jpmschweitzerandClaude Opus 4.6
a8379871ce
1. Add 5 extended atmosphere colors to biomes.toml (globe renderer reads
from toml, not body_def) — cold_arid, hot_arid, tropical, boreal,
temperate_terminator. Remove dead martian entry. Slightly differentiate
colors from base classes.
2. Fix profile table raw identifiers — apply .replace('_', ' ') to class
field in wiki body pages.
3. Fix legend text overflow — truncate labels with ellipsis when step
size is too narrow for full text.
4. Fix title panel underscores — use .replace('_', ' ') instead of
.replace('_ringed', '').
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
495 lines
18 KiB
Python
495 lines
18 KiB
Python
"""
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render_heightmap.py
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-------------------
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Renders an annotated equirectangular heightmap PNG from a terrain dict.
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This is the PRIMARY output of the planet generator pipeline.
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The globe render is a separate downstream step that reads the same terrain dict.
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Equirectangular projection:
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X axis: longitude 0°→360° (left to right)
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Y axis: latitude +90°→-90° (top to bottom, north pole at row 0)
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Each terrain grid cell maps to a block of output pixels via bicubic upscale.
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All rendering is in float32; final conversion to uint8 at save time.
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Output layers (composited in order):
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1. Biome colour — smooth-blended from Whittaker grid, not hard-snapped
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2. Elevation shading — subtle darkening in valleys, lightening on peaks
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3. Hillshade — surface normal lighting pass (makes terrain 3D-readable)
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4. Coastline — 1px dark border at sea level threshold
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5. Rivers — anti-aliased polylines from river list
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6. Lat/lon grid — every 30°, semi-transparent
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7. Title panel — body metadata strip at top
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8. Legend — biome colour swatches at bottom
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Geographic only. No settlements, roads, or cultural data.
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Those live in a separate JSON sidecar and are overlaid by the atlas app.
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Usage:
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from render_heightmap import render_heightmap
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from planet_simulation import simulate
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from body_definition_parser import parse_system
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defs = parse_system("index.md")
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terrain = simulate(defs[0])
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img = render_heightmap(defs[0], terrain)
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img.save("GJ144d_heightmap.png")
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"""
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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from scipy.ndimage import binary_dilation
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from biome_config import (
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BIOME_PALETTE as _BIOME_PALETTE_CFG,
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RIVER_RGB as _RIVER_RGB_CFG,
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COAST_RGB as _COAST_RGB_CFG,
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build_biome_rgb,
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)
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# ---------------------------------------------------------------------------
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# Output resolution
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# ---------------------------------------------------------------------------
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OUT_W = 1024
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OUT_H = 512
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UI_SCALE = OUT_W / 1024 # 1.0 at default 1024 — all pixel sizes scale with this
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# ---------------------------------------------------------------------------
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# Biome colour palette
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# Indices match planet_simulation.WHITTAKER_TABLE class IDs.
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# Extended exotic classes appended at end.
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# ---------------------------------------------------------------------------
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# Biome palette loaded from biomes.toml via biome_config.
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# Per-planet overrides can patch _BIOME_PALETTE_CFG before rendering.
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BIOME_PALETTE = _BIOME_PALETTE_CFG
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def _build_biome_rgb(mode: str = "cartographic") -> dict:
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return build_biome_rgb(mode)
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RENDER_MODE = "cartographic"
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BIOME_RGB = _build_biome_rgb(RENDER_MODE)
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def _ocean_arrays(mode: str = "cartographic"):
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return (
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np.array(BIOME_PALETTE[0][mode], dtype=np.float32),
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np.array(BIOME_PALETTE[1][mode], dtype=np.float32),
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np.array(BIOME_PALETTE[2][mode], dtype=np.float32),
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)
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OCEAN_DEEP, OCEAN_MID, OCEAN_SHALLOW = _ocean_arrays(RENDER_MODE)
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RIVER_RGB = _RIVER_RGB_CFG
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COAST_RGB = _COAST_RGB_CFG
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _upscale(grid: np.ndarray, order: int = 1) -> np.ndarray:
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"""
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Upscale a (GRID_H, GRID_W) float32 grid to (OUT_H, OUT_W).
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order=1 → bilinear (smooth, good for continuous fields)
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order=0 → nearest (sharp, good for integer class grids)
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"""
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from scipy.ndimage import zoom
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zy = OUT_H / grid.shape[0]
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zx = OUT_W / grid.shape[1]
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return zoom(grid.astype(np.float32), (zy, zx), order=order).astype(np.float32)
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def _upscale_int(grid: np.ndarray) -> np.ndarray:
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"""Nearest-neighbour upscale for integer class grids (biome, etc)."""
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from scipy.ndimage import zoom
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zy = OUT_H / grid.shape[0]
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zx = OUT_W / grid.shape[1]
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return zoom(grid.astype(np.int32), (zy, zx), order=0).astype(np.int8)
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# ---------------------------------------------------------------------------
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# Layer 1 + 2 + 3: Biome colour + elevation shading + hillshade
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# ---------------------------------------------------------------------------
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def _render_surface(terrain: dict) -> np.ndarray:
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"""
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Returns (OUT_H, OUT_W, 3) float32 RGB in [0, 1].
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Compositing order:
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biome_colour × elevation_shade × hillshade_factor
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"""
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elevation = _upscale(terrain["elevation"], order=1)
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hillshade = _upscale(terrain["hillshade"], order=1)
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biome_up = _upscale_int(terrain["biome"])
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surf_water = _upscale(terrain["surface_water"].astype(np.float32),
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order=0) > 0.5
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sea_level = terrain["sea_level"]
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H, W = elevation.shape
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# ── Biome base colour ─────────────────────────────────────────────────
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# Clamp biome index, look up palette
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# Build lookup array from active BIOME_RGB dict for vectorised indexing
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max_id = max(BIOME_RGB.keys())
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pal_arr = np.zeros((max_id + 1, 3), dtype=np.float32)
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for k, v in BIOME_RGB.items():
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pal_arr[k] = v
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biome_clamped = np.clip(biome_up, 0, max_id)
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rgb = pal_arr[biome_clamped].astype(np.float32) / 255.0
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# ── Ocean depth blending ───────────────────────────────────────────────
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# Override flat ocean biome with smooth depth gradient
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if surf_water.any():
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depth = np.clip((sea_level - elevation) / (sea_level + 1e-9), 0, 1)
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deep_col = OCEAN_DEEP / 255.0
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mid_col = OCEAN_MID / 255.0
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shallow_col = OCEAN_SHALLOW / 255.0
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# Three-stop blend: 0=shallow, 0.5=mid, 1=deep
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t1 = np.clip(depth * 2.0, 0, 1) # 0→0.5 depth: shallow→mid
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t2 = np.clip((depth - 0.5) * 2.0, 0, 1) # 0.5→1 depth: mid→deep
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ocean_rgb = (shallow_col * (1 - t1)[..., None]
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+ mid_col * (t1 * (1 - t2))[..., None]
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+ deep_col * t2[..., None])
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rgb = np.where(surf_water[..., None], ocean_rgb, rgb)
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# ── Elevation shading on land ──────────────────────────────────────────
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# Slight darkening in lowlands, brightening on ridges
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elev_norm = np.where(
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~surf_water,
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np.clip((elevation - sea_level) / (1.0 - sea_level + 1e-9), 0, 1),
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0.0)
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elev_shade = 0.88 + 0.18 * elev_norm # [0.88, 1.06] — clamp below
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rgb = np.where(~surf_water[..., None],
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np.clip(rgb * elev_shade[..., None], 0, 1),
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rgb)
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# ── Hillshade ──────────────────────────────────────────────────────────
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# Apply only on land — ocean gets its own depth shading
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# Blend factor: 0.55 hillshade + 0.45 flat (keeps colours readable)
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hs_blend = 0.55 * hillshade + 0.45
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rgb = np.where(~surf_water[..., None],
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np.clip(rgb * hs_blend[..., None], 0, 1),
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rgb)
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return rgb.astype(np.float32)
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# ---------------------------------------------------------------------------
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# Layer 4: Coastline
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# ---------------------------------------------------------------------------
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def _render_coastline(terrain: dict,
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rgb: np.ndarray) -> np.ndarray:
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"""Draw a 1–2px dark border at the sea level threshold."""
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surf_water = _upscale(terrain["surface_water"].astype(np.float32),
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order=0) > 0.5
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# Dilate water mask by 1px, XOR with original → coastline ring
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dilated = binary_dilation(surf_water, iterations=2)
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coastline = dilated & ~surf_water
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coast_col = np.array(COAST_RGB, dtype=np.float32) / 255.0
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out = rgb.copy()
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out[coastline] = coast_col
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return out
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# ---------------------------------------------------------------------------
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# Layer 5: Rivers
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# ---------------------------------------------------------------------------
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def _render_rivers(terrain: dict,
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rgb: np.ndarray) -> np.ndarray:
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"""
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Draw rivers as anti-aliased polylines.
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River list is in simulation grid coords (row, col) at GRID_H×GRID_W.
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Scale to output pixels, draw with PIL.
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"""
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rivers = terrain.get("rivers", [])
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if not rivers:
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return rgb
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GRID_H, GRID_W = terrain["_grid_h"], terrain["_grid_w"]
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scale_y = OUT_H / GRID_H
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scale_x = OUT_W / GRID_W
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# Work on a PIL image for anti-aliased line drawing
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img = Image.fromarray((rgb * 255).clip(0, 255).astype(np.uint8), mode="RGB")
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draw = ImageDraw.Draw(img)
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river_col = RIVER_RGB
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for path in rivers:
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if len(path) < 2:
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continue
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# Scale grid coords to output pixels
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pts = [(int(c * scale_x), int(r * scale_y)) for r, c in path]
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# Line width scales with path length — longer rivers are wider.
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# Base width doubled for readability at high output resolutions.
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width = max(2, min(6, len(path) // 40))
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draw.line(pts, fill=river_col, width=width, joint="curve")
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return np.array(img).astype(np.float32) / 255.0
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# ---------------------------------------------------------------------------
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# Layer 6: Lat/lon grid
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# ---------------------------------------------------------------------------
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def _render_grid(rgb: np.ndarray) -> np.ndarray:
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"""Draw lat/lon lines every 30° as semi-transparent overlays."""
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out = rgb.copy()
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col = np.array([255, 255, 255], dtype=np.float32) / 255.0
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alpha = 0.12 # very subtle
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# Latitude lines (horizontal) every 30°: at 1/6, 2/6, 3/6, 4/6, 5/6 of height
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for frac in [1/6, 2/6, 3/6, 4/6, 5/6]:
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y = int(frac * OUT_H)
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y0 = max(0, y - 1); y1 = min(OUT_H - 1, y + 1)
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out[y0:y1, :] = out[y0:y1, :] * (1 - alpha) + col * alpha
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# Longitude lines (vertical) every 30°
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for frac in [1/6, 2/6, 3/6, 4/6, 5/6]:
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x = int(frac * OUT_W)
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x0 = max(0, x - 1); x1 = min(OUT_W - 1, x + 1)
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out[:, x0:x1] = out[:, x0:x1] * (1 - alpha) + col * alpha
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return out
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# ---------------------------------------------------------------------------
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# Layer 7: Title panel
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# ---------------------------------------------------------------------------
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def _load_font(size: int):
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try:
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return ImageFont.load_default(size=size)
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except TypeError:
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return ImageFont.load_default()
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def _render_title(img: Image.Image, body_def: dict) -> Image.Image:
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"""Draw metadata strip at top of image."""
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panel_h = int(52 * UI_SCALE)
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panel = Image.new("RGBA", (OUT_W, panel_h), (12, 15, 22, 210))
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img_rgba = img.convert("RGBA")
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img_rgba.paste(panel, (0, 0), panel)
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img_out = img_rgba.convert("RGB")
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draw = ImageDraw.Draw(img_out)
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name = body_def.get("name") or body_def.get("id", "Unknown")
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bid = body_def.get("id", "")
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pclass = body_def.get("planet_class", "").replace("_", " ")
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star = body_def.get("star", {})
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orbit = body_def.get("orbit", {})
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phys = body_def.get("physical", {})
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env = body_def.get("environment", {})
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star_str = f"{star.get('type','?')}-type"
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dist_str = f"{orbit.get('distance_au', 0):.2f} AU"
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grav_str = f"{phys.get('gravity_g', '?')}g"
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atmo_str = phys.get("atmosphere", "?")
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hydro_str = env.get("hydrosphere", "?")
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px = int(14 * UI_SCALE)
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py = int(7 * UI_SCALE)
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lh = int(17 * UI_SCALE)
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title_col = (200, 210, 228)
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sub_col = (130, 145, 168)
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dim_col = (75, 88, 110)
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line1 = f"{name.upper()} · {bid} · {pclass}"
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line2 = f"{star_str} · {dist_str} · {grav_str} · atmo: {atmo_str} · hydro: {hydro_str}"
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line3 = "HEIGHTMAP · Settled Reach"
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draw.text((px, py), line1, fill=title_col, font=_load_font(int(14 * UI_SCALE)))
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draw.text((px, py + lh), line2, fill=sub_col, font=_load_font(int(12 * UI_SCALE)))
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draw.text((px, py + lh*2), line3, fill=dim_col, font=_load_font(int(11 * UI_SCALE)))
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return img_out
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# ---------------------------------------------------------------------------
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# Layer 8: Legend
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# ---------------------------------------------------------------------------
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def _biome_legend_items(terrain: dict) -> list:
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"""
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Return list of (label, RGB) for biome classes actually present
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in this terrain — no phantom legend entries.
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"""
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biome = terrain["biome"]
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present = set(np.unique(biome).tolist())
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LABELS = {
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0: "ocean deep", 1: "ocean", 2: "coastal water",
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3: "coast", 5: "trop. rainforest", 6: "trop. forest",
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7: "savanna", 8: "grassland", 9: "forest",
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10: "temp. rainforest", 11: "boreal", 12: "shrubland",
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13: "temperate desert", 14: "desert", 15: "hot desert",
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16: "tundra", 17: "ice / snow", 18: "mountain rock",
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19: "lava field", 20: "chemosyn. mat", 21: "thermophilic",
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22: "sulfuric scrub", 23: "crypto. crust", 25: "ash field",
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}
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items = []
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# Fixed display order — most common first, exotic last
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order = [0, 1, 2, 3, 7, 8, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17,
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18, 19, 20, 21, 22, 23, 25]
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for cls_id in order:
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if cls_id in present and cls_id in LABELS:
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rgb = BIOME_RGB.get(cls_id, (128, 128, 128))
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items.append((LABELS[cls_id], rgb))
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# Always include river swatch if rivers exist
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if terrain.get("rivers"):
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items.append(("river", RIVER_RGB))
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return items
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def _render_legend(img: Image.Image, terrain: dict) -> Image.Image:
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"""Draw biome legend strip at bottom of image."""
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items = _biome_legend_items(terrain)
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if not items:
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return img
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draw = ImageDraw.Draw(img)
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sw = int(14 * UI_SCALE) # swatch width
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sh = int(12 * UI_SCALE) # swatch height
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pad_x = int(14 * UI_SCALE)
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leg_y = OUT_H - int(34 * UI_SCALE)
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font = _load_font(int(10 * UI_SCALE))
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gap = int(6 * UI_SCALE)
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# Scale step to fit all items within image width
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max_items = len(items)
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max_step = (OUT_W - 2 * pad_x) // max(max_items, 1)
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step = min(int(108 * UI_SCALE), max_step)
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# Max label width = step minus swatch minus gaps
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max_label_px = step - sw - gap - int(4 * UI_SCALE)
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lx = pad_x
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for label, rgb in items:
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draw.rectangle([(lx, leg_y), (lx + sw, leg_y + sh)], fill=rgb)
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# Truncate label to fit allocated space
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display = label
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while display and draw.textlength(display, font=font) > max_label_px:
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display = display[:-1]
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if display != label and display:
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display = display[:-1] + "…"
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draw.text((lx + sw + gap, leg_y), display,
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fill=(185, 192, 205), font=font)
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lx += step
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return img
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# ---------------------------------------------------------------------------
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# Main entry point
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# ---------------------------------------------------------------------------
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def render_heightmap(body_def: dict,
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terrain: dict,
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out_w: int = OUT_W,
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out_h: int = OUT_H,
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render_mode: str = "cartographic",
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chrome: bool = True) -> Image.Image:
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"""
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Render an annotated equirectangular heightmap PNG.
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Parameters
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----------
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body_def : dict — body definition from body_definition_parser
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terrain : dict — terrain dict from planet_simulation.simulate()
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out_w, out_h — output resolution (default 1024×512)
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Returns
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-------
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PIL.Image.Image RGB
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"""
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global OUT_W, OUT_H, UI_SCALE
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OUT_W = out_w
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OUT_H = out_h
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UI_SCALE = out_w / 1024
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# Set active colour mode for this render
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global BIOME_RGB, OCEAN_DEEP, OCEAN_MID, OCEAN_SHALLOW, RENDER_MODE
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RENDER_MODE = render_mode
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BIOME_RGB = _build_biome_rgb(render_mode)
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OCEAN_DEEP, OCEAN_MID, OCEAN_SHALLOW = _ocean_arrays(render_mode)
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# Guard: require simulation data
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required = ("elevation", "biome", "surface_water", "hillshade", "sea_level")
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missing = [k for k in required if k not in terrain]
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if missing:
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raise ValueError(f"terrain dict missing keys: {missing}")
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# 1+2+3: surface colour with elevation shading and hillshade
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rgb = _render_surface(terrain)
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# 4: coastline
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rgb = _render_coastline(terrain, rgb)
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# 5: rivers
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rgb = _render_rivers(terrain, rgb)
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# 6: lat/lon grid
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rgb = _render_grid(rgb)
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# Convert to PIL for text rendering
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img = Image.fromarray(
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(rgb * 255).clip(0, 255).astype(np.uint8), mode="RGB")
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if chrome:
|
||
# 7: title panel
|
||
img = _render_title(img, body_def)
|
||
# 8: legend
|
||
img = _render_legend(img, terrain)
|
||
|
||
return img
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# CLI
|
||
# ---------------------------------------------------------------------------
|
||
|
||
if __name__ == "__main__":
|
||
import sys, json, time
|
||
|
||
if len(sys.argv) < 2:
|
||
print("Usage: python3 render_heightmap.py body_def.json [--large]")
|
||
sys.exit(1)
|
||
|
||
with open(sys.argv[1]) as f:
|
||
bd = json.load(f)
|
||
|
||
# --large flag renders at 4096×2048 for high-res review
|
||
large = "--large" in sys.argv
|
||
w, h = (4096, 2048) if large else (OUT_W, OUT_H)
|
||
|
||
from planet_simulation import simulate
|
||
|
||
print(f"Simulating: {bd['id']} ({bd['planet_class']})")
|
||
t0 = time.time()
|
||
terrain = simulate(bd)
|
||
sim_t = time.time() - t0
|
||
|
||
if not terrain:
|
||
print("Gas giant — no heightmap.")
|
||
sys.exit(0)
|
||
|
||
print(f"Rendering heightmap {w}×{h}…")
|
||
t1 = time.time()
|
||
img = render_heightmap(bd, terrain, out_w=w, out_h=h)
|
||
ren_t = time.time() - t1
|
||
|
||
out = f"/mnt/user-data/outputs/{bd['id']}_heightmap.png"
|
||
img.save(out, format="PNG")
|
||
print(f"Saved: {out}")
|
||
print(f" simulate={sim_t:.1f}s render={ren_t:.1f}s total={sim_t+ren_t:.1f}s")
|