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  • Scos
    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

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/65-scos (2016-01-11)
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  • R
    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

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/66-r (2016-01-11)
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  • OpenTurns
    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

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/67-openturns (2016-01-11)
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  • Project references
    only later on Hence nowadays several major partnerships at the international level ESREDA MUCM SAMSI or the French level IMdR have launched ambitious works to capitalise on uncertainty treatments into generic guides that would meet virtually any industrial need It aims at designing a common template to be followed as well as specific guidelines to methodology that might best fit the particular case typically according to the data and experts information availability model complexity and so on The genuine study cases considered in these projects all from different business areas illustrate a startling feature although the applications and physics involved are completely different the implicit mathematical structure seems very similar Moreover the methodology collections to shop in for each step are common and the choice is rather dependent on the criteria the underlying institutional regulation or study purpose than on the application Indeed the conceptual framework that fits conceptually at least virtually any industrial uncertainty study can be resumed by the following figure This scheme ought to be considered as a skeleton for a purely uncertainty part of an industrial study So far all the three essential steps as well as satellites B and C may be shown application free it is rather mutually dependent At a conceptual level it may be one size fits all solution meanwhile it is not assumed to be so when applied Instead the reference guide provides the methodologies to shop from and the guidelines to chose from that handleads and educates an industrial engineer over the uncertainties aspects avoiding him becoming a specialist in this complex field It may therefore be implemented as Software that would perform all the modelling propagation and criteria check and would only ask some relevant questions playing the role of Babel Fish a translator from the industrial world to the mathematical When dependency structure between the steps of this application free framework is closely investigated it appears that one step directs others in many aspects the purpose of the study often a criterion to comply with or a business decision making together with some features of the available model and the study context variables involved deterministic or random or a bit of both Therefore the very sensible approach to start with is to get it from the right end see the box above This conceptual conclusion comes as an essential point for the consolidation process of building the generic uncertainty management framework In brief the consolidation of uncertainty treatment practices aims at a kind of two side split for a generic industrial study The first one would gather all the bits specific to the particular application whereas the second one would collect the uncertainty related bits Ideally the uncertainty slice may prove to be a common to any application or can be managed to become so and might be embedded into a comprehensive Software package This would offer to an application specialist in charge of a whole study the uncertainty management toolkit applicable to his field know how In

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/projectreferences?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)
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  • Project references
    appear only later on Hence nowadays several major partnerships at the international level ESREDA MUCM SAMSI or the French level IMdR have launched ambitious works to capitalise on uncertainty treatments into generic guides that would meet virtually any industrial need It aims at designing a common template to be followed as well as specific guidelines to methodology that might best fit the particular case typically according to the data and experts information availability model complexity and so on The genuine study cases considered in these projects all from different business areas illustrate a startling feature although the applications and physics involved are completely different the implicit mathematical structure seems very similar Moreover the methodology collections to shop in for each step are common and the choice is rather dependent on the criteria the underlying institutional regulation or study purpose than on the application Indeed the conceptual framework that fits conceptually at least virtually any industrial uncertainty study can be resumed by the following figure This scheme ought to be considered as a skeleton for a purely uncertainty part of an industrial study So far all the three essential steps as well as satellites B and C may be shown application free it is rather mutually dependent At a conceptual level it may be one size fits all solution meanwhile it is not assumed to be so when applied Instead the reference guide provides the methodologies to shop from and the guidelines to chose from that handleads and educates an industrial engineer over the uncertainties aspects avoiding him becoming a specialist in this complex field It may therefore be implemented as Software that would perform all the modelling propagation and criteria check and would only ask some relevant questions playing the role of Babel Fish a translator from the industrial world to the mathematical When dependency structure between the steps of this application free framework is closely investigated it appears that one step directs others in many aspects the purpose of the study often a criterion to comply with or a business decision making together with some features of the available model and the study context variables involved deterministic or random or a bit of both Therefore the very sensible approach to start with is to get it from the right end see the box above This conceptual conclusion comes as an essential point for the consolidation process of building the generic uncertainty management framework In brief the consolidation of uncertainty treatment practices aims at a kind of two side split for a generic industrial study The first one would gather all the bits specific to the particular application whereas the second one would collect the uncertainty related bits Ideally the uncertainty slice may prove to be a common to any application or can be managed to become so and might be embedded into a comprehensive Software package This would offer to an application specialist in charge of a whole study the uncertainty management toolkit applicable to his field know how

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/projectreferences?el_mcal_month=2&el_mcal_year=2016 (2016-01-11)
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  • Results
    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

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/results?tmpl=component&print=1&page= (2016-01-11)
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  • Results
    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 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

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/results?el_mcal_month=12&el_mcal_year=2015 (2016-01-11)
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  • Results
    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 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

    Original URL path: http://www.opus-project.fr/index.php/anropusproject/results?el_mcal_month=2&el_mcal_year=2016 (2016-01-11)
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