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Number(tree.left[node])\n : Number(tree.right[node]);\n }\n return Number(tree.value[node]);\n}\n\nexport function runRightNowModel(\n name: string,\n featureMap: Record,\n): number | null {\n const model: any = (RIGHT_NOW_MODELS as any)[name];\n if (!model) return null;\n const values = model.features.map((feature: string) =>\n Number(featureMap[feature])\n );\n if (values.some((value: number) => !Number.isFinite(value))) return null;\n if (model.type === \"gradient_boost\") {\n return Number(model.init) +\n model.trees.reduce(\n (sum: number, tree: any) =>\n sum + Number(model.learningRate) * runTree(tree, values),\n 0,\n );\n }\n let estimate = Number(model.intercept) || 0;\n for (let index = 0; index < model.features.length; index += 1) {\n const scale = Math.abs(Number(model.scale[index])) > 1e-9\n ? Number(model.scale[index])\n : 1;\n estimate += ((values[index] - Number(model.mean[index])) / scale) *\n Number(model.coef[index]);\n }\n return estimate;\n}\n\nexport function tubingOpeningForecast(\n features: Record,\n fromDate: Date,\n): Date {\n const model: any = TUBING_OPEN_LIVE_MODEL;\n let estimate = Number(model.intercept) || 0;\n for (let index = 0; index < model.features.length; index += 1) {\n const raw = Number(features[model.features[index]]);\n if (!Number.isFinite(raw)) {\n throw new Error(\n `Missing tubing-opening feature ${model.features[index]}`,\n );\n }\n const scale = Math.abs(Number(model.scale[index])) > 1e-9\n ? 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type FlowPoint = { date: Date; value: number };\nexport type SnowPoint = { date: Date; value: number; median?: number | null };\nexport type Snotel = Record>;\n\nexport type BasinSnapshot = {\n capturedAt: Date;\n cutoffAt: Date;\n current: FlowPoint;\n instantaneous: FlowPoint[];\n dailyFlow: FlowPoint[];\n snotel: Snotel;\n features: Record;\n milestones: Milestones;\n sourceStatus: Record;\n};\n\nexport type Milestones = {\n runoffYear: number;\n laborDay: Date;\n first400: FlowPoint | null;\n peakDaily: FlowPoint | null;\n peakInstant: FlowPoint | null;\n peakFlowLikelyPassed: boolean;\n tubingForecastTrigger: FlowPoint | null;\n tubingOpening: Date | null;\n counts: { p300: number; p600: number; p800: number; tubing: number };\n strength: number | null;\n persistentSnow: SnowPoint | null;\n peakSwe: SnowPoint | null;\n peakSweLikelyPassed: boolean;\n};\n\nexport type PredictionValue = Record;\n\nexport type QuestionDefinition = {\n number: number;\n key: string;\n title: string;\n issueKind: \"fixed\" | \"event\";\n issueMonth?: number;\n issueDay?: number;\n trigger?: (snapshot: BasinSnapshot) => Date | null;\n calculate: (snapshot: BasinSnapshot) => PredictionValue;\n actual: (snapshot: BasinSnapshot) => PredictionValue | null;\n final: (snapshot: BasinSnapshot) => boolean;\n score: (prediction: PredictionValue, actual: PredictionValue) => boolean;\n};","label",[139],"42bbbd52-b25b-4334-8bbe-30a8c969db35","questions.ts","9c517f2c-ab9a-11f1-94dd-1607ee4eb77e",14,["D",1788881598789],"import { MODEL_BUNDLE } from \"./models.ts\";\nimport {\n addDays,\n daysBetween,\n firstMondayOfSeptember,\n mountainCalendarDate,\n mountainToUtc,\n} from \"./dates.ts\";\nimport {\n clamp,\n runRightNowModel,\n tubingOpeningForecast,\n} from \"./model-runtime.ts\";\nimport type {\n BasinSnapshot,\n PredictionValue,\n QuestionDefinition,\n} from \"./types.ts\";\n\nfunction context(snapshot: BasinSnapshot): Record {\n const m = snapshot.milestones;\n const today = mountainCalendarDate(snapshot.cutoffAt);\n const daysLeft = Math.max(\n 0,\n Math.ceil((m.laborDay.getTime() - today.getTime()) / 86_400_000),\n );\n return {\n ...snapshot.features,\n count300: m.counts.p300,\n count600: m.counts.p600,\n count800: m.counts.p800,\n countTubing: m.counts.tubing,\n daysLeft,\n };\n}\n\nfunction requiredModel(name: string, snapshot: BasinSnapshot): number {\n const value = runRightNowModel(name, context(snapshot));\n if (!Number.isFinite(value)) {\n throw new Error(`Model ${name} could not produce a prediction`);\n }\n return Number(value);\n}\n\nfunction predictionDate(\n snapshot: BasinSnapshot,\n model: string,\n maximumDays: number,\n): Date {\n const days = Math.round(\n clamp(requiredModel(model, snapshot), 0, maximumDays),\n );\n return addDays(mountainCalendarDate(snapshot.cutoffAt), days);\n}\n\nfunction countRemainingTubingDays(snapshot: BasinSnapshot): number | null {\n const opening = snapshot.milestones.tubingOpening;\n if (!opening) return null;\n const start = addDays(mountainCalendarDate(opening), 1);\n return snapshot.dailyFlow.filter((item) =>\n item.date >= start && item.date <= snapshot.milestones.laborDay &&\n item.value >= 30 && item.value < 400\n ).length;\n}\n\nconst dateActual = (date: Date | null, key = \"date\"): PredictionValue | null =>\n date ? { [key]: date.toISOString() } : null;\nconst dateHit = (\n prediction: PredictionValue,\n actual: PredictionValue,\n tolerance: number,\n key = \"date\",\n) =>\n Math.abs(\n daysBetween(\n new Date(String(prediction[key])),\n new Date(String(actual[key])),\n ),\n ) <= tolerance;\nconst numberHit = (\n prediction: PredictionValue,\n actual: PredictionValue,\n tolerance: (actual: number) => number,\n key = \"value\",\n) =>\n Math.abs(Number(prediction[key]) - Number(actual[key])) <=\n tolerance(Number(actual[key]));\n\nexport const QUESTIONS: QuestionDefinition[] = [\n {\n number: 1,\n key: \"persistent-snow\",\n title: \"First persistent headwater snowpack\",\n issueKind: \"fixed\",\n issueMonth: 10,\n issueDay: 1,\n calculate: (s) => ({\n date: predictionDate(s, \"persistent\", 100).toISOString(),\n }),\n actual: (s) => dateActual(s.milestones.persistentSnow?.date || null),\n final: (s) => Boolean(s.milestones.persistentSnow),\n score: (p, a) => dateHit(p, a, 14),\n },\n {\n number: 2,\n key: \"peak-swe\",\n title: \"Peak headwater snowpack\",\n issueKind: \"fixed\",\n issueMonth: 2,\n issueDay: 1,\n calculate: (s) => ({\n value:\n Math.round(\n Math.max(\n s.milestones.peakSwe?.value || 0,\n requiredModel(\"peak_swe\", s),\n ) * 10,\n ) / 10,\n unit: \"in\",\n }),\n actual: (s) =>\n s.milestones.peakSwe\n ? {\n value: s.milestones.peakSwe.value,\n date: s.milestones.peakSwe.date.toISOString(),\n unit: \"in\",\n }\n : null,\n final: (s) => s.milestones.peakSweLikelyPassed,\n score: (p, a) => numberHit(p, a, (value) => Math.max(2, value * 0.2)),\n },\n {\n number: 3,\n key: \"first-400\",\n title: \"First 400+ CFS day\",\n issueKind: \"fixed\",\n issueMonth: 3,\n issueDay: 1,\n calculate: (s) => ({\n date: predictionDate(s, \"first400\", 80).toISOString(),\n }),\n actual: (s) => dateActual(s.milestones.first400?.date || null),\n final: (s) => Boolean(s.milestones.first400),\n score: (p, a) => dateHit(p, a, 10),\n },\n {\n number: 4,\n key: \"season-strength\",\n title: \"May–June rafting-season strength\",\n issueKind: \"fixed\",\n issueMonth: 4,\n issueDay: 1,\n calculate: (s) => ({\n value: Math.max(0, Math.round(requiredModel(\"river_strength\", s))),\n unit: \"cfs_average\",\n }),\n actual: (s) =>\n Number.isFinite(s.milestones.strength)\n ? { value: s.milestones.strength, unit: \"cfs_average\" }\n : null,\n final: (s) => s.cutoffAt > mountainToUtc(s.milestones.runoffYear, 7, 1, 0),\n score: (p, a) => numberHit(p, a, (value) => Math.max(100, value * 0.3)),\n },\n {\n number: 5,\n key: \"spring-peak\",\n title: \"Spring peak flow\",\n issueKind: \"fixed\",\n issueMonth: 4,\n issueDay: 15,\n calculate: (s) => {\n const date = predictionDate(s, \"spring_peak_days\", 70);\n const minutes =\n Math.round(Number(MODEL_BUNDLE.seasonal_peak_time_minutes_p50) / 15) *\n 15;\n const minuteOfDay = ((minutes % 1440) + 1440) % 1440;\n const timestamp = mountainToUtc(\n date.getUTCFullYear(),\n date.getUTCMonth() + 1,\n date.getUTCDate(),\n Math.floor(minuteOfDay / 60),\n minuteOfDay % 60,\n );\n const flow = Math.round(\n Math.max(\n s.current.value,\n s.milestones.peakDaily?.value || 0,\n requiredModel(\"spring_peak_flow\", s),\n ),\n );\n return { timestamp: timestamp.toISOString(), value: flow, unit: \"cfs\" };\n },\n actual: (s) =>\n s.milestones.peakInstant\n ? {\n timestamp: s.milestones.peakInstant.date.toISOString(),\n value: s.milestones.peakInstant.value,\n unit: \"cfs\",\n }\n : null,\n final: (s) => s.cutoffAt > mountainToUtc(s.milestones.runoffYear, 8, 1, 0),\n score: (p, a) => {\n const hours =\n Math.abs(\n new Date(String(p.timestamp)).getTime() -\n new Date(String(a.timestamp)).getTime(),\n ) / 3_600_000;\n return hours <= 168 &&\n numberHit(p, a, (value) => Math.max(250, value * 0.3));\n },\n },\n {\n number: 6,\n key: \"rafting-days\",\n title: \"Rafting-flow days\",\n issueKind: \"fixed\",\n issueMonth: 5,\n issueDay: 1,\n calculate: (s) => ({\n p300: Math.round(\n clamp(\n Math.max(s.milestones.counts.p300, requiredModel(\"days_ge_300\", s)),\n s.milestones.counts.p300,\n 140,\n ),\n ),\n p600: Math.round(\n clamp(\n Math.max(s.milestones.counts.p600, requiredModel(\"days_ge_600\", s)),\n s.milestones.counts.p600,\n 140,\n ),\n ),\n p800: Math.round(\n clamp(\n Math.max(s.milestones.counts.p800, requiredModel(\"days_ge_800\", s)),\n s.milestones.counts.p800,\n 140,\n ),\n ),\n }),\n actual: (s) => ({ ...s.milestones.counts }),\n final: (s) =>\n s.cutoffAt > addDays(firstMondayOfSeptember(s.milestones.runoffYear), 1),\n score: (p, a) =>\n [\"p300\", \"p600\", \"p800\"].filter((key) =>\n Math.abs(Number(p[key]) - Number(a[key])) <= 10\n ).length >= 2,\n },\n {\n number: 7,\n key: \"tubing-opening\",\n title: \"Tubing Season Opening Day\",\n issueKind: \"event\",\n trigger: (s) => s.milestones.tubingForecastTrigger?.date || null,\n calculate: (s) => ({\n date: tubingOpeningForecast(s.features, s.cutoffAt).toISOString(),\n }),\n actual: (s) => dateActual(s.milestones.tubingOpening),\n final: (s) => Boolean(s.milestones.tubingOpening),\n score: (p, a) => dateHit(p, a, 7),\n },\n {\n number: 8,\n key: \"remaining-tubing-days\",\n title: \"Remaining tubing-range days\",\n issueKind: \"event\",\n trigger: (s) => s.milestones.tubingOpening,\n calculate: (s) => {\n const observed = countRemainingTubingDays(s) || 0;\n const preOpeningDays = Math.max(0, s.milestones.counts.tubing - observed);\n const allSeasonEstimate = Math.max(\n observed,\n requiredModel(\"tubing_days\", s) - preOpeningDays,\n );\n return {\n value: Math.round(\n clamp(allSeasonEstimate, observed, observed + context(s).daysLeft),\n ),\n unit: \"days\",\n };\n },\n actual: (s) => {\n const value = countRemainingTubingDays(s);\n return value === null ? null : { value, unit: \"days\" };\n },\n final: (s) =>\n s.cutoffAt > addDays(firstMondayOfSeptember(s.milestones.runoffYear), 1),\n score: (p, a) => Math.abs(Number(p.value) - Number(a.value)) <= 10,\n },\n];",[150],"d43d98db-ad37-477a-9717-1685b7b47eb1",["D",1788881600501],1,[28],"a22d96e8-0685-4b83-beee-f2d72e16d9bc","main.ts",["D",1788718987538],"http","/**\n * Black Hat Hydrology Lab — automated San Juan River seasonal prediction tracker.\n *\n * What this does, automatically, year after year:\n * 1. On October 1 it starts a new season (\"2025-26\", \"2026-27\", ...).\n * 2. Each seasonal prediction is locked on its fixed issue date — once locked\n * it is immutable (inputs + model version preserved forever).\n * 3. When the season provides the answer, a prediction can be scored; the\n * record keeps both the forecast and the actual result.\n * 4. The previous (now-finished) season stays available as the \"film room.\"\n * 5. The public page serves all records read-only.\n */\nimport { Database } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport const db = new Database(\"/blackhat.sqlite\");\ninit(db);\n\n// ---- Storage ---------------------------------------------------------------\n\nfunction init(db: Database) {\n db.exec(`\n CREATE TABLE IF NOT EXISTS predictions (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n season TEXT NOT NULL,\n issue_date TEXT NOT NULL,\n locked INTEGER NOT NULL DEFAULT 0,\n forecast TEXT NOT NULL, -- JSON: { inputs, model_version, headline }\n result TEXT, -- JSON: { actual, observation }\n score REAL,\n created_at TEXT NOT NULL,\n scored_at TEXT\n );\n CREATE INDEX IF NOT EXISTS idx_season ON predictions (season);\n `);\n}\n\n/** Season slug from a date: Oct 2025 -> \"2025-26\" (seasons start Oct 1, UTC). */\nfunction seasonSlug(date = new Date()): string {\n const y = date.getUTCFullYear();\n return date.getUTCMonth() >= 9 ? `${y}-${(y + 1) % 100}` : `${y - 1}-${y % 100}`;\n}\n\n/** If today is Oct 1 or later in a new season, log a fresh season marker. */\nfunction openCurrentSeason(now = new Date()): string {\n const season = seasonSlug(now);\n // Ensure a season row exists so the public page always shows the current one.\n const row = db\n .query(\"SELECT season FROM seasons WHERE season = ?\", [season])\n .next();\n if (!row) db.exec(\"INSERT OR IGNORE INTO seasons (season, opened_at) VALUES (?, ?)\", [season, now.toISOString()]);\n return season;\n}\n\n/** Lock a prediction on its fixed issue date. Refuses to overwrite once locked. */\nexport function lockPrediction(p: {\n issueDate: string; // ISO date (UTC)\n headline: string;\n inputs: object;\n modelVersion: string;\n}): { ok: boolean; id?: number; error?: string } {\n // Immutability: look up by season + issue date, never overwrite a locked one.\n const existing = db\n .query(\"SELECT id, locked FROM predictions WHERE season = ? AND issue_date = ?\", [\n seasonSlug(new Date(p.issueDate)),\n p.issueDate,\n ])\n .next();\n if (existing && existing[1] === 1) {\n return { ok: false, id: Number(existing[0]), error: \"Prediction already locked — immutable.\" };\n }\n const forecast = JSON.stringify({\n headline: p.headline,\n inputs: p.inputs,\n model_version: p.modelVersion,\n });\n if (existing) {\n db.exec(\"UPDATE predictions SET forecast = ?, locked = 1 WHERE id = ?\", [forecast, Number(existing[0])]);\n return { ok: true, id: Number(existing[0]) };\n }\n const id = db.exec(\n \"INSERT INTO predictions (season, issue_date, locked, forecast, created_at) VALUES (?, ?, 1, ?, ?)\",\n [seasonSlug(new Date(p.issueDate)), p.issueDate, forecast, new Date(p.issueDate).toISOString()],\n );\n return { ok: true, id: Number(id) };\n}\n\n/** Score a locked prediction once the season provides the answer. */\nexport function scorePrediction(\n issueDate: string,\n actual: object,\n score: number,\n): { ok: boolean; id?: number; error?: string } {\n const existing = db\n .query(\"SELECT id, locked FROM predictions WHERE season = ? AND issue_date = ?\", [\n seasonSlug(new Date(issueDate)),\n issueDate,\n ])\n .next();\n if (!existing) return { ok: false, error: \"Prediction not found.\" };\n if (existing[1] !== 1) return { ok: false, error: \"Prediction is not locked.\" };\n const id = Number(existing[0]);\n db.exec(\"UPDATE predictions SET result = ?, score = ?, scored_at = ? WHERE id = ?\", [\n JSON.stringify(actual),\n score,\n new Date().toISOString(),\n id,\n ]);\n return { ok: true, id };\n}\n\nfunction predictions(): Array> {\n return [...db.query(\"SELECT * FROM predictions ORDER BY issue_date\")].map((r) => ({\n id: r[0], season: r[1], issue_date: r[2], locked: r[3],\n forecast: JSON.parse(r[4]), result: r[5] ? JSON.parse(r[5]) : null,\n score: r[6], created_at: r[7], scored_at: r[8],\n }));\n}\n\n// ---- HTTP ------------------------------------------------------------------\n\nfunction html(records, current, body: string) {\n return `\n\nBlack Hat Hydrology Lab\n\n

🖤 Black Hat Hydrology Lab

San Juan River forecast experiment · Current season ${current} · ser

\n${body}\n

Raw JSON

\n`;\n}\n\nexport default async function handler(req: Request): Promise {\n const url = new URL(req.url);\n const current = openCurrentSeason();\n\n if (url.pathname === \"/data\") {\n return new Response(JSON.stringify(predictions(), null, 2), {\n headers: { \"content-type\": \"application/json\" },\n });\n }\n\n // Admin actions — protected by a shared secret when BH_LAB_SECRET is set.\n if (req.method === \"POST\" && url.pathname === \"/api/predict\") {\n if (!auth(req)) return new Response(\"Unauthorized\", { status: 401 });\n const body = await req.json();\n const out = lockPrediction(body);\n return new Response(JSON.stringify(out), { status: out.ok ? 201 : 400,\n headers: { \"content-type\": \"application/json\" } });\n }\n if (req.method === \"POST\" && url.pathname === \"/api/score\") {\n if (!auth(req)) return new Response(\"Unauthorized\", { status: 401 });\n const body = await req.json();\n const out = scorePrediction(body.issue_date, body.actual, body.score);\n return new Response(JSON.stringify(out), { status: out.ok ? 200 : 400,\n headers: { \"content-type\": \"application/json\" } });\n }\n\n const records = predictions();\n const bySeason = new Map();\n for (const r of records) {\n if (!bySeason.has(r.season as string)) bySeason.set(r.season as string, []);\n bySeason.get(r.season as string)!.push(r);\n }\n\n let body = `

Film room — previous season

${seasonTable(bySeason.get(current) ?? [], current)}

`;\n let closed = new Response(\"\", { status: 204 });\n for (const [season, recs] of bySeason) {\n if (season === current) continue;\n closed = null;\n body += `

${season} archived

${seasonTable(recs, season)}

`;\n }\n return new Response(html(records, current, body), {\n headers: { \"content-type\": \"text/html; charset=utf-8\" },\n });\n}\n\nfunction seasonTable(recs, season) {\n if (!recs.length) return `

No predictions locked yet for ${season}.

`;\n return `` +\n recs.map((r) => {\n const s = r.score == null ? \"—\" : `= 0 ? \"win\" : \"loss\"}\">${r.score}`;\n return ``;\n }).join(\"\") + `

Issue	Forecast	Model	Score
${r.issue_date}	${r.forecast.headline}	${r.forecast.model_version}	${s}

`;\n}\n\nfunction auth(req: Request): boolean {\n const secret = Deno.env.get(\"BH_LAB_SECRET\");\n if (!secret) return true; // no secret configured → allow while building\n return req.headers.get(\"x-lab-secret\") === secret;\n}\n",[161],"897ff7c2-3743-4e65-9376-bbd7003740f8","lab.ts","30c7c606-aa20-11f1-9651-1607ee4eb77e",["D",1788719065897],"/**\n * Black Hat Hydrology Lab — automated San Juan River seasonal prediction tracker.\n *\n * Automatic lifecycle, year after year:\n * 1. A new season opens on October 1 (\"2025-26\", \"2026-27\", ...).\n * 2. Each prediction is locked on a fixed issue date — once locked it is\n * immutable (inputs + model version preserved, cannot be overwritten).\n * 3. When the season provides the answer, the result is scored and stored\n * alongside the original forecast.\n * 4. The previous (now-finished) season remains available as the \"film room.\"\n * 5. The public page serves all records read-only.\n */\nimport { Database } from \"https://esm.town/v/std/sqlite/main.ts\";\n\n// Project-scoped persistent SQLite store.\nexport const db = new Database(\"/blackhat.sqlite\");\ninit();\n\nfunction init() {\n db.exec(`\n CREATE TABLE IF NOT EXISTS predictions (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n season TEXT NOT NULL,\n issue_date TEXT NOT NULL,\n locked INTEGER NOT NULL DEFAULT 0,\n forecast TEXT NOT NULL, -- JSON: { headline, inputs, model_version }\n result TEXT, -- JSON: actual observation\n score REAL,\n created_at TEXT NOT NULL,\n scored_at TEXT\n );\n CREATE INDEX IF NOT EXISTS idx_season ON predictions (season);\n `);\n}\n\n// ---- Helpers ---------------------------------------------------------------\n\n/** Season slug from a date: Oct 2025 -> \"2025-26\" (seasons start Oct 1, UTC). */\nfunction seasonSlug(date = new Date()): string {\n const y = date.getUTCFullYear();\n return date.getUTCMonth() >= 9 ? `${y}-${(y + 1) % 100}` : `${y - 1}-${y % 100}`;\n}\n\n/** Season currently in effect today. Oct 1 rolls this over automatically. */\nfunction currentSeason(): string {\n return seasonSlug(new Date());\n}\n\n/** Lock a prediction on its fixed issue date. Refuses to overwrite once locked. */\nexport function lockPrediction(p: {\n issueDate: string; // ISO date (UTC) the prediction is issued on\n headline: string;\n inputs: object;\n modelVersion: string;\n}): { ok: boolean; id?: number; error?: string } {\n const season = seasonSlug(new Date(p.issueDate));\n const existing = db\n .query(\"SELECT id, locked FROM predictions WHERE season = ? AND issue_date = ?\", [\n season,\n p.issueDate,\n ])\n .next();\n if (existing && existing[1] === 1) {\n return { ok: false, id: Number(existing[0]), error: \"Prediction already locked — immutable.\" };\n }\n const forecast = JSON.stringify({\n headline: p.headline,\n inputs: p.inputs,\n model_version: p.modelVersion,\n });\n if (existing) {\n db.exec(\"UPDATE predictions SET forecast = ?, locked = 1 WHERE id = ?\", [\n forecast,\n Number(existing[0]),\n ]);\n return { ok: true, id: Number(existing[0]) };\n }\n const id = db.exec(\n \"INSERT INTO predictions (season, issue_date, locked, forecast, created_at) VALUES (?, ?, 1, ?, ?)\",\n [season, p.issueDate, forecast, new Date(p.issueDate).toISOString()],\n );\n return { ok: true, id: Number(id) };\n}\n\n/** Score a locked prediction once the season provides the answer. */\nexport function scorePrediction(\n issueDate: string,\n actual: object,\n score: number,\n): { ok: boolean; id?: number; error?: string } {\n const existing = db\n .query(\"SELECT id, locked FROM predictions WHERE season = ? AND issue_date = ?\", [\n seasonSlug(new Date(issueDate)),\n issueDate,\n ])\n .next();\n if (!existing) return { ok: false, error: \"Prediction not found.\" };\n if (existing[1] !== 1) return { ok: false, error: \"Prediction is not locked.\" };\n const id = Number(existing[0]);\n db.exec(\"UPDATE predictions SET result = ?, score = ?, scored_at = ? WHERE id = ?\", [\n JSON.stringify(actual),\n score,\n new Date().toISOString(),\n id,\n ]);\n return { ok: true, id };\n}\n\n/** All records (newest last) with JSON fields parsed. */\nfunction allPredictions() {\n return [...db.query(\"SELECT * FROM predictions ORDER BY issue_date\")].map((r) => ({\n id: r[0],\n season: r[1],\n issue_date: r[2],\n locked: r[3],\n forecast: JSON.parse(r[4] as string),\n result: r[5] ? JSON.parse(r[5] as string) : null,\n score: r[6],\n created_at: r[7],\n scored_at: r[8],\n }));\n}\n\n// ---- HTTP ------------------------------------------------------------------\n\nfunction layout(body: string): string {\n return `\n\nBlack Hat Hydrology Lab\n${body}`;\n}\n\nexport default async function handler(req: Request): Promise {\n const url = new URL(req.url);\n\n // ---- Admin POST endpoints (write-only; guarded by secret when set) -------\n if (req.method === \"POST\" && url.pathname === \"/api/predict\") {\n if (!authorized(req)) return new Response(\"Unauthorized\", { status: 401 });\n const out = lockPrediction(await req.json());\n return jsonResponse(out, out.ok ? 201 : 400);\n }\n if (req.method === \"POST\" && url.pathname === \"/api/score\") {\n if (!authorized(req)) return new Response(\"Unauthorized\", { status: 401 });\n const { issue_date, actual, score } = await req.json();\n const out = scorePrediction(issue_date, actual, score);\n return jsonResponse(out, out.ok ? 200 : 400);\n }\n\n // ---- Public read-only JSON feed ----\n if (url.pathname === \"/data\") {\n return jsonResponse({ current_season: currentSeason(), records: allPredictions() });\n }\n\n // ---- Public read-only page ----\n const current = currentSeason();\n const bySeason = new Map>>();\n for (const r of allPredictions()) {\n const list = bySeason.get(r.season as string) ?? [];\n list.push(r);\n bySeason.set(r.season as string, list);\n }\n\n let body = `

🖤 Black Hat Hydrology Lab

San Juan River forecast experiment · Current season ${current} ·\nraw JSON

`;\n\n // Film room first: all completed seasons, most recent first.\n const completed = [...bySeason.keys()].filter((s) => s !== current).sort().reverse();\n for (const season of completed) {\n body +=\n `

Film room — ${season}

${seasonTable(bySeason.get(season)!, season)}

`;\n }\n // Current / active season last.\n body +=\n `

Current season — ${current}

${seasonTable(bySeason.get(current) ?? [], current)}

`;\n\n return new Response(layout(body), {\n headers: { \"content-type\": \"text/html; charset=utf-8\" },\n });\n}\n\nfunction seasonTable(recs: Array>, season: string): string {\n if (!recs.length) return `

No predictions locked yet for ${season}.

`;\n const rows = recs\n .map((r) => {\n const f = r.forecast as { headline: string; model_version: string };\n const s = r.score == null\n ? \"—\"\n : `= 0 ? \"win\" : \"loss\"}\">${r.score}`;\n return `${r.issue_date}${f.headline}${f.model_version}${s}`;\n })\n .join(\"\");\n return `${rows}

Issue	Forecast	Model	Score

`;\n}\n\nfunction authorized(req: Request): boolean {\n const secret = Deno.env.get(\"BH_LAB_SECRET\");\n if (!secret) return true; // no secret set → allow while building\n return req.headers.get(\"x-lab-secret\") === secret;\n}\n\nfunction jsonResponse(data: unknown, status = 200): Response {\n return new Response(JSON.stringify(data, null, 2), {\n status,\n headers: { \"content-type\": \"application/json\" },\n });\n}\n",[167],"8a3817b0-14da-4f14-b47b-1034d4d5d5f8","dates.ts","8d25a690-ab9a-11f1-ade0-1607ee4eb77e",8,["D",1788881573051],"import { CONFIG } from \"./config.ts\";\n\nconst DAY = 86_400_000;\n\nexport function addDays(date: Date, days: number): Date {\n return new Date(date.getTime() + days * DAY);\n}\n\nexport function isoDay(date: Date): string {\n return date.toISOString().slice(0, 10);\n}\n\nexport function mountainParts(\n date: Date,\n): { year: number; month: number; day: number; hour: number } {\n const parts = new Intl.DateTimeFormat(\"en-US\", {\n timeZone: CONFIG.timeZone,\n year: \"numeric\",\n month: \"numeric\",\n day: \"numeric\",\n hour: \"numeric\",\n hourCycle: \"h23\",\n }).formatToParts(date);\n const values = Object.fromEntries(\n parts.filter((part) => part.type !== \"literal\").map((\n part,\n ) => [part.type, Number(part.value)]),\n );\n return values as { year: number; month: number; day: number; hour: number };\n}\n\n// Converts a wall-clock time in America/Denver to UTC without requiring a date library.\nexport function mountainToUtc(\n year: number,\n month: number,\n day: number,\n hour = 0,\n minute = 0,\n): Date {\n let guess = new Date(Date.UTC(year, month - 1, day, hour, minute));\n for (let attempt = 0; attempt < 3; attempt += 1) {\n const seen = mountainParts(guess);\n const wanted = Date.UTC(year, month - 1, day, hour, minute);\n const actual = Date.UTC(\n seen.year,\n seen.month - 1,\n seen.day,\n seen.hour,\n minute,\n );\n guess = new Date(guess.getTime() + wanted - actual);\n }\n return guess;\n}\n\nexport function mountainCalendarDate(date: Date): Date {\n const p = mountainParts(date);\n return new Date(Date.UTC(p.year, p.month - 1, p.day, 12));\n}\n\nexport function waterYear(date: Date): number {\n const p = mountainParts(date);\n return p.month >= 10 ? p.year + 1 : p.year;\n}\n\nexport function fixedIssueAt(\n runoffYear: number,\n month: number,\n day: number,\n): Date {\n const year = month >= 10 ? runoffYear - 1 : runoffYear;\n return mountainToUtc(year, month, day, CONFIG.lockHourMountain);\n}\n\nexport function firstMondayOfSeptember(year: number): Date {\n const first = new Date(Date.UTC(year, 8, 1, 12));\n const offset = (8 - first.getUTCDay()) % 7;\n return addDays(first, offset);\n}\n\nexport function daysBetween(a: Date, b: Date): number {\n return Math.round(\n (mountainCalendarDate(a).getTime() - mountainCalendarDate(b).getTime()) /\n DAY,\n );\n}\n\nexport function afterMountainDay(\n date: Date,\n year: number,\n month: number,\n day: number,\n): boolean {\n return mountainCalendarDate(date) >\n new Date(Date.UTC(year, month - 1, day, 23, 59, 59));\n}",[173],"2e430288-2b75-40ad-8a18-d32903314145","config.ts","377dfa62-ab9a-11f1-ac1e-1607ee4eb77e",5,["D",1788881526228],"export const CONFIG = {\n username: \"pagosaoutside\",\n projectName: \"black-hat-hydrology\",\n timeZone: \"America/Denver\",\n lockHourMountain: 9,\n fixedLockGraceHours: 36,\n firstAutomatedWaterYear: 2027,\n // Leave this in shadow mode until the preseason checks have passed.\n mode: String(\n (globalThis as any).Deno?.env?.get?.(\"BLACK_HAT_MODE\") || \"shadow\",\n ).toLowerCase(),\n allowedOrigins: new Set([\n \"https://pagosaoutside.com\",\n \"https://www.pagosaoutside.com\",\n ]),\n usgsSite: \"09342500\",\n snotelStations: [\"840:CO:SNTL\", \"874:CO:SNTL\"],\n modelVersion: \"0.5.0-2027-game\",\n} as const;\n\nexport const IS_LIVE = CONFIG.mode === \"live\";",[180],"pulls","branches","forks","envVars",{"_193":64,"_195":64},"connections",{"_193":64,"_194":64},"count","unfilledCount","emptyCount",{"_44":179,"_33":180,"_31":181,"_141":-5,"_42":43,"_41":182,"_143":25,"_39":222,"_13":145,"_34":35,"_36":184,"_24":25,"_147":-5,"_204":223,"_27":224},{"_44":172,"_33":173,"_31":174,"_141":-5,"_42":43,"_41":175,"_143":25,"_39":219,"_13":145,"_34":35,"_36":177,"_24":25,"_147":-5,"_204":220,"_27":221},{"_44":166,"_33":167,"_31":168,"_141":-5,"_42":43,"_41":145,"_143":60,"_39":216,"_13":64,"_34":163,"_36":170,"_24":25,"_147":-5,"_204":217,"_27":218},{"_44":160,"_33":161,"_31":58,"_141":-5,"_42":43,"_41":158,"_143":25,"_39":213,"_13":64,"_34":163,"_36":164,"_24":25,"_147":-5,"_204":214,"_27":215},{"_44":156,"_33":28,"_31":32,"_141":-5,"_42":43,"_41":14,"_143":25,"_39":210,"_13":158,"_34":35,"_36":38,"_24":25,"_147":-5,"_204":211,"_27":212},{"_44":149,"_33":150,"_31":151,"_141":-5,"_42":43,"_41":152,"_143":25,"_39":207,"_13":145,"_34":35,"_36":154,"_24":25,"_147":-5,"_204":208,"_27":209},{"_44":138,"_33":139,"_31":140,"_141":-5,"_42":43,"_41":142,"_143":25,"_39":203,"_13":145,"_34":35,"_36":146,"_24":25,"_147":-5,"_204":205,"_27":206},["D",1788881595508],"children",[],[139],["D",1788881598789],[],[150],["D",1788881600501],[],[28],["D",1788718987538],[],[161],["D",1788719065897],[],[167],["D",1788881573051],[],[173],["D",1788881526228],[],[180],["D",1788718960405],["D",1789488693443],{"_101":100,"_109":105},{"_101":100,"_102":103,"_104":105,"_18":56,"_106":107,"_108":25},{"_13":90,"_33":91,"_44":43,"_92":45,"_93":-5,"_94":-5,"_95":-5,"_96":-5,"_97":-5,"_98":46,"_99":100},"ctx",{"_83":-5,"_128":263,"_264":265,"_266":267,"_268":25,"_269":25,"_270":25,"_271":60,"_272":-5,"_273":-7,"_274":-7,"_275":64,"_276":25,"_277":25,"_278":-5,"_279":25,"_280":25,"_281":25,"_282":-5},"ENV",{"_234":235,"_236":237,"_238":91,"_239":240,"_241":242,"_243":244,"_245":246,"_247":248,"_249":250,"_251":252,"_253":254,"_255":256,"_257":258,"_259":260,"_261":262},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v16