wafo 0.3.1

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Wave Analysis for Fatigue and Oceanography
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Description
===========

WAFO is a toolbox Python routines for statistical analysis and simulation of
random waves and random loads. WAFO is freely redistributable software, see WAFO
icence, cf. the GNU General Public License (GPL) and contain tools for:

Fatigue Analysis
----------------

- Fatigue life prediction for random loads
- Theoretical density of rainflow cycles

Sea modelling
-------------

- Simulation of linear and non-linear Gaussian waves
- Estimation of seamodels (spectrums)
- Joint wave height, wave steepness, wave period distributions

Statistics
------------

- Extreme value analysis
- Kernel density estimation
- Hidden markov models

Classes
-------

* TimeSeries:
Data analysis of time series. Example: extraction of turning points,
estimation of spectrum and covariance function. Estimation transformation
used in transformed Gaussian model.

* CovData:
Computation of spectral functions, linear and non-linear time series
simulation.

* SpecData:
Computation of spectral moments and covariance functions, linear and
non-linear time series simulation. Ex: common spectra implemented,
directional spectra, bandwidth measures, exact distributions for wave
characteristics.

* CyclePairs:
Cycle counting, discretization, and crossings, calculation of damage.
Simulation of discrete Markov chains, switching Markov chains,
harmonic oscillator. Ex: Rainflow cycles and matrix, discretization of
loads. Damage of a rainflow count or matrix, damage matrix, S-N plot.


Subpackages
-----------

* TRANSFORM
Modelling with linear or transformed Gaussian waves.
* STATS
Statistical tools and extreme-value distributions. Ex: generation of random
numbers, estimation of parameters, evaluation of pdf and cdf
* KDETOOLS
Kernel-density estimation.
* MISC
Miscellaneous routines.
* DOCS
Documentation of toolbox, definitions. An overview is given in the routine
wafomenu.
* DATA
Measurements from marine applications.

WAFO homepage: <http://www.maths.lth.se/matstat/wafo/>
On the WAFO home page you will find:
- The WAFO Tutorial
- List of publications related to WAFO.

Installation
============

WAFO contains some Fortran and C extensions that require a properly configured
compiler and NumPy/f2py.

Create a binary wheel package and place it in the dist folder as follows::

python setup.py bdist_wheel -d dist

And install the wheel package with::

pip install dist/wafo-X.Y.Z+abcd123-os_platform.whl

Getting started
===============

A quick introduction to some of the many features of wafo can be found in the Tutorial IPython notebooks in the
`tutorial scripts folder`_:

* Chapter 1 - `Some applications of WAFO`_

* Chapter 2 - `Modelling random loads and stochastic waves`_

* Chapter 3 - `Demonstrates distributions of wave characteristics`_

* Chapter 4 - `Fatigue load analysis and rain-flow cycles`_

* Chapter 5 - `Extreme value analysis`_

-- _tutorial scripts folder: http://nbviewer.jupyter.org/github/wafo-project/pywafo/tree/master/wafo/doc/tutorial_scripts/

.. _Some applications of WAFO: http://nbviewer.jupyter.org/github/wafo-project/pywafo/blob/master/wafo/doc/tutorial_scripts/WAFO%20Chapter%201.ipynb

.. _Modelling random loads and stochastic waves: http://nbviewer.jupyter.org/github/wafo-project/pywafo/blob/master/wafo/doc/tutorial_scripts/WAFO%20Chapter%202.ipynb

.. _Demonstrates distributions of wave characteristics: http://nbviewer.jupyter.org/github/wafo-project/pywafo/blob/master/wafo/doc/tutorial_scripts/WAFO%20Chapter%203.ipynb

.. _Fatigue load analysis and rain-flow cycles: http://nbviewer.jupyter.org/github/wafo-project/pywafo/blob/master/wafo/doc/tutorial_scripts/WAFO%20Chapter%204.ipynb

.. _Extreme value analysis: http://nbviewer.jupyter.org/github/wafo-project/pywafo/blob/master/wafo/doc/tutorial_scripts/WAFO%20Chapter%205.ipynb


Unit tests
==========

To test if the toolbox is working paste the following in an interactive
python session::

import wafo as wf
wf.test(coverage=True, doctests=True)


Note
====

This project has been set up using PyScaffold 2.4.2. For details and usage
information on PyScaffold see http://pyscaffold.readthedocs.org/.

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