The Uranie platform, v4.7.0
Index
Starting with Uranie
In order to use Uranie, once the installation is done, one should start by sourcing the proper script, depending on the shell you're using:
Then, you can call any ROOT macro that would contain Uranie objects, by doing:
root myMacro.C
or, you can run it's equivalent in python by calling:
python myMacro.py
For more information, you can refer to the usermanual (see in the left-hand side menu).
Releases notes: news from v4.7.0
Built by default with ROOT v6.24/06 (recommended)
- Correction / modifications:
- Beta version to interface FMU file using the Launcher using FMI library (only tested on Linux)
- Add the possibility to compute Shapley indices (beta version)
- Add the possibility to produce LHS doe with one or more constraints (TConstrLHS)
- Add the possibility to run nested MPI in Relauncher (a parallelised code in a parallelised loop)
- Add two new probability laws: TGeneralizedNormal and TGeneralizedNormalV2 (beta version for the last)
- Add quiet mode in fileDataRead to prevent any warning and information message (when one knows what these mean)
- Ensure and correct some sensitivity classes in order not to be computation-order sensitive (when computations are parallelised)
- User manual :
- Full split between C++ and python version. All code in the text is now only either in C++ or in python. In both cases, code within is tested.
- Python macros have been corrected to be PEP8 complient.
- Add a chapter for the MetaModelOptim chapter: distributed efficient global optimisation (EGO)
- Rewrite Relauncher to discuss more precisely the distribution process
- add the description of the TGeneralizedNormal law
- Add use case macros: metamodoptEgoHimmel, reoptimizeZoneBiSubMpi, reoptimizeZoneBiFunMpi, samplingConstrLHSLinear, samplingConstrLHSEllipses
- Methodology:
- Description of the heuristic for the constrained LHS doe
- Windows specific
- First version tested with Python 3
Uranie team