The two days of the scientific track of the conference start by a keynote talk that provide a vision and/or a report status on issues of general interest to the Scientific Python community. The rest of the day is devoted to contributed talks that present the state-of-the-art in many fields of scientific computing with Python.
The tutorials play an important role in the EuroSciPy conference and allow beginner or advanced users of the Scientific Python tools to learn from recognized experts. The first two days of the conference are devoted to the tutorials that are held in two parallel tracks.
Cameron Neylon is currently Director of Advocacy at The Public Library of Science, a non profit publisher and advocacy organization that is active in open access publishing. He started his career in academia and holds a PhD in Chemistry and is an active proponent of open access and open research.
The highest principle of network architecture design is interoperability. If Metcalfe's Law tells us that a network's value can scale as some exponent of the number of connections then our job in building networks is to ensure that those connections are as numerous, as operational, and as easy to create as possible. Where we make it easy for anyone to wire in new connections we maximise the ability of others to contribute to the value of our shared networks.
Amongst those using Python for research are a wide range different disciplines and targets but one area that stands out for me, and stretches across a range of traditional domains is the study of networks. Networks of physical interactions and social networks, of genetic control or of ecological interactions amongst many others. The scientific Python community is also amongst the most networked of research communities and amongst the most open in the sharing of research papers, of research data, tools, and even research in process in online conversations and writing.
Lifting our gaze from the networks we work on to the networks we occupy is a challenge. Our human networks are messy and contingent and our machine networks clogged with things we can't use, even if we could access them. What principles can we apply so as to build our research into networks that make the most of the network infrastructure we have around us. Where are the pitfalls? And what are the opportunities? What will it take to configure our work so as to enable "network ready research"?
Peter WangPeter holds a B.A. in Physics from Cornell University and has been developing applications professionally using Python since 2001. Before co-founding Continuum Analytics in 2011, Peter spent seven years at Enthought designing and developing applications for a variety of companies, including investment bankers, high-frequency trading firms, oil companies, and others. In 2007, Peter was named Director of Technical Architecture and served as client liaison on high-profile projects. Peter also developed Chaco, an open-source, Python-based toolkit for interactive data visualization.
While Python has been a popular and powerful language for scientific computing for a while now, its future in the broader data analytics realm is less clear, especially as market forces and technological innovation are rapidly transforming the field.
In this talk, Peter will introduce some new perspectives on "Big Data" and the evolution of programming languages, and present his thesis that Python has a central role to play in the future of not just scientific computing, but in analytics and even computing in general. As part of the discussion, many new libraries, tools, and technologies will be discussed (both Python and non-Python), both to understand why they exist and where they are driving technical evolution.
The goal is not to merely provide the audience with a new narrative about Python and data analytics. Rather, hopefully everyone will feel motivated to be proactive in evolving the language ecosystem and nurturing its adoption, in ways that will best ensure its long-term relevance and improve the field of computational analytics.
The tutorials are held in two tracks: beginner and advanced. The purpose of the beginner track is to introduce scientists to the general scientific tools that are available in Python so that the participants can start right away to work on their own problems. The speakers in the advanced track are established experts on the tools they cover. The tools covered are aimed at current users of Python who wish to learn powerful tools that are either related to a given field (statistics, for instance) or that are related to general scientific computing skills (performance optimization, for instance).
- Olivier Debeir: A small python appetizer
- Valentin Haenel: Array Manipulation with Numpy
- Nicolas P. Rougier: Matplotlib tutorial
- Philippe Gervais: Scipy package tutorial
- Emmanuelle Gouillart: Image processing with scikit-image and the SciPy stack
- Almar Klein: Modern OpenGL
- Didrik Pinte: Interactive visualization with Chaco
- Thomas Lecocq: Basemap & Cartopy - More than just base maps!
- Paul Zimmermann: Advanced Tutorial on Sage
- Nelle Varoquaux: Git and github
- Matthias Bussonnier and Min Ragan-Kelley: IPython Advance Tutorial
- Gaël Varoquaux: 3D visualization with Mayavi
As it is customary in many computing conferences, an oral session is devoted to lightning talks: participants register on site to present results, solutions or ideas in a short time. This session is very much appreciated because of the original contributions it brings and because of its lively character.
An original contribution this year is a panel discussion proposal by Mike Müller on the topic "Making Programmers" that gathered Emmanuelle Gouillart, Valentin Haenel, Ian Ozsvald, Gaël Varoquaux and Nelle Varoquaux who shared their experience on the education to programming in the scientific, academic and high-school environments.
- Marcel Stimberg: The second life of Brian
- Ian Ozsvald: How to build an open-sourced Data Science Company
- Juan Nunez-Iglesias: Learn to segment n-dimensional images with GALA
- Nicolas P. Rougier: Vispy, A Modern and Interactive Scientific Visualization
- Gael Varoquaux: Processing biggish data on commodity hardware: simple Python patterns
- Olivier Grisel: Trends in Machine Learning and the SciPy community
- Bill Little: Iris and Cartopy: open source Python libraries for weather and climate science
- Benoit Da Mota: Distributed High-Dimensional Regression with Shared Memory for Neuroimaging-Genetic Studies
- Valentin Haenel: News from the Blosc ecosystem: introducing Bloscpack
- Pierre Haessig: Computing an Optimal Control Policy for an Energy Storage
- Thomas Lecocq: Studying the Earth with ambient seismic noise
- Nicolas Baer: Elasticluster: provisioning computing clusters in the cloud with Python
- Antònia Tugores: Mobility data storage and analysis
- Jinook Oh: CATOS; Computer Aided Training/Observing System
- Pablo Olmos: Lidar data processing with Python
- Cyrille Rossant: Galry: high performance interactive data visualization in Python
- Robert Cimrman: SfePy - Overview
- Bernard UGUEN: PyLayers: A Graph Based Python Open Source Indoor Propagation Simulator
- Gorka Zamora-López: GAlib: a library for graph analysis based in numpy array manipulations
- Vladimír Lukeš: DICOM2FEM: FE meshes from CT scans
- Marcel Stimberg: The second life of Brian
- Félix Hartmann: Reaction-diffusion systems for wood formation in trees
- Denis A. Engemann: MNE-Python: MEG and EEG data analysis with Python
- Álvaro Justen: pyquality: analyzing Python code
- Dmitry Khvorostyanov: CHIMPLOT: CoHesive Interactive Model visualization tool with Paper-quaLity OuTcome
- Johann Rohwer: Parallel computation in Systems Biology with Python
- Antònia Tugores: Grid made easy
- Pierrick Brunet: Benchmarking Several Python Compilers and Interpreters on Mathematical Applications
- Jan Verschelde: modernizing PHCpack through phcpy
- Yves Hilpisch: Efficient Financial and Data Analytics with Python, pandas & Co.
- Erik Mathiesen: LCL: a library for modelling continuous systems
- Mehmet Kunt: Proposing an efficient report generation procedure based on Python modules and Latex
- Dorota Jarecka: Object-oriented programming with NumPy using CPython & PyPy; comparison with C++ & modern Fortran
- Michael McKerns: mystic: a framework for predictive science
- Horacio Andres Vargas Guzman: Dynamic Force Microscopy Simulator
- Drew Marsden: The UQ Foundation: Supporting the Right Scientific Tools for Reproducibility
- Fabrice Salvaire: Python for Image Procesing and Big Data
- Kelsey DSouza: PySTEMM - A STEM Learning Tool for Exploring and Building Executable Concept Models
- Riccardo Murri: Using the GC3Pie high-throughput library for model calibration and distributed optimization workflow
- Philippe Gervais: Memory profiling
- Pawel Chojnacki: OpenBCI: Brain-Computer Interfaces with Python in science and education
- Riccardo Murri: Performance of Python runtimes on a non-numeric scientific code.
- Stefan Richthofer: JyNI - Using native CPython-Extensions in Jython