fds 0.1.0.dev1

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# 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)