Move quickstart instructions into pipeline description

docs/source/identification_threshold.rst

@@ -3,8 +3,11 @@ Threshold-based Features
 
 Identifiy features that fall above or below a or multiple threshold(s).
 
+.. note::
+    The threshold-based identification strategies require `numba <https://numba.pydata.org/>`_ to be installed.
 
-Identification techniques
+
+Identification Strategies
 -------------------------
 
 .. autoclass:: enstools.feature.identification.threshold.DoubleThresholdIdentification

docs/source/index.rst

@@ -16,11 +16,13 @@ Content
    :maxdepth: 1
 
    installation
-   quickstart
    pipeline
    identification
    tracking
 
+A quickstart example is provided in the description of the feature pipeline.
+Output formats are described on the overview pages of the identification and tracking sub-modules.
+
 
 Acknowledgment and license
 --------------------------

docs/source/pipeline.rst

 Feature Pipeline
 ================
 
-...
+TODO: Workflow description here.
 
 
+Usage Example
+-------------
+
+TODO
+
+.. code-block:: python
+
+    from enstools.feature import FeaturePipeline
+    from enstools.feature.identification.STRATEGY import SomeIdentification
+    from enstools.feature.tracking.STRATEGY import SomeTracking
+
+    # Configure suitable identification and tracking strategies
+    ident_tech = SomeIdentification(...)
+    track_tech = SomeTracking(...)
+
+    # Configure the feature pipeline: apply the strategies chosen above and
+    # provide an input dataset
+    pipeline = FeaturePipeline(processing_mode="2d")
+    pipeline.set_identification_strategy(ident_tech)
+    pipeline.set_tracking_strategy(track_tech) # omit or set to None if no tracking desired
+    pipeline.set_data_path("path/to/input/data.nc") # or directly provide a Dataset with set_data
+
+    # Run the identification and tracking on the data
+    pipeline.execute()
+
+    # TODO: output handling
+
+
+Quickstart
+----------
+
+Here is a usage example, if you want to apply existing techniques in the code base to your data set.
+First, we need some imports, namely the
+
+- :py:class:`enstools.feature.pipeline.FeaturePipeline`, which executes the identification pipeline
+- :py:class:`enstools.feature.identification.template.IdentificationTemplate`, this is the identification technique, edit this accordingly
+- :py:class:`enstools.feature.tracking.template.TrackingTemplate`, this is the tracking technique, edit this accordingly
+- :py:class:`enstools.feature._proto_gen.template_pb2`, this is the on run auto-generated protobuf python file from the set description. Use the one that matches your detection strategy. They are named \*_pb2, where \* is the name of the identification module.
+
+.. warning::
+
+    [TODO] should not really need to set the template here, is specific to identification strategy!
+
+.. code-block:: python
+
+    from enstools.feature.pipeline import FeaturePipeline
+    from enstools.feature.identification.template import IdentificationTemplate
+    from enstools.feature.tracking.template import TrackingTemplate
+    from enstools.feature.identification._proto_gen import template_pb2
+
+Then, we initialize the pipeline with the protobuf description and optional the processing mode. For 3D data, this resembles if identification should be performed individual on 2D (latlon) or 3D subsets.
+
+.. code-block:: python
+
+    pipeline = FeaturePipeline(template_pb2, processing_mode='2d')
+
+Then, we initialize and set our strategies. The tracking can be set to `None` to be ignored.
+
+.. code-block:: python
+
+    i_strat = IdentificationTemplate(some_parameter='foo')
+    t_strat = TrackingCompareTemplate()
+    pipeline.set_identification_strategy(i_strat)
+    pipeline.set_tracking_strategy(t_strat) # or None as argument if no tracking
+
+Next, set the data to process.
+
+.. code-block:: python
+
+    pipeline.set_data_path(path)
+
+Then, the pipeline can be executed, starting the identification and subsequently the tracking.
+
+.. code-block:: python
+
+    pipeline.execute()
+    # or separated...
+    # pipeline.execute_identification()
+    # pipeline.execute_tracking()
+
+This generates an object description based on the set protobuf format. If tracking has been used, tracks based on a default simple heuristic can be generated. See docstrings for further details. The object description holds the objects, and if tracking has been executed a graph structure and the generated tracks respectively.
+
+.. code-block:: python
+
+    pipeline.generate_tracks()
+    od = pipeline.get_object_desc()
+
+The output data set and description can be saved:
+
+.. code-block:: python
+
+    pipeline.save_result(description_type='json', description_path=..., dataset_path=...)
+
+
+Class Documentation
+-------------------
+
 .. autoclass:: enstools.feature.pipeline.FeaturePipeline
 

docs/source/quickstart.rst

-Quickstart
-==========
-
-Here is a usage example, if you want to apply existing techniques in the code base to your data set.
-First, we need some imports, namely the
-
-- :py:class:`enstools.feature.pipeline.FeaturePipeline`, which executes the identification pipeline
-- :py:class:`enstools.feature.identification.template.IdentificationTemplate`, this is the identification technique, edit this accordingly
-- :py:class:`enstools.feature.tracking.template.TrackingTemplate`, this is the tracking technique, edit this accordingly
-- :py:class:`enstools.feature._proto_gen.template_pb2`, this is the on run auto-generated protobuf python file from the set description. Use the one that matches your detection strategy. They are named \*_pb2, where \* is the name of the identification module.
-
-.. warning::
-
-    [TODO] should not really need to set the template here, is specific to identification strategy!
-
-.. code-block:: python
-
-    from enstools.feature.pipeline import FeaturePipeline
-    from enstools.feature.identification.template import IdentificationTemplate
-    from enstools.feature.tracking.template import TrackingTemplate
-    from enstools.feature.identification._proto_gen import template_pb2
-
-Then, we initialize the pipeline with the protobuf description and optional the processing mode. For 3D data, this resembles if identification should be performed individual on 2D (latlon) or 3D subsets.
-
-.. code-block:: python
-
-    pipeline = FeaturePipeline(template_pb2, processing_mode='2d')
-
-Then, we initialize and set our strategies. The tracking can be set to `None` to be ignored.
-
-.. code-block:: python
-
-    i_strat = IdentificationTemplate(some_parameter='foo')
-    t_strat = TrackingCompareTemplate()
-    pipeline.set_identification_strategy(i_strat)
-    pipeline.set_tracking_strategy(t_strat) # or None as argument if no tracking
-
-Next, set the data to process.
-
-.. code-block:: python
-
-    pipeline.set_data_path(path)
-
-Then, the pipeline can be executed, starting the identification and subsequently the tracking.
-
-.. code-block:: python
-
-    pipeline.execute()
-    # or separated...
-    # pipeline.execute_identification()
-    # pipeline.execute_tracking()
-
-This generates an object description based on the set protobuf format. If tracking has been used, tracks based on a default simple heuristic can be generated. See docstrings for further details. The object description holds the objects, and if tracking has been executed a graph structure and the generated tracks respectively.
-
-.. code-block:: python
-
-    pipeline.generate_tracks()
-    od = pipeline.get_object_desc()
-
-The output data set and description can be saved:
-
-.. code-block:: python
-
-    pipeline.save_result(description_type='json', description_path=..., dataset_path=...)
-