Tim Hoffmann

At the semiconductor division of Carl Zeiss it's our mission to continuously make computer chips faster and more energy efficient. To do so, we go to the very limits of what is possible, both physically and technologically. This is only possible through massive research and development efforts. In this talk, we tell the story how Python became a central tool for our R&D activities. This includes technical aspects as well as organization and culture. How do you make sure that hundreds of people work in consistent environments? – How do you get all people on board to work together with Python? – You have lots of domain experts without much software background. How do you prevent them from creating a mess when projects get larger?

At the semiconductor division of Carl Zeiss it's our mission to continuously make computer chips faster and more energy efficient. To do so, we go to the very limits of what is possible, both physically and technologically. This is only possible through massive research and development efforts. In this talk, we tell the story how Python became a central tool for our R&D activities. This includes technical aspects as well as organization and culture. How do you make sure that hundreds of people work in consistent environments? – How do you get all people on board to work together with Python? – You have lots of domain experts without much software background. How do you prevent them from creating a mess when projects get larger?

This talk explains why Python is a good choice for research and development. It spans the arch from a conceptual, almost philosophical, understanding of the software needs of reseach and development up to concrete organiziational strategies.

At the semiconductor division of Carl Zeiss it's our mission is to continuously make computer chips faster and more energy efficient. To do so, we go to the very limits of what is possible, both physically and technologically. This is only possible through massive research and development efforts. In this talk, we tell the story of how Python became a central part in our R&D activities. We explain what worked and what didn't. This leads to a conceptual understanding of how one should regard software in R&D contexts and what is different from classical production software. Based on this we understand how we should structure our software and how we need to set up our organization to support this.

This tutorial explains the fundamental ideas and concepts of matplotlib. It's suited for complete beginners to get started as well as existing users who want to improve their plotting abilities and learn about best practices.

Matplotlib is one of the most-used and powerful visualization libraries for python. Nevertheless, there has been and still is some confusion on how use it properly. This has a number of reasons ranging from an evolution of the API and lack of good documentation to the complexity that comes with the large feature set and flexibility. But these issues can be overcome. This tutorial will explain the main concepts and intended usage patterns of matplotlib. Knowing these, lets you effectively use high-level functions for most of the cases. But you will be able to go into the details if you need to fine-tune certain aspects of the plot. We'll also touch some nowadays discouraged ways of working from the past (you should know what not to do - even though that's still found in lots of examples on the web) and we may get a glimpse into the future. Tim Hoffmann joined the matplotlib core development team almost two years ago with the mission to make matplotlib easier to use. Requirements and set up instructions: Jupyter plus any recent (>=3.0) matplotlib version will do. To be on the safe side, you may set up a new conda environment using conda create -n using-mpl matplotlib jupyterlab pandas ipympl. Notebooks: https://github.com/timhoffm/using-matplotlib