Metadata-Version: 2.0
Name: fds
Version: 0.1.0.dev1
Summary: Sensitivity analysis of chaotic simulations
Home-page: https://github.com/qiqi/fds
Author: Qiqi Wang
Author-email: qiqi.wang@gmail.com
License: GPL3
Keywords: sensitivity analysis chaotic simulation
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: dill
Requires-Dist: mpi4py (>=1.3.1)
Requires-Dist: numpy (>=1.10)
Requires-Dist: pytest
Requires-Dist: pytest-cov
Requires-Dist: python-coveralls
Requires-Dist: scipy (>=0.14)

[![Build Status](https://travis-ci.org/qiqi/fds.svg?branch=master)](https://travis-ci.org/qiqi/fds?branch=master)
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[![Dependency Status](https://dependencyci.com/github/qiqi/fds/badge)](https://dependencyci.com/github/qiqi/fds)
[![Documentation Status](https://readthedocs.org/projects/fds/badge/?version=latest)](http://fds.readthedocs.io/en/latest/?badge=latest)
[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)

# What's it for

fds is a research tool for computational simulations that exhibis
chaotic dynamics. It computes sensitivity derivatives of time averaged
quantities, a.k.a. statistics, with respect simulation parameters.

For an introduction of chaotic dynamics, I highly recommend [Strogatz's
excellent book](https://www.amazon.com/gp/product/0813349109). For a
statistical view of chaotic dynamical systems, please refer to
[Berlinger's article](http://www.uvm.edu/~pdodds/files/papers/others/1992/berliner1992a.pdf).
Algorithm used in this software is described in [the upcoming AIAA
paper](https://dl.dropbox.com/s/2e9jxjmwh375i01/fds.pdf)

# Download and use

The best way to download fds is using pip. Pip is likely included in
your Python installation. If not, see [instruction
here](https://pip.pypa.io/en/stable/installing/). To install fds
using pip, simply type

```
sudo pip install fds
```

# Tutorials

-  [First example -- Van der Pol oscillator in Python](http://fds.readthedocs.io/en/latest/tutorials/vanderpol.html)
-  [Lorenz attractor in C](http://fds.readthedocs.io/en/latest/tutorials/lorenz_c.html)
-  [Lorenz 96 in MPI and C](http://fds.readthedocs.io/en/latest/tutorials/lorenz96_mpi.html)

# Guides

-  [Chaos and statistical convergence](http://fds.readthedocs.io/en/latest/guides/statistics.html)
-  [Lyapunov exponents and time
   segmentation](http://fds.readthedocs.io/en/latest/guides/lyapunov.html)
-  [Save and restart](http://fds.readthedocs.io/en/latest/guides/save_restart.html)

# Reference

-  [The least squares shadowing algorithm](http://fds.readthedocs.io/en/latest/ref/lss_algorithm.html)
-  [Function reference](http://fds.readthedocs.io/en/latest/ref/function_ref.html)
-  [License](https://www.gnu.org/licenses/gpl-3.0.en.html)


