Add initial parallel execution setup with batch_run

Author: razvancruceanu

ships_hybrid_algorithm/config/config.json

@@ -63,7 +63,7 @@
         "min_lon": 0.0,
         "max_lon": 20.0
     },
-    "time_step_seconds": 0.001,
+    "time_step_seconds": 0.002,
     "dwa_config": {
         "max_speed": 1.0,
         "min_speed": 0.1,

ships_hybrid_algorithm/main.py

@@ -1,6 +1,9 @@
 import logging
 from datetime import datetime
 import json
+from mesa.batchrunner import batch_run, _collect_data
+from multiprocessing import freeze_support
+import pandas as pd
 
 from model import ShipModel
 from visualization import plot_simulation
@@ -13,40 +16,43 @@
     format="%(asctime)s - %(levelname)s - %(message)s"
 )
 
-def run_simulation(config_file="config/config.json"):
+if __name__ == "__main__":
+    freeze_support()
+    
+    config_file="config/config.json"
     with open(config_file) as f:
         config = json.load(f)
 
     steps = config["simulation_steps"]
 
-    model = ShipModel(
-        width=config["width"],
-        height=config["height"],
-        num_ships=config["num_ships"],
-        max_speed_range=config["max_speed_range"],
-        speed_variation=config["speed_variation"],
-        directional_variation=config["directional_variation"],
-        ports=config["ports"],
-        speed_limit_zones=config.get("speed_limit_zones", []),
-        obstacles=config["obstacles"],
-        dwa_config=config["dwa_config"],
-        resolution=config["resolution"],
-        obstacle_threshold=config["obstacle_threshold"],
-        lookahead=config["lookahead"]
-    )
-
-    # Print agent counts by type
-    for agent_type, agents in model.agents_by_type.items():
-        logging.info(f'{agent_type}: {len(agents)}')
+    model_params = {
+        "width": [config["width"]],
+        "height": [config["height"]],
+        "num_ships": [config["num_ships"]],
+        "max_speed_range": [config["max_speed_range"]], 
+        "speed_variation": [config["speed_variation"]],
+        "directional_variation": [config["directional_variation"]],
+        "ports": [config["ports"]],
+        "speed_limit_zones": [config.get("speed_limit_zones", [])],
+        "obstacles": [config["obstacles"]],
+        "dwa_config": [config["dwa_config"]],
+        "resolution": [config["resolution"]],
+        "obstacle_threshold": [config["obstacle_threshold"]],
+        "lookahead": [config["lookahead"]]
+    }
 
-    # Run the simulation
-    logging.info(f"{datetime.now()} Starting ...")
-    for t in range(steps):
-        logging.info(f"Step {t}")
-        model.step()
-    logging.info(f"{datetime.now()} Finished.")
+    results = batch_run(
+        ShipModel,
+        parameters=model_params,
+        iterations=1,
+        max_steps=steps,
+        number_processes=None,
+        data_collection_period=1,
+        display_progress=True,
+    )
 
-    agent_df = model.datacollector.get_agent_vars_dataframe().dropna()
+    results_df = pd.DataFrame(results)
+    agent_df = results_df[["Step", "AgentID", "x", "y", "AStarPath"]].dropna()
     df = agent_df.reset_index()
 
     # Save to CSV
@@ -56,6 +62,39 @@ def run_simulation(config_file="config/config.json"):
     # Run visualization
     plot_simulation(df, config)
 
+    # model = ShipModel(
+    #     width=config["width"],
+    #     height=config["height"],
+    #     num_ships=config["num_ships"],
+    #     max_speed_range=config["max_speed_range"],
+    #     speed_variation=config["speed_variation"],
+    #     directional_variation=config["directional_variation"],
+    #     ports=config["ports"],
+    #     speed_limit_zones=config.get("speed_limit_zones", []),
+    #     obstacles=config["obstacles"],
+    #     dwa_config=config["dwa_config"],
+    #     resolution=config["resolution"],
+    #     obstacle_threshold=config["obstacle_threshold"],
+    #     lookahead=config["lookahead"]
+    # )
 
-if __name__ == "__main__":
-    run_simulation()
+    # # Print agent counts by type
+    # for agent_type, agents in model.agents_by_type.items():
+    #     logging.info(f'{agent_type}: {len(agents)}')
+
+    # # Run the simulation
+    # logging.info(f"{datetime.now()} Starting ...")
+    # for t in range(steps):
+    #     logging.info(f"Step {t}")
+    #     model.step()
+    # logging.info(f"{datetime.now()} Finished.")
+
+    # agent_df = model.datacollector.get_agent_vars_dataframe().dropna()
+    # df = agent_df.reset_index()
+
+    # # Save to CSV
+    # df.to_csv("ship_simulation_output.csv", index=False)
+    # logging.info("Saved simulation data to ship_simulation_output.csv")
+
+    # # Run visualization
+    # plot_simulation(df, config)

ships_hybrid_algorithm/model.py

@@ -1,3 +1,4 @@
+import logging
 from mesa import Model
 from mesa.space import ContinuousSpace
 from mesa.datacollection import DataCollector
@@ -15,6 +16,7 @@
 class ShipModel(Model):
     def __init__(self, width, height, num_ships, max_speed_range, speed_variation, directional_variation, ports, speed_limit_zones, obstacles, dwa_config, resolution=1, obstacle_threshold=0, lookahead=3.0):
         super().__init__()
+        # self.step = 0
         self.width = width
         self.height = height
         self.max_speed_range = max_speed_range
@@ -110,5 +112,7 @@ def step(self):
         """Run one step of the model.
         
         All agents are activated in random order."""
+        # logging.info(f"Step {self.step}")
         self.schedule.step()
-        self.datacollector.collect(self)
+        self.datacollector.collect(self)
+        # self.step += 1