Datasets:
Tasks:
Tabular Regression
Modalities:
Tabular
Languages:
English
Size:
10K - 100K
Tags:
cfd
openfoam
surrogate-modeling
scientific-computing
scientific-machine-learning
physics-informed-neural-networks
License:
File size: 5,859 Bytes
23e78eb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | #!/usr/bin/env python3
"""Download N random samples from the U-bend HuggingFace dataset and visualize them.
For each sample, generates individual PNGs:
- <id>_geometry.png : fluid + solid mesh
- <id>_U.png : velocity magnitude
- <id>_p.png : pressure
- <id>_T.png : temperature (fluid + solid)
- <id>_k.png : turbulent kinetic energy
- <id>_nut.png : turbulent viscosity
Additionally, an overview grid (N rows x 6 columns) is saved as `overview.png`.
Usage:
python visualize_sample.py --n 5
"""
import argparse
import os
import random
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from huggingface_hub import hf_hub_download
from safetensors.numpy import load_file
REPO_ID = "JensDe/ubend-cfd"
parser = argparse.ArgumentParser()
parser.add_argument("--n", type=int, default=5, help="Number of random samples")
parser.add_argument("--seed", type=int, default=42, help="Random seed")
parser.add_argument("--out_dir", type=str, default="visualizations", help="Output directory")
args = parser.parse_args()
os.makedirs(args.out_dir, exist_ok=True)
random.seed(args.seed)
sample_ids = random.sample(range(10000), args.n * 3) # buffer for failed downloads
FIELD_SPECS = [
("U", "Velocity magnitude", "viridis", "|U| [m/s]"),
("p", "Pressure", "coolwarm", "p [Pa]"),
("T", "Temperature", "hot", "T [K]"),
("k", "Turbulent kinetic energy", "magma", "k [m²/s²]"),
("nut", "Turbulent viscosity", "plasma", "ν_t [m²/s]"),
]
def render_geometry(ax, x, y, sx, sy):
fluid_field = np.ones_like(x)
solid_field = np.ones_like(sx) * 2
ax.pcolormesh(x, y, fluid_field, cmap="Blues", shading="auto", vmin=0, vmax=3)
ax.pcolormesh(sx, sy, solid_field, cmap="Oranges", shading="auto", vmin=0, vmax=3)
ax.set_aspect("equal")
def render_field(ax, x, y, field, cmap, sx=None, sy=None, sfield=None):
vmin, vmax = field.min(), field.max()
if sfield is not None:
vmin = min(vmin, sfield.min())
vmax = max(vmax, sfield.max())
pcm = ax.pcolormesh(x, y, field, cmap=cmap, shading="auto", vmin=vmin, vmax=vmax)
if sfield is not None:
ax.pcolormesh(sx, sy, sfield, cmap=cmap, shading="auto", vmin=vmin, vmax=vmax)
ax.set_aspect("equal")
return pcm
def get_field(data, name):
if name == "U":
U = data["U"]
return np.sqrt(U[0]**2 + U[1]**2 + U[2]**2)
return data[name]
# Download samples
samples = []
i = 0
while len(samples) < args.n and i < len(sample_ids):
sid = sample_ids[i]
i += 1
try:
print(f"[{len(samples)+1}/{args.n}] Downloading sample {sid}...")
file_path = hf_hub_download(
repo_id=REPO_ID,
filename=f"fields/sample_{sid}.safetensors",
repo_type="dataset",
)
samples.append((sid, load_file(file_path)))
except Exception as e:
print(f" Skipping {sid}: {e.__class__.__name__}")
# Individual PNGs
for sid, data in samples:
x, y = data["coords"][0], data["coords"][1]
sx, sy = data["solid_coords"][0], data["solid_coords"][1]
base = os.path.join(args.out_dir, f"sample_{sid:05d}")
# Geometry
fig, ax = plt.subplots(figsize=(5, 7))
render_geometry(ax, x, y, sx, sy)
ax.set_title(f"Sample {sid} — Geometry")
ax.set_xlabel("x [m]"); ax.set_ylabel("y [m]")
ax.legend(handles=[Patch(facecolor="steelblue", label="Fluid"),
Patch(facecolor="orange", label="Solid")],
loc="upper center", bbox_to_anchor=(0.5, -0.1), ncol=2)
plt.tight_layout()
plt.savefig(f"{base}_geometry.png", dpi=150)
plt.close(fig)
# Fields
for fname, ftitle, cmap, label in FIELD_SPECS:
field = get_field(data, fname)
fig, ax = plt.subplots(figsize=(5, 7))
sfield = data["solid_T"] if fname == "T" else None
pcm = render_field(ax, x, y, field, cmap,
sx=sx if sfield is not None else None,
sy=sy if sfield is not None else None,
sfield=sfield)
plt.colorbar(pcm, ax=ax, label=label, orientation="horizontal", location="bottom", pad=0.08)
ax.set_title(f"Sample {sid} — {ftitle}")
ax.set_xlabel("x [m]"); ax.set_ylabel("y [m]")
plt.tight_layout()
plt.savefig(f"{base}_{fname}.png", dpi=150)
plt.close(fig)
# Overview grid: N rows x 6 columns
print(f"\nCreating overview grid...")
ncols = 1 + len(FIELD_SPECS) # geometry + fields
fig, axes = plt.subplots(args.n, ncols, figsize=(3.5 * ncols, 4.5 * args.n))
if args.n == 1:
axes = axes[None, :]
col_titles = ["Geometry"] + [s[1] for s in FIELD_SPECS]
for row, (sid, data) in enumerate(samples):
x, y = data["coords"][0], data["coords"][1]
sx, sy = data["solid_coords"][0], data["solid_coords"][1]
# Geometry
ax = axes[row, 0]
render_geometry(ax, x, y, sx, sy)
ax.set_xticks([]); ax.set_yticks([])
if row == 0:
ax.set_title(col_titles[0])
ax.set_ylabel(f"Sample {sid}", fontsize=10)
# Fields
for col, (fname, _, cmap, _) in enumerate(FIELD_SPECS, start=1):
ax = axes[row, col]
field = get_field(data, fname)
sfield = data["solid_T"] if fname == "T" else None
render_field(ax, x, y, field, cmap,
sx=sx if sfield is not None else None,
sy=sy if sfield is not None else None,
sfield=sfield)
ax.set_xticks([]); ax.set_yticks([])
if row == 0:
ax.set_title(col_titles[col])
plt.tight_layout()
overview_path = os.path.join(args.out_dir, "overview.png")
plt.savefig(overview_path, dpi=150)
plt.close(fig)
print(f"Saved overview to {overview_path}")
print(f"\nDone! {len(samples)} samples visualized in {args.out_dir}/")
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