USA; Binbin Guan, Microsoft, USA; Roland Ryf, Nokia Bell Labs, USA; Jochen Schroeder, Chalmers University of Technology, Sweden
Lab work is most efficient when data can be acquired in an automated way, especially when taking measurements over long durations. Automated acquisition avoids introducing human error and allows researchers to concentrate on the fun part of experimental work. Open source software in easy-to-learn languages such as Python provides just as much, or more features/interoperability for lab automation than alternative commercial software.
The hackathon format will consist of multiple interactive demos held in virtual breakout rooms in addition to a short introduction and a general Q&A session. Researchers with 10+ years’ experience in lab automation will show you the power of using Python to quickly get a lab experiment running and display the measurements in a web browser or GUI. Attendees will learn from companies that work in photonics and how they take advantage of Python to create easy interfaces to their software and hardware. Students will be able to show how they are developing new tools to complete their PhD.
To join please use the Remo link here.
*The OFC Hackathon will utilize the Remo platform, which may not be available to all attendees in all regions.
Demos and Leaders:
Lab Automation with Python (and 3D Printers): Do More for Less
Dr. Sébastien Popoff, ESPCI - Institut Langevin, France
LabExT – A Laboratory Automation Tool in Python
Etienne Corminboeuf, Polariton Technologies AG, Switzerland; Marco Eppenberger, ETH Zurich, Switzerland; Andreas Messner, ETH Zurich, Switzerland
LabExT provides you with a GUI to execute defined measurement routines using laboratory instruments that talk SCPI and investigate the resulting data. It is written with extensibility in mind and, aside from running experiments, features a live-instrument-viewer and automated optical coupling.
Python-Powered Design and Optimization of Photonic Integrated Circuits
Onur Düzgöl: VPI Photonics, Germany
Optical Neural Networks
Logan Wright, Cornell University, USA
A tour of optics-based physical neural networks experiments in Peter McMahon's new laboratory at Cornell.
Open Source Mask Layout in Python with Nazca Design
Ronald Broeke, Bright Photonics, Netherlands
Katarzyna Ławniczuk, Bright Photonics, Netherlands
Numpy Speed-up Tricks
Sjoerd van der Heide, Eindhoven University of Technology, Netherlands
Stylesheets and Plotting in Python
Menno van den Hout, Eindhoven University of Technology, Netherlands
Jupyter Lab and Jupyter Notebooks in a Small Lab
Andrei Isichenko, University of California, Santa Barbara, USA
To be covered: Jupyter templates and notebooks as lab notebooks, setting up a Jupyter Hub for a small lab and client/server asynchronous instrument control.
Predicting Spatiotemporal Nonlinear Dynamics in Multimode Fibre optics with a Recurrent Neural Network
Tegin Ugur, EPFL, France