A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method
- Highly efficient uncertainty analysis of N-dimensional systems
- Sensitivity analysis using Sobol indices and Global derivative based sensitivity indices
- Easy coupling to user defined models written in Python, Matlab, etc...
- The parallelization concept allows to run model evaluations in parallel
- Highly efficient adaptive algorithms allow for analysis of complex systems
- Includes highly efficient CPU and GPU (CUDA) implementations to significantly accelerate algorithmic and post-processing routines for high-dimensional and complex problems
- Includes state-of-the-art techniques such as:
- Projection: determination of optimal reduced basis
- l1-minimization: reduction of necessary model evaluations by making use of concepts from compressed sensing
- Gradient enhanced gPC: use of gradient information of the model function to increase accuracy
- Multi-element gPC: analyzing systems with discontinuities and sharp transitions
Areas of application:
pygpc can be used to analyze a variety of different of problems. It is used for example in the frameworks of:
- Nondestructive testing (Weise, K., Carlstedt, M., Ziolkowski, M., & Brauer, H. (2015). Uncertainty analysis in Lorentz force eddy current testing. IEEE Transactions on Magnetics, 52(3), 1-4.)
- Noninvasive brain stimulation (Saturnino, G. B., Thielscher, A., Madsen, K. H., Knösche, T. R., & Weise, K. (2019). A principled approach to conductivity uncertainty analysis in electric field calculations. NeuroImage, 188, 821-834.)
- Transcranial magnetic stimulation (Weise, K., Di Rienzo, L., Brauer, H., Haueisen, J., & Toepfer, H. (2015). Uncertainty analysis in transcranial magnetic stimulation using nonintrusive polynomial chaos expansion. IEEE Transactions on Magnetics, 51(7), 1-8.)
- Transcranial direct current stimulation (Kalloch, B., Weise, K., Bazin, P.-L., Lampea, L., Villringera, A., Hlawitschk, M., & Sehm, B. (2019). The influence of white matter lesions on the electrical fieldduring transcranial electric stimulation - Preliminary results of a computational sensitivity analysis, SfN Annual Meeting 2019, Chicago, Illinois, USA, October 19th-23rd 2019)
If you use pygpc in your studies, please contact Konstantin Weise to extend the list above.
Installation using pip:
pygpc can be installed via the
pip command with Python >= 3.6 and then simply run the following line from a terminal:
pip install pygpc
If you want to use the plot functionalities from pygpc, please also install matplotlib:
pip install matplotlib
Installation using the GitHub repository: Alternatively, it is possible to clone this repository and run the setup manually. This requires Cython to compile the C-extensions and Numpy for some headers. You can get Cython and Numpy by running the following command:
pip install cython numpy
Alternatively you can install the build dependencies with the following command:
pip install -r requirements.txt
Afterwards, pygpc can be installed by running the following line from the directory in which the repository was cloned:
python setup.py install
For a full API of pygpc, see https://pygpc.readthedocs.io/en/latest/. For examplary simulations and model configurations, please have a look at the jupyter notebooks provided in the /tutorial folder and the templates in the /example folder.
If you use this framework, please cite:
Saturnino, G. B., Thielscher, A., Madsen, K. H., Knösche, T. R., & Weise, K. (2019). A principled approach to conductivity uncertainty analysis in electric field calculations. NeuroImage, 188, 821-834.
If you have questions, problems or suggestions regarding pygpc, please contact Konstantin Weise.