feat(plotting): removed best marker legend entry

scripts/plots/analyze_comparisons.py

@@ -6,18 +6,17 @@ Rate, Distance Remaining).
 """
 
 import os
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 from plot_config import (
-    COLORS,
-    apply_style,
-    BEST_PERFORMER_MARKER,
-    BEST_PERFORMER_TEXT,
     BEST_PERFORMER_COLOR,
-    create_common_parser,
+    BEST_PERFORMER_TEXT,
+    COLORS,
     LEGEND_KWARGS,
+    apply_style,
+    create_common_parser,
 )
 
 
@@ -362,8 +361,8 @@ if __name__ == "__main__":
         plot_grouped_bar(
             df=df,
             metric_col="approx_max_velocity",
-            ylabel="Max Forward Velocity (cm/s)",
-            title="Graceful Degradation: Velocity Across Morphologies",
+            ylabel="",
+            title="Maximal forward velocity (in cm/s)",
             output_filename="poster_plot_velocity.png",
             output_dir=OUTPUT_DIR,
             higher_is_better=True,

scripts/plots/analyze_convergence.py

@@ -135,6 +135,9 @@ def load_metrics(file_mapping: dict[str, str]) -> pd.DataFrame:
             continue
 
         df = df[required].copy()
+        df[Columns.VELOCITY] = (df[Columns.INITIAL_XY_DIST] - df[Columns.FINAL_XY_DIST]) / df[
+            Columns.EVAL_STEPS
+        ]
         df[Columns.ARCH] = arch_name
         df[Columns.VELOCITY] = (df[Columns.INITIAL_XY_DIST] - df[Columns.FINAL_XY_DIST]) / df[
             Columns.EVAL_STEPS
@@ -165,6 +168,8 @@ def analyze_convergence(df: pd.DataFrame) -> pd.DataFrame:
     """
     results = []
 
+    centralized_base = 0
+
     for arch in df[Columns.ARCH].unique():
         arch_data = df[df[Columns.ARCH] == arch].sort_values(Columns.TIMESTEPS)
 
@@ -192,6 +197,11 @@ def analyze_convergence(df: pd.DataFrame) -> pd.DataFrame:
             }
         )
 
+        if arch == "centralized 5 arms":
+            centralized_base = reward_checkpoint
+        else:
+            print(arch, "speedup:", 1 - reward_checkpoint / centralized_base)
+
     return pd.DataFrame(results)