#!/usr/bin/env python3 """ Pod Capacity & Bandwidth Forecast — v3 (dev-status-bot aligned) Drops our v1/v2 Jira pull entirely. Uses the canonical trend data file from RocketRez/dev-status-bot (also rendered live at rocketrez-labs.com/dev-metrics): Canonical source: https://github.com/RocketRez/dev-status-bot/blob/main/reports/trend-data.json Live render: https://rocketrez-labs.com/dev-metrics/trend-data.json Methodology ----------- - Capacity unit: Jira issues_completed per Saturday-Friday week, per dev-status-bot. - Bootstrap 5,000 samples × 13 weeks → Q3 capacity distribution per pod. - Demand: Q3 roadmap items sized in T-shirts × Q3-effort-share, plus KTLO baseline (avg bugs_resolved per week × 13). - Pod team mapping (from team_id field in trend-data.json): origin = ['origin-team', 'ro'] orion = ['orion-team'] reporting = ['reporting', 'reporting-team'] Inputs ------ data/dev-metrics/trend-data.json — snapshot from the dev-status-bot repo. Refresh options (the dev-status-bot repo is private, so plain curl won't work): # Option A: GitHub CLI (recommended; handles auth) gh api repos/RocketRez/dev-status-bot/contents/reports/trend-data.json \ --jq '.content' | base64 -d > data/dev-metrics/trend-data.json # Option B: download via the live render (if your network reaches it) curl -L -o data/dev-metrics/trend-data.json \ https://rocketrez-labs.com/dev-metrics/trend-data.json # Option C: open the GitHub URL in your browser, save as https://github.com/RocketRez/dev-status-bot/blob/main/reports/trend-data.json Output ------ data/capacity/forecast.json """ import json import random import statistics from pathlib import Path from datetime import datetime, timezone random.seed(42) ROOT = Path(__file__).resolve().parent.parent.parent TREND = ROOT / "data" / "dev-metrics" / "trend-data.json" OUT = ROOT / "data" / "capacity" / "forecast.json" if not TREND.exists(): raise SystemExit( f"Missing {TREND}. Refresh with:\n" f" gh api repos/RocketRez/dev-status-bot/contents/reports/trend-data.json \\\n" f" --jq '.content' | base64 -d > {TREND.relative_to(ROOT)}\n" f" (or: curl https://rocketrez-labs.com/dev-metrics/trend-data.json > {TREND.relative_to(ROOT)})" ) with open(TREND) as f: trend = json.load(f) # --- Constants --- N_BOOTSTRAP = 5000 Q3_WEEKS = 13 PERCENTILES = [5, 25, 50, 75, 95] POD_TEAM_MAP = { "origin": ["origin-team", "ro"], "orion": ["orion-team"], "reporting": ["reporting", "reporting-team"], } # T-shirt → issue counts (calibrated to Jira-issue-equivalents, same as v1) TSHIRT_ISSUES = {"XS": 1, "S": 3, "M": 10, "L": 25, "XL": 60} # --- Q3 roadmap (T-shirt sized × Q3-effort-share) --- ROADMAP_Q3 = { "origin": [ {"title": "PCI DSS 6.4.3 CSP P1 (tail)", "size": "L", "q3_share": 0.4, "ticket": "RX-7976"}, {"title": "Toast Phase 1 (completion)", "size": "L", "q3_share": 0.5, "ticket": "RX-7403"}, {"title": "Family Memberships (continuing)", "size": "XL", "q3_share": 0.3, "ticket": "RX-6803"}, {"title": "Gifting / Deferred Activation", "size": "L", "q3_share": 0.6, "ticket": "RX-7059"}, {"title": "Insights V2 follow-on", "size": "M", "q3_share": 0.5, "ticket": "RX-8005"}, {"title": "Release cadence (3.85-3.87)", "size": "M", "q3_share": 1.0, "ticket": None}, ], "orion": [ {"title": "Operate Mariner's + waterparks", "size": "L", "q3_share": 1.0, "ticket": None}, {"title": "Membership Activation (completion)","size": "L", "q3_share": 0.5, "ticket": "ORI-1702"}, {"title": "Gift Card + Refund Waterfall", "size": "M", "q3_share": 0.6, "ticket": "ORI-1500"}, {"title": "AM/PM pass support", "size": "M", "q3_share": 0.8, "ticket": "ORI-1514"}, {"title": "Membership photo capture", "size": "M", "q3_share": 0.6, "ticket": "ORI-1781"}, {"title": "Admin terminal management UI", "size": "M", "q3_share": 0.7, "ticket": "ORI-1095"}, {"title": "E2E test automation expansion", "size": "M", "q3_share": 0.7, "ticket": None}, ], "reporting": [ {"title": "RM3 Power BI (completion)", "size": "L", "q3_share": 0.5, "ticket": "RX-6890"}, {"title": "RM4 Telerik migration (kickoff)", "size": "XL", "q3_share": 0.25,"ticket": "RX-6891"}, {"title": "Stripe Payments ODS", "size": "M", "q3_share": 0.7, "ticket": "RX-7337"}, {"title": "Mission Control expansion", "size": "L", "q3_share": 0.4, "ticket": "RX-6319"}, {"title": "Customer report mods (steady)", "size": "M", "q3_share": 1.0, "ticket": None}, ], } # --- Helpers --- def pct(sorted_vals, p): if not sorted_vals: return 0 k = (len(sorted_vals) - 1) * p / 100 f = int(k); c = min(f + 1, len(sorted_vals) - 1) return round(sorted_vals[f] + (sorted_vals[c] - sorted_vals[f]) * (k - f), 1) def bootstrap_q3(weekly_series, n=N_BOOTSTRAP): samples = [sum(random.choice(weekly_series) for _ in range(Q3_WEEKS)) for _ in range(n)] return sorted(samples) def pod_weekly_issues(pod): """Return [(week_end, issues_completed), ...] aggregated across this pod's team_ids.""" ids = set(POD_TEAM_MAP[pod]) by_week = {} for row in trend.get("issues", []): if row.get("team_id") in ids: w = row.get("week_end") by_week[w] = by_week.get(w, 0) + (row.get("completed") or 0) weeks = sorted(by_week.keys()) return [(w, by_week[w]) for w in weeks] def pod_weekly_bug_resolved(pod): """Return weekly bugs_resolved series for this pod.""" ids = set(POD_TEAM_MAP[pod]) by_week = {} for row in trend.get("bugs", []): if row.get("team_id") in ids: w = row.get("week_end") # bugs schema may vary: try both 'bugs_resolved' and 'resolved' keys count = row.get("bugs_resolved", row.get("resolved", 0)) or 0 by_week[w] = by_week.get(w, 0) + count return [by_week[w] for w in sorted(by_week.keys())] # --- Main loop --- PODS = ["origin", "orion", "reporting"] result = { "_meta": { "version": "v3", "model": "Monte Carlo bootstrap over per-week issues_completed from rocketrez-labs.com/dev-metrics", "samples": N_BOOTSTRAP, "q3Weeks": Q3_WEEKS, "weekAlignment": "Saturday-Friday", "source": "https://rocketrez-labs.com/dev-metrics/trend-data.json (RocketRez/dev-status-bot)", "generatedAt": datetime.now(timezone.utc).isoformat(), "tshirtIssues": TSHIRT_ISSUES, "podTeamMap": POD_TEAM_MAP, "forecastQuarter": "2026 Q3 (Jul-Sep), 13 weeks", "assumptions": [ "Capacity unit = Jira issues_completed per Saturday-Friday week (per dev-status-bot)", "Per-pod weekly throughput sampled with replacement over 42 historical weeks", "Q3 forecast = sum of 13 weekly samples, repeated 5,000 times", "T-shirt → issue counts (XS=1, S=3, M=10, L=25, XL=60)", "KTLO baseline = avg bugs_resolved per week × 13 (proxy for ongoing fix load)", ] }, "pods": {} } for pod in PODS: weeks_issues = pod_weekly_issues(pod) if not weeks_issues: print(f"WARN: no weekly data for {pod}") continue weeks = [w for w, _ in weeks_issues] series = [n for _, n in weeks_issues] # Bootstrap Q3 capacity q3_cap = bootstrap_q3(series) q3_cap_pcts = {f"P{p}": pct(q3_cap, p) for p in PERCENTILES} # KTLO from bug-resolved series bug_series = pod_weekly_bug_resolved(pod) ktlo_mean = (sum(bug_series) / len(bug_series)) if bug_series else 0 ktlo_q3_p50 = round(ktlo_mean * Q3_WEEKS, 1) # Roadmap demand items = ROADMAP_Q3[pod] roadmap_total = 0.0 detail = [] for it in items: issues = TSHIRT_ISSUES[it["size"]] * it["q3_share"] roadmap_total += issues detail.append({**it, "issues_q3": round(issues, 1)}) total_demand = round(roadmap_total + ktlo_q3_p50, 1) bw = {f"P{p}": round(pct(q3_cap, p) - total_demand, 1) for p in PERCENTILES} p_meet = sum(1 for s in q3_cap if s >= total_demand) / len(q3_cap) headroom_pct = round(bw["P50"] / q3_cap_pcts["P50"] * 100) if q3_cap_pcts["P50"] else 0 verdict = ( "Substantial bandwidth available" if p_meet >= 0.85 else "Meets commit, modest room" if p_meet >= 0.70 else "Tight — coin flip territory" if p_meet >= 0.45 else "Likely overcommitted" ) series_stats = { "n": len(series), "min": min(series), "max": max(series), "mean": round(statistics.mean(series), 1), "median": int(statistics.median(series)), "stdev": round(statistics.stdev(series), 1) if len(series) > 1 else 0.0, } result["pods"][pod] = { "teamIds": POD_TEAM_MAP[pod], "weeklyHistory": [ {"week_end": w, "issues_completed": n} for w, n in weeks_issues ], "monthlyStats": series_stats, "q3CapacityForecast": q3_cap_pcts, "q3KtloForecastP50": ktlo_q3_p50, "roadmapDemand": { "totalIssues": round(roadmap_total, 1), "items": detail, }, "totalDemand": total_demand, "bandwidthAtPercentile": bw, "probabilityMeetsCommit": round(p_meet, 3), "headroomPctP50": headroom_pct, "verdict": verdict, } OUT.parent.mkdir(parents=True, exist_ok=True) with open(OUT, "w") as f: json.dump(result, f, indent=2) print(f"Wrote {OUT}") print() print(f"{'Pod':<12} {'Weeks':>6} {'Avg/wk':>8} {'P50':>7} {'Demand':>8} {'BW@P50':>8} {'P(meet)':>8} {'Headroom':>10} {'Verdict':>34}") print("-" * 110) for pod in PODS: p = result["pods"][pod] s = p["monthlyStats"] print( f"{pod:<12} {s['n']:>6} {s['mean']:>8} " f"{p['q3CapacityForecast']['P50']:>7} {p['totalDemand']:>8} " f"{p['bandwidthAtPercentile']['P50']:>8} {p['probabilityMeetsCommit']:>8.1%} " f"{p['headroomPctP50']:>9}% {p['verdict']:>34}" )