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Claude
02e535cb55
- City engine: Esri ground, OSM buildings that rise with the scan, road ribbons carrying modelled traffic coloured by congestion, NYC DOT cameras, real ADS-B, MBTA GTFS lines + realtime vehicles, LAX runways with approach strobes, Fort Bragg restricted volumes, Everest terrain with contours. - Indexing keeps accelerating into a 260-capture global sweep while the planet whips a full turn. - The eye opens: aperture blades, sensor array waking, optics calibration, radar sweep, orbiting coordinate ring. - Camera wall with YOLOv8n detections and a city-pulse trace. - Ends on GOD'S EYE VIEW / OMNISCIENCE. - audio/sfx.py: sound design only, built to sit under a music track. Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01KqP857pippaUs6konSDfe8
110 lines
5.2 KiB
Python
110 lines
5.2 KiB
Python
"""Pick ~170 city centres from data/cities.json with geographic spread.
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Writes tools/flurry/city_candidates.json: ordered list of dicts with
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rank ("primary"/"backup") and region. Backups replace rejected primaries of the
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same region in build.py.
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"""
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import json
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import math
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import os
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HERE = os.path.dirname(os.path.abspath(__file__))
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ROOT = os.path.abspath(os.path.join(HERE, "..", ".."))
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AFRICA = {
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"Nigeria", "Egypt", "Congo (Kinshasa)", "South Africa", "Algeria", "Sudan", "Morocco", "Tanzania",
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"Cameroon", "Mozambique", "Libya", "Ethiopia", "Ivory Coast", "Tunisia", "Zambia", "Angola", "Ghana",
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"Uganda", "Kenya", "Guinea", "Madagascar", "Senegal", "Zimbabwe", "Mali", "Somalia", "Chad", "Niger",
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"Benin", "Eritrea", "Somaliland", "South Sudan", "Sierra Leone", "Mauritania", "Malawi", "Togo",
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"Congo (Brazzaville)", "Burkina Faso", "Rwanda", "Mauritius", "Gabon", "Equatorial Guinea", "Liberia",
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"Djibouti", "Central African Republic", "Guinea Bissau", "Lesotho", "Burundi", "Namibia", "Botswana",
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"The Gambia", "Comoros", "Cape Verde", "eSwatini",
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}
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SOUTH_AMERICA = {"Brazil", "Argentina", "Colombia", "Venezuela", "Peru", "Chile", "Ecuador", "Bolivia",
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"Paraguay", "Uruguay", "Guyana", "Suriname"}
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OCEANIA = {"Australia", "New Zealand", "Papua New Guinea", "Fiji", "French Polynesia", "Guam"}
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CENTRAL_ASIA = {"Kazakhstan", "Uzbekistan", "Turkmenistan", "Kyrgyzstan", "Tajikistan", "Afghanistan", "Mongolia"}
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# (region, primary quota, backups, per-country cap, min spacing km)
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PASSES = [
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("africa", AFRICA, 31, 16, 3, 300),
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("south_america", SOUTH_AMERICA, 22, 10, 5, 250),
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("oceania", OCEANIA, 12, 4, 5, 120),
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("central_asia", CENTRAL_ASIA, 9, 5, 3, 150),
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("rest", None, 96, 40, 11, 200),
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]
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# Picked first (in "rest" unless their country is in a quota region): fill the
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# Pacific / high-latitude longitude bands and add visually distinctive cities.
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FORCED = [
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"Honolulu", "Anchorage", "Papeete", "Agana", "Petropavlovsk-Kamchatsky", "Vancouver", "San Francisco",
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"Las Vegas", "Seattle", "Phoenix", "Denver", "Calgary", "Guatemala City", "Havana", "Reykjavík",
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"Dubai", "Doha", "Abu Dhabi", "Hong Kong", "Macau", "Venice", "Amsterdam", "København", "Oslo",
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"Stockholm", "Helsinki", "Prague", "Kathmandu", "Colombo", "Vladivostok", "Novosibirsk", "Irkutsk",
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"Yakutsk", "Norilsk", "Murmansk", "Manaus", "Mombasa", "Windhoek", "Nouakchott", "Marrakesh",
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"Samarkand", "Cairns", "Muscat", "Québec",
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]
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# Natural Earth rows whose point lands in farmland/hills rather than a dense centre.
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EXCLUDE = {"Amaravati", "Hechi", "Saidu", "Cranbourne", "Manukau", "Waitakere", "North Shore", "Wahiawa"}
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COUNTRY_CAP_TOTAL = 12
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def km(a, b):
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la1, lo1, la2, lo2 = map(math.radians, (a[1], a[0], b[1], b[0]))
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h = math.sin((la2 - la1) / 2) ** 2 + math.cos(la1) * math.cos(la2) * math.sin((lo2 - lo1) / 2) ** 2
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return 6371 * 2 * math.asin(math.sqrt(h))
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def main():
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rows = json.load(open(os.path.join(ROOT, "data", "cities.json")))["rows"]
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special = AFRICA | SOUTH_AMERICA | OCEANIA | CENTRAL_ASIA
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chosen, out = [], []
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byname = {}
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for r in rows:
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byname.setdefault(r[3], r) # first = most populous
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per = {}
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def region_of(country):
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for reg, mem, *_ in PASSES:
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if mem and country in mem:
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return reg
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return "rest"
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for nm in FORCED:
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lon, lat, pop, name, country = byname[nm]
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chosen.append((lon, lat))
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per[country] = per.get(country, 0) + 1
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out.append(dict(name=name, country=country, lat=lat, lon=lon, zoom=16, pop=pop,
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region=region_of(country), rank="primary", forced=True))
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for region, members, quota, nback, cap, spacing in PASSES:
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got = sum(1 for c in out if c["region"] == region)
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for lon, lat, pop, name, country in rows:
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if name in EXCLUDE or name in byname and any(c["name"] == name and c["country"] == country for c in out):
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continue
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inreg = (country in members) if members else (country not in special)
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if not inreg:
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continue
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if per.get(country, 0) >= cap and got < quota or per.get(country, 0) >= COUNTRY_CAP_TOTAL:
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continue
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if any(km((lon, lat), (c[0], c[1])) < spacing for c in chosen):
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continue
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rank = "primary" if got < quota else "backup"
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chosen.append((lon, lat))
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per[country] = per.get(country, 0) + 1
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out.append(dict(name=name, country=country, lat=lat, lon=lon, zoom=16, pop=pop,
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region=region, rank=rank))
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got += 1
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if got >= quota + nback:
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break
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json.dump(out, open(os.path.join(HERE, "city_candidates.json"), "w"), indent=0, ensure_ascii=False)
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prim = [c for c in out if c["rank"] == "primary"]
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print(len(prim), "primary,", len(out) - len(prim), "backup")
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from collections import Counter
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print(Counter(c["region"] for c in prim))
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print(Counter(c["country"] for c in prim).most_common(12))
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bands = Counter(int((c["lon"] + 180) // 30) for c in prim)
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print("30deg bands:", [bands.get(i, 0) for i in range(12)])
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if __name__ == "__main__":
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main()
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