feat: added to experiments library

process_bigraph/emitter.py

@@ -230,6 +230,15 @@ def get_data(self, query: Optional[List[Tuple[str]]] = None) -> Dict:
         return get_history_data_db(self.history, self.experiment_id, query)
 
     def query(self, query: Optional[List[Tuple[str]]] = None) -> Dict:
+        """API contract-wrapper for `self.get_data`. Get data based on the passed query.
+
+             Args:
+                 query: a list of tuples pointing to fields within the experiment data.
+                     In the format: [('path', 'to', 'field1'), ('path', 'to', 'field2')]
+
+            Returns:
+                 `Dict`
+        """
         return self.get_data(query)
 
     def update(self, state):

process_bigraph/experiments/smoldyn_database.py

@@ -0,0 +1,18 @@
+"""A test of the `..emitter.DatabaseEmitter` class with Smoldyn to emit and query the formatted results of
+    a Smoldyn simulation. The db workflow could be described as:
+
+        0. A local MongoDB server is spun up.  # TODO: Automatically check for this and kill/start as needed.
+        1. A Smoldyn simulation object is created by reading in a Smoldyn model.txt file.
+        2. Smoldyn "datasets" (output commands) are added using the Python API despite what `cmd` lines are
+            declared in the Smoldyn model file. (`addOutputData` && `addCommand`)
+        3. The Smoldyn simulation runs for the specified time params dependent on which simulation method is used.
+        4. The raw data is retrieved and formatted (`getOutputData` for time and listmols)
+        5. An instance of DatabaseEmitter is created.
+        6. The simulation data from #4's output is used as input for the database emitter and emitted to memory.
+        7. This process is repeated `N` times.
+        8. The user queries the vast database based on simulation run id.
+
+    Currently, this emitter works with MongoDB. TODO: include dbTypes
+"""
+
+