:py:mod:`pemtk.fit._parallel`
=============================

.. py:module:: pemtk.fit._parallel

.. autoapi-nested-parse::

   PEMtk wrappers for parallel processing for fitting

   Currently wraps xyzpy's `run_combos` for parallel functionality and data handling.
   See `the xyzpy docs <https://xyzpy.readthedocs.io/en/latest/gen_parallel.html>`_ for details.

   Other methods to be implemented.

   13/09/21  v1  Basic wrapper with XYZPY implemented



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   pemtk.fit._parallel.multiFit
   pemtk.fit._parallel.fitPara



.. py:function:: multiFit(self, nRange=[0, 10], parallel=True, num_workers=None, randomizeParams=True, seedParams=None)

   Basic wrapper for pemtk.fitClass.fit() for multiprocess execution.

   Run a batch of fits in parallel, and return results to main class structure.

   Currently wraps xyzpy's `run_combos` for parallel functionality and data handling.
   See `the xyzpy docs <https://xyzpy.readthedocs.io/en/latest/gen_parallel.html>`_ for details.

   Note: full set of results currently returned as an Xarray DataSet, then sorted back to base class as self.data[n] (integer n).
   In future may just want to use Xarray return directly?

   :param nRange: Fit indexers.
                  Set [nStart, nStop], full run will be set as list(range(nRange[0],nRange[1])).
                  TODO: more flexibility here, and auto.
   :type nRange: list
   :param parallel: Run fit jobs in parallel?
                    Note - in testing this seemed to be ignored?
   :type parallel: bool, default = True
   :param num_workers: Number of cores to use if parallel job.
                       Currently set to default to ~90% of mp.cpu_count()
   :type num_workers: int, default = None
   :param randomizeParams: Randomize seed parameters per fit?
   :type randomizeParams: bool, default = True
   :param seedParams: NOT IMPLEMENTED, but will provide an option to seed fits from a previous result.
   :type seedParams: int, default = None


.. py:function:: fitPara(data=None, n=None, randomizeParams=True, seedParams=None)

   Wrap self.fit() for XYZPY runner.