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Test Cases Monday, 04 January 2010 22:57 | Author: Administrator |    The final way to contribute to Opus is by providing test cases. While not a software contribution in the strict sense, this form of contribution is both important and precious, and deserves its own article. A test case is usually an industrial example showing the way in which the platform can be used to solve a real-life problem. The manner and production of such a test case is detailed in the Contribution Guide, but in a nutshell, the process should be along the lines of: - Clearly define the problem that will be solved, as it was in real life prior to resolution: context, quantity of interest, available data, expected difficulties and possible solutions, expected results and corresponding interpretation... - Gather the real-life data used in a format usable by Opus (ideally CSV); - Explain the method used to solve the real-life problem, from raw data to the final quantity of interest and its interpretation; - Identify the corresponding features from Opus and formulate the associated series of commands; - Check that the results obtained meet the real-life results. At this point the test case is ready to be submitted to the consortium, and, if accepted, will either be uploaded as a webpage with associated data files and command checklist or a demo script directly available from a single command in the software itself. Last Updated (Monday, 26 September 2011 00:20)

How to contribute Monday, 04 January 2010 21:47 | Author: Administrator |    This section provides a checklist explaining how to prepare and develop a contribution for Opus. Note that this is a generic method not specific to any language usable in Opus. For detailed explanations concerning each available language, see the Contribution Guide. This article also makes the assumption that the contribution is ready from a mathematical point of view, that is the mathematical method and algorithms are well-described, ideally validated on a laboratory test case, or at least on a sandbox example. Contribution Checklist - Identify existing features and necessary developments; - Choose contribution level; - Choose language/type; - Write the specifications :  functional and technical specifications; - Develop the contribution : classes and unitary tests; - Create the feature tests (use case); - Write the documentation : reference and architecture documentation, user manual, usecase manual; - Upload the contribution (Lib and Contrib levels); Detailed explanations for each step are available in the Contribution Guide. Last Updated (Monday, 26 September 2011 01:57)   Available contributions Monday, 04 January 2010 21:44 | Author: Administrator |    This section lists the available contributions for Opus. When applicable, they can be downloaded from the 'download' link below. Opus-Lib: Feel++ for Opus, v.0.91 and 0.92.0 : Feel++-OPUS is a framework for the Certified Reduced Basis (CRB) methods presented during the first OPUS workshop. It essentially provides a C++ interface for finite element codes which only supports Feel++ and an implementation of some CRB methods. See here for a pdf presentation. RPyWrapper : the wrapper RPyWrap is a link module which allows to make software contributions developed in language R interoperable with Open TURNS. Open TURNS PC library : Open TURNS is a C++ library and a Python module that implements the global methodology for uncertainty propagation that Opus also uses. The Opus project created new features for Openturns, especially a Polynomial Chaos Expansion (PCE) library. This is now used on real industrial cases on a regular basis and is available for the whole community for business, academic or teaching purposes. See website and blog for details. This is a joint work of EADS, EDF and Phiméca. The contribution of EADS to these developments has been mainly funded by OPUS, while the contribution of EDF and Phiméca has been funded by own resources and by the ANR project MIRADOR (Modélisation interactive des risques associés au développement d’ouvrages robustes, ref. ANR-06-RGCU-008). NISP toolbox for Scilab : The goal of this toolbox is to provide a tool to manage uncertainties in simulated models for the Scilab platform. This Scilab toolbox is based on the NISP C++ library, where NISP stands for "Non-Intrusive Spectral Projection". The NISP library is based on a set of 3 C++ classes so that it provides an object-oriented framework for uncertainty analysis. The Scilab toolbox provides a pseudo-object oriented interface to this library, so that the two approaches are consistent. See wiki for details. Opus Contrib : Octave/Matlab toolbox for kriging : a toolbox for carrying out kriging regression in Matlab (also compatible with Octave, with some limitations). See website on sourceforge for more details. Various R scripts for inverse probabilistic modeling were created, dealing with : MCMC estimation usecase scripts: The idea here was to develop the scripts applied to a usecase while reusing existing tools based on the R language when relevant. Two R packages available on the CRAN site have been identified, and both packages may be used conjointly with the R package CODA which allows output analysis and diagnostics for Markov Chain Monte Carlo simulations.These packages are MCMC package and MCMCpack package. MLE estimation scripts: ECME (Expectation Conditional Maximization Either) by iterated linearizations (to deal with non-linear cases), based on G. Celeux et al. (Identifying intrinsic variability in multivariate systems through linearised inverse methods, Rapport de recherche INRIA RR-6400, 2007); S(A)EM (Stochastic - Approximation version of - Expectation Maximization) which allows to perform SEM or SAEM. Relevant publications to SA(E)M : G. Celeux and J. Diebolt, The SEM Algorithm: a Probabilistic 101/128 ANR OPUS Final Report D-WP0/11/03/A Teacher Algorithm Derived from the EM Algorithm for the Mixture Problem, Computational Statistics Quaterly, 2:73–82, 1985 and E. Kuhn, Estimation par maximum de vraisemblance dans des problèmes inverses non linéaires, Mémoire de thèse, Université Paris XI d’Orsay, 2003). Opus-Forum : High quantile estimation by Multi-Element Polynomial Chaos expansions. This contribution comes mainly from the work of the post-doc Jordan Ko (Paris Diderot). It is a brief summary of a paper submitted to Journal of Computational Physics. Functional sensitivity analysis: variable selection using varying coefficient modeling and application to diesel engine smoke depollution. This contribution came mainly from the works of the DICE consortium. It is thus a contribution to the platform, ’external’ to the OPUS project team. A commented R script implementing the proposed method, as well as data files have been provided. Download contributions Last Updated (Monday, 26 September 2011 02:15)   Opus Forum Monday, 04 January 2010 21:44 | Author: Administrator |    OPUS-Forum. The contributor uses the forum space available in the OPUS web site, to discuss and propose features or ideas in a completely free way, from a simple algorithm to a bit of code or even a complete module (albeit incompatible with other OPUS contributions). Scripts, articles, commentaries concerning a method or its software development as well as preprints are typical ways to contribute at the -Forum level. Apart from adding features, the contributor can also provide test cases, usually industrial examples showing the way in which OPUS methods and tools can be used to solve a real-life problem. While not a software contribution, this form of contribution is both important and precious. Note, however, that a test case providing useful insights into uncertainty study is perfectly acceptable as a contribution even if it is not tailored to use the software features developed within OPUS. The various contributions can be shared on the Web site on the download tab. Last Updated (Monday, 26 September 2011 00:20)   Opus Contrib Monday, 04 January 2010 21:43 | Author: Administrator |    OPUS-Contrib. This is the main way in which features are supposed to be provided by the community. Most of restricting rules proper to to the -Lib level have been released. The main points to note are: Contributions at this level can be written in any of the following supported languages: C++, Python, R, Scilab, Matlab/Octave; The contribution’s quality is the contributor’s responsibility: it is up to him/her to ensure the quality of the code, the respect of the programming rules as well as the mathematical correctness; Different kinds of OPUS-Contrib contribution types exist, differentiated with respect to the integration with the others: Integration: the package is created using the OPUS module system, basically a C++ module with the corresponding Python layer, and written according to the relevant rules; however, the contributor chooses not to aim at OPUS-Lib for a variety of reasons (e.g. releasing some constraints due to programming quality). Compatibility: the package is compatible with the rest of OPUS, i.e. written using a language or formalism allowing it to directly interact with OPUS. This means either: 1. a C++ code, which can use all -Lib and C++ -Contrib features. The code may be contributed with or without the corresponding Python layer; 2. a Python script, which can use all of the Lib and all installed Python features, C++/Python features and Opus-style modules from -Contrib: 3. a Scilab script using OPUS features; Interoperability: the package is not usable directly with OPUS, but can interact with it, e.g. via text files; Other: the package is not usable with OPUS at all, but has relevancy for uncertainty studies, or adds value to OPUS in any other way. Last Updated (Monday, 26 September 2011 00:19)