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MuPIF platform for multiscale/multiphysics modeling

Project description

MuPIF: Multi-Physics Integration Framework

Multi-Physics Integration Framework [MuPIF](http://mupif.org) is an integration framework, that will facilitate the implementation of multi-physics and multi-level simulations, built from independently developed components. The principal role of the framework is to steer individual components (applications) and to provide high-level data-exchange services. Each application should implement an interface that allows to steer application and execute data requests. The design supports various coupling strategies, discretization techniques, and also the distributed applications.

MuPIF has been developped at Czech Technical University by Borek Patzak and coworkers and is available under GNU Library or Lesser General Public License version 3.0 (LGPLv3).

Getting Started

MuPIF is distributed as a module with the following directory tree structure:

MuPIF_TOP_DIR - contains source code and other files of the MuPIF package
├── mupif - contains source code of the MuPIF package
│ ├── doc - documentation (reference manual and User guide)
│ ├── examples - examples and tests
│ ├── Physics - module for units
│ ├── tools - various supportive tools
│ ├── tests - tests for nosetests module
│ ├── *.py - MuPIF classes
│ └── __init__.py - description for mupif module
├── LICENSE.txt - LGPL license conditions
├── MANIFEST.in - support for setuptools
├── README.txt - general description
└──setup.py - support for setuptools, Pypi upload etc.

Prerequisites

MuPIF requires the python interpreter, version 3.2 or newer. It has been tested on Linux / Windows systems. Network communication relies on Pyro4 module.

Installing

There are two options for MuPIF installation. The first one relies on Python Package Index (run as pip3 or pip) which is installed via pip3 install mupif systemwide, needing root priviledges or pip3 install mupif –user as a user

To uninstall, run pip3 uninstall mupif

The second option relies on the most advanced version on github git clone https://github.com/mupif/mupif.git mupif.git

mupif.fast

Some operations in mupif can be accelerated by using compiled modules. All such features will be enabled automatically if detected, no user interaction is necessary. They are collectivelly called mupif.fast.

  1. [minieigen](https://pypi.python.org/pypi/minieigen) module will be used for faster bounding-box implementation.
  2. Experimental mupif.fastOctant will be compiled if useCxx=True is manually set in setup.py (Linux-only). The compilation requires boost_python and [Eigen](http://eigen.tuxfamily.org>); runtime requires minieigen as in the previous point.

Running the tests

Run nosetests3 anywhere in MuPIF source tree to check basic functionality. Add –verbose for verbose output and, if you have rednose installed, add –rednose for prettier output.

Subdirectory examples contains more advanced examples for MuPIF testing. They start with a simple test on a local computer and continue through demonstrating network communication and job manager functionality. It is possible to run tests on a single computer or in distributed manner using ssh tunnels or VPN. The tests also contain stationary and nonstationary thermo-mechanical linked simulations with VTK output. Consult README files in individual directories for instructions.

Bugs

Please mail all bug reports and suggestions to mailto:info@oofem.org. I will try to give satisfaction, if the time is at least partially on my side.

Versioning

For the versions available, see the [git repository](https://github.com/mupif/mupif.git). or [pip archive](https://pypi.python.org/pypi/mupif).

Authors

Bořek Patzák Vít Šmilauer Stanislav Šulc Václav Šmilauer Martin Horák Guillaume Pacquaut

License

MuPIF is available under GNU Library or Lesser General Public License version 3.0 (LGPLv3)

Acknowledgements

The MuPIF development has been supported by Grant Agency of the Czech Republic (Project No. P105/10/1402), by EU under 7th Framework programme (MMP project, Grant agreement no: 604279) and by Horizon 2020 programme (Composelector project, Project reference: 721105).

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