Cleanup

celeste/celeste_ai/plotting/plot_best_action.py

@@ -3,6 +3,7 @@ import numpy as np
 from pathlib import Path
 import matplotlib as mpl
 import matplotlib.pyplot as plt
+import json
 
 # All of the following are required to load
 # a pickled model.
@@ -15,7 +16,8 @@ def best_action(
 	model_file: Path,
 	out_filename: Path,
 	*,
-	device = torch.device("cpu")
+	device = torch.device("cpu"),
+	draw_path = True
 ):
 	if not model_file.is_file():
 		raise Exception(f"Bad model file {model_file}")
@@ -81,12 +83,34 @@ def best_action(
 		vmin = 0,
 		vmax = 8
 	)
+
+
+	if draw_path:
+		d = None
+		with Path("model_data/solved_4layer/paths.json").open("r") as f:
+			for l in f.readlines():
+				t = json.loads(l)
+				if t["current_image"] == model_file.name:
+					break
+			d = t
+		assert d is not None
+
+		plt.plot(
+			[max(0,x["xpos"]) for x in d["hist"]],
+			[max(0,x["ypos"] + 5) for x in d["hist"]],
+			marker = "",
+			markersize = 0,
+			linestyle = "-",
+			linewidth = 5,
+			color = "white",
+			solid_capstyle = "round",
+			solid_joinstyle = "round"
+		)
+
 	ax.invert_yaxis()
 	cbar = fig.colorbar(plot, ticks = list(range(0, 9)))
 	cbar.ax.set_yticklabels(Celeste.action_space)
 
-	
-
 	fig.savefig(out_filename)
 	plt.close()
 

celeste/celeste_ai/record_paths.py

@@ -7,7 +7,7 @@ from celeste_ai import DQN
 
 
 
-model_data_root = Path("model_data/solved_1")
+model_data_root = Path("model_data/current")
 
 compute_device = torch.device(
 	"cuda" if torch.cuda.is_available() else "cpu"
@@ -94,7 +94,7 @@ def on_state_after(celeste, before_out):
 			f.write(json.dumps(
 				{
 					"hist": state_history,
-					"current_image": str(current_path)
+					"current_image": current_path.name
 				}
 			) + "\n")