daily largest-events refresh

README.md

@@ -33,10 +33,10 @@ configs:
 
 # The 1,000 largest US layoff events on record under the WARN Act, 1988-2026
 
-**Rebuilt 2026-09-12. Largest on file: United Airlines, 2020-07-01 to 2020-10-01 — 45,360
+**Rebuilt 2026-09-13. Largest on file: United Airlines, 2020-07-01 to 2020-10-01 — 45,360
 workers across 25 notices in 14 state(s). The table's floor is
-835 workers; 558 of the 1,000 events span more than one notice and
-380 span more than one state.**
+844 workers; 560 of the 1,000 events span more than one notice and
+381 span more than one state.**
 
 A state WARN portal lists one row per site per notice. "What was the biggest layoff?" is an
 employer-level question, and answering it takes three steps no portal performs: 48 agencies'
@@ -124,39 +124,3 @@ employers.
 
 *Automated publisher (APProjects). Not affiliated with any government agency. Verify critical
 figures against the state source linked from each notice.*
-
-<!-- warn-feed:offer:start -->
-## Use it, or keep watching it
-
-This dataset is one cut of a single daily rebuild: **61,304 US WARN Act
-layoff notices from 48 state agencies, 1988 to today**, one schema, no
-login, no delay, CC BY 4.0. Snapshot as of **2026-09-13**; the files above are
-rebuilt every day, so the live count is the truth.
-
-**Look something up right now — free, no signup, nothing to install.**
-[Check any employer or state against the last 180 days
-→](https://approjects-warn-act-notices.static.hf.space/watch-now.html) It runs in your browser against these same files.
-
-**Building something with it?** The same files are a free HTTP API — JSON and
-CSV, no key, no signup, `access-control-allow-origin: *` so `fetch()` works from
-a browser: [endpoints, schema and curl examples →](https://approjects-warn-act-notices.static.hf.space/api.html)
-
-**Or have it watch a list for you.** Coming back to look is the part a CSV cannot
-do. **WARN Watch — $49 for a year**, one payment, nothing auto-renews, 14-day
-refund, no login: up to 500 employer names plus whole states, matched on every
-daily refresh, delivered to a private alert page + calendar (.ics) + RSS +
-an optional Slack / Discord / Teams webhook. Every alert carries that employer's
-whole filing history from the archive, which a keyword rule on an RSS feed cannot
-see. There is no built-in email — we do not claim one.
-
-- [See a real alert page before paying](https://approjects-warn-act-notices.static.hf.space/watch-sample.html) · [what you get](https://approjects-warn-act-notices.static.hf.space/watch.html)
-- [Try it free for 30 days, no card](https://approj.gumroad.com/l/warn-free-watch) · [Buy — $49/year](https://approj.gumroad.com/l/warn-watch)
-
-**Reaching a human.** WARN Feed is published by APProjects, an automated data
-publisher — that is stated plainly rather than dressed up. Corrections, coverage
-gaps, schema questions and refund requests all go here and are read:
-[open an issue](https://github.com/APVentureEngine/warn-act-notices/issues/new/choose). Payments are handled by Gumroad as merchant of
-record, so an invoice can carry your company name.
-
-[Source, scrapers and methodology](https://github.com/APVentureEngine/warn-act-notices) · [the 48-state site](https://approjects-warn-act-notices.static.hf.space/)
-<!-- warn-feed:offer:end -->

hf_largest_events.py

@@ -353,6 +353,79 @@ def render(res):
     return card, svg
 
 
+MD_PATH = os.path.join(HERE, "repo", "LARGEST-LAYOFF-EVENTS.md")
+MD_TOP = 100
+
+MD = """# The {rows:,} largest US layoff events on record (WARN Act, {y0}-{y1})
+
+*Rebuilt {asof} from the daily-normalized archive of 48 state WARN portals. Full table:
+[`data/largest_layoff_events.csv`](data/largest_layoff_events.csv) ({rows:,} rows, CC BY 4.0) ·
+[Hugging Face card]({hf}) · [how the numbers are made](#how-an-event-is-built).*
+
+**Largest on file: {top_employer}, {top_start} to {top_end} — {top_workers:,} workers across
+{top_notices} notices in {top_states} state(s).** {multi_notice:,} of the {rows:,} events span more
+than one notice; {multi_state:,} span more than one state; the table's floor is {min_workers:,}
+workers.
+
+A state portal lists one row per site per notice. "What was the biggest layoff?" is an
+employer-level question, and it takes three steps no portal performs: one schema across 48
+agencies, an employer's spellings resolved to one name (Boeing files under 22), and rolling
+per-site notices clustered into one event.
+
+## Top {mdtop}
+
+| # | employer | period | workers reported | notices | sites | states |
+|---|---|---|---|---|---|---|
+{table}
+
+## How an event is built
+
+* **Event** = one alias-merged employer's notices with no gap over {gap} days between consecutive
+  notice dates and no more than {maxdays} days end to end. A rolling programme is one event; a
+  later round is a separate one.
+* **`workers reported`** sums `employees_affected` after dropping exact duplicate portal rows.
+  Successive notices for the same site are summed (a portal does not say whether the second is
+  cumulative), so a rolling programme can be overstated; `sites` is the conservative figure.
+* **Coverage is uneven before ~2020** — most state portals begin between 2010 and 2023, so an
+  early-year rank is a rank among the states we hold. The CSV's `states_covered_in_year` column
+  says how many.
+* Single-filing events are only as reliable as that one portal row (`single_notice` in the CSV);
+  verify at the state source before repeating a number.
+* Employer resolution: [`product/alias_merge.py`](product/alias_merge.py). It merges spellings,
+  not corporate parents.
+
+Get told the day an employer on your list files, in any of the 48 states: [free 30-day
+watch]({free_watch}) (no card) or [WARN Watch, $49/year]({watch}) for a list of up to 500
+employers. Automated publisher (APProjects); not affiliated with any government agency.
+"""
+
+
+def write_md(res, path=MD_PATH):
+    """GitHub-channel twin (c327 reasoning: the repo is the most human surface; site pages
+    carry a host-injected canonical header). Same in-memory result as the card and CSV."""
+    rows, st = res["rows"], res["stats"]
+    if st["rows"] < MIN_EVENTS:
+        raise SystemExit(f"hf_largest_events: only {st['rows']} events; refusing to write md")
+    top = rows[0]
+    table = "\n".join(
+        f"| {e['rank']} | {('[' + e['employer'] + '](' + e['employer_page'] + ')') if e['employer_page'] else e['employer']} | "
+        f"{e['event_start']} to {e['event_end']} | {e['workers_reported']:,} | {e['notices']} | "
+        f"{e['sites']} | {e['states'].replace(';', ' ')} |"
+        for e in rows[:MD_TOP])
+    md = MD.format(rows=st["rows"], y0=min(st["years"]), y1=max(st["years"]), asof=st["asof"],
+                   hf=HF_URL, top_employer=top["employer"], top_start=top["event_start"],
+                   top_end=top["event_end"], top_workers=top["workers_reported"],
+                   top_notices=top["notices"], top_states=top["states_count"],
+                   multi_notice=st["multi_notice_rows"], multi_state=st["multi_state_rows"],
+                   min_workers=st["min_workers_in_table"], mdtop=min(MD_TOP, st["rows"]),
+                   table=table, gap=st["gap_days"], maxdays=MAX_EVENT_DAYS,
+                   free_watch=FREE_WATCH, watch=WATCH)
+    assert not re.findall(r"\{[a-z_0-9]+\}", md)
+    open(path, "w", encoding="utf-8").write(md)
+    print(f"hf_largest_events: wrote {os.path.basename(path)} ({len(md):,} chars)")
+    return md
+
+
 README_ANCHOR = "<!--largest-events-readme-->"
 README_PATH = os.path.join(HERE, "repo", "README.md")
 HF_URL = f"https://huggingface.co/datasets/APProjects/{DATASET_NAME}"
@@ -365,7 +438,7 @@ def inject_readme(res, readme_path=README_PATH):
         return False
     top = res["rows"][0]
     line = (f"{README_ANCHOR} \U0001F3ED **[The {st['rows']:,} largest US layoff events since "
-            f"{min(st['years'])}](data/largest_layoff_events.csv)** — notices clustered per resolved "
+            f"{min(st['years'])}](LARGEST-LAYOFF-EVENTS.md)** — notices clustered per resolved "
             f"employer (rolling programmes = one event); #1 {top['employer']} {top['year']}, "
             f"{top['workers_reported']:,} workers over {top['notices']} notices. "
             f"[Card on Hugging Face]({HF_URL}).")
@@ -482,6 +555,10 @@ def selftest():
     assert not left, f"unformatted placeholder: {left}"
     assert "<svg" in svg and "| 1 | Employer" in card and "45 days" in card
     import tempfile
+    mdtmp = tempfile.NamedTemporaryFile("w", suffix=".md", delete=False, encoding="utf-8"); mdtmp.close()
+    md = write_md(res2, mdtmp.name)
+    assert "| 1 | Employer" in md and md.count("\n| ") >= MD_TOP + 1, "md top table missing"
+    os.unlink(mdtmp.name)
     tmp = tempfile.NamedTemporaryFile("w", suffix=".md", delete=False, encoding="utf-8")
     tmp.write("# T\n\n<!--metro-readme--> metro line\n<!--state-rates-readme--> rates line\n\n## Next\n")
     tmp.close()
@@ -500,6 +577,7 @@ def main():
     write_csv(OUT_CSV, res["rows"])
     write_csv(REPO_CSV, res["rows"])
     try:
+        write_md(res)
         inject_readme(res)
     except Exception as e:  # noqa: BLE001 — a README line must never block the upload
         print(f"hf_largest_events: WARN README injection failed ({e})")