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settled-reach/tooling/planet-gen/sol_data/venus.py
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jpmschweitzerandClaude Opus 4.6 18bdb1ed3d feat(assets): add Sol system handcrafted terrain pipeline
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>
2026-04-07 22:22:18 +02:00

127 lines
5.2 KiB
Python

"""
Venus (GJ0c) terrain builder.
Data source:
- Elevation: Magellan radar altimetry from USGS Astrogeology
Global topography at ~4.6 km/px, PDS format.
Venus properties:
- Surface: volcanic, extremely hot (~735K), dense CO2 atmosphere
- No liquid water, thick clouds
- Min elevation: ~-2000 m (lowlands)
- Max elevation: ~11000 m (Maxwell Montes on Ishtar Terra)
- planet_class: "volcanic", atmosphere: "toxic"
"""
import numpy as np
from pathlib import Path
from sol_data.download import ensure_cached
from sol_data.shared import (
GRID_W, GRID_H,
load_tiff_as_array, load_raw_binary, resample_to_grid, normalize_01,
compute_hillshade, assemble_terrain,
temperature_grid_analytical,
)
# Magellan topography — USGS GeoTIFF (reliable, PIL-loadable)
MAGELLAN_TIFF_URL = "https://planetarymaps.usgs.gov/mosaic/Venus_Magellan_Topography_Global_4641m_v02.tif"
MAGELLAN_TIFF_FILE = "Venus_Magellan_Topography_Global_4641m_v02.tif"
# PDS fallback (raw binary, dimensions may vary)
MAGELLAN_PDS_URL = "https://pds-geosciences.wustl.edu/mgn/mgn-v-rdrs-5-dim-v1/mg_3002/gedr/gtdr/gtdr_shtplt.img"
MAGELLAN_PDS_FILE = "venus_magellan_gtdr.img"
VENUS_MIN_ELEV_M = -2000.0
VENUS_MAX_ELEV_M = 11000.0
VENUS_SURFACE_TEMP_K = 735.0 # nearly uniform due to dense atmosphere
def _load_magellan() -> np.ndarray:
"""Load Magellan topography data."""
# Try USGS GeoTIFF first (reliable, well-defined format)
try:
path = ensure_cached(MAGELLAN_TIFF_URL, MAGELLAN_TIFF_FILE)
print(f" loading Magellan GeoTIFF: {path}")
arr = load_tiff_as_array(str(path))
# Handle nodata
arr[arr < -20000] = 0.0
arr[arr > 20000] = 0.0
print(f" Magellan shape: {arr.shape}, "
f"range: [{arr.min():.0f}, {arr.max():.0f}] m")
return arr
except Exception as e:
print(f" GeoTIFF failed ({e}), trying PDS binary...")
# PDS fallback — try common dimension/format combinations
try:
path = ensure_cached(MAGELLAN_PDS_URL, MAGELLAN_PDS_FILE)
print(f" loading Magellan PDS: {path}")
for w, h in [(4096, 2048), (2048, 1024), (8192, 4096)]:
try:
arr = load_raw_binary(str(path), w, h, dtype=">i2", offset=0)
arr[arr > 20000] = 0.0
arr[arr < -20000] = 0.0
print(f" Magellan PDS: {w}x{h}, range: [{arr.min():.0f}, {arr.max():.0f}]")
return arr
except ValueError:
continue
except Exception as e3:
print(f" PDS also failed ({e3})")
# All sources failed — fall through to procedural generation
print(f" WARNING: all Magellan sources failed, using procedural")
return None
def build_terrain(body_def: dict) -> dict:
"""Build Venus terrain dict from Magellan data."""
import sys
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from planet_simulation import compute_biome
print(" Venus: loading Magellan data...")
# ── 1. Elevation ────────────────────────────────────────────────────
raw = _load_magellan()
if raw is None:
# Fall back to procedural simulation
import sys
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from planet_simulation import simulate
return simulate(body_def)
from sol_data.shared import greenwich_to_dateline
shifted = greenwich_to_dateline(raw)
elevation_m = resample_to_grid(shifted, GRID_H, GRID_W, order=1)
elevation = normalize_01(elevation_m, VENUS_MIN_ELEV_M, VENUS_MAX_ELEV_M)
sea_level = 0.0
surface_water = np.zeros((GRID_H, GRID_W), dtype=bool)
# ── 2. Temperature ──────────────────────────────────────────────────
# Venus has nearly uniform surface temperature due to dense atmosphere
temperature_K = temperature_grid_analytical(
base_T_K=VENUS_SURFACE_TEMP_K,
elevation=elevation,
lapse_rate_K_per_unit=50.0, # slight cooling at altitude
lat_gradient_K=5.0, # almost no lat variation (thick atmo)
)
# ── 3. Moisture ─────────────────────────────────────────────────────
moisture = np.zeros((GRID_H, GRID_W), dtype=np.float32)
# ── 4. Biome ────────────────────────────────────────────────────────
biome = compute_biome(body_def, elevation, sea_level, surface_water,
temperature_K, moisture)
# ── 5. Hillshade ────────────────────────────────────────────────────
hillshade = compute_hillshade(elevation)
return assemble_terrain(
elevation=elevation, temperature_K=temperature_K,
moisture=moisture, biome=biome,
surface_water=surface_water, hillshade=hillshade,
rivers=[], sea_level=sea_level,
)