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Results

Project results

 

The final report has been submitted to the ANR.

Publications made within the project

 

A sequential Bayesian algorithm to estimate a probability of failure.

Emmanuel Vazquez, Julien Bect.  15th IFAC Symposium on System Identification, SYSID 2009 (2009).

OpenTURNS, an Open Source initiative to Treat Uncertainties, Risks'N Statistics in a structured industrial approach.

Dutfoy A.,Dutka-Malen I., Pasanisi A., Lebrun R., Mangeant F., Sen Gupta J., Pendola M., Yalamas T., 41èmes Journées de Statistique, Bordeaux, mai 2009.

Software

Using R features from Opus


This document aims at presenting the Opus-R link module. It will allow calling R features directly from Opus.

The module was developed in Python, so as to allow use of the Opus TUI. It acts as a wrapper allowing interaction with an R-Python interface called Rpy2.

The module converts variables from Opus types to Python types and vice-versa. The variables are then used by Rpy2 to interact with R. This Opus-R link is called rpyWrap.

In essence, this allows the Opus user to call R functions with Opus-type arguments, and receive outputs in Opus-types too.

The conversion of variables is made by the conv() function. This function will call the overload method of the variable type sent in argument. The conv() function works both ways.

As not all variable types have equivalents in the other language, an exhaustive list of the possible conversions has been created (as of February, 2011).

There are more complex types to translate, such as vector, matrix, formula and the board of data.

Note that a Vector’s type depends on its contents (which are homogenous). Consequently, there are several vector types: vectors of floats, integers, characters, booleans as well as vectors of vectors.

Type equivalence:

tableau_correspondance_final

 

For more details read this document.

Type conversion does not modify the data, so R functions can be called directly with Opus-type arguments and return Opus-type variables without loss of information.

From a practical point of view, using the wrapper is simple, as a single function makes the conversion both ways. However, in order to call R functions, the user has to know the correct Rpy syntax to call functions.

Actually we try to convert R-function in Opus-function and vice-versa.

 

 

 

Last Updated (Wednesday, 05 October 2011 19:46)

 

OpenTurns

http://trac.openturns.org/chrome/site/newlogoOT.png

Openturns

Introduction

OpenTURNS is an Open source initiative to Treat Uncertainties, Risks’N Statistics in a structured industrial approach.

Since the beginning of 2005, a partnership of three companies has been working on building together a tool designed to perform uncertainty treatment and reliability analyses.

Key characteristics

OpenTURNS is a Unix/Linux software with three main components :

  • a scientific C++ library including an internal data model and algorithms dedicated to the treatment of uncertainties. The main function of that library is giving to specific applications all the functionalities needed to treat uncertainties in studies. Targeted users are all engineers who want to introduce the probabilistic dimension in their so far deterministic studies.
  • an independent application with a graphical user interface. This application was built to become the work environment for the specialist of the treatment of uncertainties. Targeted users are once again industrial practitioners: those who identify the treatment of uncertainties as a full task, which can be spread to multiple engineering domains.
  • a python module with high level operators in the probabilistic and statistical field. The interest of this language is to be both a powerful scientific language (capable of using C++ libraries), and a user friendly interpreted language like Matlab’s one. This python module was designed to make the development of prototypes easier (in order to test new algorithms and methods for example), to become an easy-to-use support for educational works, … This module intends to become a natural environment capable of integrating new developments within the field of uncertainty and sensitivity analysis. The targeted users are here research centres and the academic community.

Website

Last Updated (Monday, 11 January 2010 22:39)

 

R

http://www.r-project.org/Rlogo.jpg

 

R

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation's GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS. The R environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

  • an effective data handling and storage facility,
  • a suite of operators for calculations on arrays, in particular matrices,
  • a large, coherent, integrated collection of intermediate tools for data analysis,
  • graphical facilities for data analysis and display either on-screen or on hardcopy, and
  • a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term "environment" is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.

Website

Last Updated (Monday, 11 January 2010 22:39)

 

Scos

Logo scos


Scos

The following is the quotation from an RNTL SCOS project statement launched in 2006 :

The SCOS consortium shares the same values and vision concerning scientific and high performance computing. Too many investments are duplicated inside each organisation, where many of them could be shared between multiple organisations.

Defining and promoting some common standards and normalize platforms and applications appears as the best way to face the international competitiveness need, by saving a huge amount of time and money in the same time: Validate once, use as much as possible !

SCOS project has gathered 22 leading Industrial and Academic players in the Complex System Design and Scientific Computing. By the end of 2007 it is posed to deliver the detailed interoperability specifications thus establishing a deeply entrenched standard.

Website


Last Updated (Monday, 11 January 2010 22:38)

 

Scilab

Logo Scilab

Scilab

The following is the quotation from www.scilab.org, official SCILAB Web resource.

Scilab is a scientific software package for numerical computations providing a powerful open computing environment for engineering and scientific applications.

Scilab is an open source software. Since 1994 it has been distributed freely along with the source code via the Internet. It is currently used in educational and industrial environments around the world. Scilab is now the responsibility of the Scilab Consortium, launched in May 2003. There are currently 25 members in Scilab Consortium.

Scilab includes hundreds of mathematical functions with the possibility to add interactively programs from various languages (FORTRAN, C, C++, JAVA…). It has sophisticated data structures (including lists, polynomials, rational functions, linear systems…), an interpreter and a high level programming language. Scilab has been conceived to be an open system where the user can define new data types and operations on these data types.

Nowadays SCILAB is used by +250.000 users worldwide as a high performing and user friendly Scientific Computing tool.

Website




Last Updated (Monday, 11 January 2010 22:38)

 
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