Spaces:
Sleeping
Sleeping
File size: 16,397 Bytes
8474a22 ede1cc2 8474a22 9ed3c47 f86283e 8474a22 9ed3c47 8474a22 3ef4825 8474a22 3ef4825 8474a22 ede1cc2 8474a22 ede1cc2 8474a22 0f8c82e 8474a22 ede1cc2 8474a22 3d6c3a7 8474a22 3d6c3a7 83d974b 1a437b8 8474a22 0f8c82e 8474a22 82f79f3 8474a22 3d6c3a7 8474a22 3d6c3a7 8474a22 f1c4d57 1a437b8 f1c4d57 1a437b8 f1c4d57 1a437b8 f1c4d57 1a437b8 f1c4d57 1a437b8 f1c4d57 1a437b8 f1c4d57 1a437b8 f1c4d57 1a437b8 3d6c3a7 0f8c82e 8474a22 3d6c3a7 8474a22 0f8c82e 8474a22 0f8c82e 8474a22 1a437b8 8474a22 1a437b8 0f8c82e 3d6c3a7 8474a22 0f8c82e f86283e 0f8c82e 82f79f3 f1c4d57 82f79f3 9ed3c47 82f79f3 0f8c82e ac96c88 f1c4d57 ac96c88 1a437b8 ac96c88 0f8c82e 1a437b8 0f8c82e 1a437b8 0f8c82e 3d6c3a7 8474a22 3d6c3a7 8474a22 1a437b8 8474a22 1a437b8 0f8c82e 3d6c3a7 8474a22 3d6c3a7 8474a22 3d6c3a7 8474a22 36ac372 8474a22 36ac372 8474a22 36ac372 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 | import os
import json
from datetime import datetime
import math
from functools import lru_cache
from typing import Dict, Any, List
import numpy as np
from fastapi import FastAPI, Query
from fastapi.responses import JSONResponse, HTMLResponse
import gradio as gr
# Local import: vendored from working project
from backend.grib_wave_puller import GRIBWavePuller
app = FastAPI(title="Wave Visualizer API")
def _compute_uv_from_wave(height: np.ndarray, direction_deg: np.ndarray, scale: float = 0.1):
"""Compute U/V components from wave height and meteorological 'from' direction.
- height: significant wave height array (m)
- direction_deg: wave direction (deg, meteorological, coming from)
- scale: visualization scaling factor
"""
dir_rad = np.deg2rad(direction_deg)
mag = np.clip(height, 0, np.nanmax(height)) * scale
# Eastward (u) and northward (v) components; negative on v because 'from'
u = mag * np.sin(dir_rad)
v = -mag * np.cos(dir_rad)
return u, v
def _build_velocity_grib_json(lats: np.ndarray, lons: np.ndarray, u: np.ndarray, v: np.ndarray, ref_time: str) -> List[Dict[str, Any]]:
"""Build leaflet-velocity compatible JSON (Wind/Earth GRIB-like format).
Data must be provided on a regular lat-lon grid. Arrays are 2D with shape (ny, nx)
where ny=len(lats), nx=len(lons). Latitude should be provided in descending order
(north to south) to match common GRIB conventions; reorder if needed.
"""
# Ensure 1D coordinate arrays
lats_1d = lats if lats.ndim == 1 else lats[:, 0]
lons_1d = lons if lons.ndim == 1 else lons[0, :]
ny = int(len(lats_1d))
nx = int(len(lons_1d))
# If latitude increases northward, reverse to north->south
if ny > 1 and lats_1d[0] < lats_1d[-1]:
lats_1d = lats_1d[::-1]
u = np.flipud(u)
v = np.flipud(v)
# Normalize longitudes to [-180, 180) to avoid 0..360 grids causing clipping
# Then ensure they ascend west->east and reorder u/v columns accordingly.
if nx > 1:
lons_wrapped = ((np.asarray(lons_1d, dtype=float) + 180.0) % 360.0) - 180.0
order = np.argsort(lons_wrapped)
lons_1d = lons_wrapped[order]
if u.ndim == 2 and v.ndim == 2 and u.shape[1] == nx and v.shape[1] == nx:
u = u[:, order]
v = v[:, order]
la1 = float(lats_1d[0])
la2 = float(lats_1d[-1])
lo1 = float(lons_1d[0])
lo2 = float(lons_1d[-1])
# Grid spacing (approx)
dy = float(abs(lats_1d[1] - lats_1d[0])) if ny > 1 else 0.0
dx = float(abs(lons_1d[1] - lons_1d[0])) if nx > 1 else 0.0
# Sanitize arrays: replace NaN/Inf with zeros for JSON compliance
u = np.nan_to_num(np.asarray(u, dtype=float), nan=0.0, posinf=0.0, neginf=0.0)
v = np.nan_to_num(np.asarray(v, dtype=float), nan=0.0, posinf=0.0, neginf=0.0)
# Optional clamp to reasonable range (avoid absurd values)
# Here, clamp to [-20, 20] m/s just for safety in visualization
u = np.clip(u, -20.0, 20.0)
v = np.clip(v, -20.0, 20.0)
# Flatten row-major (lat-major first, then lon) matching header
u_data = u.flatten().tolist()
v_data = v.flatten().tolist()
header_common = {
"lo1": lo1,
"la1": la1,
"lo2": lo2,
"la2": la2,
"nx": nx,
"ny": ny,
"dx": dx,
"dy": dy,
"refTime": ref_time,
}
u_record = {
"header": {
**header_common,
"parameterCategory": 2,
"parameterNumber": 2, # U component
"parameterUnit": "m/s",
},
"data": u_data,
}
v_record = {
"header": {
**header_common,
"parameterCategory": 2,
"parameterNumber": 3, # V component
"parameterUnit": "m/s",
},
"data": v_data,
}
return [u_record, v_record]
@lru_cache(maxsize=16)
def get_puller() -> GRIBWavePuller:
return GRIBWavePuller()
@app.get("/data/points")
def data_points(hour: int = Query(0, ge=0, le=240)):
puller = get_puller()
result = puller.fetch_global_wave_data(hour)
if not result:
return JSONResponse(status_code=503, content={"error": "No data available"})
def _sanitize(obj):
if isinstance(obj, dict):
return {k: _sanitize(v) for k, v in obj.items()}
if isinstance(obj, list):
return [_sanitize(v) for v in obj]
if isinstance(obj, (np.floating,)):
v = float(obj)
return None if not math.isfinite(v) else v
if isinstance(obj, (np.integer,)):
return int(obj)
if isinstance(obj, float):
return None if not math.isfinite(obj) else obj
return obj
payload = {
"type": "points",
"refTime": result.get("timestamp"),
"points": result.get("sample_points", []),
}
return JSONResponse(content=_sanitize(payload))
@app.get("/data/velocity")
def data_velocity(hour: int = Query(0, ge=0, le=240), scale: float = Query(0.1)):
puller = get_puller()
result = puller.fetch_global_wave_data(hour)
if not result:
return JSONResponse(status_code=503, content={"error": "No data available"})
# If we have a downsampled UV grid, return leaflet-velocity JSON
grid_uv = result.get("grid_uv")
if grid_uv:
lats = np.array(grid_uv['lats'])
lons = np.array(grid_uv['lons'])
u = np.array(grid_uv['u'])
v = np.array(grid_uv['v'])
# Validate grid content; if empty or trivial, fall back to points
if (
u.size < 16 or v.size < 16 or
not np.isfinite(u).any() or not np.isfinite(v).any() or
(np.nanmax(np.abs(u)) < 1e-6 and np.nanmax(np.abs(v)) < 1e-6)
):
sample_points = result.get("sample_points", [])
return JSONResponse(content={"type": "points", "refTime": result.get("timestamp"), "points": sample_points})
payload = _build_velocity_grib_json(lats, lons, u, v, ref_time=result.get("timestamp", datetime.utcnow().isoformat()))
return JSONResponse(content=payload)
# Fallback to points if no grid is present
sample_points = result.get("sample_points", [])
payload = {"type": "points", "refTime": result.get("timestamp"), "points": sample_points}
# Sanitize for JSON compliance
def _san(obj):
if isinstance(obj, dict):
return {k: _san(v) for k, v in obj.items()}
if isinstance(obj, list):
return [_san(v) for v in obj]
if isinstance(obj, (np.floating,)):
v = float(obj)
return None if not math.isfinite(v) else v
if isinstance(obj, (np.integer,)):
return int(obj)
if isinstance(obj, float):
return None if not math.isfinite(obj) else obj
return obj
return JSONResponse(content=_san(payload))
def leaflet_html() -> str:
return """
<!doctype html>
<html>
<head>
<meta charset=\"utf-8\" />
<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />
<link rel=\"stylesheet\" href=\"https://unpkg.com/leaflet@1.9.4/dist/leaflet.css\" />
<style>
html, body, #map { height: 100%; margin: 0; }
.leaflet-control-container .leaflet-top.leaflet-left { z-index: 1000; }
.control { position:absolute; top:10px; left:10px; z-index:1000; background:#fff; padding:8px; border-radius:4px; box-shadow:0 1px 3px rgba(0,0,0,0.3); pointer-events:auto; }
/* Animated flow for arrow polylines */
@keyframes arrow-dash {
0% { stroke-dashoffset: 0; }
100% { stroke-dashoffset: -20; }
}
/* Leaflet renders polylines as SVG paths */
.leaflet-overlay-pane path.arrow-line {
vector-effect: non-scaling-stroke;
fill: none;
stroke-linecap: butt;
stroke-dasharray: 6 10;
animation: arrow-dash 1.2s linear infinite;
}
</style>
</head>
<body>
<div id=\"map\"></div>
<div class=\"control\">
<label>Forecast hour: <input type=\"number\" id=\"hour\" min=\"0\" max=\"240\" step=\"6\" value=\"0\" /></label>
<button id=\"load\">Load Waves</button>
<span id=\"status\" style=\"margin-left:8px; font-size:12px; color:#333\"></span>
</div>
<script src=\"https://unpkg.com/leaflet@1.9.4/dist/leaflet.js\"></script>
<!-- Use a known-good Leaflet-Velocity build -->
<script src=\"https://cdn.jsdelivr.net/npm/leaflet-velocity@1.8.0/dist/leaflet-velocity.min.js\"></script>
<script>
const map = L.map('map').setView([20, 0], 2);
L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
maxZoom: 6,
attribution: '© OpenStreetMap contributors'
}).addTo(map);
let velocityLayer = null;
let pointLayer = null;
// Create a short dash polyline with attached metadata (wind-like particle)
function mkArrow(p, opts = {}) {
const dir = p.wave_direction ?? 0;
const height = p.wave_height ?? 0.5;
const period = p.wave_period ?? null;
// Keep segments short to resemble particles rather than long arrows
const len = Math.max(15000, Math.min(60000, height * 35000)); // meters
const rad = dir * Math.PI/180.0;
const dx = Math.sin(rad) * len;
const dy = -Math.cos(rad) * len;
const poly = L.polyline(
[[p.lat, p.lon], [p.lat + dy/1e6, p.lon + dx/1e6]],
{ color: '#e5242a', weight: 1.0, opacity: 0.9, className: 'arrow-line', ...opts }
);
// Attach metadata so we can tune styles after added to map
poly._waveMeta = { height, period, direction: dir };
return poly;
}
// Tune per-feature animation speed/appearance using period and height
function tuneArrowStyles(group) {
if (!group) return;
const tune = (layer) => {
const el = (layer.getElement && layer.getElement()) || layer._path;
if (!el || !layer._waveMeta) return;
const { height, period } = layer._waveMeta;
// Stroke weight by height (clamped)
const w = Math.max(0.6, Math.min(1.6, 0.8 + (height || 0) * 0.25));
layer.setStyle && layer.setStyle({ weight: w });
// Particle-like short dashes
const dashLen = Math.max(2, Math.min(10, 3 + (height || 0) * 1.2));
const gapLen = Math.round(dashLen * 1.4);
el.style.strokeDasharray = `${dashLen} ${gapLen}`;
// Animation speed by period: longer period => faster flow (shorter duration)
let dur;
if (period && isFinite(period)) {
// Map 2s..20s -> 1.2s..0.5s duration for livelier particles
const p = Math.max(2, Math.min(20, period));
dur = 1.2 - (p - 2) * ((1.2 - 0.5) / (20 - 2));
} else {
dur = 0.9; // default
}
el.style.animationDuration = `${dur.toFixed(2)}s`;
};
// Defer slightly to ensure SVG paths exist
setTimeout(() => {
group.eachLayer(tune);
}, 0);
}
async function load(hour) {
if (velocityLayer) { map.removeLayer(velocityLayer); velocityLayer = null; }
if (pointLayer) { map.removeLayer(pointLayer); pointLayer = null; }
const status = document.getElementById('status');
status.textContent = 'Loading...';
const res = await fetch(`/data/velocity?hour=${hour}`);
if (!res.ok) { alert('Failed to fetch data'); return; }
const payload = await res.json();
console.log('velocity payload', payload);
if (payload && payload.type === 'points') {
// Fallback: draw particle-like markers with direction
const features = payload.points.map(p => mkArrow(p));
pointLayer = L.layerGroup(features).addTo(map);
tuneArrowStyles(pointLayer);
status.textContent = `Rendered ${features.length} wave arrows`;
} else {
try {
// Expected: array of two GRIB-like records (u and v)
if (Array.isArray(payload) && payload.length >= 2 && payload[0].data && payload[0].data.length) {
// Quick sanity check: some non-zero magnitudes
const sample = payload[0].data.slice(0, 200);
const nz = sample.reduce((acc, v) => acc + Math.abs(v), 0);
if (nz < 1e-3) {
throw new Error('Velocity grid near-zero; fallback to points');
}
velocityLayer = L.velocityLayer({
data: payload,
displayValues: true,
displayOptions: {
velocityType: 'Wave',
position: 'bottomleft',
emptyString: 'No wave data',
speedUnit: 'm/s',
angleConvention: 'bearingCW',
showCardinal: true
},
// Settings aligned with the working wind demo
velocityScale: 0.01,
opacity: 0.9,
maxVelocity: 20,
particleMultiplier: 0.002,
lineWidth: 1.2,
frameRate: 15,
particleAge: 40,
fadeOpacity: 0,
animationDuration: 0,
// Remove strict bounds/wrap to support 0..360 or -180..180 grids
// Red gradient color scale
colorScale: [
"#4c0000", "#660000", "#800000", "#990000", "#b30000",
"#cc0000", "#e60000", "#ff0000", "#ff3333", "#ff6666", "#ff9999"
],
});
velocityLayer.addTo(map);
status.textContent = 'Velocity layer active';
// Also overlay a sparse set of arrows for immediate visual feedback
try {
const resPts = await fetch(`/data/points?hour=${hour}`);
if (resPts.ok) {
const pld = await resPts.json();
const pts = (pld.points || []).slice(0, 300);
const arrs = pts.map(p => mkArrow(p, { color: '#e5242a', opacity: 0.85 }));
pointLayer = L.layerGroup(arrs).addTo(map);
tuneArrowStyles(pointLayer);
}
} catch (e2) { console.warn('arrow overlay failed', e2); }
} else {
// Final fallback: fetch points explicitly
const res2 = await fetch(`/data/points?hour=${hour}`);
if (res2.ok) {
const payload2 = await res2.json();
console.log('points payload', payload2);
const features = (payload2.points || []).map(p => mkArrow(p));
if (features.length) {
pointLayer = L.layerGroup(features).addTo(map);
tuneArrowStyles(pointLayer);
status.textContent = `Rendered ${features.length} wave arrows`;
} else {
status.textContent = 'No wave data available';
}
} else {
status.textContent = 'Failed to fetch data';
}
}
} catch (e) {
console.warn('Velocity layer failed, falling back to points:', e);
const res2 = await fetch(`/data/points?hour=${hour}`);
const payload2 = await res2.json();
const features = payload2.points.map(p => mkArrow(p));
pointLayer = L.layerGroup(features).addTo(map);
tuneArrowStyles(pointLayer);
status.textContent = `Rendered ${features.length} wave arrows`;
}
}
}
document.getElementById('load').onclick = () => {
const h = parseInt(document.getElementById('hour').value || '0', 10);
load(h);
};
</script>
</body>
</html>
"""
@app.get("/map", response_class=HTMLResponse)
def map_page():
return leaflet_html()
@app.get("/", response_class=HTMLResponse)
def root_page():
return leaflet_html()
# Optional Gradio UI under /ui
with gr.Blocks(title="Wave Visualizer UI") as demo:
gr.Markdown("# Wave Visualizer\nUse the link below to open the map page.")
gr.HTML('<p><a href="/map" target="_blank">Open Map</a></p>')
from gradio.routes import mount_gradio_app
app = mount_gradio_app(app, demo, path="/ui")
|