Keywords
Python; Research software; Software sustainability; Environmental modelling; Multi-paradigm modelling
Start Date
15-9-2020 12:40 PM
End Date
15-9-2020 1:00 PM
Abstract
The open-source PCRaster environmental modelling platform (http://www.pcraster.eu) is a long-time developed toolbox for the construction of earth science simulation models, providing tailored model building blocks with main applications in hydrology, ecology, and environmental health. We present the recent developments of the PCRaster package. The distribution supports the conda package manager for a simplified installation on Linux, macOS and Windows, the usage of PCRaster in Jupyter Notebooks, the "multicore" module providing multithreaded field-based operations, significantly reduced build complexity, and a refactored code basis to recent C and C++ standards. While PCRaster originally started as a raster-based toolbox, it is nowadays required to enrich support for agent–based modelling due to the increasing scientific demand for constructing and analysing multi-disciplinary models, e.g. calculating personal human exposures to environmental variables by modelling human activity patterns and environmental variables. A major research focus is therefore on LUE, a new conceptual and physical data model capable to store fields and agents. The conceptual data model is a generalisation of field-based and agent-based data models and implemented as a physical data model using HDF5 and C++, and providing a Python API to expose functionality. Our LUE data model is part of a new modelling language, allowing for operations accepting both fields and agents as arguments, and therefore resembling and extending the map algebra approach to modelling. A major software development focus is on providing model building blocks usable in a high-performance computing context. We currently work on a framework with parallel and distributed model building blocks using the HPX C++ library, allowing to scale environmental algorithms from single nodes to entire compute clusters. In our presentation we give an overview of the 30 year development history of PCRaster, the major challenges in synchronising scientific and technical progress, and our approaches thereto.
Thirty years of spatio-temporal modelling with PCRaster
The open-source PCRaster environmental modelling platform (http://www.pcraster.eu) is a long-time developed toolbox for the construction of earth science simulation models, providing tailored model building blocks with main applications in hydrology, ecology, and environmental health. We present the recent developments of the PCRaster package. The distribution supports the conda package manager for a simplified installation on Linux, macOS and Windows, the usage of PCRaster in Jupyter Notebooks, the "multicore" module providing multithreaded field-based operations, significantly reduced build complexity, and a refactored code basis to recent C and C++ standards. While PCRaster originally started as a raster-based toolbox, it is nowadays required to enrich support for agent–based modelling due to the increasing scientific demand for constructing and analysing multi-disciplinary models, e.g. calculating personal human exposures to environmental variables by modelling human activity patterns and environmental variables. A major research focus is therefore on LUE, a new conceptual and physical data model capable to store fields and agents. The conceptual data model is a generalisation of field-based and agent-based data models and implemented as a physical data model using HDF5 and C++, and providing a Python API to expose functionality. Our LUE data model is part of a new modelling language, allowing for operations accepting both fields and agents as arguments, and therefore resembling and extending the map algebra approach to modelling. A major software development focus is on providing model building blocks usable in a high-performance computing context. We currently work on a framework with parallel and distributed model building blocks using the HPX C++ library, allowing to scale environmental algorithms from single nodes to entire compute clusters. In our presentation we give an overview of the 30 year development history of PCRaster, the major challenges in synchronising scientific and technical progress, and our approaches thereto.
Stream and Session
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