* SEO: generate curated city set for crawlable climate pages
gen_cities.py reuses the GeoNames index places.py already parses to select the top
~500 metros by population, assigns each a stable URL-safe slug (dropping admin1 when
it repeats the city name), and writes committed backend/cities.json. cities.py loads
it lazily with slug lookup, all_slugs(), display_name(), and by_country() grouping
for the upcoming hub + sitemap.
* SEO: rendering core, robots.txt, sitemap.xml, and metadata hygiene
- content.py: Jinja2 environment + HTML responder (ETag/304), dynamic /robots.txt
(disallows /api and /alerts, points at the sitemap) and /sitemap.xml (enumerates
the home/static pages plus every city, month, and records URL from cities.py).
Registered on the app before the StaticFiles mount so the routes win.
- templates/base.html.j2: shared layout with unique title/description, self-
referential canonical, Open Graph, favicon/manifest, header nav (adds a Climate
link) and a footer link graph.
- Give each existing page a unique <meta description> (were 5x identical) and a
self-referential <link rel=canonical>; add WebApplication JSON-LD to the home page.
- Pin jinja2.
* SEO: server-rendered per-city climate page (/climate/{slug})
The keystone crawlable page: for a city it snaps to the grid cell, loads the
archive (fetching once if missing, self-healing), and renders as real HTML — a
'how today compares' block (grade + percentile per metric from grade_day, tinted
by tier), a monthly normals table (climatology at each month's 15th, shown in °F
and °C), all-time records (new grading.all_time_records helper), a breadcrumb,
Dataset+Place+BreadcrumbList JSON-LD, self-referential canonical, and links into
the interactive tool + month/records pages. Content-page CSS added to style.css
(renamed the table class to avoid colliding with the app's .normals flex row).
* SEO: month (/climate/{slug}/{month}) and records (/climate/{slug}/records) pages
Month pages render the exact-month long-tail ('average weather in {city} in
{month}') with that month's average high/low, typical p10-p90 range, month-specific
records, and prev/next month links. Records pages show all-time record highs/lows
per metric with dates (grading.all_time_records). Shared _resolve_city helper; the
literal /records route is registered before the {month} param and month names are
validated (unknown month -> 404).
* SEO: climate hub, weather glossary, and about/methodology pages
- /climate: crawlable directory of all ~500 cities grouped by country — the
internal-link graph that lets search engines discover every city page.
- /glossary + /glossary/{term}: plain-language definitions (climate normal,
percentile, temperature anomaly, feels-like, heat index, wind chill, humidity,
reanalysis) with DefinedTerm JSON-LD and cross-links into the tool.
- /about: methodology page (ERA5 data source, 45-year baseline, +/-7-day window,
percentile grading) for E-E-A-T. All linked from the shared footer.
* SEO: archive warmer, content-page tests, and deploy docs
- warm_cities.py: paced, idempotent offline warmer that pre-fetches each city
cell's archive so /climate pages serve from cache and a crawl can't burst the
archive quota (pages self-heal if hit before warming).
- tests/test_content.py: city-set slug uniqueness/lookup, robots.txt, sitemap
enumerating city/month/records URLs, and that a rendered city page carries the
stats + canonical + Dataset JSON-LD in the HTML; plus month/records/hub/glossary/
about routing and 404s.
- DEPLOY.md: document the content pages, the warm step, and submitting the sitemap.
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
"""Offline generator for backend/cities.json — the finite set of cities that get
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crawlable climate pages (/climate/<slug>). Run occasionally to refresh the list:
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python gen_cities.py [N] # default N=500 top metros by population
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It reuses the GeoNames index that places.py already downloads/parses (calling
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places._load() synchronously fills places._data), takes the top-N places by
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population, and assigns each a stable, unique, URL-safe slug. Committing the output
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keeps the routable city set explicit and reviewable, and decouples page-serving
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from the async place-name loader.
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"""
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import json
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import os
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import re
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import sys
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import unicodedata
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import places
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OUT_PATH = os.path.join(os.path.dirname(__file__), "cities.json")
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# GeoNames entry tuple layout (see places._load): the fields we keep.
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_NAME, _ADMIN1, _COUNTRY, _CC, _LAT, _LON, _POP = 1, 2, 3, 4, 5, 6, 7
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def slugify(*parts: str) -> str:
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"""ASCII, lowercase, hyphenated slug from name/admin/country parts."""
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text = " ".join(p for p in parts if p)
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text = unicodedata.normalize("NFKD", text).encode("ascii", "ignore").decode()
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text = re.sub(r"[^a-zA-Z0-9]+", "-", text).strip("-").lower()
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return re.sub(r"-{2,}", "-", text)
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def build(n: int = 500) -> list[dict]:
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places._load() # synchronous parse; fills places._data (entries are pop-desc)
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if not places._data:
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raise SystemExit("GeoNames index failed to load (see logs); cannot generate cities.")
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entries = places._data[0]
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out: list[dict] = []
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seen_slugs: set[str] = set()
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for e in entries:
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if len(out) >= n:
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break
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name, admin1, country, cc = e[_NAME], e[_ADMIN1], e[_COUNTRY], e[_CC]
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# Drop admin1 from the slug when it just repeats the city name
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# (e.g. Tokyo/Tokyo, Singapore/Singapore) to avoid "tokyo-tokyo-jp".
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admin_part = admin1 if admin1 and slugify(admin1) != slugify(name) else ""
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base = slugify(name, admin_part, cc or "")
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if not base:
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continue
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slug = base
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i = 2
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while slug in seen_slugs: # disambiguate the rare collision
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slug = f"{base}-{i}"
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i += 1
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seen_slugs.add(slug)
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out.append({
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"slug": slug,
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"name": name,
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"admin1": admin1,
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"country": country,
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"country_code": cc,
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"lat": round(e[_LAT], 5),
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"lon": round(e[_LON], 5),
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"population": e[_POP],
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})
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return out
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def main() -> None:
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n = int(sys.argv[1]) if len(sys.argv) > 1 else 500
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cities = build(n)
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with open(OUT_PATH, "w", encoding="utf-8") as f:
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json.dump(cities, f, ensure_ascii=False, indent=0, separators=(",", ":"))
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f.write("\n")
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print(f"wrote {len(cities)} cities -> {OUT_PATH}")
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print("sample:", ", ".join(c["slug"] for c in cities[:8]))
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if __name__ == "__main__":
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main()
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