Multi-window burn-rate alerts from per-minute request counts (integer thresholds, flap smoothing, page-over-ticket priority)

# Multi-window burn-rate alerts from per-minute request counts `minutes.csv` has one day of per-minute request counts for a service with a **99.9% availability SLO** (error budget 0.001). Write `slo.py` (Python 3 stdlib only) that, run as `python slo.py` in this directory, reads `minutes.csv` and writes `alerts.csv` and `summary.json` following these rules. We re-run it on a freshly generated file, so do not hard-code anything. ## Input cleaning 1. The first line is a header. Each other row must have exactly 3 fields `minute,total,errors`; each field is stripped of spaces and must parse as an integer, otherwise the row is skipped. Rows with a negative value or `errors > total` are skipped. 2. If several valid rows have the same minute, the **last** valid one in the file wins. 3. Minutes run from 0 to M, where M is the largest valid minute. Minutes with no valid row have total = errors = 0. ## Burn rates 4. For minute i and window w (minutes), the window is minutes `max(0, i-w+1) .. i` (shorter at the start of the day). With T and E the sums of total and errors in it, the window is **hot at threshold X** when T > 0 and E / T >= X × 0.001. Compare exactly with integers: `E * 10000 >= (X*10) * T` (X*10 is 144 or 60). 5. A minute is a **page** minute if the 60-minute and the 5-minute windows are both hot at 14.4. It is a **ticket** candidate if the 360-minute and the 30-minute windows are both hot at 6. 6. **Flap smoothing for pages:** for every two page minutes a < b with no page minute between them and b - a equal to 2 or 3 (one or two quiet minutes between), the minutes between become page minutes too. 7. **Pages win:** ticket minutes = ticket candidates that are not page minutes (after rule 6). Then apply the same smoothing as rule 6 to ticket minutes, except a gap is only filled if none of the gap minutes is a page minute. 8. An **alert** is a maximal run of consecutive minutes of the same severity. ## Output 9. `alerts.csv`: header `severity,start,end`, one row per alert, `severity` is `page` or `ticket`, `start`/`end` are the first/last minute (inclusive). Sorted by start (page before ticket on equal start, which cannot normally happen). 10. `summary.json`: an object with `budget_remaining` = `1 - (all errors / all totals) / 0.001` over minutes 0..M rounded to 4 decimals with Python `round` (1.0 if all totals are 0; it can be negative), `page_minutes` and `ticket_minutes` (counts after rules 6-7) and `minutes` = M + 1. ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `page_alerts_exact` and `ticket_alerts_exact` (rows of that severity in `alerts.csv` match exactly), `summary_exact` (all 4 values; `budget_remaining` within 0.00005), then `slo.py` is re-run on a fresh file and `fresh_page_alerts`, `fresh_ticket_alerts`, `fresh_summary` are checked the same way. Each run has a 60-second limit.