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tools.realwallpaper: draw fires as dots from FIRMS detections
The GIBS VIIRS_NOAA20_Thermal_Anomalies_375m_All raster shows each
detection as a coarse blob, far bigger than its 375 m footprint at
wallpaper scale, so the fire layer was loud and distracting.
Fetch FIRMS' keyless global 24 h VIIRS CSVs of NOAA-20, NOAA-21 and
Suomi NPP instead (cached, refreshed hourly like the old layer), drop
low-confidence detections and draw each one as an antialiased dot in
screen space through the same forward mapping as the markers: about
1 px at 1080p, growing slightly with the lens zoom, opacity from FRP on
a log scale between 1 and 500 MW. The colour and max blend under the
clouds stay as before.
author
Hermes
committer
lassulus
date
(Oct 17, 2026, 12:37 AM CEST)
commit
996812a3
996812a32666e72d5b52a4fcc7cb10a260b1b16d
parent
0d32dfeb
0d32dfebee03e149ec3d8fe121f592c4dee0fa14
+78
-11
+78
-11
tools/realwallpaper/realwallpaper.py
+78
-11
tools/realwallpaper/realwallpaper.py
···
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import argparse
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import dataclasses
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import concurrent.futures
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import csv
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import datetime as dt
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import hashlib
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import io
···
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def dh(self, s):
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return np.interp(s, self._s, self._dh)
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def zoom(self, xs, ys):
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"""Lens magnification (tangential) at screen px, 1 outside the lens."""
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s = self.norm(xs - self.px, ys - self.py)
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si = np.minimum(s, 1.0)
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with np.errstate(divide="ignore", invalid="ignore"):
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return np.where(s < 1, np.where(si > 1e-9, si / self.h(si), self.m), 1.0)
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def distance_for_demand(self, q: float) -> float:
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"""Radius (km) around home inside which some pixel needs finer than q."""
···
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if path.exists() and time.time() - path.stat().st_mtime < ttl:
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return path
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try:
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t0 = time.monotonic()
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body, _ = http_get(url, timeout=30)
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if not body:
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raise FetchError(f"{url}: empty response")
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tmp = path.with_name(f".{path.name}.tmp")
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tmp.write_bytes(body)
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os.replace(tmp, path)
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log(f"fetched {name} ({len(body) / 1e6:.1f} MB) in {time.monotonic() - t0:.1f}s")
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except Exception as e: # noqa: BLE001
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log(f"warning: {name}: {e}")
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return path if path.exists() else None
···
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transform=lambda c: smoothstep(0.15, 0.9, c),
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),
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Layer(
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"fire",
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None,
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Wms("fire", GIBS, "VIIRS_NOAA20_Thermal_Anomalies_375m_All", 0.375, 3600,
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days_back=(0, 1)),
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prep_scalar,
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),
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Layer(
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"chlorophyll",
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None,
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Wms("chlorophyll", GIBS, "VIIRS_NOAA20_Chlorophyll_a", 1.0, 6 * 3600,
···
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# can end up coarser than the next one
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layer.log_res = np.maximum.accumulate(np.log([lvl.res for lvl in layer.levels]))
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out[layer.name] = layer
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return out
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# --------------------------------------------------------------------------
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# fires: VIIRS 375 m active fire detections of the last 24 h from FIRMS,
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# drawn as screen-space dots
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FIRMS = "https://firms.modaps.eosdis.nasa.gov/data/active_fire"
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FIRMS_CSVS = [
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"noaa-20-viirs-c2/csv/J1_VIIRS_C2_Global_24h.csv",
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"noaa-21-viirs-c2/csv/J2_VIIRS_C2_Global_24h.csv",
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"suomi-npp-viirs-c2/csv/SUOMI_VIIRS_C2_Global_24h.csv",
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]
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FIRE_DOT = 1.6 # dot diameter in px at scale 1 (1800 px tall)
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FIRE_LENS_GROWTH = 0.25 # dots grow with the lens zoom to this power
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FIRE_FRP = (1.0, 500.0) # fire radiative power (MW) of the faintest and full dots
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@dataclass
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class Fires:
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lat: np.ndarray
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lon: np.ndarray
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frp: np.ndarray
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def load_fires(cache: Cache) -> Fires:
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lat, lon, frp = [], [], []
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for rel in FIRMS_CSVS:
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path = cache.get_url(f"firms-{Path(rel).name}", f"{FIRMS}/{rel}", 3600)
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if path is None:
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continue
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try:
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with path.open(newline="") as f:
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for row in csv.DictReader(f):
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if row["confidence"] == "l":
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continue
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lat.append(float(row["latitude"]))
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lon.append(float(row["longitude"]))
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frp.append(float(row["frp"]))
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except Exception as e: # noqa: BLE001 - a broken file drops its satellite
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log(f"warning: {path.name}: {e}")
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return Fires(np.array(lat), np.array(lon), np.array(frp))
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def fire_mask(proj: LensMap, fires: Fires, scale: float) -> np.ndarray:
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"""Fire opacity per screen px: antialiased dots, the max where they overlap."""
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W, H = proj.width, proj.height
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out = np.zeros((H, W), np.float32)
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if len(fires.lat) == 0:
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return out
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x, y = proj.forward(fires.lat, fires.lon)
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radius = FIRE_DOT * scale / 2 * proj.zoom(x, y) ** FIRE_LENS_GROWTH
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lo, hi = FIRE_FRP
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alpha = 0.2 + 0.8 * np.clip(np.log(np.maximum(fires.frp, lo) / lo) / math.log(hi / lo), 0, 1)
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k = math.ceil(radius.max() + 0.5)
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for oy in range(-k, k + 1):
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for ox in range(-k, k + 1):
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cx = np.floor(x).astype(np.int64) + ox
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cy = np.floor(y).astype(np.int64) + oy
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cover = np.clip(radius + 0.5 - np.hypot(cx + 0.5 - x, cy + 0.5 - y), 0, 1)
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ok = (cover > 0) & (cx >= 0) & (cx < W) & (cy >= 0) & (cy < H)
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np.maximum.at(out, (cy[ok], cx[ok]), (cover * alpha)[ok].astype(np.float32))
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return out
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···
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return top * (1 - fy) + bottom * fy
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def render_base(proj: LensMap, lay: dict[str, Layer], sun: tuple[float, float]) -> np.ndarray:
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def render_base(proj: LensMap, lay: dict[str, Layer], fires: Fires,
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sun: tuple[float, float]) -> np.ndarray:
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W, H = proj.width, proj.height
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ground = np.zeros((H, W, 3), np.float32)
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cloud = np.zeros((H, W), np.float32)
···
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light = smoothstep(-0.10, 0.08, cosz)
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day_lit = day * (0.55 + 0.45 * np.sqrt(np.clip(cosz, 0, 1)))[:, None]
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color = lerp(day * moonlight, day_lit, light)
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if (s := get("fire")) is not None:
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color = np.maximum(color, fire_rgb * 0.8 * s[0][:, 0][:, None])
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if (s := get("clouds")) is not None:
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cloud[rows] = np.clip(s[0][:, 0], 0, 1).reshape(len(ys), W)
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if (s := get("lights")) is not None:
···
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q_f[rows] = q.reshape(len(ys), W)
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scale = H / 1800
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ground = np.maximum(ground, fire_rgb * 0.8 * fire_mask(proj, fires, scale)[..., None])
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light = smoothstep(-0.10, 0.08, cosz_f)
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if "clouds" in lay:
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cloud = cloud_detail(cloud, lat_f, lon_f, q_f)
···
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sun_image = cache.get_url("sun.jpg", "https://sdo.gsfc.nasa.gov/assets/img/latest/latest_512_0171.jpg", 3600)
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marker_file = cache.get_url("markers.json", args.marker_url, 0) if args.marker_url else None
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lay = load_layers(proj, cache, when)
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fires = load_fires(cache)
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log(f"{len(fires.lat)} fire detections")
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log(f"data ready in {time.monotonic() - t0:.1f}s")
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try:
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sun = sky.bodies["sun"] if sky else (0.0, 0.0)
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t0 = time.monotonic()
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base = Image.fromarray(render_base(proj, lay, sun)).convert("RGBA")
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base = Image.fromarray(render_base(proj, lay, fires, sun)).convert("RGBA")
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log(f"rendered in {time.monotonic() - t0:.1f}s")
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scale = args.height / 1800