Daniele Bigoni – Uncertainty Quantification LibraryImportant : All projects are available on LauchPad. Questions and bugs need to be reported there. This library is a collection of tools for Uncertainty Quantification. The main features are:
The library is written in Python and is composed by 4 building blocks. Depending on the personal need one can install only some them, even if some dependences exist. The installation of the library is straightforward, but I reccomend to always work in a virtual environment – e.g. virtualenv –, with Numpy and Scipy installed (optionally Matplotlib and mpi4py) Reference - link
Bigoni, D & Engsig-Karup, AP 2015, Uncertainty Quantification with Applications to Engineering Problems. Ph.D. thesis, Technical University of Denmark, Kgs. Lyngby. DTU Compute PHD-2014, no. 359 bibtex/biber
@phdthesis{Bigoni2014PhD, PyORTHPOLPython (and C) porting of the Fortran package “orthpol”, written by W. Gautschi [1]. Installation: $ pip install orthpol Source code and tracker : LaunchPad [1] Gautschi, W. (1994). Algorithm 726: ORTHPOL–a package of routines for generating orthogonal polynomials and Gauss-type quadrature rules. ACM Transactions on Mathematical Software (TOMS), 20(1), 21–62. doi:10.1145/174603.174605 SpectralToolboxFeatures:
Requirements: Non-standard polynomials are constructed using the package PyORTHPOL. Installation: $ pip install SpectralToolbox Source code and tracker : LaunchPad TensorToolboxFeatures:
Requirements: the SpectralToolbox is needed for the construction of the surrogates. Installation: $ pip install TensorToolbox Source code and tracker : LaunchPad UQToolboxFeatures:
Requirements: some parts of the software require the SpectralToolbox. Installation: $ pip install UQToolbox Source code and tracker : LaunchPad mpi_mapThis package uses mpi4py and marshal to spawn processes and execute them. Installation: $ pip install mpi_map Source code and tracker : BitBucket phantom_schedulerPhantom scheduler used to administer the job submission to clusters. This program runs a scheduler and a number of children that keep submitting jobs to a cluster queue. It is particularly useful when one has many short jobs which cannot be submitted all at once. This permits not to lose the priority in the queue. “With great power comes great responsibility” – Uncle Ben Installation: $ pip install phantom_scheduler Source code and tracker : LaunchPad |