thermograph/gen_cities.py
Emi Griffith 7adcb3f77b SEO: expand city set to 1000 (extended English-speaking / high-proficiency) (#99)
Add a third tier to gen_cities.py: the top-population cities from the remaining
English-official countries plus countries where >35% speak English (Eurobarometer/
EF), that weren't already chosen. cities.json grows to 1000 (500 global + 250
core-English + 250 extended), pulling in the biggest cities of India, Nigeria,
Pakistan, the Philippines, plus Amsterdam/Stockholm/Nairobi/Tel Aviv, etc.

gen_flavor.py is now incremental (fetches only cities missing a blurb, prunes stale
ones; --full to rebuild); cities_flavor.json refreshed to cover 942/1000.
2026-07-16 01:14:30 +00:00

126 lines
5 KiB
Python

"""Offline generator for backend/cities.json — the finite set of cities that get
crawlable climate pages (/climate/<slug>). Run occasionally to refresh the list:
python gen_cities.py [N] # default N=500 top metros by population
It reuses the GeoNames index that places.py already downloads/parses (calling
places._load() synchronously fills places._data), takes the top-N places by
population, and assigns each a stable, unique, URL-safe slug. Committing the output
keeps the routable city set explicit and reviewable, and decouples page-serving
from the async place-name loader.
"""
import json
import os
import re
import sys
import unicodedata
import places
OUT_PATH = os.path.join(os.path.dirname(__file__), "cities.json")
# GeoNames entry tuple layout (see places._load): the fields we keep.
_NAME, _ADMIN1, _COUNTRY, _CC, _LAT, _LON, _POP = 1, 2, 3, 4, 5, 6, 7
def slugify(*parts: str) -> str:
"""ASCII, lowercase, hyphenated slug from name/admin/country parts."""
text = " ".join(p for p in parts if p)
text = unicodedata.normalize("NFKD", text).encode("ascii", "ignore").decode()
text = re.sub(r"[^a-zA-Z0-9]+", "-", text).strip("-").lower()
return re.sub(r"-{2,}", "-", text)
# Core English-speaking countries — the first English top-up tier.
ENGLISH_CC = {"US", "GB", "CA", "AU", "NZ", "IE", "ZA"}
# A second English top-up tier: the remaining countries where English is an official
# language, plus countries where >35% of the population speaks English (Eurobarometer
# 2012 "can hold a conversation in English" / EF EPI). Both drive English-language
# search demand. Editable — this is a judgment call, not a hard rule.
ENGLISH_EXTENDED_CC = {
# English official (beyond the core seven)
"IN", "PK", "PH", "SG", "HK", "MY", "LK", "PG", "FJ",
"NG", "KE", "GH", "UG", "TZ", "ZW", "ZM", "MW", "BW", "NA", "RW", "SL", "LR", "MU", "SS", "SZ", "LS", "GM", "SC",
"JM", "TT", "BB", "BS", "BZ", "GY", "GD", "LC", "VC", "AG", "DM", "KN", "MT",
# >35% English proficiency (non-official)
"NL", "SE", "DK", "NO", "IS", "FI", "DE", "AT", "BE", "CH", "LU", "CY", "SI", "GR", "EE", "LV", "LT", "FR", "IL",
}
def _to_city(e, seen_slugs: set[str]) -> dict | None:
name, admin1, country, cc = e[_NAME], e[_ADMIN1], e[_COUNTRY], e[_CC]
# Drop admin1 from the slug when it just repeats the city name
# (e.g. Tokyo/Tokyo, Singapore/Singapore) to avoid "tokyo-tokyo-jp".
admin_part = admin1 if admin1 and slugify(admin1) != slugify(name) else ""
base = slugify(name, admin_part, cc or "")
if not base:
return None
slug = base
i = 2
while slug in seen_slugs: # disambiguate the rare collision
slug = f"{base}-{i}"
i += 1
seen_slugs.add(slug)
return {
"slug": slug, "name": name, "admin1": admin1,
"country": country, "country_code": cc,
"lat": round(e[_LAT], 5), "lon": round(e[_LON], 5),
"population": e[_POP],
}
def build(n_global: int = 500, n_english: int = 250, n_extended: int = 250) -> list[dict]:
"""Three tiers, population-descending, de-duplicated:
1. top n_global cities worldwide,
2. up to n_english more from core English-speaking countries,
3. up to n_extended more from the remaining English-official + >35%-English
countries — all not already chosen."""
places._load() # synchronous parse; fills places._data (entries are pop-desc)
if not places._data:
raise SystemExit("GeoNames index failed to load (see logs); cannot generate cities.")
entries = places._data[0]
out: list[dict] = []
seen_slugs: set[str] = set()
chosen_ids: set = set() # (name, admin1, cc) already added, so tiers don't overlap
def add_from(pred, limit: int) -> int:
added = 0
for e in entries:
if added >= limit:
break
if not pred(e):
continue
ident = (e[_NAME], e[_ADMIN1], e[_CC])
if ident in chosen_ids:
continue
c = _to_city(e, seen_slugs)
if c:
out.append(c)
chosen_ids.add(ident)
added += 1
return added
add_from(lambda e: True, n_global) # tier 1: global
add_from(lambda e: e[_CC] in ENGLISH_CC, n_english) # tier 2: core English
add_from(lambda e: e[_CC] in ENGLISH_EXTENDED_CC, n_extended) # tier 3: extended English
return out
def main() -> None:
a = sys.argv[1:]
n_global = int(a[0]) if len(a) > 0 else 500
n_english = int(a[1]) if len(a) > 1 else 250
n_extended = int(a[2]) if len(a) > 2 else 250
cities = build(n_global, n_english, n_extended)
with open(OUT_PATH, "w", encoding="utf-8") as f:
json.dump(cities, f, ensure_ascii=False, indent=0, separators=(",", ":"))
f.write("\n")
print(f"wrote {len(cities)} cities ({n_global} global + {n_english} core-English + "
f"{n_extended} extended-English) -> {OUT_PATH}")
print("sample:", ", ".join(c["slug"] for c in cities[:8]))
if __name__ == "__main__":
main()