graphlib-backport

from graphlib_backport import TopologicalSorter # Example graph: keys are nodes, values are sets of predecessors graph = {"D": {"B", "C"}, "C": {"A"}, "B": {"A"}, "A": set()} ts = TopologicalSorter(graph) # Get the nodes in topological order using static_order (convenience method) order = list(ts.static_order()) print(f"Topological order (static): {order}") # Example with dynamic processing (simulating parallel work) print("\nProcessing nodes dynamically:") ts_dynamic = TopologicalSorter(graph) ts_dynamic.prepare() # Must be called before is_active, get_ready, or done processed_nodes = [] while ts_dynamic.is_active(): ready_nodes = ts_dynamic.get_ready() if ready_nodes: print(f" Ready to process: {ready_nodes}") # Simulate processing for each ready node for node in ready_nodes: processed_nodes.append(node) ts_dynamic.done(*ready_nodes) # Mark nodes as done to unblock successors else: # This branch should not be reached in a valid DAG if is_active is true # unless there's an unexpected state (e.g., cycle detected after prepare, or bug). print(" No nodes ready, but sorter is active. Potential issue or cycle?") break print(f"Dynamic processing complete. Order: {processed_nodes}")