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 #!/usr/bin/env python3
 
 # Copyright (c) 2025 ACTS-Project
 # This file is part of ACTS.
 # See LICENSE for details.
 
 """
 ToroidFieldMap Benchmark and Visualization.
 
 This script provides optimized benchmarking and visualization of ACTS ToroidField
 vs ToroidFieldMap (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 matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.colors import LogNorm
 
 
 def create_analytical_field():
     """Create the analytical toroid field"""
     config = acts.ToroidField.Config()
     return acts.ToroidField(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 = acts.ToroidField.Config()
     analytical_field = acts.ToroidField(config)
 
     # Create LUT normally (this will recompute, but we have the data cached)
     lut_field = acts.toroidFieldMapCyl(
         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="ToroidField vs ToroidFieldMap 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("=== Toroid 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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