#!/usr/bin/env python3
"""Gera plugin/contents/ui/milkyway.jpg a partir do mapa all-sky do Gaia.

Fonte: "Gaia's sky in colour" (ESA/Gaia/DPAC, CC BY-SA 3.0 IGO), 8000x4000,
projeção HAMMER em coordenadas galácticas (elipse, centro l=0/b=0, l crescendo
para a esquerda, b=+90 no topo). Este script reprojeta para plate carrée
(mesma convenção de l/b) e reduz para textura leve.

O dancer.html amostra a textura por pixel via (l, b) reais — a banda é a
Via Láctea fotografada de verdade, projetada em tempo real.

Requer Pillow e numpy. Uso: python3 tools/build_skymap.py
"""

import io
import math
import urllib.request
from pathlib import Path

import numpy as np
from PIL import Image

URL = ("https://upload.wikimedia.org/wikipedia/commons/e/ea/"
       "Gaia%E2%80%99s_sky_in_colour_ESA393127.png")
CACHE = Path.home() / ".cache/skeledance/gaia_allsky.png"
OUT = Path(__file__).resolve().parent.parent / "plugin/contents/ui/milkyway.jpg"
SIZE = (3072, 1536)    # textura final (plate carrée 2:1)
OVERSAMPLE = 2         # reprojeta a 2x e reduz com LANCZOS (anti-aliasing)
QUALITY = 85


def fetch() -> bytes:
    if CACHE.exists():
        print(f"Usando cache {CACHE}")
        return CACHE.read_bytes()
    print(f"Baixando {URL} ...")
    req = urllib.request.Request(URL, headers={"User-Agent": "skeledance-build/1.0"})
    raw = urllib.request.urlopen(req, timeout=180).read()
    CACHE.parent.mkdir(parents=True, exist_ok=True)
    CACHE.write_bytes(raw)
    print(f"  {len(raw) / 1e6:.1f} MB (cacheado)")
    return raw


def main():
    Image.MAX_IMAGE_PIXELS = None
    src = np.asarray(Image.open(io.BytesIO(fetch())).convert("RGB"))
    sh, sw = src.shape[:2]

    tw, th = SIZE[0] * OVERSAMPLE, SIZE[1] * OVERSAMPLE
    # Grade de saída plate carrée: l = 180 -> -180 (esq -> dir), b = 90 -> -90
    l = np.deg2rad(180 - (np.arange(tw) + 0.5) * 360 / tw)
    b = np.deg2rad(90 - (np.arange(th) + 0.5) * 180 / th)
    L, B = np.meshgrid(l, b)

    # Projeção Hammer direta -> posição na imagem-fonte
    denom = np.sqrt(1 + np.cos(B) * np.cos(L / 2))
    x = 2 * math.sqrt(2) * np.cos(B) * np.sin(L / 2) / denom   # + = esquerda
    y = math.sqrt(2) * np.sin(B) / denom                       # + = cima
    sx = np.clip(((1 - x / (2 * math.sqrt(2))) * sw / 2).astype(int), 0, sw - 1)
    sy = np.clip(((1 - y / math.sqrt(2)) * sh / 2).astype(int), 0, sh - 1)

    out = Image.fromarray(src[sy, sx])
    out = out.resize(SIZE, Image.LANCZOS)
    out.save(OUT, "JPEG", quality=QUALITY, optimize=True)
    print(f"{OUT} ({OUT.stat().st_size / 1024:.0f} KB, {SIZE[0]}x{SIZE[1]})")


if __name__ == "__main__":
    main()