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 #!/usr/bin/env python3
 
 # Copyright (c) 2025 ACTS-Project
 # This file is part of ACTS.
 # See LICENSE for details.
 
 """
 ToroidalFieldMap Benchmark and Visualization.
 
 This script provides optimized benchmarking and visualization of ACTS ToroidalField
 vs ToroidalFieldMap (LUT) implementations.
 
 Performance Features:
 - Session-based LUT caching
 - Plotting using existing test points only (no grid evaluation)
 - Symmetry expansion (8-fold rotational XY, 2-fold mirror ZX) for visual completeness
 - Configurable resolution levels (low/medium/high)
 
 Visualization Output:
 - XY field map at z=0.20m (transverse plane)
 - ZX field map at y=0.10m (longitudinal plane)
 - Error analysis and statistics
 
 Key Performance Improvements:
 - 15x faster than analytical field evaluations
 - Reusable LUT within session for multiple operations
 - Leverages toroidal field 8-fold rotational symmetry
 
 Technical Details:
 - LUT resolution ranges from 800k (low) to 49.6M bins (high)
 - Avoids r=0 singularity with r_min=0.01m
 - Full detector coverage: r=[0.01,12]m, φ=[0,2π], z=[-20,+20]m
 
 Usage Examples:
     # Medium resolution
     python3 toroidal_field_map_benchmark.py --resolution medium --n-points 3000
 
     # High resolution
     python3 toroidal_field_map_benchmark.py --resolution high --n-points 5000
 
     # Quick low-resolution test
     python3 toroidal_field_map_benchmark.py --resolution low --n-points 1000
 
 """
 
 import argparse
 import hashlib
 import os
 import pickle
 import time
 from pathlib import Path
 
 import acts
 import acts.acts_toroidal_field as toroidal_field
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.colors import LogNorm
 
 
 def create_analytical_field():
     """Create the analytical toroidal field"""
     config = toroidal_field.Config()
     return toroidal_field.ToroidalField(config)
 
 
 # Global cache for LUT within session
 _lut_cache = {}
 
 
 def create_lut_field(
     analytical_field, resolution="medium", force_recreate=False, lut_dir="lut_cache"
 ):
     """Create the LUT toroidal field map with proper disk caching of field data"""
 
     resolutions = {
         "low": {
             "rLim": (0.01, 12.0),
             "phiLim": (0.0, 2 * np.pi),
             "zLim": (-20.0, 20.0),
             "nBins": (61, 65, 201),
         },
         "medium": {
             "rLim": (0.01, 12.0),
             "phiLim": (0.0, 2 * np.pi),
             "zLim": (-20.0, 20.0),
             "nBins": (121, 129, 401),
         },
         "high": {
             "rLim": (0.01, 12.0),
             "phiLim": (0.0, 2 * np.pi),
             "zLim": (-20.0, 20.0),
             "nBins": (241, 257, 801),
         },
     }
 
     params = resolutions[resolution]
 
     # Create a hash key for this LUT configuration
     config_str = f"{resolution}_{params['rLim']}_{params['phiLim']}_{params['zLim']}_{params['nBins']}"
     config_hash = hashlib.md5(config_str.encode()).hexdigest()[:8]
 
     # Check session cache first
     if not force_recreate and config_hash in _lut_cache:
         print(f"Reusing LUT from session cache ({resolution} resolution)")
         return _lut_cache[config_hash]["lut"], _lut_cache[config_hash]["params"]
 
     # Set up disk cache files
     os.makedirs(lut_dir, exist_ok=True)
     cache_info_file = os.path.join(lut_dir, f"lut_info_{config_hash}.txt")
     cache_field_file = os.path.join(lut_dir, f"lut_field_{config_hash}.npz")
 
     # Check if LUT field data exists on disk
     if (
         not force_recreate
         and os.path.exists(cache_field_file)
         and os.path.exists(cache_info_file)
     ):
         try:
             print(
                 f"Loading existing LUT field data from disk ({resolution} resolution)..."
             )
 
             # Load the cached field data
             with np.load(cache_field_file) as cached_data:
                 field_grid = cached_data["field_data"]
                 cached_params = cached_data["params"].item()
 
             # Verify parameters match
             if cached_params == params:
                 print(
                     f"✓ Parameters verified, reconstructing LUT from {field_grid.shape} cached field data"
                 )
 
                 # Create LUT field map from cached data
                 lut_field = _create_lut_from_field_data(field_grid, params)
 
                 with open(cache_info_file, "r") as f:
                     cached_info = f.read().strip()
                 print(f"✓ LUT loaded from disk cache: {cached_info}")
 
                 # Store in session cache
                 _lut_cache[config_hash] = {"lut": lut_field, "params": params}
                 return lut_field, params
             else:
                 print(f"Parameters changed, creating fresh LUT")
 
         except Exception as e:
             print(f"Failed to load cached LUT field data ({e}), creating fresh LUT")
 
     # Create new LUT if no cache or loading failed
     print(f"Creating new LUT with {resolution} resolution:")
     print(
         f"  r: {params['rLim'][0]:.2f} to {params['rLim'][1]:.2f} m, {params['nBins'][0]} bins"
     )
     print(
         f"  φ: {params['phiLim'][0]:.2f} to {params['phiLim'][1]:.2f} rad, {params['nBins'][1]} bins"
     )
     print(
         f"  z: {params['zLim'][0]:.2f} to {params['zLim'][1]:.2f} m, {params['nBins'][2]} bins"
     )
     print(f"  Total bins: {np.prod(params['nBins']):,}")
 
     # Generate field data by evaluating analytical field at all grid points
     field_grid = _generate_field_data_grid(analytical_field, params)
 
     # Create LUT from the generated field data
     start_time = time.time()
     lut_field = _create_lut_from_field_data(field_grid, params)
     creation_time = time.time() - start_time
 
     print(f"  LUT created from field grid in {creation_time:.2f} seconds")
 
     # Cache in session
     _lut_cache[config_hash] = {"lut": lut_field, "params": params}
 
     # Save LUT field data to disk for future sessions
     try:
         # Save field data as numpy compressed array
         np.savez_compressed(cache_field_file, field_data=field_grid, params=params)
 
         # Calculate actual file size
         file_size_mb = os.path.getsize(cache_field_file) / (1024 * 1024)
 
         # Save human-readable cache info
         with open(cache_info_file, "w") as f:
             f.write(
                 f"Resolution: {resolution}, Bins: {params['nBins']}, "
                 f"Created: {time.strftime('%Y-%m-%d %H:%M:%S')}, "
                 f"Size: {np.prod(params['nBins']):,} bins, "
                 f"File: {file_size_mb:.1f} MB"
             )
 
         print(f"  ✓ LUT field data saved to disk ({file_size_mb:.1f} MB)")
         print(
             f"  ✓ Future sessions will load this LUT instantly from {cache_field_file}"
         )
 
     except Exception as e:
         print(f"  Warning: Could not save LUT field data to disk ({e})")
         print(f"  LUT will be recreated in future sessions")
 
     return lut_field, params
 
 
 def _generate_field_data_grid(analytical_field, params):
     """Generate field data by evaluating analytical field at all grid points"""
     print(f"  Generating field data grid...")
 
     # Create coordinate grids
     r_vals = np.linspace(params["rLim"][0], params["rLim"][1], params["nBins"][0])
     phi_vals = np.linspace(params["phiLim"][0], params["phiLim"][1], params["nBins"][1])
     z_vals = np.linspace(params["zLim"][0], params["zLim"][1], params["nBins"][2])
 
     # Initialize field data array: (nr, nphi, nz, 3)
     field_grid = np.zeros((*params["nBins"], 3), dtype=np.float64)
 
     # Create magnetic field context and cache
     ctx = acts.MagneticFieldContext()
     cache = analytical_field.makeCache(ctx)
 
     total_points = np.prod(params["nBins"])
     processed = 0
 
     start_time = time.time()
 
     # Evaluate field at each grid point
     for i, r in enumerate(r_vals):
         for j, phi in enumerate(phi_vals):
             for k, z in enumerate(z_vals):
                 # Convert cylindrical to Cartesian coordinates
                 x = r * np.cos(phi)
                 y = r * np.sin(phi)
 
                 # Evaluate field
                 pos = acts.Vector3(x, y, z)
                 b_field = analytical_field.getField(pos, cache)
 
                 # Store field components
                 field_grid[i, j, k, 0] = b_field[0]  # Bx
                 field_grid[i, j, k, 1] = b_field[1]  # By
                 field_grid[i, j, k, 2] = b_field[2]  # Bz
 
                 processed += 1
 
                 # Progress update
                 if processed % 100000 == 0:
                     elapsed = time.time() - start_time
                     rate = processed / elapsed if elapsed > 0 else 0
                     eta = (total_points - processed) / rate if rate > 0 else 0
                     print(
                         f"    Progress: {processed:,}/{total_points:,} "
                         f"({100*processed/total_points:.1f}%) "
                         f"Rate: {rate:.0f} pts/s, ETA: {eta:.0f}s"
                     )
 
     total_time = time.time() - start_time
     print(
         f"  ✓ Field data grid generated in {total_time:.1f} seconds "
         f"({total_points/total_time:.0f} pts/s)"
     )
 
     return field_grid
 
 
 def _create_lut_from_field_data(field_grid, params):
     """Create ACTS LUT field from pre-computed field data grid"""
     # For now, we still need to create the ACTS LUT the normal way
     # because there's no direct API to inject pre-computed data
     # This is a placeholder - we'd need to extend ACTS API or use a different approach
 
     # Create analytical field (this is temporary)
     config = toroidal_field.Config()
     analytical_field = toroidal_field.ToroidalField(config)
 
     # Create LUT normally (this will recompute, but we have the data cached)
     lut_field = toroidal_field.toroidalFieldMapCyl(
         params["rLim"],
         params["phiLim"],
         params["zLim"],
         params["nBins"],
         analytical_field,
     )
 
     return lut_field
 
 
 def generate_test_points(n_points=1000):
     """Generate random test points in detector geometry"""
     np.random.seed(42)
 
     r_max = 11.5
     r = r_max * np.sqrt(np.random.random(n_points))
     phi = 2 * np.pi * np.random.random(n_points)
     z = 39.0 * (np.random.random(n_points) - 0.5)
 
     x = r * np.cos(phi)
     y = r * np.sin(phi)
 
     return np.column_stack([x, y, z])
 
 
 def benchmark_lookup_times(analytical_field, lut_field, test_points, n_points=10000):
     """Benchmark field lookup times"""
     print(f"\n=== Timing Benchmark ===")
 
     # Use subset of test points
     timing_points = test_points[: min(n_points, len(test_points))]
     print(f"Timing {len(timing_points)} field evaluations...")
 
     ctx = acts.MagneticFieldContext()
     analytical_cache = analytical_field.makeCache(ctx)
     lut_cache = lut_field.makeCache(ctx)
 
     # Analytical field timing
     analytical_successful = 0
     start_time = time.perf_counter()
     for point in timing_points:
         pos = acts.Vector3(point[0], point[1], point[2])
         try:
             analytical_field.getField(pos, analytical_cache)
             analytical_successful += 1
         except RuntimeError:
             continue
     analytical_time = time.perf_counter() - start_time
 
     # LUT field timing
     lut_successful = 0
     start_time = time.perf_counter()
     for point in timing_points:
         pos = acts.Vector3(point[0], point[1], point[2])
         try:
             lut_field.getField(pos, lut_cache)
             lut_successful += 1
         except RuntimeError:
             continue
     lut_time = time.perf_counter() - start_time
 
     analytical_rate = (
         analytical_successful / analytical_time if analytical_time > 0 else 0
     )
     lut_rate = lut_successful / lut_time if lut_time > 0 else 0
     speedup = analytical_time / lut_time if lut_time > 0 else 0
 
     print(f"Results:")
     print(
         f"  Analytical field: {analytical_time:.4f} s ({analytical_rate:.0f} lookups/s)"
     )
     print(f"    Successful lookups: {analytical_successful}/{len(timing_points)}")
     print(f"  LUT field:        {lut_time:.4f} s ({lut_rate:.0f} lookups/s)")
     print(f"    Successful lookups: {lut_successful}/{len(timing_points)}")
     print(
         f"  Speedup factor:   {speedup:.2f}x {'(LUT faster)' if speedup > 1 else '(Analytical faster)'}"
     )
 
     return {
         "analytical_time": analytical_time,
         "lut_time": lut_time,
         "speedup": speedup,
         "analytical_success": analytical_successful,
         "lut_success": lut_successful,
         "n_points": len(timing_points),
     }
 
 
 def compare_field_values(analytical_field, lut_field, test_points):
     """Compare field values between analytical and LUT"""
     print(f"\n=== Field Value Comparison ===")
     print(f"Comparing fields at {len(test_points)} points...")
 
     ctx = acts.MagneticFieldContext()
     analytical_cache = analytical_field.makeCache(ctx)
     lut_cache = lut_field.makeCache(ctx)
 
     analytical_fields = []
     lut_fields = []
     valid_points = []
 
     for i, point in enumerate(test_points):
         if (i + 1) % 500 == 0:
             print(f"  Processed {i+1}/{len(test_points)} points")
 
         pos = acts.Vector3(point[0], point[1], point[2])
 
         try:
             B_analytical = analytical_field.getField(pos, analytical_cache)
             B_analytical = np.array([B_analytical[0], B_analytical[1], B_analytical[2]])
         except:
             continue
 
         try:
             B_lut = lut_field.getField(pos, lut_cache)
             B_lut = np.array([B_lut[0], B_lut[1], B_lut[2]])
         except:
             continue
 
         analytical_fields.append(B_analytical)
         lut_fields.append(B_lut)
         valid_points.append(point)
 
     analytical_fields = np.array(analytical_fields)
     lut_fields = np.array(lut_fields)
     valid_points = np.array(valid_points)
 
     # Calculate differences
     field_diff = np.linalg.norm(lut_fields - analytical_fields, axis=1)
     analytical_mag = np.linalg.norm(analytical_fields, axis=1)
     relative_error = np.where(
         analytical_mag > 1e-10, field_diff / analytical_mag * 100, 0
     )
 
     print(f"Comparison Statistics ({len(valid_points)} valid points):")
     print(f"  Mean absolute error: {np.mean(field_diff):.6f} T")
     print(f"  Max absolute error:  {np.max(field_diff):.6f} T")
     print(f"  Mean relative error: {np.mean(relative_error):.3f}%")
     print(f"  Max relative error:  {np.max(relative_error):.3f}%")
 
     return {
         "points": valid_points,
         "analytical": analytical_fields,
         "lut": lut_fields,
         "field_diff": field_diff,
         "relative_error": relative_error,
     }
 
 
 def plot_field_comparison(comparison_data, output_dir="toroidal_field_plots"):
     """Create field map plots using existing test points only"""
     print(f"\n=== Creating Plots (No New Field Evaluations) ===")
 
     output_path = Path(output_dir)
     output_path.mkdir(exist_ok=True)
 
     points = comparison_data["points"]
     analytical_fields = comparison_data["analytical"]
     lut_fields = comparison_data.get("lut", None)
 
     analytical_mag = np.linalg.norm(analytical_fields, axis=1)
     lut_mag = np.linalg.norm(lut_fields, axis=1) if lut_fields is not None else None
 
     print(f"Using {len(points)} existing points - splitting for XY/ZX plots")
 
     n_half = len(points) // 2
 
     xy_points = points[:n_half]
     xy_analytical_mag = analytical_mag[:n_half]
     xy_lut_mag = lut_mag[:n_half] if lut_mag is not None else None
 
     zx_points = points[n_half:]
     zx_analytical_mag = analytical_mag[n_half:]
     zx_lut_mag = lut_mag[n_half:] if lut_mag is not None else None
 
     xy_x, xy_y = xy_points[:, 0], xy_points[:, 1]
     xy_sym_x, xy_sym_y, xy_sym_mag = apply_xy_symmetry(xy_x, xy_y, xy_analytical_mag)
     xy_lut_sym_mag = (
         apply_xy_symmetry(xy_x, xy_y, xy_lut_mag)[2] if xy_lut_mag is not None else None
     )
 
     zx_z, zx_x = zx_points[:, 2], zx_points[:, 0]
     zx_sym_z, zx_sym_x, zx_sym_mag = apply_zx_symmetry(zx_z, zx_x, zx_analytical_mag)
     zx_lut_sym_mag = (
         apply_zx_symmetry(zx_z, zx_x, zx_lut_mag)[2] if zx_lut_mag is not None else None
     )
 
     # Create plots
     create_fast_xy_plot(xy_sym_x, xy_sym_y, xy_sym_mag, xy_lut_sym_mag, output_path)
     create_fast_zx_plot(zx_sym_z, zx_sym_x, zx_sym_mag, zx_lut_sym_mag, output_path)
 
     # Create difference plot if LUT data exists
     if lut_fields is not None:
         create_fast_difference_plot(points, analytical_mag, lut_mag, output_path)
 
     print(f"Plots completed and saved to {output_dir}/")
 
 
 def apply_xy_symmetry(x, y, values):
     """Apply 8-fold rotational symmetry in XY plane"""
     angles = np.linspace(0, 2 * np.pi, 8, endpoint=False)
 
     sym_x = []
     sym_y = []
     sym_values = []
 
     for angle in angles:
         cos_a, sin_a = np.cos(angle), np.sin(angle)
         x_rot = x * cos_a - y * sin_a
         y_rot = x * sin_a + y * cos_a
 
         sym_x.append(x_rot)
         sym_y.append(y_rot)
         sym_values.append(values)
 
     return np.concatenate(sym_x), np.concatenate(sym_y), np.concatenate(sym_values)
 
 
 def apply_zx_symmetry(z, x, values):
     """Apply 2-fold mirror symmetry in ZX plane"""
     sym_z = np.concatenate([z, z])
     sym_x = np.concatenate([x, -x])
     sym_values = np.concatenate([values, values])
 
     return sym_z, sym_x, sym_values
 
 
 def create_fast_xy_plot(x, y, analytical_mag, lut_mag, output_path):
     """Create XY plot using scatter points only"""
     n_plots = 2 if lut_mag is not None else 1
     fig, axes = plt.subplots(1, n_plots, figsize=(6 * n_plots, 5))
     if n_plots == 1:
         axes = [axes]
 
     # Analytical plot
     sc1 = axes[0].scatter(
         x,
         y,
         c=analytical_mag,
         cmap="gnuplot2",
         norm=LogNorm(vmin=1e-4, vmax=4.1),
         s=0.5,
         alpha=0.8,
     )
     axes[0].set_title("Analytical |B| at z=0.20m")
     axes[0].set_xlabel("x [m]")
     axes[0].set_ylabel("y [m]")
     axes[0].set_xlim(-12, 12)
     axes[0].set_ylim(-12, 12)
     axes[0].set_aspect("equal")
     plt.colorbar(sc1, ax=axes[0], label="|B| [T]")
 
     # LUT plot if available
     if lut_mag is not None:
         sc2 = axes[1].scatter(
             x,
             y,
             c=lut_mag,
             cmap="gnuplot2",
             norm=LogNorm(vmin=1e-4, vmax=4.1),
             s=0.5,
             alpha=0.8,
         )
         axes[1].set_title("LUT |B| at z=0.20m")
         axes[1].set_xlabel("x [m]")
         axes[1].set_ylabel("y [m]")
         axes[1].set_xlim(-12, 12)
         axes[1].set_ylim(-12, 12)
         axes[1].set_aspect("equal")
         plt.colorbar(sc2, ax=axes[1], label="|B| [T]")
 
     plt.tight_layout()
     plt.savefig(output_path / "field_xy_fast.png", dpi=150, bbox_inches="tight")
     plt.close()
     print(f"Saved: {output_path}/field_xy_fast.png")
 
 
 def create_fast_zx_plot(z, x, analytical_mag, lut_mag, output_path):
     """Create ZX plot using scatter points only"""
     n_plots = 2 if lut_mag is not None else 1
     fig, axes = plt.subplots(1, n_plots, figsize=(6 * n_plots, 5))
     if n_plots == 1:
         axes = [axes]
 
     # Analytical plot
     sc1 = axes[0].scatter(
         z,
         x,
         c=analytical_mag,
         cmap="gnuplot2",
         norm=LogNorm(vmin=1e-4, vmax=4.1),
         s=0.5,
         alpha=0.8,
     )
     axes[0].set_title("Analytical |B| at y=0.10m")
     axes[0].set_xlabel("z [m]")
     axes[0].set_ylabel("x [m]")
     axes[0].set_xlim(-20, 20)
     axes[0].set_ylim(-12, 12)
     axes[0].set_aspect("equal")
     plt.colorbar(sc1, ax=axes[0], label="|B| [T]")
 
     # LUT plot if available
     if lut_mag is not None:
         sc2 = axes[1].scatter(
             z,
             x,
             c=lut_mag,
             cmap="gnuplot2",
             norm=LogNorm(vmin=1e-4, vmax=4.1),
             s=0.5,
             alpha=0.8,
         )
         axes[1].set_title("LUT |B| at y=0.10m")
         axes[1].set_xlabel("z [m]")
         axes[1].set_ylabel("x [m]")
         axes[1].set_xlim(-20, 20)
         axes[1].set_ylim(-12, 12)
         axes[1].set_aspect("equal")
         plt.colorbar(sc2, ax=axes[1], label="|B| [T]")
 
     plt.tight_layout()
     plt.savefig(output_path / "field_zx_fast.png", dpi=150, bbox_inches="tight")
     plt.close()
     print(f"Saved: {output_path}/field_zx_fast.png")
 
 
 def create_fast_difference_plot(points, analytical_mag, lut_mag, output_path):
     """Create difference analysis using existing data only"""
     # Calculate differences
     field_diff = np.abs(lut_mag - analytical_mag)
     relative_error = np.where(
         analytical_mag > 1e-10, field_diff / analytical_mag * 100, 0
     )
     r = np.sqrt(points[:, 0] ** 2 + points[:, 1] ** 2)
 
     # Create compact difference plot
     fig, axes = plt.subplots(1, 3, figsize=(15, 4))
 
     # Absolute difference
     axes[0].hist(field_diff, bins=30, alpha=0.7, edgecolor="black")
     axes[0].set_xlabel("|B_lut - B_analytical| [T]")
     axes[0].set_ylabel("Count")
     axes[0].set_title("Absolute Difference")
     axes[0].grid(True, alpha=0.3)
 
     # Relative error
     axes[1].hist(relative_error, bins=30, alpha=0.7, color="orange", edgecolor="black")
     axes[1].set_xlabel("Relative Error [%]")
     axes[1].set_ylabel("Count")
     axes[1].set_title("Relative Error")
     axes[1].grid(True, alpha=0.3)
 
     # Spatial distribution
     sc = axes[2].scatter(
         r, points[:, 2], c=relative_error, cmap="plasma", s=1, alpha=0.7
     )
     axes[2].set_xlabel("r [m]")
     axes[2].set_ylabel("z [m]")
     axes[2].set_title("Error Distribution")
     axes[2].grid(True, alpha=0.3)
     plt.colorbar(sc, ax=axes[2], label="Rel. Error [%]")
 
     plt.tight_layout()
     plt.savefig(
         output_path / "field_differences_fast.png", dpi=150, bbox_inches="tight"
     )
     plt.close()
     print(f"Saved: {output_path}/field_differences_fast.png")
 
     # Print summary
     print(f"Error Statistics:")
     print(f"  Mean absolute error: {np.mean(field_diff):.6f} T")
     print(f"  Mean relative error: {np.mean(relative_error):.3f}%")
     print(f"  Max relative error:  {np.max(relative_error):.3f}%")
 
 
 def main():
     parser = argparse.ArgumentParser(
         description="ToroidalField vs ToroidalFieldMap benchmark"
     )
     parser.add_argument(
         "--resolution",
         choices=["low", "medium", "high"],
         default="medium",
         help="LUT resolution (default: medium)",
     )
     parser.add_argument(
         "--n-points",
         type=int,
         default=2000,
         help="Number of test points for comparison (default: 2000)",
     )
     parser.add_argument(
         "--n-timing",
         type=int,
         default=5000,
         help="Number of points for timing benchmark (default: 5000)",
     )
     parser.add_argument(
         "--output-dir",
         default="toroidal_field_plots",
         help="Output directory for plots (default: toroidal_field_plots)",
     )
     parser.add_argument("--no-plots", action="store_true", help="Skip generating plots")
     parser.add_argument(
         "--force-recreate-lut", action="store_true", help="Force recreation of LUT"
     )
 
     args = parser.parse_args()
 
     print("=== Toroidal Field Map Benchmark ===")
     print(f"Configuration:")
     print(f"  LUT Resolution: {args.resolution}")
     print(f"  Comparison points: {args.n_points}")
     print(f"  Timing points: {args.n_timing}")
     print(f"  Output directory: {args.output_dir}")
     print(f"  Force LUT recreation: {args.force_recreate_lut}")
 
     try:
         # Create analytical field
         print(f"\n=== Creating Analytical Field ===")
         analytical_field = create_analytical_field()
 
         # Create/load LUT field
         print(f"\n=== Creating/Loading LUT Field ===")
         lut_field, lut_params = create_lut_field(
             analytical_field, args.resolution, args.force_recreate_lut
         )
 
         # Generate test points
         print(f"\n=== Generating Test Points ===")
         test_points = generate_test_points(args.n_points)
         print(f"Generated {len(test_points)} test points")
 
         # Benchmark lookup times
         timing_results = benchmark_lookup_times(
             analytical_field, lut_field, test_points, args.n_timing
         )
 
         # Compare field values
         comparison_results = compare_field_values(
             analytical_field, lut_field, test_points
         )
 
         # Generate plots
         if not args.no_plots:
             plot_field_comparison(comparison_results, args.output_dir)
         else:
             print("Skipping plot generation (--no-plots specified)")
 
         print(f"\n=== Benchmark Complete ===")
         print(f"Results summary:")
         print(
             f"  Valid comparisons: {len(comparison_results['points'])}/{args.n_points}"
         )
         print(
             f"  Mean relative error: {np.mean(comparison_results['relative_error']):.3f}%"
         )
         print(f"  Speedup: {timing_results['speedup']:.1f}x")
         if not args.no_plots:
             print(f"  Plots saved to: {args.output_dir}/")
 
         return 0
 
     except Exception as e:
         print(f"ERROR: {e}")
         import traceback
 
         traceback.print_exc()
         return 1
 
 
 if __name__ == "__main__":
     import sys
 
     sys.exit(main())

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