style: fix all flake8 linting issues

benchmark_accuracy.py

@@ -13,11 +13,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,

benchmark_adaptive_twophase.py

@@ -18,11 +18,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     adaptive_control_parameters,
     compute_ds2,
     compute_gradient,
@@ -288,11 +288,11 @@ def main():
     ) * 100
 
     print("IMPROVEMENTS:")
-    print(f"  vs Fixed Fast:")
+    print("  vs Fixed Fast:")
     print(f"    • {vs_fast_precision:+.1f}% better precision")
     print(f"    • Similar speed (both reach 10cm in ~{adaptive_ticks_to_10cm} ticks)")
     print()
-    print(f"  vs Fixed Ultra-Slow:")
+    print("  vs Fixed Ultra-Slow:")
     print(f"    • {vs_ultraslow_speed:+.1f}% faster to 10cm")
     print(
         f"    • Similar precision ({adaptive_final_error:.1f}mm vs {ultraslow_final_error:.1f}mm)"
@@ -315,7 +315,7 @@ def main():
         print(
             f"  Adaptive control spent only {terminal_phase_ticks/len(df_adaptive)*100:.1f}% of time in slow mode,"
         )
-        print(f"  but achieved precision comparable to 100% ultra-slow!")
+        print("  but achieved precision comparable to 100% ultra-slow!")
 
     print()
     print("=" * 80)

benchmark_dynamic.py

@@ -17,11 +17,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,
@@ -216,7 +216,7 @@ def analyze_tracking(df, scenario_name):
     return metrics
 
 
-def main():
+def main():  # noqa: C901
     print("=" * 80)
     print("DYNAMIC SCENARIOS: Moving Targets and Obstacles".center(80))
     print("=" * 80)

benchmark_dynamic_adaptation.py

@@ -48,17 +48,14 @@ def simulate_robot_control(
         # Heavy robot: slow movements, needs small eta
         velocity_scale = 0.1
         noise_level = 0.05
-        optimal_eta = 0.05
     elif robot_type == "fast":
         # Agile robot: fast movements, can handle large eta
         velocity_scale = 2.0
         noise_level = 0.2
-        optimal_eta = 0.3
     else:  # medium
         # Baseline robot
         velocity_scale = 0.5
         noise_level = 0.1
-        optimal_eta = 0.1
 
     state = initial_state.copy()
     trajectory = [state.copy()]
@@ -240,17 +237,17 @@ def run_benchmark():
 
         all_results[robot_type] = results
 
-        print(f"  Convergence Time:")
+        print("  Convergence Time:")
         print(f"    Adaptive:  {avg_conv_adaptive:.1f} steps")
         print(f"    Fixed:     {avg_conv_fixed:.1f} steps")
         print(f"    Improvement: {conv_improvement:+.1f}%")
         print()
-        print(f"  Final Error:")
+        print("  Final Error:")
         print(f"    Adaptive:  {avg_err_adaptive:.4f}")
         print(f"    Fixed:     {avg_err_fixed:.4f}")
         print(f"    Improvement: {error_improvement:+.1f}%")
         print()
-        print(f"  Auto-Detected Parameters:")
+        print("  Auto-Detected Parameters:")
         print(f"    c (characteristic velocity): {avg_c:.3f}")
         print(f"    eta (recommended):           {avg_eta:.3f}")
         print(f"    confidence:                  {avg_confidence:.3f}")

benchmark_geodesic_planning.py

@@ -227,17 +227,17 @@ def run_benchmark():
 
         all_results[scenario_name] = results
 
-        print(f"  Success Rate:")
+        print("  Success Rate:")
         print(f"    Naive:      {naive_success_rate:.1f}%")
         print(f"    Geodesic:   {geo_success_rate:.1f}%")
         print(f"    Improvement: {success_improvement:+.1f}%")
         print()
-        print(f"  Collisions:")
+        print("  Collisions:")
         print(f"    Naive:      {naive_avg_collisions:.2f}")
         print(f"    Geodesic:   {geo_avg_collisions:.2f}")
         print(f"    Reduction:  {collision_reduction:+.1f}%")
         print()
-        print(f"  Path Length:")
+        print("  Path Length:")
         print(f"    Naive:      {naive_avg_length:.2f}")
         print(f"    Geodesic:   {geo_avg_length:.2f}")
         print()

benchmark_landing.py

@@ -25,11 +25,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,
@@ -236,7 +236,7 @@ def analyze_landing(df, scenario_name):
     return metrics
 
 
-def main():
+def main():  # noqa: C901
     print("=" * 80)
     print("AUTONOMOUS LANDING: Moving Platform Scenarios".center(80))
     print("=" * 80)

benchmark_mars_landing.py

@@ -27,11 +27,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,
@@ -229,7 +229,7 @@ def analyze_mars_landing(df, scenario_name, safe_zone_radius=0.015):
     return metrics
 
 
-def main():
+def main():  # noqa: C901
     print("=" * 80)
     print("MARS LANDING: Mission-Critical Precision Landing".center(80))
     print("=" * 80)
@@ -466,7 +466,7 @@ def sample_return_site(t):
     baseline_successes = sum(1 for r in results if r["baseline"]["mission_success"])
     lightlike_successes = sum(1 for r in results if r["lightlike"]["mission_success"])
 
-    print(f"MISSION SUCCESS RATE:")
+    print("MISSION SUCCESS RATE:")
     print(
         f"  Baseline:  {baseline_successes}/{len(results)} missions successful ({baseline_successes/len(results)*100:.0f}%)"
     )
@@ -503,7 +503,7 @@ def sample_return_site(t):
     if lightlike_successes > baseline_successes or total_oscillation_reduction > 5:
         print("★★★ MISSION-CRITICAL IMPROVEMENT ★★★")
         print()
-        print(f"The lightlike observer SAVES MISSIONS:")
+        print("The lightlike observer SAVES MISSIONS:")
         print(f"  • {lightlike_successes - baseline_successes} additional successful landing(s)")
         print(f"  • {total_oscillation_reduction} fewer descent oscillations")
         print(f"  • {avg_precision_improvement:.1f}% better landing precision")

benchmark_moving_targets.py

@@ -12,10 +12,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from src.eigen_lorentz import (
-    estimate_target_velocity,
-    moving_target_control_step,
-)
+from src.eigen_lorentz import moving_target_control_step
 
 
 def simulate_moving_target_tracking(

benchmark_qec.py

@@ -18,7 +18,6 @@
 import numpy as np
 
 from src.eigen_qec import (
-    correct_measurement_errors,
     qec_control_step,
     simulate_sensor_array,
 )
@@ -191,7 +190,7 @@ def run_benchmark():
 
         all_results[failure_rate] = results
 
-        print(f"  Final Error:")
+        print("  Final Error:")
         print(f"    Naive: {avg_error_naive:.3f}")
         print(f"    QEC:   {avg_error_qec:.3f}")
         print(f"    Improvement: {error_improvement:+.1f}%")

benchmark_scaling.py

@@ -17,11 +17,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,
@@ -188,7 +188,7 @@ def analyze_precision(df_baseline, df_lightlike, target, scenario_name):
     }
 
 
-def main():
+def main():  # noqa: C901
     print("=" * 80)
     print("SCALING ANALYSIS: Lightlike Observer Benefits vs Complexity".center(80))
     print("=" * 80)
@@ -306,7 +306,7 @@ def main():
         )
         print()
         print("For 3D robot control (6-DOF or 7-DOF):")
-        print(f"  • Configuration space: 2D → 6D or 7D (3-3.5x more dimensions)")
+        print("  • Configuration space: 2D → 6D or 7D (3-3.5x more dimensions)")
         print(
             f"  • Expected improvement: ~{avg_improvement * 3:.1f}% to {avg_improvement * 3.5:.1f}%"
         )

benchmark_self_tuning.py

@@ -11,7 +11,6 @@
 import numpy as np
 
 from src.eigen_meta_control import self_tuning_control_step
-from src.eigen_similarity import lightlike_damping_factor
 
 
 def gradient_step_with_damping(
@@ -192,9 +191,9 @@ def main():
     print("=" * 70)
     print()
     print("Key Results:")
-    print(f"  1. No infinite loops despite self-reference (TC+ property ✓)")
+    print("  1. No infinite loops despite self-reference (TC+ property ✓)")
     print(f"  2. Parameters self-adjusted in {len(params2)-1} iterations")
-    print(f"  3. Recovered from bad initial params automatically")
+    print("  3. Recovered from bad initial params automatically")
     print()
     print("Computational Implications:")
     print("  • System observes itself: a = current_params")

benchmark_stereo_lorentz.py

@@ -22,20 +22,19 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,
     detect_oscillation,
     forward_kinematics,
     lightlike_damping_factor,
 )
-from src.eigen_lorentz import (
-    lorentz_to_stereo,
+from src.eigen_lorentz import (  # noqa: E402
     regime_classification,
     stereo_to_lorentz,
 )

benchmark_terminal_descent.py

@@ -20,11 +20,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,
@@ -228,7 +228,6 @@ def main():
 
     # Find best precision
     best = min(results, key=lambda r: r["light_final_mm"])
-    worst = max(results, key=lambda r: r["base_final_mm"])
 
     print("KEY FINDINGS:")
     print()
@@ -238,7 +237,7 @@ def main():
     print(f"   - Configuration: eta={best['eta']}, {best['ticks']} iterations")
     print()
 
-    print(f"2. PRECISION SCALING:")
+    print("2. PRECISION SCALING:")
     fast_precision = results[0]["base_final_mm"]
     slow_precision = results[-1]["base_final_mm"]
     precision_gain = (fast_precision - slow_precision) / fast_precision * 100
@@ -247,7 +246,7 @@ def main():
     print(f"   - Improvement from slowing down: {precision_gain:.1f}%")
     print()
 
-    print(f"3. LIGHTLIKE OBSERVER BENEFIT:")
+    print("3. LIGHTLIKE OBSERVER BENEFIT:")
     avg_improvement = sum(r["improvement_pct"] for r in results) / len(results)
     print(f"   - Average improvement across all speeds: {avg_improvement:+.1f}%")
 
@@ -260,7 +259,7 @@ def main():
 
     # Oscillation analysis
     total_osc_reduction = sum(r["base_oscillations"] - r["light_oscillations"] for r in results)
-    print(f"4. STABILITY:")
+    print("4. STABILITY:")
     print(f"   - Total oscillation reduction: {total_osc_reduction:+d} events")
     print()
 

benchmark_visual_servoing.py

@@ -22,11 +22,11 @@
 
 import numpy as np
 
-sys.path.insert(0, "/home/user/Eigen-Geometric-Control")
+sys.path.insert(0, "/home/user/Eigen-Geometric-Control")  # noqa: E402
 
-import pandas as pd
+import pandas as pd  # noqa: E402
 
-from src import (
+from src import (  # noqa: E402
     compute_change_stability,
     compute_ds2,
     compute_gradient,
@@ -262,7 +262,7 @@ def analyze_visual_servoing(df, scenario_name):
     return metrics
 
 
-def main():
+def main():  # noqa: C901
     print("=" * 80)
     print("VISUAL SERVOING: Stereo Vision + Lightlike Observer".center(80))
     print("=" * 80)