:py:mod:`mlair.workflows`
=========================

.. py:module:: mlair.workflows


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   abstract_workflow/index.rst
   custom_workflows/index.rst
   default_workflow/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   mlair.workflows.Workflow
   mlair.workflows.DefaultWorkflow
   mlair.workflows.DefaultWorkflowHPC




.. py:class:: Workflow(name=None, log_level_stream=None)

   Abstract workflow class to handle sequence of stages (run modules). An inheriting class has to first initialise
   this mother class and can afterwards add an arbitrary number of stages by using the add method. The execution order
   is equal to the ordering of the stages have been added. To run the workflow, finally, a single call of the run
   method is sufficient. It must be taken care for inter-stage dependencies, this workflow class only handles the
   execution but not the dependencies (workflow would probably fail in this case).

   .. py:method:: add(self, stage, **kwargs)

      Add a new stage with optional kwargs.


   .. py:method:: run(self)

      Run workflow embedded in a run environment and according to the stage's ordering.



.. py:class:: DefaultWorkflow(stations=None, train_model=None, create_new_model=None, window_history_size=None, experiment_date='testrun', variables=None, statistics_per_var=None, start=None, end=None, target_var=None, target_dim=None, window_lead_time=None, dimensions=None, interpolation_method=None, time_dim=None, limit_nan_fill=None, train_start=None, train_end=None, val_start=None, val_end=None, test_start=None, test_end=None, use_all_stations_on_all_data_sets=None, fraction_of_train=None, experiment_path=None, plot_path=None, forecast_path=None, bootstrap_path=None, overwrite_local_data=None, sampling=None, permute_data_on_training=None, extreme_values=None, extremes_on_right_tail_only=None, transformation=None, train_min_length=None, val_min_length=None, test_min_length=None, plot_list=None, model=None, batch_size=None, epochs=None, data_handler=None, log_level_stream=None, **kwargs)

   Bases: :py:obj:`mlair.workflows.abstract_workflow.Workflow`

   A default workflow executing ExperimentSetup, PreProcessing, ModelSetup, Training and PostProcessing in exact
   the mentioned ordering.

   .. py:method:: _setup(self, **kwargs)

      Set up default workflow.



.. py:class:: DefaultWorkflowHPC(stations=None, train_model=None, create_new_model=None, window_history_size=None, experiment_date='testrun', variables=None, statistics_per_var=None, start=None, end=None, target_var=None, target_dim=None, window_lead_time=None, dimensions=None, interpolation_method=None, time_dim=None, limit_nan_fill=None, train_start=None, train_end=None, val_start=None, val_end=None, test_start=None, test_end=None, use_all_stations_on_all_data_sets=None, fraction_of_train=None, experiment_path=None, plot_path=None, forecast_path=None, bootstrap_path=None, overwrite_local_data=None, sampling=None, permute_data_on_training=None, extreme_values=None, extremes_on_right_tail_only=None, transformation=None, train_min_length=None, val_min_length=None, test_min_length=None, plot_list=None, model=None, batch_size=None, epochs=None, data_handler=None, log_level_stream=None, **kwargs)

   Bases: :py:obj:`DefaultWorkflow`

   A default workflow for Jülich HPC systems executing ExperimentSetup, PreProcessing, PartitionCheck, ModelSetup,
   Training and PostProcessing in exact the mentioned ordering.

   .. py:method:: _setup(self, **kwargs)

      Set up default workflow.