jpmschweitzerandClaude Opus 4.6
18bdb1ed3d
Custom pipeline for GJ-0 (Sol) that imports real NASA/USGS planetary data instead of procedural generation. Produces the same output format (heightmap.png, globe.png, markers.json). Real data bodies: - Earth: ETOPO2022 elevation + WorldClim climate + 14 rivers - Mars: MOLA DEM + ferric biome classes + terraformed water - Luna: LOLA DEM + lunar biome palette Procedural fallback for Mercury, Venus, Phobos, Deimos. Synthetic elevation from albedo for Io, Europa, Ganymede, Callisto, Titan, Enceladus. Gas giants use existing renderer. New biome classes 34-36 (ferric_dust/highland/lowland) for Mars iron oxide surface. Earth features: 50 cities (smart scatter by continent), 15 named rivers, oceans, mountains. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
118 lines
4.7 KiB
Python
118 lines
4.7 KiB
Python
"""
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Io (GJ0f-1) terrain builder.
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Io is the most volcanically active body in the solar system due to
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tidal heating from Jupiter. Surface is covered in sulfur and volcanic
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deposits. No published global DEM exists at useful resolution — we use
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the Galileo/Voyager global mosaic (albedo) to derive synthetic elevation.
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Data source:
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- Surface: USGS Io Galileo/Voyager global mosaic
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- Elevation: synthetic from albedo (dark = caldera/lava, bright = sulfur)
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Properties:
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- Surface temp: ~130K background, 400-1800K at volcanic hotspots
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- planet_class: "volcanic", atmosphere: "none"
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"""
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import numpy as np
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from pathlib import Path
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from scipy.ndimage import gaussian_filter
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from sol_data.download import ensure_cached
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from sol_data.shared import (
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GRID_W, GRID_H,
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load_image_as_elevation, resample_to_grid, normalize_01,
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compute_hillshade, assemble_terrain,
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temperature_grid_analytical,
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)
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# Io global mosaic (Galileo SSI + Voyager) — JPEG from USGS
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# If direct download isn't available, fall back to procedural
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IO_MOSAIC_URL = "https://astrogeology.usgs.gov/cache/images/bf08a5b6fa0c2ed73117dc1b6c516fa8_io_galileo_voyager_global_mosaic_1km.jpg"
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IO_MOSAIC_FILE = "io_galileo_mosaic.jpg"
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IO_BACKGROUND_TEMP_K = 130.0
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IO_HOTSPOT_TEMP_K = 600.0
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def _load_io_mosaic() -> np.ndarray:
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"""Load Io global mosaic and convert to synthetic elevation."""
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try:
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path = ensure_cached(IO_MOSAIC_URL, IO_MOSAIC_FILE)
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print(f" loading Io mosaic: {path}")
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albedo = load_image_as_elevation(str(path), invert=False)
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except Exception as e:
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print(f" WARNING: Io mosaic unavailable ({e}), generating synthetic")
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return _synthetic_io_terrain()
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# Resample to grid
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albedo = resample_to_grid(albedo, GRID_H, GRID_W, order=1)
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# Convert albedo to elevation:
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# Dark regions (low albedo) = calderas/lava flows = low elevation
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# Bright regions (high albedo) = sulfur deposits = high elevation
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# Smooth to create plausible topography
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elevation = gaussian_filter(albedo, sigma=3.0)
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elevation = normalize_01(elevation)
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return elevation
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def _synthetic_io_terrain() -> np.ndarray:
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"""Generate synthetic Io-like terrain if mosaic unavailable."""
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rng = np.random.default_rng(42)
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base = rng.random((GRID_H, GRID_W)).astype(np.float32)
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base = gaussian_filter(base, sigma=8.0)
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# Add volcanic calderas (circular depressions)
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for _ in range(30):
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cy, cx = rng.integers(0, GRID_H), rng.integers(0, GRID_W)
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r = rng.integers(3, 15)
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y, x = np.ogrid[-cy:GRID_H-cy, -cx:GRID_W-cx]
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mask = x*x + y*y <= r*r
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base[mask] *= 0.3
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return normalize_01(base)
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def build_terrain(body_def: dict) -> dict:
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"""Build Io terrain dict."""
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import sys
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from planet_simulation import compute_biome
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print(" Io: loading data...")
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# ── 1. Elevation ────────────────────────────────────────────────────
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elevation = _load_io_mosaic()
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sea_level = 0.0
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surface_water = np.zeros((GRID_H, GRID_W), dtype=bool)
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# ── 2. Temperature ──────────────────────────────────────────────────
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# Background ~130K, volcanic hotspots much hotter
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temperature_K = temperature_grid_analytical(
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base_T_K=IO_BACKGROUND_TEMP_K,
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elevation=elevation,
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lapse_rate_K_per_unit=-200.0, # low elevation = hot (lava)
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lat_gradient_K=10.0,
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)
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# Volcanic hotspots: low-elevation areas are hot
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hotspot_mask = elevation < 0.25
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temperature_K[hotspot_mask] += 300.0
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# ── 3. Moisture ─────────────────────────────────────────────────────
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moisture = np.zeros((GRID_H, GRID_W), dtype=np.float32)
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# ── 4. Biome ────────────────────────────────────────────────────────
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biome = compute_biome(body_def, elevation, sea_level, surface_water,
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temperature_K, moisture)
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# ── 5. Hillshade ────────────────────────────────────────────────────
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hillshade = compute_hillshade(elevation)
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return assemble_terrain(
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elevation=elevation, temperature_K=temperature_K,
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moisture=moisture, biome=biome,
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surface_water=surface_water, hillshade=hillshade,
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rivers=[], sea_level=sea_level,
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)
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