Skip to content
josephmure edited this page Jul 25, 2024 · 38 revisions

Modules

  • otagrum: create a distribution from a Bayesian Network using aGrUM
  • otfftw: Fast Fourier Transform algorithm (e.g. for stochastic processes) using FFTW
  • otfmi: FMI models manipulation using PyFMI
  • otmixmod: build mixtures of a multivariate Normal distribution from a sample
  • otmorris: Morris screening method module
  • otpmml: manages PMML files for meta-modeling exchanges
  • otpod: A module to build Probability of Detection for Non Destructive Testing
  • otrobopt: robust optimization
  • otsvm: Support Vector regression and classification with libsvm
  • otwrapy: Python wrapper tools
  • otbenchmark: benchmark problems for reliability and sensitivity analysis

Installation

conda install otrobopt
  • From pip (except osx, otagrum):
pip install otmixmod
  • From otconda
  • On Linux, you can install modules either from our Debian or RPM repositories, see instructions
  • On Linux, from sources:
  1. You will need to install the development packages first, check the required dependencies
  2. Launch installation, for example the robopt module:
git clone https://github.com/openturns/otrobopt.git
cd otrobopt
cmake -DCMAKE_INSTALL_PREFIX=~/.local .
make install

Other modules

  • othdrplot: high density region algorithm for functional outlier detection
  • otsurrogate: surrogate metamodels
  • otsklearn: metamodels with the scikit-learn estimator API (fit/predict)
  • ottensap: reuse tensor metamodels from tensap as ot.Function
  • otusecases: use cases suitable for OpenTURNS (functions and datasets)
  • otmarkov: simulates Markov chains (experimental)
  • otsensitivity: sensitivity analysis with density based measures
  • otshapley: compute Shapley effects (fork of shapley-effects which is compatible with python 3.9+)
  • batman: Statistical analysis for expensive computer codes made easy.
  • otak: Active learning Kriging methods for reliability analysis.
  • otsmt: Surrogate models from SMT into OpenTURNS PythonFunctions.
  • otkerneldesign: Kernel Herding-based designs of experiments.
  • otchaoskriging: Chaos/Kriging metamodels (experimental)
  • otsliced: SIR method (experimental)

Installation

Some of these modules are available on pypi:

pip install otbenchmark
pip install othdrplot

A tutorial to create a Python package with OpenTURNS Python modules is provided at:

otquickmodule : Easy Python Packaging (7 steps to publish on PyPI).

A tutorial to create a Python module is provided at:

https://github.com/sofianehaddad/ottemplatepython

Clone this wiki locally