File size: 6,374 Bytes
b871dba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | #!/usr/bin/env python3
"""Render PointCFD ground truth, prediction, and absolute field errors."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
from typing import Any, Dict, List
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt # noqa: E402
import numpy as np # noqa: E402
PROJECT_ROOT = Path(__file__).resolve().parents[1]
project_root_string = str(PROJECT_ROOT)
if project_root_string in sys.path:
sys.path.remove(project_root_string)
sys.path.insert(0, project_root_string)
from scripts.common import resolve_path, write_json # noqa: E402
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--predictions",
type=Path,
default=PROJECT_ROOT / "results" / "predictions.npz",
)
parser.add_argument(
"--output-dir",
type=Path,
default=PROJECT_ROOT / "results" / "figures",
)
parser.add_argument("--num-cases", type=int, default=3)
parser.add_argument("--case-offset", type=int, default=0)
return parser.parse_args()
def main() -> None:
args = parse_args()
predictions_path = resolve_path(PROJECT_ROOT, str(args.predictions))
output_dir = resolve_path(PROJECT_ROOT, str(args.output_dir))
if not predictions_path.is_file():
raise FileNotFoundError(f"Prediction archive not found: {predictions_path}")
if args.num_cases <= 0:
raise ValueError("num-cases must be positive")
if args.case_offset < 0:
raise ValueError("case-offset cannot be negative")
with np.load(predictions_path, allow_pickle=False) as archive:
required = {"coordinates", "predictions", "targets", "case_indices", "target_names"}
missing = required - set(archive.files)
if missing:
raise ValueError(f"Prediction archive is missing keys: {sorted(missing)}")
coordinates = np.asarray(archive["coordinates"], dtype=np.float32)
predictions = np.asarray(archive["predictions"], dtype=np.float32)
targets = np.asarray(archive["targets"], dtype=np.float32)
case_indices = np.asarray(archive["case_indices"], dtype=np.int64)
target_names = [str(name) for name in archive["target_names"].tolist()]
if coordinates.ndim != 3 or coordinates.shape[-1] != 2:
raise ValueError(f"coordinates must be [cases,points,2], got {coordinates.shape}")
if predictions.shape != targets.shape or predictions.ndim != 3:
raise ValueError("predictions and targets must share [cases,points,variables]")
if coordinates.shape[:2] != predictions.shape[:2]:
raise ValueError("Coordinate and field case/point dimensions do not match")
if predictions.shape[-1] != len(target_names):
raise ValueError("target_names does not match prediction channels")
if not (np.isfinite(coordinates).all() and np.isfinite(predictions).all() and np.isfinite(targets).all()):
raise ValueError("Visualization inputs contain NaN or Infinity")
stop = min(args.case_offset + args.num_cases, coordinates.shape[0])
if args.case_offset >= stop:
raise ValueError("case-offset is beyond the available predictions")
output_dir.mkdir(parents=True, exist_ok=True)
generated: List[str] = []
case_summaries: List[Dict[str, Any]] = []
for local_index in range(args.case_offset, stop):
xy = coordinates[local_index]
case_prediction = predictions[local_index]
case_target = targets[local_index]
absolute_error = np.abs(case_prediction - case_target)
rows = len(target_names)
figure, axes = plt.subplots(rows, 3, figsize=(13.5, 4.1 * rows), squeeze=False)
variable_summary: Dict[str, Any] = {}
for channel, name in enumerate(target_names):
lower = float(min(case_target[:, channel].min(), case_prediction[:, channel].min()))
upper = float(max(case_target[:, channel].max(), case_prediction[:, channel].max()))
if upper <= lower:
upper = lower + 1.0e-12
panels = (
(case_target[:, channel], "Ground truth", lower, upper, "viridis"),
(case_prediction[:, channel], "Prediction", lower, upper, "viridis"),
(absolute_error[:, channel], "Absolute error", 0.0, None, "magma"),
)
for column, (values, title, vmin, vmax, color_map) in enumerate(panels):
axis = axes[channel, column]
scatter = axis.scatter(
xy[:, 0],
xy[:, 1],
c=values,
s=8,
marker="o",
linewidths=0,
cmap=color_map,
vmin=vmin,
vmax=vmax,
)
axis.set_aspect("equal", adjustable="box")
axis.set_xlabel("x")
axis.set_ylabel("y")
axis.set_title(f"{name}: {title}")
figure.colorbar(scatter, ax=axis, fraction=0.046, pad=0.04)
variable_summary[name] = {
"mean_absolute_error": float(np.mean(absolute_error[:, channel])),
"max_absolute_error": float(np.max(absolute_error[:, channel])),
}
case_index = int(case_indices[local_index])
figure.suptitle(f"PointCFD fixed test case {case_index}")
figure.tight_layout()
output_path = output_dir / f"case_{case_index:04d}_fields.png"
figure.savefig(output_path, dpi=180, bbox_inches="tight")
plt.close(figure)
generated.append(str(output_path))
case_summaries.append({"case_index": case_index, "variables": variable_summary})
print(f"figure={output_path}", flush=True)
summary = {
"predictions": str(predictions_path),
"visualization_method": (
"direct point scatter without triangulation or interpolation because mesh topology "
"and obstacle boundaries are not provided"
),
"generated_files": generated,
"cases": case_summaries,
}
summary_path = output_dir / "visualization_summary.json"
write_json(summary_path, summary)
print(f"visualization_summary={summary_path}", flush=True)
if __name__ == "__main__":
main()
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