Metadata-Version: 2.1
Name: pce
Version: 0.1.2
Summary: Polynomial Chaos Expansion method
Home-page: https://github.com/giaccone/pce
Author: Luca Giaccone
Author-email: luca.giaccone@polito.it
License: UNKNOWN
Project-URL: example of use, https://github.com/giaccone/cogen_eval
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# 1. pce
This repository includes an implementation of the Polynomial Chaos Expansion method.

## 1.1 Brief description
More comprehensive tools on the same subject are available (e.g. Chaospy), this repository is born during a self-learning activity of the authors.

At the moment, one can use this module to study the uncertainty propagation of a model with uncertain inputs. The following aspects are implemented:

* each uncertain variable can be associated to a uniform or normal distribution
* evaluation of the coefficient with spectral projection method
* global sensitivity analysis with Sobol' indices

## 1.2 How can I use it? How can I cite this module?
If you use this module you can consider to cite the following paper [direct link](https://www.mdpi.com/2079-9292/9/9/1471).

*Giaccone, L.; Lazzeroni, P.; Repetto, M. Uncertainty Quantification in Energy Management Procedures. Electronics 2020, 9, 1471. https://doi.org/10.3390/electronics9091471*

In this paper the `pce` module has been used successfully to estimate uncertainties. You can also find all codes associated to the paper here [https://github.com/giaccone/cogen_eval](https://github.com/giaccone/cogen_eval).

## 1.3 Requirements

The project is developed using Python 3. The installer requires a Python version `>= 3.6`.

Other requirements (I tend to use always the latest version of the following libraries):

* numpy
* scipy
* matplotlib
* joblib

# 2. Installation

This project is deployed through the Python Package Index, therefore, it can be easily obtained by running the following command:

```bash
pip install pce
```

