"""Surface-field screenshots of the benchmark predictions on one reference test run. Renders the surface fields seen from below, looking towards +Z. One PNG per model per quantity; the panels are composed side by side in the paper's LaTeX rather than montaged here. Outputs, under ``outputs/fields/``: cp_truth.png CFD ground-truth surface C_p cp_pred_.png predicted C_p, one per model cp_err_.png signed error (prediction - truth), one per model ws_truth.png CFD ground-truth |w_s| ws_pred_.png predicted |w_s|, one per model Run surface_metrics.py first: the panels are ordered by each model's mean $C_l$ error. """ from __future__ import annotations import sys from pathlib import Path import numpy as np import pyvista as pv sys.path.insert(0, str(Path(__file__).resolve().parent)) from inference_io import ( # noqa: E402 field_keys, here, model_labels, load_coefficients, mesh_files, read_field, relative_error, reported_models, ) pv.OFF_SCREEN = True reference_run = "0169_7575" reference_split = "stratified" output_dir = here / "outputs" / "fields" output_dir.mkdir(parents=True, exist_ok=True) cp_field = "MeanPressureCoefficient" ws_field = "MeanWallShearStress" assert set(field_keys) == {cp_field, ws_field}, field_keys colormap = "turbo" window_size = (1700, 780) # (colour limits, colourbar title) per rendered quantity specs = { "cp": ((-2.0, 1.0), r"$C_p$"), "cp_err": ((-0.3, 0.3), r"$\Delta C_p$"), "ws": ((0.0, 25.0), r"$w_s$"), } def ranked_models() -> list[str]: """Model keys ordered by mean relative Cl error on the stratified split, best first.""" err = { model: relative_error(load_coefficients("stratified", model), "Cl").mean() for model in reported_models() } return sorted(err, key=err.get) def attach(mesh, values, name="field"): """Attach `values` to the mesh, choosing cell or point association by length.""" if len(values) == mesh.n_cells: mesh.cell_data[name] = values elif len(values) == mesh.n_points: mesh.point_data[name] = values else: raise ValueError( f"{len(values)} values match neither n_cells={mesh.n_cells} " f"nor n_points={mesh.n_points}" ) return name def field_values(mesh, field: str, kind: str) -> np.ndarray: """Ground truth, prediction or signed error (prediction - truth) of one field. The wall shear stress is a vector, and the paper reports its magnitude. """ truth = read_field(mesh, field) pred = read_field(mesh, f"{field}Pred") if truth.ndim == 2: truth = np.linalg.norm(truth, axis=1) pred = np.linalg.norm(pred, axis=1) return {"truth": truth, "pred": pred, "error": pred - truth}[kind] def render(mesh, name: str, clim, title: str, path: Path) -> Path: """Bottom view of one field: camera below the wing, looking towards +Z.""" plotter = pv.Plotter(off_screen=True, window_size=window_size) plotter.set_background("white") plotter.add_mesh( mesh, scalars=name, cmap=colormap, clim=clim, lighting=False, show_scalar_bar=False ) plotter.add_scalar_bar( title=title, vertical=False, position_x=0.20, position_y=0.10, width=0.60, height=0.055, title_font_size=34, label_font_size=28, color="black", n_labels=5, fmt="%.1f", ) # +X (chord) points up in the frame, so the span runs horizontally. Parallel projection # with an explicit scale keeps the framing pixel-identical across every panel. plotter.view_xy(negative=True) plotter.camera.up = (1.0, 0.0, 0.0) plotter.enable_parallel_projection() bounds = mesh.bounds scale = ((bounds.y_max - bounds.y_min) / 2) / (window_size[0] / window_size[1]) / 0.96 mid_x = (bounds.x_min + bounds.x_max) / 2 mid_y = (bounds.y_min + bounds.y_max) / 2 mid_z = (bounds.z_min + bounds.z_max) / 2 lift = 0.34 * scale # frees the bottom band of the frame for the colourbar plotter.camera.parallel_scale = scale plotter.camera.focal_point = (mid_x - lift, mid_y, mid_z) plotter.camera.position = (mid_x - lift, mid_y, bounds.z_min - 5.0) plotter.add_axes( color="black", xlabel="x", ylabel="y", zlabel="z", viewport=(0.005, 0.02, 0.095, 0.27), label_size=(0.20, 0.13), ) plotter.screenshot(str(path)) plotter.close() return path def emit(mesh, field: str, kind: str, spec: str, path: Path) -> Path: """Render one panel.""" values = field_values(mesh, field, kind) clim, title = specs[spec] render(mesh, attach(mesh, values), clim, title, path) return path def main() -> None: order = ranked_models() print("reference run :", reference_run, f"({reference_split} split)") print("model order :", ", ".join(model_labels[m] for m in order)) # Surface C_p: ground truth, the six predictions, and the six signed error fields. The # ground truth is the same CFD field on the same mesh for every model, so it is rendered # once, from the best-ranked model's copy. truth_mesh = pv.read(mesh_files(reference_split, order[0])[reference_run]) emit(truth_mesh, cp_field, "truth", "cp", output_dir / "cp_truth.png") print("rendered C_p ground truth") for model in order: mesh = pv.read(mesh_files(reference_split, model)[reference_run]) emit(mesh, cp_field, "pred", "cp", output_dir / f"cp_pred_{model}.png") emit(mesh, cp_field, "error", "cp_err", output_dir / f"cp_err_{model}.png") print(f"rendered C_p {model_labels[model]}") # Surface wall-shear-stress magnitude: ground truth and the six predictions. truth_mesh = pv.read(mesh_files(reference_split, order[0])[reference_run]) emit(truth_mesh, ws_field, "truth", "ws", output_dir / "ws_truth.png") print("rendered |w_s| ground truth") for model in order: mesh = pv.read(mesh_files(reference_split, model)[reference_run]) emit(mesh, ws_field, "pred", "ws", output_dir / f"ws_pred_{model}.png") print(f"rendered |w_s| {model_labels[model]}") print(f"\nwrote 20 panels to {output_dir}") if __name__ == "__main__": main()