2nd Keynote: A Snake in Space - The rise of scientific Python in Astrodynamics and Astronomy - Francesco Biscani

duffyd
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Max Planck Institute for Astronomy, European Space Agency.

PaGMO and Astropy - will mainly talk about PaGMO as wrote this and more familiar with this.

PaGMO - Parallel Global Multiobjective Optimiser. Optimisation tool. Initially a trajectory optimisation tool, evolved as a general-purpose optimiser.

Focused on parallel and distributed computing. Can use via C++ or Python.

Astropy (opens new window) - community effort to develop a single core package for Astronomy.

Optimisation - a large area of applied mathematics. “The selection of a best element (with regard to some criteria) from some set of available alternatives.” E.g. Travelling salesman (TSP).

E.g. algorithms: gradient-based methods, evolutionary algorithms, stochastic algorithms.

Interplanetary trajectories - space mission trajectories are defined by sets of parameters: launch date, initial velocity vector, sequence of flybys, sequence of deep-space manoeuvres (DSM).

Usually we want to minimise fuel consumption.

The resulting optimisation problem is hard: multimodal objective function, highly nonlinear, highly dimensional.

Traditionally tackled by teams of human experts.

  • genetic algorithms
  • differential evolution
  • ant-colony optimisation
  • artificial bee-colony optimisation

Island model - name inspired by Darwin’s trip to the Galápagos Islands.

History of PaGMO - pattern of scientific programming code: created as part of some research and then abandoned for many years; not useable by anyone else and then picked up later and made more consumable by others.

Initially created Python bindings to initially created C/C++ ‘research’ code (2008-2009) and followed 'eat own dog food’ approach by using it a lot for internal research. Been through 2 GSoCs and now 'fully-fledged’ open source project.

  • Emphasis on correctness & reproducibility

  • Powerful driver for innovations in HPC

  • Wheel re-invention

  • Code is often written with a one-paper-horizon mindset

  • Most scientists are not trained in software engineering

  • The abstract island class includes a problem, an algorithm and a population of candidate solutions, and a virtual evolve() method that dispatches the optimisation (to a thread or a process on another machine)

Implemented via Boost.Python.

  • serialisation across language boundaries involving virtual classes, base pointers, etc

  • extension from Python of C++ base classes

  • working around some of Python’s limitation wrt parallel programming (GIL)

  • Scientific Python stack: NumPy (crunching results), SciPy (optimisation algorithms), matplotlib, IPython, etc

PaGMO uses: evolution of neural networks for autonomous Martian rovers, selection of Near Earth Asteroids for future human missions.

  • Handle practical needs of astronomers: units, coordinates, FITS files, cosmological calculations

  • “One of best community packages ever seen”

  • Not research package like PaGMO

  • Heavy reliance on NumPy