|
|
| """
|
| 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
|
|
|
|
|
| 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)
|
|
|
| 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
|
| 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)
|
|
|
| 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)
|
|
|
|
|
| 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,
|
| }
|
|
|
|
|
| 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)
|
|
|
|
|
| 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
|
|
|
|
|
| mask = y_plus_full <= Re_tau
|
| y_plus = y_plus_full[mask]
|
| y_mm = y_plus * delta_nu
|
|
|
|
|
| U_plus = profiles['U_plus'][mask, 0]
|
| V_plus = profiles['U_plus'][mask, 1]
|
| W_plus = profiles['U_plus'][mask, 2]
|
|
|
|
|
| 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,
|
| }
|
|
|
|
|
| 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
|
|
|
|
|
| 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, :]
|
|
|
|
|
| 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')
|
|
|
|
|
| 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)
|
|
|
|
|
| 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
|
|
|
|
|
| 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 {
|
|
|
| 'ux': piv.ux * 1000,
|
| 'uy': piv.uy * 1000,
|
| 'uz': piv.uz * 1000,
|
|
|
| 'uu': piv.uu * 1e6,
|
| 'vv': piv.vv * 1e6,
|
| 'ww': piv.ww * 1e6,
|
|
|
| 'uv': piv.uv * 1e6,
|
| 'uw': piv.uw * 1e6,
|
| 'vw': piv.vw * 1e6,
|
|
|
| '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']
|
|
|
|
|
|
|
| valid_mask = ~np.isnan(x)
|
| valid_rows = np.any(valid_mask, axis=1)
|
| valid_cols = np.any(valid_mask, axis=0)
|
|
|
|
|
| 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
|
|
|
|
|
| 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
|
|
|
|
|
| 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")
|
|
|
|
|
| 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)}")
|
|
|
|
|
| y_valid_mask = ~np.isnan(y_full)
|
|
|
|
|
| profiles = {}
|
|
|
| for var in ['ux', 'uy', 'uz', 'uu', 'vv', 'ww', 'uv', 'uw', 'vw']:
|
| data = piv_data[var]
|
|
|
| row_means = np.nanmean(data[:, x_mask], axis=1)
|
| profiles[var] = row_means[y_valid_mask]
|
|
|
|
|
| profiles['y_mm'] = y_full[y_valid_mask]
|
|
|
|
|
| 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,
|
|
|
| 'U_plus': profiles['U'] / u_tau,
|
| 'V_plus': profiles['V'] / u_tau,
|
| 'W_plus': profiles['W'] / u_tau,
|
|
|
| 'uu_plus': profiles['uu'] / u_tau2,
|
| 'vv_plus': profiles['vv'] / u_tau2,
|
| 'ww_plus': profiles['ww'] / u_tau2,
|
|
|
| '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]
|
|
|
|
|
| gt_interp = interp1d(y_gt, gt_plus[var], kind='linear',
|
| bounds_error=False, fill_value=np.nan)
|
| gt_vals = gt_interp(y_compare)
|
|
|
|
|
| valid = ~np.isnan(piv_vals) & ~np.isnan(gt_vals)
|
| if valid.sum() == 0:
|
| continue
|
|
|
| piv_valid = piv_vals[valid]
|
| gt_valid = gt_vals[valid]
|
|
|
|
|
| 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
|
|
|
|
|
|
|
|
|
| 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)
|
|
|
|
|
| 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: $U^+ = \frac{1}{\kappa}\ln(y^+) + B$')
|
|
|
|
|
| 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)
|
|
|
|
|
|
|
|
|
| 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)
|
|
|
|
|
|
|
|
|
| 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)
|
|
|
|
|
|
|
|
|
| fig, axes = plt.subplots(1, 3, figsize=(16, 5))
|
|
|
|
|
| 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)
|
|
|
|
|
| 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)
|
|
|
|
|
| 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))
|
|
|
|
|
| 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))
|
|
|
|
|
| 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))
|
|
|
|
|
| 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))
|
|
|
|
|
| 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))
|
|
|
|
|
| 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))
|
|
|
|
|
| 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))
|
|
|
|
|
| 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)
|
|
|
|
|
| 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 $-\overline{u'v'}^+$")
|
|
|
|
|
| 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))
|
|
|
|
|
| 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)
|
|
|
|
|
| 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)
|
|
|
|
|
| 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)
|
|
|
|
|
| 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']
|
|
|
|
|
| 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))
|
|
|
|
|
| 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)")
|
|
|
| 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)
|
|
|
|
|
| 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)
|
|
|
|
|
| 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."""
|
|
|
|
|
| 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")
|
|
|
|
|
| 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
|
| 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_idx = np.argsort(piv_plus['y_plus'])
|
| for key in piv_plus:
|
| piv_plus[key] = piv_plus[key][sort_idx]
|
|
|
| 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}")
|
|
|
|
|
| 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,
|
| }
|
|
|
| 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("\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}")
|
|
|
|
|
| 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)
|
|
|
