Metadata-Version: 2.0
Name: fds
Version: 0.0.3.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: numpy (>=1.10)
Requires-Dist: pytest
Requires-Dist: pytest-cov
Requires-Dist: scipy (>=0.14)

.. figure:: https://travis-ci.org/qiqi/fds.svg?branch=master
   :alt: Travis CI

.. toctree::
   :maxdepth: 5

   tutorials/src/vanderpol_python/vanderpol


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 <tutorials/src/vanderpol_python/vanderpol>`__
-  `Lorenz attractor in C <docs/tutorials/lorenz_c.md>`__
-  `Lorenz 96 in MPI and C <docs/tutorials/lorenz96_mpi.md>`__

Guides
~~~~~~

-  `Chaos and statistical convergence <docs/guides/statistics.md>`__
-  `Lyapunov exponents and time
   segmentation <docs/guides/lyapunov.md>`__
-  `Save and restart <docs/guides/save_restart.md>`__

Reference
~~~~~~~~~

-  `The least squares shadowing algorithm <docs/ref/lss_algorithm.md>`__
-  `Function reference <docs/ref/function_ref.md>`__
-  `License <LICENSE.md>`__


