created abstract and default workflow modules

src/__init__.py

@@ -5,7 +5,7 @@ __version_info__ = {
 }
 
 from src.run_modules import *
-from src.run import run
+from src.workflows.default_workflow import DefaultWorkflow
 
 
 def get_version():

src/configuration/defaults.py

@@ -2,12 +2,7 @@ __author__ = "Lukas Leufen"
 __date__ = '2020-06-25'
 
 
-DEFAULT_STATIONS = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBY052', 'DEBY032', 'DEBW022', 'DEBY004',
-                    'DEBY020', 'DEBW030', 'DEBW037', 'DEBW031', 'DEBW015', 'DEBW073', 'DEBY039', 'DEBW038', 'DEBW081',
-                    'DEBY075', 'DEBW040', 'DEBY053', 'DEBW059', 'DEBW027', 'DEBY072', 'DEBW042', 'DEBW039', 'DEBY001',
-                    'DEBY113', 'DEBY089', 'DEBW024', 'DEBW004', 'DEBY037', 'DEBW056', 'DEBW029', 'DEBY068', 'DEBW010',
-                    'DEBW026', 'DEBY002', 'DEBY079', 'DEBW084', 'DEBY049', 'DEBY031', 'DEBW019', 'DEBW001', 'DEBY063',
-                    'DEBY005', 'DEBW046', 'DEBW103', 'DEBW052', 'DEBW034', 'DEBY088', ]
+DEFAULT_STATIONS = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
 DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'maximum', 'u': 'average_values',
                         'v': 'average_values', 'no': 'dma8eu', 'no2': 'dma8eu', 'cloudcover': 'average_values',
                         'pblheight': 'maximum'}

src/workflows/abstract_workflow.py

+"""Abstract workflow."""
+
+__author__ = "Lukas Leufen"
+__date__ = '2020-06-26'
+
+from collections import OrderedDict
+
+from src import RunEnvironment
+
+
+class AbstractWorkflow:
+    """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)."""
+
+    def __init__(self):
+        self._registry = OrderedDict()
+
+    def add(self, stage, **kwargs):
+        """Add a new stage with optional kwargs."""
+        self._registry[stage] = kwargs
+
+    def run(self):
+        """Run workflow embedded in a run environment and according to the stage's ordering."""
+        with RunEnvironment():
+            for stage, kwargs in self._registry.items():
+                stage(**kwargs)
\ No newline at end of file

src/workflows/default_workflow.py

+"""Default workflow."""
+
+__author__ = "Lukas Leufen"
+__date__ = '2020-06-26'
+
+import inspect
+from src.helpers import remove_items
+from src.run_modules import ExperimentSetup, PreProcessing, PartitionCheck, ModelSetup, Training, PostProcessing
+from src.workflows.abstract_workflow import AbstractWorkflow
+
+
+class DefaultWorkflow(AbstractWorkflow):
+    """A default workflow executing ExperimentSetup, PreProcessing, PartitionCheck, ModelSetup, Training and
+    PostProcessing in exact the mentioned ordering."""
+
+    def __init__(self, stations=None,
+        station_type=None,
+        trainable=None, create_new_model=None,
+        window_history_size=None,
+        experiment_date="testrun",
+        network=None,
+        variables=None, statistics_per_var=None,
+        start=None, end=None,
+        target_var=None, target_dim=None,
+        window_lead_time=None,
+        dimensions=None,
+        interpolate_method=None, interpolate_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,
+        evaluate_bootstraps=None, number_of_bootstraps=None, create_new_bootstraps=None,
+        plot_list=None,
+        model=None,
+        batch_size=None,
+        epochs=None):
+        super().__init__()
+
+        # extract all given kwargs arguments
+        params = remove_items(inspect.getfullargspec(self.__init__).args, "self")
+        kwargs = {k: v for k, v in locals().items() if k in params and v is not None}
+        self._setup(**kwargs)
+
+    def _setup(self, **kwargs):
+        """Set up default workflow."""
+        self.add(ExperimentSetup, **kwargs)
+        self.add(PreProcessing)
+        self.add(PartitionCheck)
+        self.add(ModelSetup)
+        self.add(Training)
+        self.add(PostProcessing)