"trend_extrapolation" package
-----------------------------

This is the `antiCPy.trend_extrapolation` package. It contains

#. the class `CPSegmentFit` (basic serial implementation),
#. the class `BatchedCPSegmentFit` (strongly parallelized version).

`CPSegmentFit` incorporates all attributes needed to implement the Bayesian non-parametric linear segment fit
which takes into account possible change points (CPs). The basic procedure is described in [vdL14]_ [K14]_
and the nomenclature is chosen congruent to that. Each of the calculation steps is realized
by a class method of `CP_segment_fit`. You can follow the instructions of the cited
papers to interpret the coding. For example, the segment fit can be applied to drift slope
estimate :math:`\hat{\zeta}(t) \equiv y(x)` time series computed with the `antiCPy.early_warnings` module.

The simple serial implementation `CPSegmentFit` can be rather time consuming. A first improvement is to used its
multiprocessing option which computes each CP configuration in parallel with a predefined number of workers.
Additionally, large amounts of CP configurations will without a doubt result in memory errors.
The `BatchedCPSegmentFit` class solves these issues by parallel computation of batches of CP configurations while
each worker only constructs a suitable subset of configurations. This leads to a major computation time improvement and
avoids memory issues for a complicated CP segment fit with an arbitrary number of CPs.

.. automodule:: antiCPy.trend_extrapolation.batched_cp_segment_fit
    :members:
    :show-inheritance:

.. automodule:: antiCPy.trend_extrapolation.cp_segment_fit
    :members:


"batched_configs_helper" subpackage
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The helper package enables the construction of CP configuration batches to avoid memory errors. It implements
methods which are required for the memory efficient version of the parallelized CP analysis.

.. automodule:: antiCPy.trend_extrapolation.batched_configs_helper.create_configs_helper
    :members:


Bibliography
^^^^^^^^^^^^


.. [vdL14]	Linden, W., Dose, V., & Toussaint, U. (2014). Bayesian Probability Theory: 
			Applications in the Physical Sciences. Cambridge: Cambridge University Press. 
			doi:10.1017/CBO9781139565608

.. [K14]	A. Klöckner, F. van der Linden, and D. Zimmer, in Proceedings of the 10th International 
			Modelica Conference, March 10-12, 2014, Lund, Sweden (Linköping University Electronic Press, 2014)