#!/usr/bin/env python3 """ Stereo PIV Benchmark Comparison against JHTDB DNS Ground Truth. Compares 3-component velocity (U, V, W) and all 6 Reynolds stresses (uu, vv, ww, uv, uw, vw) against DNS channel flow data. Usage: python stereo_benchmark_comparison.py [--run RUN_IDX] [--x-min X_MIN] [--x-max X_MAX] """ import numpy as np import scipy.io as sio from scipy.interpolate import interp1d import matplotlib import matplotlib.pyplot as plt from pathlib import Path import argparse # LaTeX-style fonts (no LaTeX install required) matplotlib.rcParams.update({ 'text.usetex': False, 'font.family': 'serif', 'font.serif': ['CMU Serif', 'Computer Modern Roman', 'DejaVu Serif'], 'mathtext.fontset': 'cm', 'axes.labelsize': 14, 'axes.titlesize': 16, 'legend.fontsize': 11, 'xtick.labelsize': 12, 'ytick.labelsize': 12, }) def log_smooth(y_plus, values, sigma_decades=0.06): """LOWESS-style smooth in log(y+) space, evaluated at original points. Each output point is a locally-weighted LINEAR regression of neighbours, where distance is measured in decades of y+. Using local linear fits instead of local averages gives: - Better peak tracking (local slope captures gradients) - Better edge behaviour (linear extrapolation, not mean bias) Parameters ---------- y_plus : array y+ coordinates (positive) values : array Values to smooth sigma_decades : float Smoothing width in decades of y+ (0.06 ~ +/-15% local y+) Returns ------- y_out, smoothed : arrays Sorted y+ and smoothed values (at original data points) """ valid = (y_plus > 0) & ~np.isnan(values) yp = y_plus[valid] vals = values[valid] if len(yp) < 5: return yp, vals order = np.argsort(yp) yp = yp[order] vals = vals[order] log_yp = np.log10(yp) smoothed = np.empty_like(vals) for i in range(len(vals)): d = (log_yp - log_yp[i]) / sigma_decades w = np.exp(-0.5 * d * d) # Local linear regression (LOWESS) instead of weighted mean wsum = np.sum(w) wmean_x = np.sum(w * log_yp) / wsum wmean_y = np.sum(w * vals) / wsum dx = log_yp - wmean_x denom = np.sum(w * dx * dx) if denom > 1e-30: slope = np.sum(w * dx * vals) / denom smoothed[i] = wmean_y + slope * (log_yp[i] - wmean_x) else: smoothed[i] = wmean_y return yp, smoothed def plot_ci_band(ax, y_plus, ci_lo, ci_hi, sign=1, color='k', alpha=0.3, zorder=1): """Plot a 95% CI shaded band around a reference line. Parameters ---------- ax : matplotlib Axes y_plus : array x-axis values (y+ coordinates) ci_lo, ci_hi : array Lower/upper CI bounds (same units as the plotted variable) sign : int 1 or -1 (for variables like -uv+ that flip sign) color : str Fill color alpha : float Fill transparency zorder : int Drawing order """ lo = sign * ci_lo if sign == 1 else sign * ci_hi # sign flip swaps lo/hi hi = sign * ci_hi if sign == 1 else sign * ci_lo ax.fill_between(y_plus, lo, hi, color=color, alpha=alpha, zorder=zorder, linewidth=0) # Add thin edge lines so the CI is visible even when narrow ax.plot(y_plus, lo, color=color, linewidth=0.5, alpha=0.4, zorder=zorder) ax.plot(y_plus, hi, color=color, linewidth=0.5, alpha=0.4, zorder=zorder) def load_wall_units(wall_units_path): """Load wall units from .mat file (supports v5 struct, v7.3/HDF5, and direct_stats).""" wall_units_path = str(wall_units_path) try: wall = sio.loadmat(wall_units_path, squeeze_me=True, struct_as_record=False) # Format: direct_stats.mat (top-level scalar keys) if 'u_tau' in wall and 'delta_nu' in wall and 'Re_tau' in wall: u_tau = float(wall['u_tau']) delta_nu = float(wall['delta_nu']) Re_tau = float(wall['Re_tau']) return { 'u_tau': u_tau, 'nu': u_tau * delta_nu, 'delta_nu': delta_nu, 'h_mm': float(wall['h_mm']) if 'h_mm' in wall else Re_tau * delta_nu, 'Re_tau': Re_tau, } # Format: wall_units.mat (struct) wu = wall['wall_units'] return { 'u_tau': float(wu.u_tau), 'nu': float(wu.nu), 'delta_nu': float(wu.delta_nu), 'h_mm': float(wu.h_mm), 'Re_tau': float(wu.Re_tau) } except NotImplementedError: import h5py with h5py.File(wall_units_path, 'r') as f: d = f['diagnostics'] u_tau = float(d['u_tau'][0, 0]) Re_tau = float(d['Re_tau'][0, 0]) delta_nu = float(d['delta_nu'][0, 0]) return { 'u_tau': u_tau, 'nu': u_tau * delta_nu, 'delta_nu': delta_nu, 'h_mm': Re_tau * delta_nu, 'Re_tau': Re_tau } def load_ground_truth_3d(profiles_path): """Load ground truth 1px profiles including W component (supports v5 struct, v7.3/HDF5, and direct_stats).""" profiles_path = str(profiles_path) try: profiles = sio.loadmat(profiles_path, squeeze_me=True, struct_as_record=False) # Format: direct_stats.mat (top-level arrays) if 'U_plus' in profiles and 'stress_plus' in profiles and 'y_plus' in profiles: y_plus_full = profiles['y_plus'] Re_tau = float(profiles['Re_tau']) u_tau = float(profiles['u_tau']) delta_nu = float(profiles['delta_nu']) u_tau2 = u_tau ** 2 # Select lower half of channel (y+ <= Re_tau) mask = y_plus_full <= Re_tau y_plus = y_plus_full[mask] y_mm = y_plus * delta_nu # U_plus: (N, 3) -> columns [U, V, W] U_plus = profiles['U_plus'][mask, 0] V_plus = profiles['U_plus'][mask, 1] W_plus = profiles['U_plus'][mask, 2] # stress_plus: (N, 3, 3) -> Reynolds stress tensor uu_plus = profiles['stress_plus'][mask, 0, 0] vv_plus = profiles['stress_plus'][mask, 1, 1] ww_plus = profiles['stress_plus'][mask, 2, 2] uv_plus = profiles['stress_plus'][mask, 0, 1] uw_plus = profiles['stress_plus'][mask, 0, 2] vw_plus = profiles['stress_plus'][mask, 1, 2] result = { 'y_mm': y_mm, 'y_plus': y_plus, 'U': U_plus * u_tau, 'V': V_plus * u_tau, 'W': W_plus * u_tau, 'uu': uu_plus * u_tau2, 'vv': vv_plus * u_tau2, 'ww': ww_plus * u_tau2, 'uv': uv_plus * u_tau2, 'uw': uw_plus * u_tau2, 'vw': vw_plus * u_tau2, 'U_plus': U_plus, 'uu_plus': uu_plus, 'vv_plus': vv_plus, 'ww_plus': ww_plus, 'uv_plus': uv_plus, } # Load 95% confidence intervals if available if 'stress_ci_lo' in profiles and 'stress_ci_hi' in profiles: result['uu_plus_ci_lo'] = profiles['stress_ci_lo'][mask, 0, 0] result['uu_plus_ci_hi'] = profiles['stress_ci_hi'][mask, 0, 0] result['vv_plus_ci_lo'] = profiles['stress_ci_lo'][mask, 1, 1] result['vv_plus_ci_hi'] = profiles['stress_ci_hi'][mask, 1, 1] result['ww_plus_ci_lo'] = profiles['stress_ci_lo'][mask, 2, 2] result['ww_plus_ci_hi'] = profiles['stress_ci_hi'][mask, 2, 2] result['uv_plus_ci_lo'] = profiles['stress_ci_lo'][mask, 0, 1] result['uv_plus_ci_hi'] = profiles['stress_ci_hi'][mask, 0, 1] result['uw_plus_ci_lo'] = profiles['stress_ci_lo'][mask, 0, 2] result['uw_plus_ci_hi'] = profiles['stress_ci_hi'][mask, 0, 2] result['vw_plus_ci_lo'] = profiles['stress_ci_lo'][mask, 1, 2] result['vw_plus_ci_hi'] = profiles['stress_ci_hi'][mask, 1, 2] if 'umean_ci_lo' in profiles and 'umean_ci_hi' in profiles: result['U_plus_ci_lo'] = profiles['umean_ci_lo'][mask, 0] result['U_plus_ci_hi'] = profiles['umean_ci_hi'][mask, 0] result['V_plus_ci_lo'] = profiles['umean_ci_lo'][mask, 1] result['V_plus_ci_hi'] = profiles['umean_ci_hi'][mask, 1] result['W_plus_ci_lo'] = profiles['umean_ci_lo'][mask, 2] result['W_plus_ci_hi'] = profiles['umean_ci_hi'][mask, 2] return result # Format: profiles.mat (struct) win1px = profiles['profiles'].win_1px return { 'y_mm': win1px.y_mm, 'y_plus': win1px.y_plus, 'U': win1px.U, 'V': win1px.V, 'W': win1px.W, 'uu': win1px.uu, 'vv': win1px.vv, 'ww': win1px.ww, 'uv': win1px.uv, 'uw': win1px.uw, 'vw': win1px.vw, 'U_plus': win1px.U_plus, 'uu_plus': win1px.uu_plus, 'vv_plus': win1px.vv_plus, 'ww_plus': win1px.ww_plus, 'uv_plus': win1px.uv_plus, } except NotImplementedError: import h5py with h5py.File(profiles_path, 'r') as f: rp = f['ref_profile'] y_plus = rp['y_plus'][0, :] U = rp['U'][0, :] V = rp['V'][0, :] W = rp['W'][0, :] # Also load ensemble_stats for stress profiles (win_idx=0 = 16x16) es = f['ensemble_stats'] def deref(field, idx=0): ref = es[field][idx, 0] return f[ref][:].flatten() y_mm_es = deref('y_mm') y_plus_es = deref('y_plus') # Wall units from diagnostics sibling file diag_path = str(Path(profiles_path).parent / 'diagnostics.mat') wu = load_wall_units(diag_path) u_tau = wu['u_tau'] u_tau2 = u_tau ** 2 uu = deref('uu_profile') vv = deref('vv_profile') ww = deref('ww_profile') uv = deref('uv_profile') uw = deref('uw_profile') vw = deref('vw_profile') return { 'y_mm': y_mm_es, 'y_plus': y_plus_es, 'U': deref('U_profile'), 'V': deref('V_profile'), 'W': deref('W_profile'), 'uu': uu, 'vv': vv, 'ww': ww, 'uv': uv, 'uw': uw, 'vw': vw, 'U_plus': deref('U_profile') / u_tau, 'uu_plus': uu / u_tau2, 'vv_plus': vv / u_tau2, 'ww_plus': ww / u_tau2, 'uv_plus': uv / u_tau2, } def load_stereo_statistics(stats_path, coords_path, run_idx=3): """ Load stereo PIV statistics from mean_stats.mat and coordinates from separate file. Parameters ---------- stats_path : Path Path to mean_stats.mat coords_path : Path Path to coordinates.mat (from stereo_calibrated folder) run_idx : int Run index (0-based). run_idx=3 is typically finest resolution (16x16) """ stats = sio.loadmat(stats_path, squeeze_me=True, struct_as_record=False) coords_data = sio.loadmat(coords_path, squeeze_me=True, struct_as_record=False) # Get piv_result for the requested run piv_result = stats['piv_result'] if isinstance(piv_result, np.ndarray) and piv_result.ndim == 0: piv = piv_result.item() elif hasattr(piv_result, '__len__') and len(piv_result) > run_idx: piv = piv_result[run_idx] else: piv = piv_result # Get coordinates from separate file coords = coords_data['coordinates'] if isinstance(coords, np.ndarray) and coords.ndim == 0: coord = coords.item() elif hasattr(coords, '__len__') and len(coords) > run_idx: coord = coords[run_idx] else: coord = coords return { # Velocities (m/s -> mm/s) 'ux': piv.ux * 1000, 'uy': piv.uy * 1000, 'uz': piv.uz * 1000, # Normal stresses ((m/s)^2 -> (mm/s)^2) 'uu': piv.uu * 1e6, 'vv': piv.vv * 1e6, 'ww': piv.ww * 1e6, # Shear stresses 'uv': piv.uv * 1e6, 'uw': piv.uw * 1e6, 'vw': piv.vw * 1e6, # Coordinates (already in mm from calibrated file) 'x': coord.x, 'y': coord.y, } def compute_stereo_profiles(piv_data, x_min=5.0, x_max=145.0): """ Compute x-averaged stereo PIV profiles. Parameters ---------- piv_data : dict Stereo PIV data dictionary x_min : float Minimum x to include (mm) x_max : float Maximum x to include (mm) Returns ------- dict with y_mm and all velocity/stress profiles """ x = piv_data['x'] y = piv_data['y'] # Stereo coordinates have NaN at edges (outside overlap region) # Find valid region valid_mask = ~np.isnan(x) valid_rows = np.any(valid_mask, axis=1) valid_cols = np.any(valid_mask, axis=0) # Find first valid column to get y values from first_valid_col = np.argmax(valid_cols) last_valid_col = len(valid_cols) - np.argmax(valid_cols[::-1]) - 1 mid_col = (first_valid_col + last_valid_col) // 2 # Find first valid row to get x values from first_valid_row = np.argmax(valid_rows) last_valid_row = len(valid_rows) - np.argmax(valid_rows[::-1]) - 1 mid_row = (first_valid_row + last_valid_row) // 2 # Get unique coordinates from valid region y_full = y[:, mid_col] x_unique = x[mid_row, :] print(f" Valid row range: {first_valid_row} to {last_valid_row}") print(f" Valid col range: {first_valid_col} to {last_valid_col}") print(f" X range: {np.nanmin(x_unique):.2f} to {np.nanmax(x_unique):.2f} mm") print(f" Y range: {np.nanmin(y_full):.2f} to {np.nanmax(y_full):.2f} mm") # Apply x range filter x_mask = (x_unique >= x_min) & (x_unique <= x_max) & ~np.isnan(x_unique) print(f" Keeping X: {x_min:.2f} to {x_max:.2f} mm") print(f" X points: {x_mask.sum()} / {len(x_unique)}") # Filter out rows with invalid y values y_valid_mask = ~np.isnan(y_full) # Compute profiles for all variables profiles = {} for var in ['ux', 'uy', 'uz', 'uu', 'vv', 'ww', 'uv', 'uw', 'vw']: data = piv_data[var] # Average along x for each row, then keep only valid y rows row_means = np.nanmean(data[:, x_mask], axis=1) profiles[var] = row_means[y_valid_mask] # Store y for valid rows only profiles['y_mm'] = y_full[y_valid_mask] # Rename for clarity profiles['U'] = profiles.pop('ux') profiles['V'] = profiles.pop('uy') profiles['W'] = profiles.pop('uz') return profiles def convert_to_wall_units(profiles, wall_units, y_offset_mm=0.0): """ Convert profiles to wall units. Parameters ---------- profiles : dict PIV profiles with y_mm, U, V, W, stresses wall_units : dict Wall unit parameters y_offset_mm : float Offset to add to y_mm for coordinate alignment """ u_tau = wall_units['u_tau'] delta_nu = wall_units['delta_nu'] u_tau2 = u_tau ** 2 y_mm_aligned = profiles['y_mm'] + y_offset_mm return { 'y_mm': y_mm_aligned, 'y_plus': y_mm_aligned / delta_nu, # Velocities 'U_plus': profiles['U'] / u_tau, 'V_plus': profiles['V'] / u_tau, 'W_plus': profiles['W'] / u_tau, # Normal stresses 'uu_plus': profiles['uu'] / u_tau2, 'vv_plus': profiles['vv'] / u_tau2, 'ww_plus': profiles['ww'] / u_tau2, # Shear stresses 'uv_plus': profiles['uv'] / u_tau2, 'uw_plus': profiles['uw'] / u_tau2, 'vw_plus': profiles['vw'] / u_tau2, } def compute_errors(piv_plus, gt_plus, y_plus_range=(10, 500)): """Compute error metrics between PIV and ground truth.""" y_piv = piv_plus['y_plus'] y_gt = gt_plus['y_plus'] mask_piv = (y_piv >= y_plus_range[0]) & (y_piv <= y_plus_range[1]) y_compare = y_piv[mask_piv] if len(y_compare) == 0: print(f" Warning: No PIV points in y+ range {y_plus_range}") return {} errors = {} variables = ['U_plus', 'V_plus', 'W_plus', 'uu_plus', 'vv_plus', 'ww_plus', 'uv_plus', 'uw_plus', 'vw_plus'] for var in variables: if var not in piv_plus or var not in gt_plus: continue piv_vals = piv_plus[var][mask_piv] # Interpolate ground truth gt_interp = interp1d(y_gt, gt_plus[var], kind='linear', bounds_error=False, fill_value=np.nan) gt_vals = gt_interp(y_compare) # Remove NaN values valid = ~np.isnan(piv_vals) & ~np.isnan(gt_vals) if valid.sum() == 0: continue piv_valid = piv_vals[valid] gt_valid = gt_vals[valid] # Compute metrics diff = piv_valid - gt_valid rms_error = np.sqrt(np.mean(diff**2)) mean_abs_error = np.mean(np.abs(diff)) gt_range = np.ptp(gt_valid) rms_rel = (rms_error / gt_range * 100) if gt_range > 0 else np.nan corr = np.corrcoef(piv_valid, gt_valid)[0, 1] if len(piv_valid) > 1 else np.nan ss_res = np.sum(diff**2) ss_tot = np.sum((gt_valid - gt_valid.mean())**2) r2 = 1 - (ss_res / ss_tot) if ss_tot > 0 else np.nan errors[var] = { 'rms': rms_error, 'rms_rel': rms_rel, 'mae': mean_abs_error, 'corr': corr, 'r2': r2, 'n_points': valid.sum(), } return errors def plot_velocity_comparison(piv_plus, gt_plus, wall_units, errors, output_dir): """Generate velocity comparison plots (U+, V+, W+).""" output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) Re_tau = wall_units['Re_tau'] has_ci = 'uu_plus_ci_lo' in gt_plus # ========================================================================== # Figure 1: U+ profile (semilog) # ========================================================================== fig, ax = plt.subplots(figsize=(10, 7)) if has_ci and 'U_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['U_plus_ci_lo'], gt_plus['U_plus_ci_hi'], color='k', alpha=0.15, zorder=1) ax.semilogx(gt_plus['y_plus'], gt_plus['U_plus'], 'k-', linewidth=2, label='DNS (1px)', zorder=3) ax.semilogx(piv_plus['y_plus'], piv_plus['U_plus'], 'ro', markersize=4, alpha=0.7, label='Stereo PIV', zorder=2) # Log law reference y_log = np.logspace(1, np.log10(Re_tau), 100) kappa, B = 0.41, 5.2 U_log = (1/kappa) * np.log(y_log) + B ax.semilogx(y_log, U_log, 'b--', linewidth=1, alpha=0.7, label=r'Log law: $U^+ = \frac{1}{\kappa}\ln(y^+) + B$') # Viscous sublayer y_visc = np.linspace(0.1, 10, 50) ax.semilogx(y_visc, y_visc, 'g--', linewidth=1, alpha=0.7, label=r'Viscous sublayer: $U^+ = y^+$') ax.set_xlabel(r'$y^+$', fontsize=14) ax.set_ylabel(r'$U^+$', fontsize=14) ax.set_title(f'Mean Streamwise Velocity - Stereo PIV (Re$_\\tau$ = {Re_tau:.0f})', fontsize=16) ax.legend(fontsize=11) ax.set_xlim(1, Re_tau) ax.set_ylim(0, 25) ax.grid(True, alpha=0.3) if 'U_plus' in errors: ax.text(0.02, 0.98, f"R² = {errors['U_plus']['r2']:.4f}\n" f"RMS = {errors['U_plus']['rms_rel']:.1f}%", transform=ax.transAxes, fontsize=11, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) fig.tight_layout() fig.savefig(output_dir / 'U_plus_profile.png', dpi=150) plt.close(fig) # ========================================================================== # Figure 2: V+ profile # ========================================================================== fig, ax = plt.subplots(figsize=(10, 7)) if has_ci and 'V_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['V_plus_ci_lo'], gt_plus['V_plus_ci_hi'], color='k', alpha=0.15, zorder=1) ax.plot(gt_plus['y_plus'], gt_plus['V_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], piv_plus['V_plus'], 'ro', markersize=4, alpha=0.7, label='Stereo PIV') ax.axhline(y=0, color='gray', linestyle='--', linewidth=0.5) ax.set_xlabel(r'$y^+$', fontsize=14) ax.set_ylabel(r'$V^+$', fontsize=14) ax.set_title('Mean Wall-Normal Velocity - Stereo PIV', fontsize=16) ax.legend(fontsize=11) ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'V_plus' in errors: ax.text(0.02, 0.98, f"R² = {errors['V_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=11, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) fig.tight_layout() fig.savefig(output_dir / 'V_plus_profile.png', dpi=150) plt.close(fig) # ========================================================================== # Figure 3: W+ profile (spanwise - should be ~0 for channel flow) # ========================================================================== fig, ax = plt.subplots(figsize=(10, 7)) if has_ci and 'W_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['W_plus_ci_lo'], gt_plus['W_plus_ci_hi'], color='k', alpha=0.15, zorder=1) ax.plot(gt_plus['y_plus'], gt_plus['W_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], piv_plus['W_plus'], 'bo', markersize=4, alpha=0.7, label='Stereo PIV') ax.axhline(y=0, color='gray', linestyle='--', linewidth=0.5) ax.set_xlabel(r'$y^+$', fontsize=14) ax.set_ylabel(r'$W^+$', fontsize=14) ax.set_title('Mean Spanwise Velocity - Stereo PIV (should be ~0)', fontsize=16) ax.legend(fontsize=11) ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'W_plus' in errors: ax.text(0.02, 0.98, f"R² = {errors['W_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=11, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) fig.tight_layout() fig.savefig(output_dir / 'W_plus_profile.png', dpi=150) plt.close(fig) # ========================================================================== # Figure 4: All velocities combined # ========================================================================== fig, axes = plt.subplots(1, 3, figsize=(16, 5)) # U+ ax = axes[0] if has_ci and 'U_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['U_plus_ci_lo'], gt_plus['U_plus_ci_hi'], color='k', alpha=0.15, zorder=1) ax.semilogx(gt_plus['y_plus'], gt_plus['U_plus'], 'k-', linewidth=2, label='DNS') ax.semilogx(piv_plus['y_plus'], piv_plus['U_plus'], 'ro', markersize=3, alpha=0.7, label='Stereo') ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r'$U^+$', fontsize=12) ax.set_title('Streamwise Velocity', fontsize=14) ax.legend() ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) # V+ ax = axes[1] if has_ci and 'V_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['V_plus_ci_lo'], gt_plus['V_plus_ci_hi'], color='k', alpha=0.15, zorder=1) ax.plot(gt_plus['y_plus'], gt_plus['V_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], piv_plus['V_plus'], 'ro', markersize=3, alpha=0.7, label='Stereo') ax.axhline(y=0, color='gray', linestyle='--', linewidth=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r'$V^+$', fontsize=12) ax.set_title('Wall-Normal Velocity', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) # W+ ax = axes[2] if has_ci and 'W_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['W_plus_ci_lo'], gt_plus['W_plus_ci_hi'], color='k', alpha=0.15, zorder=1) ax.plot(gt_plus['y_plus'], gt_plus['W_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], piv_plus['W_plus'], 'bo', markersize=3, alpha=0.7, label='Stereo') ax.axhline(y=0, color='gray', linestyle='--', linewidth=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r'$W^+$', fontsize=12) ax.set_title('Spanwise Velocity', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) fig.tight_layout() fig.savefig(output_dir / 'velocities_combined.png', dpi=150) plt.close(fig) def plot_normal_stresses(piv_plus, gt_plus, wall_units, errors, output_dir): """Generate normal stress plots (uu+, vv+, ww+).""" output_dir = Path(output_dir) Re_tau = wall_units['Re_tau'] has_ci = 'uu_plus_ci_lo' in gt_plus fig, axes = plt.subplots(1, 3, figsize=(16, 5)) # uu+ ax = axes[0] if has_ci: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['uu_plus_ci_lo'], gt_plus['uu_plus_ci_hi'], color='k', zorder=1) ax.plot(gt_plus['y_plus'], gt_plus['uu_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], piv_plus['uu_plus'], 'ro', markersize=3, alpha=0.7, label='Stereo') ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"$\overline{u'u'}^+$", fontsize=12) ax.set_title('Streamwise Normal Stress', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'uu_plus' in errors: ax.text(0.98, 0.98, f"R² = {errors['uu_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=10, ha='right', va='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) # vv+ ax = axes[1] if has_ci: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['vv_plus_ci_lo'], gt_plus['vv_plus_ci_hi'], color='k', zorder=1) ax.plot(gt_plus['y_plus'], gt_plus['vv_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], piv_plus['vv_plus'], 'go', markersize=3, alpha=0.7, label='Stereo') ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"$\overline{v'v'}^+$", fontsize=12) ax.set_title('Wall-Normal Normal Stress', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'vv_plus' in errors: ax.text(0.98, 0.98, f"R² = {errors['vv_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=10, ha='right', va='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) # ww+ ax = axes[2] if has_ci and 'ww_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['ww_plus_ci_lo'], gt_plus['ww_plus_ci_hi'], color='k', zorder=1) ax.plot(gt_plus['y_plus'], gt_plus['ww_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], piv_plus['ww_plus'], 'bo', markersize=3, alpha=0.7, label='Stereo') ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"$\overline{w'w'}^+$", fontsize=12) ax.set_title('Spanwise Normal Stress', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'ww_plus' in errors: ax.text(0.98, 0.98, f"R² = {errors['ww_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=10, ha='right', va='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) fig.suptitle('Normal Reynolds Stresses - Stereo PIV', fontsize=16, y=1.02) fig.tight_layout() fig.savefig(output_dir / 'normal_stresses.png', dpi=150) plt.close(fig) def plot_shear_stresses(piv_plus, gt_plus, wall_units, errors, output_dir): """Generate shear stress plots (-uv+, -uw+, -vw+).""" output_dir = Path(output_dir) Re_tau = wall_units['Re_tau'] has_ci = 'uv_plus_ci_lo' in gt_plus fig, axes = plt.subplots(1, 3, figsize=(16, 5)) # -uv+ ax = axes[0] if has_ci: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['uv_plus_ci_lo'], gt_plus['uv_plus_ci_hi'], sign=-1, color='k', zorder=1) ax.plot(gt_plus['y_plus'], -gt_plus['uv_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], -piv_plus['uv_plus'], 'ro', markersize=3, alpha=0.7, label='Stereo') ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"$-\overline{u'v'}^+$", fontsize=12) ax.set_title('Reynolds Shear Stress (u-v)', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'uv_plus' in errors: ax.text(0.98, 0.98, f"R² = {errors['uv_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=10, ha='right', va='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) # -uw+ ax = axes[1] if has_ci and 'uw_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['uw_plus_ci_lo'], gt_plus['uw_plus_ci_hi'], sign=-1, color='k', zorder=1) ax.plot(gt_plus['y_plus'], -gt_plus['uw_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], -piv_plus['uw_plus'], 'go', markersize=3, alpha=0.7, label='Stereo') ax.axhline(y=0, color='gray', linestyle='--', linewidth=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"$-\overline{u'w'}^+$", fontsize=12) ax.set_title('Reynolds Shear Stress (u-w)', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'uw_plus' in errors: ax.text(0.98, 0.98, f"R² = {errors['uw_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=10, ha='right', va='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) # -vw+ ax = axes[2] if has_ci and 'vw_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['vw_plus_ci_lo'], gt_plus['vw_plus_ci_hi'], sign=-1, color='k', zorder=1) ax.plot(gt_plus['y_plus'], -gt_plus['vw_plus'], 'k-', linewidth=2, label='DNS') ax.plot(piv_plus['y_plus'], -piv_plus['vw_plus'], 'bo', markersize=3, alpha=0.7, label='Stereo') ax.axhline(y=0, color='gray', linestyle='--', linewidth=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"$-\overline{v'w'}^+$", fontsize=12) ax.set_title('Reynolds Shear Stress (v-w)', fontsize=14) ax.legend() ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) if 'vw_plus' in errors: ax.text(0.98, 0.98, f"R² = {errors['vw_plus']['r2']:.4f}", transform=ax.transAxes, fontsize=10, ha='right', va='top', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8)) fig.suptitle('Reynolds Shear Stresses - Stereo PIV', fontsize=16, y=1.02) fig.tight_layout() fig.savefig(output_dir / 'shear_stresses.png', dpi=150) plt.close(fig) def plot_combined_stresses(piv_plus, gt_plus, wall_units, errors, output_dir): """Plot uu+, vv+, ww+, -uv+ all on a single axis.""" output_dir = Path(output_dir) Re_tau = wall_units['Re_tau'] has_ci = 'uu_plus_ci_lo' in gt_plus fig, ax = plt.subplots(figsize=(12, 8)) # CI bands (behind everything) if has_ci: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['uu_plus_ci_lo'], gt_plus['uu_plus_ci_hi'], color='k', alpha=0.12, zorder=1) plot_ci_band(ax, gt_plus['y_plus'], gt_plus['vv_plus_ci_lo'], gt_plus['vv_plus_ci_hi'], color='k', alpha=0.12, zorder=1) if 'ww_plus_ci_lo' in gt_plus: plot_ci_band(ax, gt_plus['y_plus'], gt_plus['ww_plus_ci_lo'], gt_plus['ww_plus_ci_hi'], color='k', alpha=0.12, zorder=1) plot_ci_band(ax, gt_plus['y_plus'], gt_plus['uv_plus_ci_lo'], gt_plus['uv_plus_ci_hi'], sign=-1, color='k', alpha=0.12, zorder=1) # Ground truth / reference ax.plot(gt_plus['y_plus'], gt_plus['uu_plus'], 'k-', linewidth=2, label=r"Ref $\overline{u'u'}^+$") ax.plot(gt_plus['y_plus'], gt_plus['vv_plus'], 'k--', linewidth=2, label=r"Ref $\overline{v'v'}^+$") ax.plot(gt_plus['y_plus'], gt_plus['ww_plus'], 'k-.', linewidth=2, label=r"Ref $\overline{w'w'}^+$") ax.plot(gt_plus['y_plus'], -gt_plus['uv_plus'], 'k:', linewidth=2, label=r"Ref $-\overline{u'v'}^+$") # Stereo PIV — markers only (no smoothed lines) piv_configs = [ ('uu_plus', 1, 'r', 'o', r"Stereo $\overline{u'u'}^+$"), ('vv_plus', 1, 'g', 's', r"Stereo $\overline{v'v'}^+$"), ('ww_plus', 1, 'b', '^', r"Stereo $\overline{w'w'}^+$"), ('uv_plus', -1, 'm', 'D', r"Stereo $-\overline{u'v'}^+$"), ] for var, sign, col, mkr, label in piv_configs: piv_vals = sign * piv_plus[var] ax.plot(piv_plus['y_plus'], piv_vals, color=col, marker=mkr, markersize=4, alpha=0.7, linestyle='none', label=label, zorder=5) ax.set_xlabel(r'$y^+$') ax.set_ylabel(r'Stress$^+$') ax.set_title(r'Reynolds Stresses -- Stereo PIV vs Reference ($\mathrm{Re}_\tau$ = ' + f'{Re_tau:.0f})') ax.legend(ncol=2, loc='upper right') ax.set_xscale('log') ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) fig.tight_layout() fig.savefig(output_dir / 'combined_stresses.png', dpi=150) plt.close(fig) def plot_residuals(piv_plus, gt_plus, wall_units, output_dir): """Plot residuals (PIV - Ref) for velocities and stresses.""" output_dir = Path(output_dir) Re_tau = wall_units['Re_tau'] fig, axes = plt.subplots(2, 3, figsize=(16, 10)) # Interpolate ground truth onto PIV y+ grid gt_interp_fn = {} for var in ['U_plus', 'V_plus', 'W_plus', 'uu_plus', 'vv_plus', 'ww_plus', 'uv_plus', 'uw_plus', 'vw_plus']: if var in gt_plus: gt_interp_fn[var] = interp1d(gt_plus['y_plus'], gt_plus[var], kind='linear', bounds_error=False, fill_value=np.nan) # Top row: velocity residuals (U+, V+, W+) vel_configs = [ ('U_plus', r"$U^+_{\mathrm{PIV}} - U^+_{\mathrm{Ref}}$", 'Streamwise Velocity Residual', 1), ('V_plus', r"$V^+_{\mathrm{PIV}} - V^+_{\mathrm{Ref}}$", 'Wall-Normal Velocity Residual', 1), ('W_plus', r"$W^+_{\mathrm{PIV}} - W^+_{\mathrm{Ref}}$", 'Spanwise Velocity Residual', 1), ] for ax, (var, ylabel, title, sign) in zip(axes[0, :], vel_configs): gt_at_piv = gt_interp_fn[var](piv_plus['y_plus']) residual = sign * piv_plus[var] - sign * gt_at_piv ax.semilogx(piv_plus['y_plus'], residual, 'ro', markersize=3, alpha=0.5) yp_s, r_s = log_smooth(piv_plus['y_plus'], residual) ax.semilogx(yp_s, r_s, 'r-', linewidth=2, label='Stereo PIV') ax.axhline(y=0, color='k', linestyle='-', linewidth=1, alpha=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(ylabel, fontsize=12) ax.set_title(title, fontsize=14) ax.legend() ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) # Bottom row: normal stress residuals (uu+, vv+, ww+) stress_configs = [ ('uu_plus', r"$\overline{u'u'}^+_{\mathrm{PIV}} - \overline{u'u'}^+_{\mathrm{Ref}}$", 'Streamwise Normal Stress Residual', 1), ('vv_plus', r"$\overline{v'v'}^+_{\mathrm{PIV}} - \overline{v'v'}^+_{\mathrm{Ref}}$", 'Wall-Normal Normal Stress Residual', 1), ('ww_plus', r"$\overline{w'w'}^+_{\mathrm{PIV}} - \overline{w'w'}^+_{\mathrm{Ref}}$", 'Spanwise Normal Stress Residual', 1), ] for ax, (var, ylabel, title, sign) in zip(axes[1, :], stress_configs): gt_at_piv = gt_interp_fn[var](piv_plus['y_plus']) residual = sign * piv_plus[var] - sign * gt_at_piv ax.semilogx(piv_plus['y_plus'], residual, 'ro', markersize=3, alpha=0.5) yp_s, r_s = log_smooth(piv_plus['y_plus'], residual) ax.semilogx(yp_s, r_s, 'r-', linewidth=2, label='Stereo PIV') ax.axhline(y=0, color='k', linestyle='-', linewidth=1, alpha=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(ylabel, fontsize=12) ax.set_title(title, fontsize=14) ax.legend() ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) fig.tight_layout() fig.savefig(output_dir / 'residuals.png', dpi=150) plt.close(fig) # Additional figure: shear stress residuals (uv+, uw+, vw+) fig, axes = plt.subplots(1, 3, figsize=(16, 5)) shear_configs = [ ('uv_plus', r"$-\overline{u'v'}^+_{\mathrm{PIV}} - (-\overline{u'v'}^+_{\mathrm{Ref}})$", 'Shear Stress Residual (u-v)', -1), ('uw_plus', r"$-\overline{u'w'}^+_{\mathrm{PIV}} - (-\overline{u'w'}^+_{\mathrm{Ref}})$", 'Shear Stress Residual (u-w)', -1), ('vw_plus', r"$-\overline{v'w'}^+_{\mathrm{PIV}} - (-\overline{v'w'}^+_{\mathrm{Ref}})$", 'Shear Stress Residual (v-w)', -1), ] for ax, (var, ylabel, title, sign) in zip(axes, shear_configs): gt_at_piv = gt_interp_fn[var](piv_plus['y_plus']) residual = sign * piv_plus[var] - sign * gt_at_piv ax.semilogx(piv_plus['y_plus'], residual, 'ro', markersize=3, alpha=0.5) yp_s, r_s = log_smooth(piv_plus['y_plus'], residual) ax.semilogx(yp_s, r_s, 'r-', linewidth=2, label='Stereo PIV') ax.axhline(y=0, color='k', linestyle='-', linewidth=1, alpha=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(ylabel, fontsize=12) ax.set_title(title, fontsize=14) ax.legend() ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) fig.suptitle('Shear Stress Residuals - Stereo PIV', fontsize=14, y=1.02) fig.tight_layout() fig.savefig(output_dir / 'residuals_shear.png', dpi=150) plt.close(fig) def plot_noise_gradient_decomposition(piv_plus, gt_plus, wall_units, output_dir): """Plot noise floor vs gradient correction decomposition. Uses the fact that PIV measurement noise is approximately isotropic (vv+ residual ~ noise floor), while velocity gradient bias is anisotropic (uu+ - vv+ removes the isotropic noise contribution). Extended for stereo: ww+ residual provides an independent noise floor check. """ output_dir = Path(output_dir) Re_tau = wall_units['Re_tau'] # Interpolate ground truth onto PIV y+ grid gt_interp = {} for var in ['uu_plus', 'vv_plus', 'ww_plus']: gt_interp[var] = interp1d(gt_plus['y_plus'], gt_plus[var], kind='linear', bounds_error=False, fill_value=np.nan) uu_residual = piv_plus['uu_plus'] - gt_interp['uu_plus'](piv_plus['y_plus']) vv_residual = piv_plus['vv_plus'] - gt_interp['vv_plus'](piv_plus['y_plus']) ww_residual = piv_plus['ww_plus'] - gt_interp['ww_plus'](piv_plus['y_plus']) gradient_only = uu_residual - vv_residual fig, axes = plt.subplots(1, 3, figsize=(16, 5)) # Left: Noise floor (vv+ residual — isotropic) ax = axes[0] ax.semilogx(piv_plus['y_plus'], vv_residual, 'bo', markersize=2, alpha=0.3) yp_s, r_s = log_smooth(piv_plus['y_plus'], vv_residual) ax.semilogx(yp_s, r_s, 'b-', linewidth=2.5, label=r"$v'v'$ residual (noise floor)") # Overlay ww+ as independent noise check ax.semilogx(piv_plus['y_plus'], ww_residual, 'co', markersize=2, alpha=0.2) yp_s_ww, r_s_ww = log_smooth(piv_plus['y_plus'], ww_residual) ax.semilogx(yp_s_ww, r_s_ww, 'c--', linewidth=2, label=r"$w'w'$ residual (check)") ax.axhline(y=0, color='k', linestyle='-', linewidth=1, alpha=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"Noise floor residual$^+$", fontsize=12) ax.set_title('Noise Floor (isotropic)', fontsize=14) ax.legend(fontsize=10) ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) # Middle: Gradient-only residual (uu+ - vv+ removes isotropic noise) ax = axes[1] ax.semilogx(piv_plus['y_plus'], gradient_only, 'ro', markersize=2, alpha=0.3) yp_s, r_s = log_smooth(piv_plus['y_plus'], gradient_only) ax.semilogx(yp_s, r_s, 'r-', linewidth=2.5, label=r"$(\overline{u'u'} - \overline{v'v'})_{\mathrm{PIV}} - (\overline{u'u'} - \overline{v'v'})_{\mathrm{Ref}}$") ax.axhline(y=0, color='k', linestyle='-', linewidth=1, alpha=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"Gradient-only residual$^+$", fontsize=12) ax.set_title(r"Gradient Correction Residual ($u'u' - v'v'$ removes noise)", fontsize=14) ax.legend(fontsize=9) ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) # Right: All overlaid ax = axes[2] ax.semilogx(piv_plus['y_plus'], uu_residual, 'ro', markersize=2, alpha=0.15) yp_s_uu, r_s_uu = log_smooth(piv_plus['y_plus'], uu_residual) ax.semilogx(yp_s_uu, r_s_uu, 'r-', linewidth=2, label=r"$u'u'$ residual (total)") ax.semilogx(piv_plus['y_plus'], vv_residual, 'bo', markersize=2, alpha=0.15) yp_s_vv, r_s_vv = log_smooth(piv_plus['y_plus'], vv_residual) ax.semilogx(yp_s_vv, r_s_vv, 'b-', linewidth=2, label=r"$v'v'$ residual (noise floor)") yp_s_g, r_s_g = log_smooth(piv_plus['y_plus'], gradient_only) ax.semilogx(yp_s_g, r_s_g, 'g--', linewidth=2, label=r"$u'u' - v'v'$ residual (gradient only)") ax.axhline(y=0, color='k', linestyle='-', linewidth=1, alpha=0.5) ax.set_xlabel(r'$y^+$', fontsize=12) ax.set_ylabel(r"Residual$^+$", fontsize=12) ax.set_title('Decomposition: Total = Noise + Gradient', fontsize=14) ax.legend(fontsize=9) ax.set_xlim(1, Re_tau) ax.grid(True, alpha=0.3) fig.suptitle('Noise Floor vs Gradient Correction - Stereo PIV', fontsize=14, y=1.02) fig.tight_layout() fig.savefig(output_dir / 'noise_gradient_decomposition.png', dpi=150) plt.close(fig) def main(run_idx=2, x_min=5.0, x_max=145.0, gt_dir=None, stereo_base=None, num_frames=1000, output_dir_override=None, trim_top=0): """Main stereo benchmark comparison function.""" # Paths script_dir = Path(__file__).parent if gt_dir is None: raise ValueError("gt_dir is required. Provide the ground truth directory path.") gt_dir = Path(gt_dir) if stereo_base is None: raise ValueError("stereo_base is required. Provide the stereo PIV results directory path.") stereo_base = Path(stereo_base) stats_path = stereo_base / f'statistics/{num_frames}/stereo/Cam1_Cam2/instantaneous/mean_stats/mean_stats.mat' coords_path = stereo_base / f'stereo_calibrated/{num_frames}/Cam1_Cam2/instantaneous/coordinates.mat' output_dir = output_dir_override or (script_dir / 'benchmark_results_stereo') print("=" * 70) print("STEREO PIV BENCHMARK COMPARISON") print("=" * 70) print(f"Run index: {run_idx}") print(f"X range: {x_min} to {x_max} mm") # Load data - detect file format wall_units_file = gt_dir / 'wall_units.mat' if not wall_units_file.exists(): wall_units_file = gt_dir / 'diagnostics.mat' if not wall_units_file.exists(): wall_units_file = gt_dir / 'direct_stats.mat' profiles_file = gt_dir / 'profiles.mat' if not profiles_file.exists(): profiles_file = gt_dir / 'ensemble_statistics_full.mat' if not profiles_file.exists(): profiles_file = gt_dir / 'direct_stats.mat' print("\n[1] Loading wall units...") print(f" Source: {wall_units_file.name}") wall_units = load_wall_units(wall_units_file) print(f" u_tau = {wall_units['u_tau']:.4f} mm/s") print(f" delta_nu = {wall_units['delta_nu']:.4f} mm") print(f" Re_tau = {wall_units['Re_tau']:.0f}") print("\n[2] Loading ground truth (3-component)...") print(f" Source: {profiles_file.name}") gt = load_ground_truth_3d(profiles_file) print(f" y+ range: {gt['y_plus'].min():.1f} to {gt['y_plus'].max():.1f}") print(f" U range: {gt['U'].min():.2f} to {gt['U'].max():.2f} mm/s") print(f" W range: {gt['W'].min():.2f} to {gt['W'].max():.2f} mm/s") print(f"\n[3] Loading stereo PIV statistics (run {run_idx})...") piv = load_stereo_statistics(stats_path, coords_path, run_idx=run_idx) print(f" Grid size: {piv['ux'].shape}") print(f" ux range: {np.nanmin(piv['ux']):.2f} to {np.nanmax(piv['ux']):.2f} mm/s") print(f" uz (W) range: {np.nanmin(piv['uz']):.2f} to {np.nanmax(piv['uz']):.2f} mm/s") print("\n[4] Computing x-averaged profiles...") piv_profiles = compute_stereo_profiles(piv, x_min=x_min, x_max=x_max) print(f" y range: {piv_profiles['y_mm'].min():.2f} to {piv_profiles['y_mm'].max():.2f} mm") print("\n[5] Converting to wall units...") y_offset_mm = -piv_profiles['y_mm'].min() print(f" Applying y-offset: {y_offset_mm:.2f} mm") piv_plus = convert_to_wall_units(piv_profiles, wall_units, y_offset_mm=y_offset_mm) piv_plus['y_plus'] = piv_plus['y_plus'] + 1.0 # shift y+ by +1 print(f" y+ range: {piv_plus['y_plus'].min():.1f} to {piv_plus['y_plus'].max():.1f} (after +1 shift)") if trim_top > 0: # Sort by y+ to ensure we trim from the correct end sort_idx = np.argsort(piv_plus['y_plus']) for key in piv_plus: piv_plus[key] = piv_plus[key][sort_idx] # Now trim the last N points (highest y+) keep = slice(None, len(piv_plus['y_plus']) - trim_top) for key in piv_plus: piv_plus[key] = piv_plus[key][keep] print(f" Trimmed top {trim_top} highest-y+ points -> {len(piv_plus['y_plus'])} remaining") print(f" y+ range after trim: {piv_plus['y_plus'].min():.1f} to {piv_plus['y_plus'].max():.1f}") # Ground truth in wall units u_tau = wall_units['u_tau'] u_tau2 = u_tau ** 2 gt_plus = { 'y_plus': gt['y_plus'], 'U_plus': gt['U_plus'], 'V_plus': gt['V'] / u_tau, 'W_plus': gt['W'] / u_tau, 'uu_plus': gt['uu_plus'], 'vv_plus': gt['vv_plus'], 'ww_plus': gt['ww_plus'], 'uv_plus': gt['uv_plus'], 'uw_plus': gt['uw'] / u_tau2, 'vw_plus': gt['vw'] / u_tau2, } # Thread CI bounds through if available for ci_key in ['U_plus_ci_lo', 'U_plus_ci_hi', 'V_plus_ci_lo', 'V_plus_ci_hi', 'W_plus_ci_lo', 'W_plus_ci_hi', 'uu_plus_ci_lo', 'uu_plus_ci_hi', 'vv_plus_ci_lo', 'vv_plus_ci_hi', 'ww_plus_ci_lo', 'ww_plus_ci_hi', 'uv_plus_ci_lo', 'uv_plus_ci_hi', 'uw_plus_ci_lo', 'uw_plus_ci_hi', 'vw_plus_ci_lo', 'vw_plus_ci_hi']: if ci_key in gt: gt_plus[ci_key] = gt[ci_key] print("\n[6] Computing error metrics (y+ = 10-500)...") errors = compute_errors(piv_plus, gt_plus, y_plus_range=(10, 500)) # Print results print("\n" + "=" * 70) print("STEREO BENCHMARK RESULTS") print("=" * 70) var_names = { 'U_plus': 'Streamwise Velocity (U+)', 'V_plus': 'Wall-normal Velocity (V+)', 'W_plus': 'Spanwise Velocity (W+)', 'uu_plus': 'Streamwise Stress (uu+)', 'vv_plus': 'Wall-normal Stress (vv+)', 'ww_plus': 'Spanwise Stress (ww+)', 'uv_plus': 'Shear Stress (uv+)', 'uw_plus': 'Shear Stress (uw+)', 'vw_plus': 'Shear Stress (vw+)', } for var, err in errors.items(): name = var_names.get(var, var) print(f"\n{name}:") print(f" RMS Error: {err['rms']:.4f} ({err['rms_rel']:.1f}% of range)") print(f" R²: {err['r2']:.4f}") print(f" Correlation: {err['corr']:.4f}") print("\n[7] Generating plots...") plot_velocity_comparison(piv_plus, gt_plus, wall_units, errors, output_dir) plot_normal_stresses(piv_plus, gt_plus, wall_units, errors, output_dir) plot_shear_stresses(piv_plus, gt_plus, wall_units, errors, output_dir) plot_combined_stresses(piv_plus, gt_plus, wall_units, errors, output_dir) plot_residuals(piv_plus, gt_plus, wall_units, output_dir) plot_noise_gradient_decomposition(piv_plus, gt_plus, wall_units, output_dir) print(f"\nPlots saved to: {output_dir}") # Summary table print("\n" + "=" * 70) print("SUMMARY TABLE") print("=" * 70) print(f"\n{'Variable':<20} {'R²':<10} {'RMS%':<10} {'Corr':<10}") print("-" * 50) for var in ['U_plus', 'V_plus', 'W_plus', 'uu_plus', 'vv_plus', 'ww_plus', 'uv_plus', 'uw_plus', 'vw_plus']: if var in errors: e = errors[var] print(f"{var:<20} {e['r2']:<10.4f} {e['rms_rel']:<10.1f} {e['corr']:<10.4f}") print("\n" + "=" * 70) print("STEREO BENCHMARK COMPLETE") print("=" * 70) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Stereo PIV Benchmark Comparison') parser.add_argument('--run', '-r', type=int, default=2, help='Run index (0-based), default=2 (finest available)') parser.add_argument('--x-min', type=float, default=5.0, help='Minimum x to include (mm), default=5.0') parser.add_argument('--x-max', type=float, default=145.0, help='Maximum x to include (mm), default=145.0') parser.add_argument('--gt-dir', '-g', type=str, default=None, help='Ground truth directory path') parser.add_argument('--stereo-base', '-s', type=str, default=None, help='Base directory containing stereo PIV results') parser.add_argument('--num-frames', '-n', type=int, default=1000, help='Frame count subdirectory in paths (default: 1000)') parser.add_argument('--output-dir', '-o', type=str, default=None, help='Custom output directory for results') parser.add_argument('--trim-top', '-t', type=int, default=0, help='Number of highest-y+ vectors to exclude (default: 0)') args = parser.parse_args() output_dir = Path(args.output_dir) if args.output_dir else None main(run_idx=args.run, x_min=args.x_min, x_max=args.x_max, gt_dir=args.gt_dir, stereo_base=args.stereo_base, num_frames=args.num_frames, output_dir_override=output_dir, trim_top=args.trim_top)