feat(intro): add the First Light intro sequence

A deterministic WebGL film of God's Eye View coming online: a seed on Null
Island grows into a culture that draws the planet, indexes the surface
tile by tile, ingests the live feeds and outgrows its dish, ending on the
logo. Renders 16:9 and 9:16 frame by frame in headless Chromium; the
procedural score is locked to the same clock. Data and imagery are
fetched by the scripts in tools/ and are not committed.

Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01KqP857pippaUs6konSDfe8

intro-sequence/tools/imagery/build_clouds.py

@@ -0,0 +1,76 @@
+"""Deliverable 3: clouds.jpg (4096x2048) grayscale cloud opacity map for one recent day.
+
+Source: NASA GIBS daily VIIRS Corrected Reflectance True Colour for the same UTC
+date from three satellites (NOAA-20, NOAA-21, Suomi NPP), EPSG:4326 level 3
+(10x5 tiles of 512px = 5120x2560), downsampled to 4096x2048.
+
+Cloud metric per satellite:
+    whiteness = min(R,G,B)            (clouds are bright in every channel)
+    greyness  = 1 - saturation        (clouds are colourless)
+    c = ramp(whiteness) * smoothstep(greyness)
+The same metric computed on the Esri base map (earth_day_4k.jpg) is subtracted
+to suppress bright deserts, salt flats, snow and ice that would otherwise read
+as cloud. The three per-satellite results are combined with a per-pixel median
+(nan-aware), which removes swath-edge haze / glint streaks that only one
+satellite shows, and fills swath gaps. Remaining no-data (polar night) -> 0.
+
+Usage: python3 build_clouds.py [YYYY-MM-DD]
+"""
+import os
+import sys
+import numpy as np
+from PIL import Image
+from common import OUT, gibs_mosaic
+
+Image.MAX_IMAGE_PIXELS = None
+DATE = sys.argv[1] if len(sys.argv) > 1 else "2026-10-15"
+LAYERS = [
+    "VIIRS_NOAA20_CorrectedReflectance_TrueColor",
+    "VIIRS_NOAA21_CorrectedReflectance_TrueColor",
+    "VIIRS_SNPP_CorrectedReflectance_TrueColor",
+]
+W, H = 4096, 2048
+
+
+def smooth(x, a, b):
+    t = np.clip((x - a) / (b - a), 0, 1)
+    return t * t * (3 - 2 * t)
+
+
+def metric(rgb):
+    rgb = rgb.astype(np.float32) / 255.0
+    mn = rgb.min(2)
+    mx = rgb.max(2)
+    sat = (mx - mn) / (mx + 1e-4)
+    white = np.clip((mn - 0.14) / 0.86, 0, 1) ** 1.15  # linear-ish: keeps texture inside thick cloud
+    return white * smooth(1.0 - sat, 0.65, 0.95), mx
+
+
+def main():
+    base = np.asarray(Image.open(os.path.join(OUT, "earth_day_4k.jpg")).convert("RGB"))
+    base_c, _ = metric(base)
+    # dilate the base metric a few px so small Esri/VIIRS coastline misregistration
+    # does not leave bright outlines along desert and ice coasts
+    from PIL import ImageFilter
+    base_c = np.asarray(Image.fromarray((base_c * 255).astype(np.uint8)).filter(ImageFilter.MaxFilter(3)).filter(ImageFilter.GaussianBlur(2))).astype(np.float32) / 255
+    stack = []
+    for L in LAYERS:
+        m = gibs_mosaic(L, DATE, "250m", 3, 5, 10, "jpg").resize((W, H), Image.LANCZOS)
+        c, mx = metric(np.asarray(m))
+        c = np.clip(c - base_c * 1.1, 0, 1)
+        c[mx < 0.03] = np.nan  # no-data (black)
+        stack.append(c)
+    stack = np.stack(stack)
+    with np.errstate(all="ignore"):
+        import warnings
+        warnings.simplefilter("ignore")
+        cloud = np.nanmedian(stack, axis=0)
+    cloud = np.nan_to_num(cloud, nan=0.0)
+    cloud = np.clip((cloud - 0.06) / 0.94, 0, 1)  # black point: kill faint surface/haze residue
+    img = Image.fromarray(np.clip(cloud * 255 + 0.5, 0, 255).astype(np.uint8), "L")
+    img.save(os.path.join(OUT, "clouds.jpg"), quality=92, optimize=True, progressive=True)
+    print("done", DATE, "mean opacity %.3f" % cloud.mean())
+
+
+if __name__ == "__main__":
+    main()