.. _examples:

Code examples
#############

.. note:: More examples will be provided on request.

Plot time series and power spectral density
*******************************************

Import the time series database, load data from file and plot all time series.

.. literalinclude:: examples/plot.py
   :language: python
   :linenos:
   :lines: 1-16
   :emphasize-lines: 16

You can also select a subset of the timeseries (suports wildcards) as shown below.

.. literalinclude:: examples/plot.py
   :language: python
   :linenos:
   :lineno-start: 15
   :lines: 18-19
   :emphasize-lines: 2

You can also plot the power spectral density of the selected time series. Because the 'surge' time series was sampled at
a varying time step the the time series are resampled to a constant time step of 0.1 seconds before the FFT.

.. literalinclude:: examples/plot.py
   :language: python
   :linenos:
   :lineno-start: 15
   :lines: 21-22
   :emphasize-lines: 2

Count cycles using the Rainflow algorithm
*****************************************

.. note::
    Some of the syntax shown below is not applicable for ``version <= 4.6.1``, in particular the unpacking of cycles.
    The changes implemented since 4.6.1 are backwards compatible, however; the syntax suggested below is recommended due
    to better performance. One may also experience minor changes in rebinned cycles. Finally, the mesh established by
    :func:`qats.fatigue.rainflow.mesh()` (and used by :meth:`TimeSeries.plot_cycle_rangemean3d`) was not correct for
    version <= 4.6.1. For more details, please refer to the
    `CHANGELOG <https://github.com/dnvgl/qats/blob/master/CHANGELOG.md>`_.

Fetch a single time series from the time series database, extract and visualize the cycle distribution using the
Rainflow algorithm.

.. literalinclude:: examples/rainflow.py
   :language: python
   :linenos:
   :lines: 1-29

.. figure:: img/ts_cycle_range.png
    :figclass: align-center
    :target: _images/ts_cycle_range.png

    Single cycle range distribution.

.. figure:: img/ts_cycle_range_mean.png
    :figclass: align-center
    :target: _images/ts_cycle_mean_range.png

    Single cycle range-mean distribution.

.. figure:: img/ts_cycle_range_mean_3d.png
    :figclass: align-center
    :target: _images/ts_cycle_range_mean_3d.png

    Single 3D cycle range-mean distribution

Compare the cycle range and range-mean distribution from several time series using the methods on the TsDB class.

.. literalinclude:: examples/rainflow.py
   :language: python
   :linenos:
   :lineno-start: 30
   :lines: 30-34

.. figure:: img/tsdb_cycle_range.png
    :figclass: align-center
    :target: _images/tsdb_cycle_range.png

    Comparison of several cycle range distributions.

.. figure:: img/tsdb_cycle_range_mean.png
    :figclass: align-center
    :target: _images/tsdb_cycle_range_mean.png

    Comparison of several cycle range-mean distributions.


Calculate fatigue damage in mooring lines
*****************************************

Using a traditional S-N curve with constant slope and intercept.

.. literalinclude:: examples/mooring_fatigue.py
   :language: python
   :linenos:
   :lines: 1-


Apply low-pass and high-pass filters to time series
***************************************************

Initiate time series database and load time series file in one go. Plot the low-passed and high-passed time series and
the sum of those.

.. literalinclude:: examples/timeseries_filter.py
   :language: python
   :linenos:
   :lines: 1-


Tail fitting with Weibull
*************************

Fit a parametric Weibull distribution to the largest 13% of the peak sample. Often called tail fitting.

.. literalinclude:: examples/tail_fitting.py
   :language: python
   :linenos:
   :lines: 1-

.. figure:: img/weibull_tail_fit.png
    :figclass: align-center
    :target: _images/weibull_tail_fit.png

    Comparison of empirical sample distribution and fitted parametric distribution on linearized scales.


Merge files and export to different format
******************************************

.. todo:: Coming soon