Stéfan van der Walt
I work at the intersection of computation and research, with a focus on improving open source tooling and supporting the community of developers. I've been involved with scientific Python since the early 2000s, and founded scikit-image in 2009. I am a co-author of Elegant SciPy, a community leader in the Scientific Python project (https://scientific-python.org), and thoroughly enjoy my collaborations with members of this community. I am originally from South Africa, and was privileged to attend my first EuroSciPy (which I loved!) in 2011.
University of California, Berkeley
Homepage – Git*hub|lab –Sessions
Scientists are producing more and more images with telescopes, microscopes, MRI scanners, etc. They need automatable tools to measure what they've imaged and help them turn these images into knowledge. This tutorial covers the fundamentals of algorithmic image analysis, starting with how to think of images as NumPy arrays, moving on to basic image filtering, and finishing with a complete workflow: segmenting a 3D image into regions and making measurements on those regions.
Learn more about the Scientific Python project (https://scientific.python.org): what it aims to achieve (helping the developer community), recent progress that has been made, and how to become involved.
Scientific python libraries struggle with the existence of several array and dataframe providers. Many important libraries currently mainly support NumPy arrays or pandas dataframes.
However, as library authors we wish to allow users to smoothly use other array provides and simplify for example the use of GPUs without the need for explicit use of cuda enabled libraries.
This session will be split into three related discussions around efforts to tackle this situation:
* Dispatching and backend selection discussion
* Array API adoption progress and discussion
* Dataframe compatibility layer discussion