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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 ? Number(model.scale[index])\n : 1;\n estimate += ((raw - Number(model.mean[index])) / scale) *\n Number(model.coef[index]);\n }\n return addDays(\n mountainCalendarDate(fromDate),\n Math.round(clamp(estimate, 0, 70)),\n );\n}\n\nimport {\n MODEL_BUNDLE,\n SMALL_RIGHT_NOW_MODELS,\n TUBING_OPEN_LIVE_MODEL,\n} from \"./models-core.ts\";\nimport { FIRST400_TREES_1 } from \"./first400-trees-1.ts\";\nimport { FIRST400_TREES_2 } from \"./first400-trees-2.ts\";\nimport { FIRST400_TREES_3 } from \"./first400-trees-3.ts\";\nimport { FIRST400_TREES_4 } from \"./first400-trees-4.ts\";\nimport { FIRST400_TREES_5 } from \"./first400-trees-5.ts\";\n\nexport { MODEL_BUNDLE, TUBING_OPEN_LIVE_MODEL };\nexport const RIGHT_NOW_MODELS = {\n ...SMALL_RIGHT_NOW_MODELS,\n first400: {\n ...{\n \"type\": \"extra_trees\",\n \"features\": [\n \"log_flow\",\n \"log_flow_mean_7d\",\n \"log_flow_mean_30d\",\n \"relative_flow_change_7d\",\n \"swe_in\",\n \"swe_ratio\",\n \"swe_change_7d\",\n \"temperature_scaled\",\n \"prec_total_7d\",\n \"prec_total_30d\",\n \"snow_present\",\n \"season_sin\",\n \"season_cos\",\n \"season_sin_2\",\n \"season_cos_2\",\n \"melt_pressure\",\n \"rain_without_snow\",\n ],\n \"audit\": {\n \"model\": \"extra_trees\",\n \"hit_rate\": 0.8112244897959183,\n \"mae\": 6.468764022471184,\n \"n\": 196,\n },\n },\n trees: [\n ...FIRST400_TREES_1,\n ...FIRST400_TREES_2,\n ...FIRST400_TREES_3,\n ...FIRST400_TREES_4,\n ...FIRST400_TREES_5,\n ],\n },\n} as const;","import {\n MODEL_BUNDLE,\n SMALL_RIGHT_NOW_MODELS,\n TUBING_OPEN_LIVE_MODEL,\n} from \"./models-core.ts\";\nimport { FIRST400_TREES_1 } from \"./first400-trees-1.ts\";\nimport { FIRST400_TREES_2 } from \"./first400-trees-2.ts\";\nimport { FIRST400_TREES_3 } from \"./first400-trees-3.ts\";\nimport { FIRST400_TREES_4 } from \"./first400-trees-4.ts\";\nimport { FIRST400_TREES_5 } from \"./first400-trees-5.ts\";\n\nexport { MODEL_BUNDLE, TUBING_OPEN_LIVE_MODEL };\nexport const RIGHT_NOW_MODELS = {\n ...SMALL_RIGHT_NOW_MODELS,\n first400: {\n ...{\n \"type\": \"extra_trees\",\n \"features\": [\n \"log_flow\",\n \"log_flow_mean_7d\",\n \"log_flow_mean_30d\",\n \"relative_flow_change_7d\",\n \"swe_in\",\n \"swe_ratio\",\n \"swe_change_7d\",\n \"temperature_scaled\",\n \"prec_total_7d\",\n \"prec_total_30d\",\n \"snow_present\",\n \"season_sin\",\n \"season_cos\",\n \"season_sin_2\",\n \"season_cos_2\",\n \"melt_pressure\",\n \"rain_without_snow\",\n ],\nimport { MODEL_BUNDLE, SMALL_RIGHT_NOW_MODELS, TUBING_OPEN_LIVE_MODEL } from \"./models-core.ts\";\nimport { FIRST400_TREES_1 } from \"./first400-trees-1.ts\";\nimport { FIRST400_TREES_2 } from \"./first400-trees-2.ts\";\nimport { FIRST400_TREES_3 } from \"./first400-trees-3.ts\";\nimport { FIRST400_TREES_4 } from \"./first400-trees-4.ts\";\nimport { FIRST400_TREES_5 } from \"./first400-trees-5.ts\";\n\nexport { MODEL_BUNDLE, TUBING_OPEN_LIVE_MODEL };\nexport const RIGHT_NOW_MODELS = {\n ...SMALL_RIGHT_NOW_MODELS,\n first400: { ...{\"type\":\"extra_trees\",\"features\":[\"log_flow\",\"log_flow_mean_7d\",\"log_flow_mean_30d\",\"relative_flow_change_7d\",\"swe_in\",\"swe_ratio\",\"swe_change_7d\",\"temperature_scaled\",\"prec_total_7d\",\"prec_total_30d\",\"snow_present\",\"season_sin\",\"season_cos\",\"season_sin_2\",\"season_cos_2\",\"melt_pressure\",\"rain_without_snow\"],\"audit\":{\"model\":\"extra_trees\",\"hit_rate\":0.8112244897959183,\"mae\":6.468764022471184,\"n\":196}}, trees: [...FIRST400_TREES_1, ...FIRST400_TREES_2, ...FIRST400_TREES_3, ...FIRST400_TREES_4, ...FIRST400_TREES_5] },\n} as const;\n \"audit\": { \"model\": \"extra_trees\", \"hit_rate\": 0.8112244897959183, \"mae\": 6.468764022471184,\n \"n\": 196,\n },\n },\n trees: [\n ...FIRST400_TREES_1,\n ...FIRST400_TREES_2,\n ...FIRST400_TREES_3,\n ...FIRST400_TREES_4,\n ...FIRST400_TREES_5,\n ],\n },\n} as 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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",[151],"2486aa70-b6f7-4a88-8449-dfd269df371b","storage.ts","a3869a0c-ab9a-11f1-b55c-1607ee4eb77e",50,["D",1788882421895],3,"// Val-scoped SQLite keeps this project's records isolated and returns rows as\n// named objects (the legacy account-global database returns arrays).\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport type StoredPrediction = {\n water_year: number;\n question_number: number;\n question_key: string;\n title: string;\n mode: string;\n issue_kind: string;\n issue_due_at: string;\n locked_at: string;\n input_cutoff_at: string;\n model_version: string;\n model_hash: string;\n prediction_json: string;\n inputs_json: string;\n inputs_hash: string;\n source_status_json: string;\n status: string;\n actual_json: string | null;\n score: string | null;\n scored_at: string | null;\n};\n\nexport async function initDb(): Promise {\n await sqlite.batch([\n `CREATE TABLE IF NOT EXISTS bh_seasons (\n water_year INTEGER PRIMARY KEY,\n opened_at TEXT NOT NULL,\n state TEXT NOT NULL DEFAULT 'active',\n model_bundle_version TEXT NOT NULL,\n completed_at TEXT\n )`,\n `CREATE TABLE IF NOT EXISTS bh_predictions (\n water_year INTEGER NOT NULL,\n question_number INTEGER NOT NULL,\n question_key TEXT NOT NULL,\n title TEXT NOT NULL,\n mode TEXT NOT NULL CHECK(mode IN ('shadow','live')),\n issue_kind TEXT NOT NULL,\n issue_due_at TEXT NOT NULL,\n locked_at TEXT NOT NULL,\n input_cutoff_at TEXT NOT NULL,\n model_version TEXT NOT NULL,\n model_hash TEXT NOT NULL,\n prediction_json TEXT NOT NULL,\n inputs_json TEXT NOT NULL,\n inputs_hash TEXT NOT NULL,\n source_status_json TEXT NOT NULL,\n status TEXT NOT NULL DEFAULT 'locked',\n actual_json TEXT,\n score TEXT CHECK(score IN ('hit','miss') OR score IS NULL),\n scored_at TEXT,\n PRIMARY KEY (water_year, question_number, mode)\n )`,\n `CREATE TABLE IF NOT EXISTS bh_runs (\n id TEXT PRIMARY KEY,\n ran_at TEXT NOT NULL,\n mode TEXT NOT NULL,\n success INTEGER NOT NULL,\n message TEXT NOT NULL,\n source_status_json TEXT\n )`,\n `CREATE TABLE IF NOT EXISTS bh_events (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n water_year INTEGER NOT NULL,\n question_number INTEGER,\n event_type TEXT NOT NULL,\n occurred_at TEXT NOT NULL,\n payload_json TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS bh_public_cache (\n cache_key TEXT PRIMARY KEY,\n value_json TEXT NOT NULL,\n updated_at TEXT NOT NULL\n )`,\n `CREATE TRIGGER IF NOT EXISTS bh_locked_prediction_immutable\n BEFORE UPDATE OF issue_due_at, locked_at, input_cutoff_at, model_version, model_hash, prediction_json, inputs_json, inputs_hash\n ON bh_predictions\n BEGIN SELECT RAISE(ABORT, 'Locked Black Hat prediction fields are immutable'); END`,\n ]);\n}\n\nexport async function ensureSeason(\n waterYear: number,\n modelVersion: string,\n openedAt: Date,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT OR IGNORE INTO bh_seasons(water_year, opened_at, model_bundle_version) VALUES (?, ?, ?)`,\n args: [waterYear, openedAt.toISOString(), modelVersion],\n });\n}\n\nexport async function getPrediction(\n waterYear: number,\n questionNumber: number,\n mode: string,\n): Promise {\n const result = await sqlite.execute({\n sql:\n `SELECT * FROM bh_predictions WHERE water_year=? AND question_number=? AND mode=?`,\n args: [waterYear, questionNumber, mode],\n });\n return (result.rows[0] as any) || null;\n}\n\nexport async function lockPrediction(row: StoredPrediction): Promise {\n const result = await sqlite.execute({\n sql: `INSERT OR IGNORE INTO bh_predictions(\n water_year, question_number, question_key, title, mode, issue_kind, issue_due_at, locked_at,\n input_cutoff_at, model_version, model_hash, prediction_json, inputs_json, inputs_hash,\n source_status_json, status, actual_json, score, scored_at\n ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n row.water_year,\n row.question_number,\n row.question_key,\n row.title,\n row.mode,\n row.issue_kind,\n row.issue_due_at,\n row.locked_at,\n row.input_cutoff_at,\n row.model_version,\n row.model_hash,\n row.prediction_json,\n row.inputs_json,\n row.inputs_hash,\n row.source_status_json,\n row.status,\n row.actual_json,\n row.score,\n row.scored_at,\n ],\n });\n return Number(result.rowsAffected) === 1;\n}\n\nexport async function finalizePrediction(\n waterYear: number,\n questionNumber: number,\n mode: string,\n actualJson: string,\n hit: boolean,\n scoredAt: Date,\n): Promise {\n await sqlite.execute({\n sql:\n `UPDATE bh_predictions SET actual_json=?, score=?, status='scored', scored_at=?\n WHERE water_year=? AND question_number=? AND mode=? AND scored_at IS NULL`,\n args: [\n actualJson,\n hit ? \"hit\" : \"miss\",\n scoredAt.toISOString(),\n waterYear,\n questionNumber,\n mode,\n ],\n });\n}\n\nexport async function recordEvent(\n waterYear: number,\n questionNumber: number | null,\n eventType: string,\n occurredAt: Date,\n payload: unknown,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT INTO bh_events(water_year, question_number, event_type, occurred_at, payload_json) VALUES (?, ?, ?, ?, ?)`,\n args: [\n waterYear,\n questionNumber,\n eventType,\n occurredAt.toISOString(),\n JSON.stringify(payload),\n ],\n });\n}\n\nexport async function recordRun(\n success: boolean,\n mode: string,\n message: string,\n sourceStatus: unknown = null,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT INTO bh_runs(id, ran_at, mode, success, message, source_status_json) VALUES (?, ?, ?, ?, ?, ?)`,\n args: [\n crypto.randomUUID(),\n new Date().toISOString(),\n mode,\n success ? 1 : 0,\n message,\n sourceStatus ? JSON.stringify(sourceStatus) : null,\n ],\n });\n}\n\nexport async function listPredictions(\n waterYear: number,\n mode: string,\n): Promise {\n const result = await sqlite.execute({\n sql:\n `SELECT * FROM bh_predictions WHERE water_year=? AND mode=? ORDER BY question_number`,\n args: [waterYear, mode],\n });\n return result.rows as any;\n}\n\nexport async function recentRuns(limit = 10): Promise {\n const result = await sqlite.execute({\n sql:\n `SELECT ran_at, mode, success, message, source_status_json FROM bh_runs ORDER BY ran_at DESC LIMIT ?`,\n args: [limit],\n });\n return result.rows as any;\n}\n\nexport async function cachePublicFeed(\n key: string,\n value: unknown,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT INTO bh_public_cache(cache_key, value_json, updated_at) VALUES (?, ?, ?)\n ON CONFLICT(cache_key) DO UPDATE SET value_json=excluded.value_json, updated_at=excluded.updated_at`,\n args: [key, JSON.stringify(value), new Date().toISOString()],\n });\n}\n\nexport async function getPublicFeed(\n key: string,\n): Promise<{ value: any; updatedAt: string } | null> {\n const result = await sqlite.execute({\n sql: `SELECT value_json, updated_at FROM bh_public_cache WHERE cache_key=?`,\n args: [key],\n });\n const row: any = result.rows[0];\n return row\n ? {\n value: JSON.parse(String(row.value_json)),\n updatedAt: String(row.updated_at),\n }\n : null;\n}\n\n// Val-scoped SQLite keeps this project's records isolated and returns rows as\n// named objects (the legacy account-global database returns arrays).\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport type StoredPrediction = {\n water_year: number;\n question_number: number;\n question_key: string;\n title: string;\n mode: string;\n issue_kind: string;\n issue_due_at: string;\n locked_at: string;\n input_cutoff_at: string;\n model_version: string;\n model_hash: string;\n prediction_json: string;\n inputs_json: string;\n inputs_hash: string;\n source_status_json: string;\n status: string;\n actual_json: string | null;\n score: string | null;\n scored_at: string | null;\n};\n\nexport async function initDb(): Promise {\n await sqlite.batch([\n `CREATE TABLE IF NOT EXISTS bh_seasons (\n water_year INTEGER PRIMARY KEY,\n opened_at TEXT NOT NULL,\n state TEXT NOT NULL DEFAULT 'active',\n model_bundle_version TEXT NOT NULL,\n completed_at TEXT\n )`,\n `CREATE TABLE IF NOT EXISTS bh_predictions (\n water_year INTEGER NOT NULL,\n question_number INTEGER NOT NULL,\n question_key TEXT NOT NULL,\n title TEXT NOT NULL,\n mode TEXT NOT NULL CHECK(mode IN ('shadow','live')),\n issue_kind TEXT NOT NULL,\n issue_due_at TEXT NOT NULL,\n locked_at TEXT NOT NULL,\n input_cutoff_at TEXT NOT NULL,\n model_version TEXT NOT NULL,\n model_hash TEXT NOT NULL,\n prediction_json TEXT NOT NULL,\n inputs_json TEXT NOT NULL,\n inputs_hash TEXT NOT NULL,\n source_status_json TEXT NOT NULL,\n status TEXT NOT NULL DEFAULT 'locked',\n actual_json TEXT,\n score TEXT CHECK(score IN ('hit','miss') OR score IS NULL),\n scored_at TEXT,\n PRIMARY KEY (water_year, question_number, mode)\n )`,\n `CREATE TABLE IF NOT EXISTS bh_runs (\n id TEXT PRIMARY KEY,\n ran_at TEXT NOT NULL,\n mode TEXT NOT NULL,\n success INTEGER NOT NULL,\n message TEXT NOT NULL,\n source_status_json TEXT\n )`,\n `CREATE TABLE IF NOT EXISTS bh_events (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n water_year INTEGER NOT NULL,\n question_number INTEGER,\n event_type TEXT NOT NULL,\n occurred_at TEXT NOT NULL,\n payload_json TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS bh_public_cache (\n cache_key TEXT PRIMARY KEY,\n value_json TEXT NOT NULL,\n updated_at TEXT NOT NULL\n )`,\n `CREATE TRIGGER IF NOT EXISTS bh_locked_prediction_immutable\n BEFORE UPDATE OF issue_due_at, locked_at, input_cutoff_at, model_version, model_hash, prediction_json, inputs_json, inputs_hash\n ON bh_predictions\n BEGIN SELECT RAISE(ABORT, 'Locked Black Hat prediction fields are immutable'); END`,\n ]);\n}\n\nexport async function ensureSeason(\n waterYear: number,\n modelVersion: string,\n openedAt: Date,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT OR IGNORE INTO bh_seasons(water_year, opened_at, model_bundle_version) VALUES (?, ?, ?)`,\n args: [waterYear, openedAt.toISOString(), modelVersion],\n });\n}\n\nexport async function getPrediction(\n waterYear: number,\n questionNumber: number,\n mode: string,\n): Promise {\n const result = await sqlite.execute({\n sql:\n `SELECT * FROM bh_predictions WHERE water_year=? AND question_number=? AND mode=?`,\n args: [waterYear, questionNumber, mode],\n });\n return (result.rows[0] as any) || null;\n}\n\nexport async function lockPrediction(row: StoredPrediction): Promise {\n const result = await sqlite.execute({\n sql: `INSERT OR IGNORE INTO bh_predictions(\n water_year, question_number, question_key, title, mode, issue_kind, issue_due_at, locked_at,\n input_cutoff_at, model_version, model_hash, prediction_json, inputs_json, inputs_hash,\n source_status_json, status, actual_json, score, scored_at\n ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n row.water_year,\n row.question_number,\n row.question_key,\n row.title,\n row.mode,\n row.issue_kind,\n row.issue_due_at,\n row.locked_at,\n row.input_cutoff_at,\n row.model_version,\n row.model_hash,\n row.prediction_json,\n row.inputs_json,\n row.inputs_hash,\n row.source_status_json,\n row.status,\n row.actual_json,\n row.score,\n row.scored_at,\n ],\n });\n return Number(result.rowsAffected) === 1;\n}\n\nexport async function finalizePrediction(\n waterYear: number,\n questionNumber: number,\n mode: string,\n actualJson: string,\n hit: boolean,\n scoredAt: Date,\n): Promise {\n await sqlite.execute({\n sql:\n `UPDATE bh_predictions SET actual_json=?, score=?, status='scored', scored_at=?\n WHERE water_year=? AND question_number=? AND mode=? AND scored_at IS NULL`,\n args: [\n actualJson,\n hit ? \"hit\" : \"miss\",\n scoredAt.toISOString(),\n waterYear,\n questionNumber,\n mode,\n ],\n });\n}\n\nexport async function recordEvent(\n waterYear: number,\n questionNumber: number | null,\n eventType: string,\n occurredAt: Date,\n payload: unknown,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT INTO bh_events(water_year, question_number, event_type, occurred_at, payload_json) VALUES (?, ?, ?, ?, ?)`,\n args: [\n waterYear,\n questionNumber,\n eventType,\n occurredAt.toISOString(),\n JSON.stringify(payload),\n ],\n });\n}\n\nexport async function recordRun(\n success: boolean,\n mode: string,\n message: string,\n sourceStatus: unknown = null,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT INTO bh_runs(id, ran_at, mode, success, message, source_status_json) VALUES (?, ?, ?, ?, ?, ?)`,\n args: [\n crypto.randomUUID(),\n new Date().toISOString(),\n mode,\n success ? 1 : 0,\n message,\n sourceStatus ? JSON.stringify(sourceStatus) : null,\n ],\n });\n}\n\nexport async function listPredictions(\n waterYear: number,\n mode: string,\n): Promise {\n const result = await sqlite.execute({\n sql:\n `SELECT * FROM bh_predictions WHERE water_year=? AND mode=? ORDER BY question_number`,\n args: [waterYear, mode],\n });\n return result.rows as any;\n}\n\nexport async function recentRuns(limit = 10): Promise {\n const result = await sqlite.execute({\n sql:\n `SELECT ran_at, mode, success, message, source_status_json FROM bh_runs ORDER BY ran_at DESC LIMIT ?`,\n args: [limit],\n });\n return result.rows as any;\n}\n\nexport async function cachePublicFeed(\n key: string,\n value: unknown,\n): Promise {\n await sqlite.execute({\n sql:\n `INSERT INTO bh_public_cache(cache_key, value_json, updated_at) VALUES (?, ?, ?)\n ON CONFLICT(cache_key) DO UPDATE SET value_json=excluded.value_json, updated_at=excluded.updated_at`,\n args: [key, JSON.stringify(value), new Date().toISOString()],\n });\n}\n\nexport async function getPublicFeed(\n key: string,\n): Promise<{ value: any; updatedAt: string } | null> {\n const result = await sqlite.execute({\n sql: `SELECT value_json, updated_at FROM bh_public_cache WHERE cache_key=?`,\n args: [key],\n });\n const row: any = result.rows[0];\n return row\n ? {\n value: JSON.parse(String(row.value_json)),\n updatedAt: String(row.updated_at),\n }\n : null;\n}",[162],"4bb600a6-f590-41ee-9e8a-eeed86394cd7","sources.ts","9fe9133e-ab9a-11f1-b085-1607ee4eb77e",63,["D",1788882623133],6,"import { CONFIG } from \"./config.ts\";\nimport {\n addDays,\n firstMondayOfSeptember,\n isoDay,\n mountainCalendarDate,\n waterYear,\n} from \"./dates.ts\";\nimport type { BasinSnapshot, FlowPoint, Milestones, Snotel } from \"./types.ts\";\n\nconst average = (values: unknown[]): number | null => {\n const valid = values.map(Number).filter(Number.isFinite);\n return valid.length\n ? valid.reduce((sum, value) => sum + value, 0) / valid.length\n : null;\n};\n\nasync function fetchJson(url: string, timeout = 20_000): Promise {\n const controller = new AbortController();\n const timer = setTimeout(() => controller.abort(), timeout);\n try {\n const response = await fetch(url, {\n signal: controller.signal,\n headers: { Accept: \"application/json\" },\n });\n if (!response.ok) {\n const parsed = new URL(url);\n throw new Error(\n `${parsed.hostname}${parsed.pathname} returned ${response.status}`,\n );\n }\n return await response.json();\n } finally {\n clearTimeout(timer);\n }\n}\n\nfunction parseUsgs(payload: any, cutoffAt: Date): FlowPoint[] {\n const series = payload?.value?.timeSeries?.[0]?.values?.[0]?.value || [];\n return series.map((item: any) => ({\n date: new Date(item.dateTime),\n value: Number(item.value),\n }))\n .filter((item: FlowPoint) =>\n !Number.isNaN(item.date.getTime()) && item.date <= cutoffAt &&\n Number.isFinite(item.value) && item.value >= 0\n )\n .sort((a: FlowPoint, b: FlowPoint) => a.date.getTime() - b.date.getTime());\n}\n\nfunction parseSnotel(payload: any, cutoffAt: Date): Snotel {\n const output: Snotel = {};\n for (const station of payload || []) {\n const stationId = String(station.stationTriplet);\n output[stationId] ||= {};\n for (const series of station.data || []) {\n const code = series.stationElement?.elementCode;\n if (!code) continue;\n output[stationId][code] = (series.values || []).map((item: any) => ({\n date: new Date(`${item.date}T12:00:00Z`),\n value: Number(item.value),\n median: item.median === null || item.median === undefined\n ? null\n : Number(item.median),\n })).filter((item: any) =>\n !Number.isNaN(item.date.getTime()) && item.date <= cutoffAt &&\n Number.isFinite(item.value)\n );\n }\n }\n return output;\n}\n\nfunction latestAtOrBefore(\n points: T[],\n date: Date,\n): T | null {\n const eligible = (points || []).filter((point) => point.date <= date);\n return eligible.length ? eligible[eligible.length - 1] : null;\n}\n\nfunction stationAverage(\n snotel: Snotel,\n element: string,\n date: Date,\n field: \"value\" | \"median\" = \"value\",\n): number | null {\n return average(\n Object.values(snotel).map((station) =>\n (latestAtOrBefore(station[element] || [], date) as any)?.[field]\n ),\n );\n}\n\nfunction waterYearDay(date: Date): number {\n const year = date.getUTCMonth() >= 9\n ? date.getUTCFullYear()\n : date.getUTCFullYear() - 1;\n return Math.round(\n (Date.UTC(date.getUTCFullYear(), date.getUTCMonth(), date.getUTCDate()) -\n Date.UTC(year, 9, 1)) / 86_400_000,\n );\n}\n\nfunction buildFeatures(\n flowDaily: FlowPoint[],\n currentFlow: number,\n snotel: Snotel,\n now: Date,\n): Record {\n const date7 = addDays(now, -7);\n const date30 = addDays(now, -30);\n const recent7 = flowDaily.filter((point) =>\n point.date >= date7 && point.date <= now\n ).map((point) => point.value);\n const recent30 = flowDaily.filter((point) =>\n point.date >= date30 && point.date <= now\n ).map((point) => point.value);\n const flow7Ago = latestAtOrBefore(flowDaily, date7)?.value ?? currentFlow;\n const flowMean7 = average(recent7) ?? currentFlow;\n const flowMean30 = average(recent30) ?? flowMean7;\n const swe = stationAverage(snotel, \"WTEQ\", now) ?? 0;\n const sweMedian = stationAverage(snotel, \"WTEQ\", now, \"median\") ?? 0;\n const swe7Ago = stationAverage(snotel, \"WTEQ\", date7) ?? swe;\n const sweRatio = sweMedian > 0.05\n ? Math.min(3, Math.max(0, swe / sweMedian))\n : swe <= 0.05\n ? 0\n : 3;\n const stationIds = Object.keys(snotel);\n const temperatures: number[] = [];\n for (const id of stationIds) {\n for (const point of snotel[id].TAVG || []) {\n if (point.date >= date7 && point.date <= now) {\n temperatures.push(point.value);\n }\n }\n }\n const temp7 = average(temperatures) ?? 32;\n const precipitationChange = (startDate: Date) =>\n average(stationIds.map((id) => {\n const current = latestAtOrBefore(snotel[id].PREC || [], now)?.value;\n const earlier = latestAtOrBefore(snotel[id].PREC || [], startDate)?.value;\n return Number.isFinite(current) && Number.isFinite(earlier)\n ? Math.max(0, Number(current) - Number(earlier))\n : null;\n })) ?? 0;\n const precip7 = precipitationChange(date7);\n const precip30 = precipitationChange(date30);\n const snowPresent = swe > 0.05 ? 1 : 0;\n const angle = 2 * Math.PI * waterYearDay(now) / 365.25;\n return {\n log_flow: Math.log1p(currentFlow),\n log_flow_mean_7d: Math.log1p(flowMean7),\n log_flow_mean_30d: Math.log1p(flowMean30),\n relative_flow_change_7d: (currentFlow - flow7Ago) /\n (Math.abs(flowMean7) + 30),\n swe_in: swe,\n swe_ratio: sweRatio,\n swe_change_7d: swe - swe7Ago,\n temperature_scaled: (temp7 - 32) / 20,\n prec_total_7d: precip7,\n prec_total_30d: precip30,\n snow_present: snowPresent,\n season_sin: Math.sin(angle),\n season_cos: Math.cos(angle),\n season_sin_2: Math.sin(2 * angle),\n season_cos_2: Math.cos(2 * angle),\n melt_pressure: swe * Math.max(temp7 - 32, 0) / 100,\n rain_without_snow: precip7 * (1 - snowPresent),\n current_flow: currentFlow,\n swe_median: sweMedian,\n temp_7d: temp7,\n precip_7d: precip7,\n };\n}\n\nasync function fetchInstantaneousPeak(\n dailyFlow: FlowPoint[],\n runoffYear: number,\n cutoffAt: Date,\n): Promise {\n const start = new Date(Date.UTC(runoffYear, 3, 1));\n const end = new Date(\n Math.min(cutoffAt.getTime(), Date.UTC(runoffYear, 6, 31, 23, 59)),\n );\n const candidate = dailyFlow.filter((item) =>\n item.date >= start && item.date <= end\n )\n .reduce(\n (best, item) => !best || item.value > best.value ? item : best,\n null,\n );\n if (!candidate) return null;\n const url =\n `https://waterservices.usgs.gov/nwis/iv/?format=json&sites=${CONFIG.usgsSite}&startDT=${\n isoDay(addDays(candidate.date, -1))\n }&endDT=${\n isoDay(addDays(candidate.date, 2))\n }¶meterCd=00060&siteStatus=all`;\n const values = parseUsgs(await fetchJson(url), cutoffAt);\n return values.reduce(\n (best, item) => !best || item.value > best.value ? item : best,\n null,\n );\n}\n\nfunction findTubingOpening(\n series: FlowPoint[],\n after: Date | null,\n): Date | null {\n if (!after) return null;\n const ordered = series.filter((item) => item.date > after).sort((a, b) =>\n a.date.getTime() - b.date.getTime()\n );\n for (let index = 0; index < ordered.length; index += 1) {\n const endPoint = ordered[index];\n const startTime = endPoint.date.getTime() - 24 * 3_600_000;\n const window = ordered.filter((item) =>\n item.date.getTime() >= startTime && item.date <= endPoint.date\n );\n if (window.length < 4) continue;\n if (\n window.at(-1)!.date.getTime() - window[0].date.getTime() < 23 * 3_600_000\n ) continue;\n if (window.every((item) => item.value >= 30 && item.value < 400)) {\n return endPoint.date;\n }\n }\n return null;\n}\n\nfunction deriveMilestones(\n dailyFlow: FlowPoint[],\n instantaneous: FlowPoint[],\n snotel: Snotel,\n current: FlowPoint,\n peakInstant: FlowPoint | null,\n): Milestones {\n const runoffYear = waterYear(current.date);\n const waterYearStart = new Date(Date.UTC(runoffYear - 1, 9, 1, 12));\n const springStart = new Date(Date.UTC(runoffYear, 2, 1, 12));\n const peakWindowStart = new Date(Date.UTC(runoffYear, 3, 1, 12));\n const peakWindowEnd = new Date(Date.UTC(runoffYear, 6, 31, 23, 59));\n const recreationStart = new Date(Date.UTC(runoffYear, 4, 1, 12));\n const laborDay = firstMondayOfSeptember(runoffYear);\n laborDay.setUTCHours(23, 59, 59, 999);\n const waterYearDaily = dailyFlow.filter((item) =>\n item.date >= waterYearStart && item.date <= current.date\n );\n const springDaily = waterYearDaily.filter((item) => item.date >= springStart);\n const first400 = springDaily.find((item) => item.value >= 400) || null;\n const peakDaily = springDaily.filter((item) =>\n item.date >= peakWindowStart && item.date <= peakWindowEnd\n )\n .reduce(\n (best, item) => !best || item.value > best.value ? item : best,\n null,\n );\n const daysSincePeak = peakDaily\n ? Math.floor(\n (current.date.getTime() - peakDaily.date.getTime()) / 86_400_000,\n )\n : 0;\n const dropFromPeak = peakDaily?.value\n ? 1 - current.value / peakDaily.value\n : 0;\n const peakFlowLikelyPassed = Boolean(\n peakDaily && daysSincePeak >= 6 && dropFromPeak >= 0.18,\n );\n let tubingForecastTrigger: FlowPoint | null = null;\n if (peakDaily) {\n const peakIndex = springDaily.findIndex((item) =>\n isoDay(item.date) === isoDay(peakDaily.date) &&\n item.value === peakDaily.value\n );\n for (\n let index = Math.max(2, peakIndex + 1);\n index < springDaily.length;\n index += 1\n ) {\n const item = springDaily[index];\n const fallingThreeDay = item.value < springDaily[index - 2].value;\n const eligible = peakDaily.value >= 800\n ? item.value < 800\n : peakFlowLikelyPassed;\n if (eligible && fallingThreeDay) {\n tubingForecastTrigger = item;\n break;\n }\n }\n }\n const dailyTubingFallback = peakDaily\n ? springDaily.find((item) =>\n item.date > peakDaily.date && item.value >= 30 && item.value < 400\n )?.date || null\n : null;\n const tubingOpening =\n findTubingOpening(instantaneous, peakDaily?.date || null) ||\n dailyTubingFallback;\n const seasonDaily = waterYearDaily.filter((item) =>\n item.date >= recreationStart && item.date <= laborDay &&\n item.date <= current.date\n );\n const counts = {\n p300: seasonDaily.filter((item) => item.value >= 300).length,\n p600: seasonDaily.filter((item) => item.value >= 600).length,\n p800: seasonDaily.filter((item) => item.value >= 800).length,\n tubing:\n seasonDaily.filter((item) => item.value >= 30 && item.value < 400).length,\n };\n const strengthDays = waterYearDaily.filter((item) =>\n item.date >= recreationStart &&\n item.date <= new Date(Date.UTC(runoffYear, 5, 30, 23, 59))\n );\n const strength = average(strengthDays.map((item) => item.value));\n const snowDates = [\n ...new Set(\n Object.values(snotel).flatMap((station) =>\n (station.WTEQ || []).map((item) => isoDay(item.date))\n ),\n ),\n ]\n .map((value) => new Date(`${value}T12:00:00Z`)).filter((date) =>\n date >= waterYearStart && date <= current.date\n ).sort((a, b) => a.getTime() - b.getTime());\n const snowSeries = snowDates.map((date) => ({\n date,\n value: stationAverage(snotel, \"WTEQ\", date),\n }))\n .filter((item): item is { date: Date; value: number } =>\n Number.isFinite(item.value)\n );\n let persistentSnow = null;\n for (let index = 0; index <= snowSeries.length - 7; index += 1) {\n const window = snowSeries.slice(index, index + 7);\n if (window[0].value >= 1 && window.every((item) => item.value >= 0.5)) {\n persistentSnow = window[0];\n break;\n }\n }\n const peakSwe = snowSeries.reduce<{ date: Date; value: number } | null>(\n (best, item) => !best || item.value > best.value ? item : best,\n null,\n );\n const daysSincePeakSwe = peakSwe\n ? Math.floor((current.date.getTime() - peakSwe.date.getTime()) / 86_400_000)\n : 0;\n const currentSwe = snowSeries.at(-1)?.value || 0;\n const peakSweLikelyPassed = Boolean(\n peakSwe && peakSwe.value >= 1 && daysSincePeakSwe >= 14 &&\n currentSwe <= peakSwe.value * 0.7,\n );\n return {\n runoffYear,\n laborDay,\n first400,\n peakDaily,\n peakInstant,\n peakFlowLikelyPassed,\n tubingForecastTrigger,\n tubingOpening,\n counts,\n strength,\n persistentSnow,\n peakSwe,\n peakSweLikelyPassed,\n };\n}\n\nexport async function fetchBasinSnapshot(\n cutoffAt = new Date(),\n): Promise {\n const runoffYear = waterYear(cutoffAt);\n // Models use 7- and 30-day history. Starting 45 days before the water year\n // also prevents the October 1 lock from running with an empty daily series.\n const waterYearStart = new Date(Date.UTC(runoffYear - 1, 9, 1, 12));\n const start = isoDay(addDays(waterYearStart, -45));\n const end = isoDay(cutoffAt);\n const dailyUrl =\n `https://waterservices.usgs.gov/nwis/dv/?format=json&sites=${CONFIG.usgsSite}&startDT=${start}&endDT=${end}¶meterCd=00060&statCd=00003&siteStatus=all`;\n const instantUrl =\n `https://waterservices.usgs.gov/nwis/iv/?format=json&sites=${CONFIG.usgsSite}&period=P60D¶meterCd=00060&siteStatus=all`;\n const stationParam = encodeURIComponent(CONFIG.snotelStations.join(\",\"));\n const snotelUrl =\n `https://wcc.sc.egov.usda.gov/awdbRestApi/services/v1/data?stationTriplets=${stationParam}&elements=WTEQ%2CSNWD%2CPREC%2CTAVG&duration=DAILY&beginDate=${start}&endDate=${end}&periodRef=END¢ralTendencyType=MEDIAN&returnFlags=false&returnOriginalValues=false&returnSuspectData=false`;\n const [dailyPayload, instantPayload, snowPayload] = await Promise.all([\n fetchJson(dailyUrl),\n fetchJson(instantUrl),\n fetchJson(snotelUrl),\n ]);\n const dailyFlow = parseUsgs(dailyPayload, cutoffAt);\n const instantaneous = parseUsgs(instantPayload, cutoffAt);\n if (!dailyFlow.length || !instantaneous.length) {\n throw new Error(\"The Pagosa USGS gauge did not return enough data\");\n }\n const snotel = parseSnotel(snowPayload, cutoffAt);\n if (!Object.keys(snotel).length) {\n throw new Error(\"The headwater SNOTEL stations did not return enough data\");\n }\n const latestSnotelAt = Object.values(snotel)\n .flatMap((station) => Object.values(station).flat())\n .reduce(\n (latest, point) => !latest || point.date > latest ? point.date : latest,\n null,\n );\n const current = instantaneous.at(-1)!;\n const features = buildFeatures(\n dailyFlow,\n current.value,\n snotel,\n current.date,\n );\n const peakInstant = await fetchInstantaneousPeak(\n dailyFlow,\n runoffYear,\n cutoffAt,\n ).catch(() => null);\n const milestones = deriveMilestones(\n dailyFlow,\n instantaneous,\n snotel,\n current,\n peakInstant,\n );\n return {\n capturedAt: new Date(),\n cutoffAt,\n current,\n instantaneous,\n dailyFlow,\n snotel,\n features,\n milestones,\n sourceStatus: {\n usgs_site: CONFIG.usgsSite,\n usgs_latest_at: current.date.toISOString(),\n usgs_daily_points: dailyFlow.length,\n usgs_instant_points: instantaneous.length,\n snotel_stations: Object.keys(snotel),\n snotel_latest_day: latestSnotelAt ? isoDay(latestSnotelAt) : null,\n },\n };\n}",[170],"7c5e5777-1df3-420b-9574-ea18bfcd7bfd","questions.ts","9c517f2c-ab9a-11f1-94dd-1607ee4eb77e",64,["D",1788882624992],5,"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];",[178],"04c3b251-4154-4418-808d-f713b122556f","new-file-4901.ts","5fc26ee2-ab9d-11f1-80fd-1607ee4eb77e",69,["D",1788882782986],[186],"e606d5c4-9c9d-4419-92de-0ee0d46dda2e",["D",1788882784046],[28],"98a0163b-4643-4ce8-be3c-518473f83708","models-core.ts","3bf39924-ab9c-11f1-b3b0-1607ee4eb77e",37,["D",1788882295796],1,[195],"bb6c2a4d-77ab-4075-86fe-bf36540039a8","model-runtime.ts","9e38bf58-ab9a-11f1-9797-1607ee4eb77e",62,["D",1788882618399],4,"import { RIGHT_NOW_MODELS, TUBING_OPEN_LIVE_MODEL } from \"./models.ts\";\nimport { addDays, mountainCalendarDate } from \"./dates.ts\";\n\nexport function clamp(value: number, min: number, max: number): number {\n return Math.min(max, Math.max(min, value));\n}\n\nfunction runTree(tree: any, values: number[]): number {\n let node = 0;\n while (Number(tree.feature[node]) >= 0) {\n const featureIndex = Number(tree.feature[node]);\n node = values[featureIndex] <= Number(tree.threshold[node])\n ? 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 ${model.features[index]}`,\n );\n }\n const scale = Math.abs(Number(model.scale[index])) > 1e-9\n ? Number(model.scale[index])\n : 1;\n estimate += ((raw - Number(model.mean[index])) / scale) *\n Number(model.coef[index]);\n }\n return addDays(\n mountainCalendarDate(fromDate),\n Math.round(clamp(estimate, 0, 70)),\n );\n}",[202],"71a8dd41-9d07-48a4-a2e5-49b174c0d3a7","main.ts",67,["D",1788882631456],"http","import { CONFIG } from \"./config.ts\";\nimport { getPublicFeed, initDb } from \"./storage.ts\";\n\nfunction cors(origin: string | null): HeadersInit {\n const headers: Record = {\n \"Access-Control-Allow-Methods\": \"GET, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type\",\n \"Cache-Control\": \"public, max-age=300, stale-while-revalidate=3600\",\n \"Content-Type\": \"application/json; charset=utf-8\",\n \"Vary\": \"Origin\",\n };\n if (origin && CONFIG.allowedOrigins.has(origin)) {\n headers[\"Access-Control-Allow-Origin\"] = origin;\n }\n return headers;\n}\n\nexport default async function (req: Request): Promise {\n const origin = req.headers.get(\"origin\");\n if (req.method === \"OPTIONS\") {\n return new Response(null, { status: 204, headers: cors(origin) });\n }\n if (req.method !== \"GET\") {\n return Response.json({ error: \"Read-only endpoint\" }, {\n status: 405,\n headers: cors(origin),\n });\n }\n await initDb();\n const url = new URL(req.url);\n const key = url.searchParams.get(\"year\")\n ? `scoreboard-${url.searchParams.get(\"year\")}`\n : \"scoreboard-current\";\n const cached = await getPublicFeed(key);\n if (!cached) {\n return Response.json({\n error: \"The scoreboard has not completed its first run yet.\",\n }, { status: 503, headers: cors(origin) });\n }\n return Response.json({\n ...cached.value,\n served_at: new Date().toISOString(),\n cache_updated_at: cached.updatedAt,\n }, { headers: cors(origin) });\n}",[210],"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",[217],"cd4e6e69-78bd-4cf6-b43c-911ea0c17652","first400-trees-5.ts","480a27d2-ab9c-11f1-9a57-1607ee4eb77e",47,["D",1788882319621],[223],"ff4cade3-377c-486f-8c32-07fb15d4b8f1","first400-trees-4.ts","443c880c-ab9c-11f1-abb3-1607ee4eb77e",45,["D",1788882312683],[229],"82ac7bdc-eb46-4a8d-87cd-70644e230d73","first400-trees-3.ts","420ad214-ab9c-11f1-8ed7-1607ee4eb77e",43,["D",1788882306326],[235],"00e5f497-a106-4771-925a-87050315d243","first400-trees-2.ts","3df70fa8-ab9c-11f1-a43c-1607ee4eb77e",41,["D",1788882302686],[241],"1830c816-3f3d-4665-81c8-75dff77b9f4a","first400-trees-1.ts","41098f18-ab9c-11f1-99ad-1607ee4eb77e",54,["D",1788882510486],[247],"207f3d62-22f6-4eb4-bf9b-145fa1b36c6a","engine.ts","a6362ff6-ab9a-11f1-9797-1607ee4eb77e",65,["D",1788882627143],"import { CONFIG, IS_LIVE } from \"./config.ts\";\nimport { addDays, fixedIssueAt, mountainToUtc, waterYear } from \"./dates.ts\";\nimport { MODEL_BUNDLE } from \"./models.ts\";\nimport { QUESTIONS } from \"./questions.ts\";\nimport { fetchBasinSnapshot } from \"./sources.ts\";\nimport {\n cachePublicFeed,\n ensureSeason,\n finalizePrediction,\n getPrediction,\n initDb,\n listPredictions,\n lockPrediction,\n recentRuns,\n recordEvent,\n recordRun,\n} from \"./storage.ts\";\nimport type { BasinSnapshot, QuestionDefinition } from \"./types.ts\";\n\nfunction canonical(value: any): string {\n if (Array.isArray(value)) return `[${value.map(canonical).join(\",\")}]`;\n if (value && typeof value === \"object\") {\n return `{${\n Object.keys(value).sort().map((key) =>\n `${JSON.stringify(key)}:${canonical(value[key])}`\n ).join(\",\")\n }}`;\n }\n return JSON.stringify(value);\n}\n\nasync function sha256(value: string): Promise {\n const digest = await crypto.subtle.digest(\n \"SHA-256\",\n new TextEncoder().encode(value),\n );\n return [...new Uint8Array(digest)].map((byte) =>\n byte.toString(16).padStart(2, \"0\")\n ).join(\"\");\n}\n\nfunction inputRecord(snapshot: BasinSnapshot): Record {\n const m = snapshot.milestones;\n return {\n captured_at: snapshot.capturedAt.toISOString(),\n cutoff_at: snapshot.cutoffAt.toISOString(),\n current: {\n date: snapshot.current.date.toISOString(),\n value: snapshot.current.value,\n },\n features: snapshot.features,\n milestones: {\n runoff_year: m.runoffYear,\n first_400: m.first400 &&\n { date: m.first400.date.toISOString(), value: m.first400.value },\n peak_daily: m.peakDaily &&\n { date: m.peakDaily.date.toISOString(), value: m.peakDaily.value },\n peak_instant: m.peakInstant &&\n { date: m.peakInstant.date.toISOString(), value: m.peakInstant.value },\n tubing_trigger: m.tubingForecastTrigger &&\n {\n date: m.tubingForecastTrigger.date.toISOString(),\n value: m.tubingForecastTrigger.value,\n },\n tubing_opening: m.tubingOpening?.toISOString() || null,\n counts: m.counts,\n strength: m.strength,\n persistent_snow: m.persistentSnow &&\n {\n date: m.persistentSnow.date.toISOString(),\n value: m.persistentSnow.value,\n },\n peak_swe: m.peakSwe &&\n { date: m.peakSwe.date.toISOString(), value: m.peakSwe.value },\n },\n };\n}\n\nfunction dueAt(\n question: QuestionDefinition,\n runoffYear: number,\n snapshot: BasinSnapshot,\n): Date | null {\n if (question.issueKind === \"fixed\") {\n return fixedIssueAt(runoffYear, question.issueMonth!, question.issueDay!);\n }\n return question.trigger?.(snapshot) || null;\n}\n\nfunction publicPrediction(row: any): Record {\n return {\n number: Number(row.question_number),\n key: row.question_key,\n title: row.title,\n issue_kind: row.issue_kind,\n issue_due_at: row.issue_due_at,\n locked_at: row.locked_at,\n input_cutoff_at: row.input_cutoff_at,\n model_version: row.model_version,\n prediction: JSON.parse(row.prediction_json),\n status: row.status,\n actual: row.actual_json ? JSON.parse(row.actual_json) : null,\n score: row.score,\n scored_at: row.scored_at,\n };\n}\n\nasync function refreshPublicCache(runoffYear: number): Promise {\n const live = await listPredictions(runoffYear, \"live\");\n const shadow = await listPredictions(runoffYear, \"shadow\");\n const runs = await recentRuns(5);\n const feed = {\n schema_version: 1,\n water_year: runoffYear,\n game_state: live.length ? \"live\" : \"shadow-testing\",\n model: MODEL_BUNDLE,\n updated_at: new Date().toISOString(),\n predictions: live.map(publicPrediction),\n shadow_summary: {\n locked: shadow.length,\n scored: shadow.filter((row) => row.score).length,\n },\n health: runs.map((run) => ({\n ran_at: run.ran_at,\n mode: run.mode,\n success: Boolean(run.success),\n message: run.message,\n })),\n };\n await cachePublicFeed(`scoreboard-${runoffYear}`, feed);\n await cachePublicFeed(\"scoreboard-current\", feed);\n}\n\nexport async function runAutomation(\n now = new Date(),\n): Promise> {\n await initDb();\n const mode = IS_LIVE ? \"live\" : \"shadow\";\n const runoffYear = waterYear(now);\n if (runoffYear < CONFIG.firstAutomatedWaterYear) {\n // September is a preseason health check. It deliberately creates no locks,\n // but proves the official sources, models, database, cron and public cache work.\n try {\n const current = await fetchBasinSnapshot(now);\n const message =\n `Preseason ${mode} check passed. Water year ${CONFIG.firstAutomatedWaterYear} becomes eligible on October 1.`;\n await recordRun(true, mode, message, current.sourceStatus);\n await refreshPublicCache(CONFIG.firstAutomatedWaterYear);\n return {\n ok: true,\n mode,\n preseason: true,\n water_year: CONFIG.firstAutomatedWaterYear,\n message,\n source_status: current.sourceStatus,\n };\n } catch (error) {\n const message = error instanceof Error ? error.message : String(error);\n await recordRun(false, mode, `Preseason check failed: ${message}`);\n throw error;\n }\n }\n const openedAt = mountainToUtc(runoffYear - 1, 10, 1, 0);\n await ensureSeason(runoffYear, MODEL_BUNDLE.source_model_version, openedAt);\n let current: BasinSnapshot;\n try {\n current = await fetchBasinSnapshot(now);\n } catch (error) {\n const message = error instanceof Error ? error.message : String(error);\n await recordRun(false, mode, message);\n throw error;\n }\n\n const actions: string[] = [];\n const lockSnapshots = new Map();\n for (const question of QUESTIONS) {\n let stored = await getPrediction(runoffYear, question.number, mode);\n if (!stored) {\n const issueAt = dueAt(question, runoffYear, current);\n if (issueAt && now >= issueAt) {\n const ageHours = (now.getTime() - issueAt.getTime()) / 3_600_000;\n if (ageHours > CONFIG.fixedLockGraceHours) {\n actions.push(\n `P${question.number} needs review: issue window was missed`,\n );\n } else {\n const cacheKey = issueAt.toISOString();\n let lockSnapshot = lockSnapshots.get(cacheKey);\n if (!lockSnapshot) {\n lockSnapshot = await fetchBasinSnapshot(issueAt);\n lockSnapshots.set(cacheKey, lockSnapshot);\n }\n const prediction = question.calculate(lockSnapshot);\n const inputs = inputRecord(lockSnapshot);\n const predictionJson = canonical(prediction);\n const inputsJson = canonical(inputs);\n const inserted = await lockPrediction({\n water_year: runoffYear,\n question_number: question.number,\n question_key: question.key,\n title: question.title,\n mode,\n issue_kind: question.issueKind,\n issue_due_at: issueAt.toISOString(),\n locked_at: now.toISOString(),\n input_cutoff_at: issueAt.toISOString(),\n model_version: MODEL_BUNDLE.source_model_version,\n model_hash: MODEL_BUNDLE.sha256,\n prediction_json: predictionJson,\n inputs_json: inputsJson,\n inputs_hash: await sha256(inputsJson),\n source_status_json: canonical(lockSnapshot.sourceStatus),\n status: \"locked\",\n actual_json: null,\n score: null,\n scored_at: null,\n });\n if (inserted) {\n await recordEvent(\n runoffYear,\n question.number,\n `${mode}_lock_created`,\n now,\n { issue_at: issueAt.toISOString(), prediction },\n );\n actions.push(`P${question.number} ${mode} lock created`);\n stored = await getPrediction(runoffYear, question.number, mode);\n }\n }\n }\n }\n if (stored && !stored.scored_at && question.final(current)) {\n const actual = question.actual(current);\n if (actual) {\n const prediction = JSON.parse(stored.prediction_json);\n const hit = question.score(prediction, actual);\n await finalizePrediction(\n runoffYear,\n question.number,\n mode,\n canonical(actual),\n hit,\n now,\n );\n await recordEvent(\n runoffYear,\n question.number,\n `${mode}_score_finalized`,\n now,\n { actual, score: hit ? \"hit\" : \"miss\" },\n );\n actions.push(`P${question.number} scored ${hit ? \"HIT\" : \"MISS\"}`);\n }\n }\n }\n const message = actions.length\n ? actions.join(\"; \")\n : \"Data refreshed; no lock or score was due.\";\n await recordRun(true, mode, message, current.sourceStatus);\n await refreshPublicCache(runoffYear);\n return {\n ok: true,\n mode,\n water_year: runoffYear,\n actions,\n source_status: current.sourceStatus,\n };\n}",[253],"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}",[260],"94d96d84-45ff-4a1b-b85e-b472f10c1645","cron.ts","a7fd641c-ab9a-11f1-83fc-1607ee4eb77e",66,["D",1788882629303],"interval","import { runAutomation } from \"./engine.ts\";\n\nexport default async function (_interval: { lastRunAt?: Date }): Promise {\n const result = await runAutomation(new Date());\n console.log(JSON.stringify(result));\n}",[267],"2e430288-2b75-40ad-8a18-d32903314145","config.ts","377dfa62-ab9a-11f1-ac1e-1607ee4eb77e",["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\";",[275],"pulls","branches","forks","envVars",{"_287":63,"_289":63},"connections",{"_287":63,"_288":63},"count","unfilledCount","emptyCount",{"_43":274,"_32":275,"_30":276,"_153":-5,"_41":42,"_40":182,"_155":25,"_38":364,"_13":157,"_33":34,"_35":278,"_24":25,"_159":-5,"_310":365,"_27":366},{"_43":266,"_32":267,"_30":268,"_153":-5,"_41":42,"_40":269,"_155":25,"_38":361,"_13":174,"_33":271,"_35":272,"_24":25,"_159":-5,"_310":362,"_27":363},{"_43":259,"_32":260,"_30":261,"_153":-5,"_41":42,"_40":262,"_155":25,"_38":358,"_13":157,"_33":34,"_35":264,"_24":25,"_159":-5,"_310":359,"_27":360},{"_43":252,"_32":253,"_30":254,"_153":-5,"_41":42,"_40":255,"_155":25,"_38":355,"_13":182,"_33":34,"_35":257,"_24":25,"_159":-5,"_310":356,"_27":357},{"_43":246,"_32":247,"_30":248,"_153":-5,"_41":42,"_40":249,"_155":25,"_38":352,"_13":199,"_33":34,"_35":110,"_24":25,"_159":-5,"_310":353,"_27":354},{"_43":240,"_32":241,"_30":242,"_153":-5,"_41":42,"_40":243,"_155":25,"_38":349,"_13":157,"_33":34,"_35":110,"_24":25,"_159":-5,"_310":350,"_27":351},{"_43":234,"_32":235,"_30":236,"_153":-5,"_41":42,"_40":237,"_155":25,"_38":346,"_13":199,"_33":34,"_35":110,"_24":25,"_159":-5,"_310":347,"_27":348},{"_43":228,"_32":229,"_30":230,"_153":-5,"_41":42,"_40":231,"_155":25,"_38":343,"_13":199,"_33":34,"_35":110,"_24":25,"_159":-5,"_310":344,"_27":345},{"_43":222,"_32":223,"_30":224,"_153":-5,"_41":42,"_40":225,"_155":25,"_38":340,"_13":199,"_33":34,"_35":110,"_24":25,"_159":-5,"_310":341,"_27":342},{"_43":216,"_32":217,"_30":218,"_153":-5,"_41":42,"_40":157,"_155":59,"_38":337,"_13":63,"_33":213,"_35":220,"_24":25,"_159":-5,"_310":338,"_27":339},{"_43":209,"_32":210,"_30":57,"_153":-5,"_41":42,"_40":211,"_155":25,"_38":334,"_13":166,"_33":213,"_35":214,"_24":25,"_159":-5,"_310":335,"_27":336},{"_43":201,"_32":202,"_30":203,"_153":-5,"_41":42,"_40":204,"_155":25,"_38":331,"_13":206,"_33":34,"_35":207,"_24":25,"_159":-5,"_310":332,"_27":333},{"_43":194,"_32":195,"_30":196,"_153":-5,"_41":42,"_40":197,"_155":25,"_38":328,"_13":199,"_33":34,"_35":110,"_24":25,"_159":-5,"_310":329,"_27":330},{"_43":191,"_32":28,"_30":31,"_153":-5,"_41":42,"_40":14,"_155":59,"_38":325,"_13":174,"_33":34,"_35":37,"_24":25,"_159":-5,"_310":326,"_27":327},{"_43":185,"_32":186,"_30":187,"_153":-5,"_41":42,"_40":188,"_155":25,"_38":322,"_13":63,"_33":34,"_35":110,"_24":25,"_159":-5,"_310":323,"_27":324},{"_43":177,"_32":178,"_30":179,"_153":-5,"_41":42,"_40":180,"_155":25,"_38":319,"_13":182,"_33":34,"_35":183,"_24":25,"_159":-5,"_310":320,"_27":321},{"_43":169,"_32":170,"_30":171,"_153":-5,"_41":42,"_40":172,"_155":25,"_38":316,"_13":174,"_33":34,"_35":175,"_24":25,"_159":-5,"_310":317,"_27":318},{"_43":161,"_32":162,"_30":163,"_153":-5,"_41":42,"_40":164,"_155":25,"_38":313,"_13":166,"_33":34,"_35":167,"_24":25,"_159":-5,"_310":314,"_27":315},{"_43":150,"_32":151,"_30":152,"_153":-5,"_41":42,"_40":154,"_155":25,"_38":309,"_13":157,"_33":34,"_35":158,"_24":25,"_159":-5,"_310":311,"_27":312},["D",1788881595508],"children",[],[151],["D",1788882421895],[],[162],["D",1788882623133],[],[170],["D",1788882624992],[],[178],["D",1788882782986],[],[186],["D",1788882784046],[],[28],["D",1788882295796],[],[195],["D",1788882618399],[],[202],["D",1788882631456],[],[210],["D",1788719065897],[],[217],["D",1788882319621],[],[223],["D",1788882312683],[],[229],["D",1788882306326],[],[235],["D",1788882302686],[],[241],["D",1788882510486],[],[247],["D",1788882627143],[],[253],["D",1788881573051],[],[260],["D",1788882629303],[],[267],["D",1788881526228],[],[275],["D",1788718960405],["D",1789488693443],{"_100":99,"_108":104},{"_100":99,"_101":102,"_103":104,"_18":55,"_105":106,"_107":25},{"_13":89,"_32":90,"_43":42,"_91":44,"_92":-5,"_93":-5,"_94":-5,"_95":-5,"_96":-5,"_97":45,"_98":99},"ctx",{"_82":-5,"_128":405,"_406":407,"_408":409,"_410":25,"_411":25,"_412":25,"_413":59,"_414":-5,"_415":-7,"_416":-7,"_417":63,"_418":25,"_419":25,"_420":-5,"_421":25,"_422":25,"_423":25,"_424":-5},"ENV",{"_376":377,"_378":379,"_380":90,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392,"_393":394,"_395":396,"_397":398,"_399":400,"_401":402,"_403":404},"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 · v70