closes #160. progress bar optional, plus untested dag bugfix. (#161)

CHANGELOG.rst

@@ -1,3 +1,17 @@
+Mass_Composition 0.6.1 (2024-05-16)
+===================================
+
+Feature
+-------
+
+- DAG progress bar now optional.
+
+Bugfix
+------
+
+- Attempted fix for DAG.run ValueError('generator already running') (#160)
+
+
 Mass_Composition 0.6.0 (2024-05-16)
 ===================================
 

elphick/mass_composition/dag.py

@@ -89,7 +89,7 @@ def add_output(self, name: str, stream: str) -> 'DAG':
     def _topological_sort(self) -> List[str]:
         return list(nx.topological_sort(self.graph))
 
-    def run(self, input_streams: dict):
+    def run(self, input_streams: dict, progress_bar: bool = True):
         """
         Executes the Directed Acyclic Graph (DAG).
 
@@ -102,6 +102,7 @@ def run(self, input_streams: dict):
         Parameters:
         input_streams (dict): A dictionary mapping node names to Stream objects. These are the initial Stream objects
                     for the input nodes of the DAG.
+        progress_bar (bool): If True, a progress bar is displayed during the execution of the DAG.
 
         Returns:
         None
@@ -114,8 +115,9 @@ def run(self, input_streams: dict):
         for node in self.graph.nodes:
             self.node_executed[node] = False
 
-        # Initialise a progressbar that will count up to the number of nodes in the graph
-        pbar = tqdm(total=len(self.graph.nodes), desc="Executing nodes", unit="node")
+        if progress_bar:
+            # Initialise a progressbar that will count up to the number of nodes in the graph
+            pbar = tqdm(total=len(self.graph.nodes), desc="Executing nodes", unit="node")
 
         executed_nodes = set()  # Keep track of nodes that have been executed
 
@@ -153,11 +155,12 @@ def run(self, input_streams: dict):
                 for edge, strm in updated_edges.items():
                     logger.debug(f"Updating edge {edge} with stream {strm.name}")
                     self.graph.edges[edge]['mc'] = strm
-                # update the progress bar by one step
-                pbar.set_postfix_str(f"Processed node: {node}")
-                pbar.update()
-
-        pbar.close()  # Close the progress bar
+                if progress_bar:
+                    # update the progress bar by one step
+                    pbar.set_postfix_str(f"Processed node: {node}")
+                    pbar.update()
+        if progress_bar:
+            pbar.close()  # Close the progress bar
         logger.info(f"DAG execution complete for the nodes: {executed_nodes}")
 
     def execute_node(self, node: str, strms: dict):
@@ -234,7 +237,7 @@ def execute_node(self, node: str, strms: dict):
                 for successor in self.graph.successors(node):
                     updated_edges[(node, successor)] = result
 
-        return node, result, updated_edges
+        return node, list(result), updated_edges
 
     def plot(self):
         plt.figure(figsize=(8, 6))

examples/502_dag.py

@@ -39,9 +39,14 @@
              kwargs={'fraction': 0.3, 'name_1': 'lump', 'name_2': 'fines'})
 dag.add_step(name='split_2', operation=Stream.split, streams=['lump'],
              kwargs={'fraction': 0.3, 'name_1': 'lumpier', 'name_2': 'less_lumpy'})
-dag.add_output(name='lumpier', stream='lumpier')
-dag.add_output(name='mid', stream='less_lumpy')
-dag.add_output(name='fines', stream='fines')
+dag.add_step(name='split_3', operation=Stream.split, streams=['fines'],
+             kwargs={'fraction': 0.3, 'name_1': 'finer', 'name_2': 'less_fine'})
+dag.add_step(name='joiner_1', operation=Stream.add, streams=['less_lumpy', 'less_fine'],
+             kwargs={'name': 'mix_1'})
+dag.add_step(name='joiner_2', operation=Stream.add, streams=['lumpier', 'finer'],
+             kwargs={'name': 'mix_2'})
+dag.add_output(name='product_1', stream='mix_1')
+dag.add_output(name='product_2', stream='mix_2')
 
 
 # %%
@@ -53,7 +58,7 @@
 
 dag.run({'feed_1': mc_sample,
          'feed_2': deepcopy(mc_sample).rename('sample_2')  # names must be unique
-         })
+         }, progress_bar=False)
 
 # %%
 # Create a Flowsheet object from the dag, enabling all the usual network plotting and analysis methods.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mass-composition"
-version = "0.6.0"
+version = "0.6.1"
 description = "For managing multi-dimensional mass-composition datasets, supporting weighted mathematical operations and visualisation."
 authors = ["Greg <greg@elphick.com.au>"]
 packages = [{ include = "elphick/mass_composition" }]