[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_948":949,"_950":951},"routes/_app.x.$handle.$name",{"_100":99,"_32":45,"_109":90,"_27":110,"_13":14,"_111":-5,"_11":112,"_113":114,"_115":116,"_117":118,"_119":120,"_20":-5,"_121":-5,"_122":-5,"_123":25,"_124":125,"_126":59,"_127":-5,"_128":-5,"_129":130},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":57,"_58":59,"_60":-5,"_61":-5,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71,"_72":-5,"_73":74,"_75":74,"_76":77,"_78":77,"_79":77,"_80":74,"_81":74,"_82":83,"_84":85,"_86":87,"_88":25},"version",261,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","backtest.ts","latestPath","fileId","9e5e92f6-abcb-11f1-8a7a-1607ee4eb77e","name","fileType","script","content","import { CONFIG } from \"./config.ts\";\nimport {\n addDays,\n firstMondayOfSeptember,\n fixedIssueAt,\n isoDay,\n mountainToUtc,\n} from \"./dates.ts\";\nimport { QUESTIONS } from \"./questions.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): Promise {\n const response = await fetch(url, {\n headers: { Accept: \"application/json\" },\n });\n if (!response.ok) {\n throw new Error(`${new URL(url).hostname} returned ${response.status}`);\n }\n return response.json();\n}\n\nfunction parseUsgs(payload: any): FlowPoint[] {\n const series = payload?.value?.timeSeries?.[0]?.values?.[0]?.value || [];\n return series.map((item: any) => ({\n date: new Date(item.dateTime),\n value: Number(item.value),\n }))\n .filter((item: FlowPoint) =>\n !Number.isNaN(item.date.getTime()) && Number.isFinite(item.value) &&\n item.value >= 0\n )\n .sort((a: FlowPoint, b: FlowPoint) => a.date.getTime() - b.date.getTime());\n}\n\nfunction parseSnotel(payload: any): Snotel {\n const output: Snotel = {};\n for (const station of payload || []) {\n const stationId = String(station.stationTriplet);\n output[stationId] ||= {};\n for (const series of station.data || []) {\n const code = series.stationElement?.elementCode;\n if (!code) continue;\n output[stationId][code] = (series.values || []).map((item: any) => ({\n date: new Date(`${item.date}T12:00:00Z`),\n value: Number(item.value),\n median: item.median == null ? null : Number(item.median),\n })).filter((item: any) =>\n !Number.isNaN(item.date.getTime()) && 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 filteredSnotel(snotel: Snotel, cutoff: Date): Snotel {\n return Object.fromEntries(\n Object.entries(snotel).map((\n [id, station],\n ) => [\n id,\n Object.fromEntries(\n Object.entries(station).map((\n [code, points],\n ) => [\n code,\n points.filter((point) => point.date <= cutoff),\n ]),\n ),\n ]),\n );\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 startYear = 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(startYear, 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), date30 = addDays(now, -30);\n const recent7 = flowDaily.filter((p) => p.date >= date7 && p.date <= now).map(\n (p) => p.value\n );\n const recent30 = flowDaily.filter((p) => p.date >= date30 && p.date <= now)\n .map((p) => p.value);\n const flow7Ago = latestAtOrBefore(flowDaily, date7)?.value ?? currentFlow;\n const flowMean7 = average(recent7) ?? currentFlow,\n flowMean30 = average(recent30) ?? flowMean7;\n const swe = stationAverage(snotel, \"WTEQ\", now) ?? 0,\n 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), 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 = (start: Date) =>\n average(stationIds.map((id) => {\n const current = latestAtOrBefore(snotel[id].PREC || [], now)?.value;\n const earlier = latestAtOrBefore(snotel[id].PREC || [], start)?.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 precip30 = precipitationChange(date30),\n 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\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 let left = 0, outOfRange = 0;\n for (let index = 0; index < ordered.length; index += 1) {\n const endPoint = ordered[index],\n startTime = endPoint.date.getTime() - 24 * 3_600_000;\n if (endPoint.value < 30 || endPoint.value >= 400) outOfRange += 1;\n while (left <= index && ordered[left].date.getTime() < startTime) {\n if (ordered[left].value < 30 || ordered[left].value >= 400) {\n outOfRange -= 1;\n }\n left += 1;\n }\n if (\n index - left + 1 >= 4 &&\n endPoint.date.getTime() - ordered[left].date.getTime() >=\n 23 * 3_600_000 &&\n outOfRange === 0\n ) return endPoint.date;\n }\n return null;\n}\n\nfunction deriveMilestones(\n dailyFlow: FlowPoint[],\n instantaneous: FlowPoint[],\n snotel: Snotel,\n current: FlowPoint,\n): Milestones {\n const runoffYear = current.date.getUTCMonth() >= 9\n ? current.date.getUTCFullYear() + 1\n : current.date.getUTCFullYear();\n const waterYearStart = new Date(Date.UTC(runoffYear - 1, 9, 1, 12)),\n springStart = new Date(Date.UTC(runoffYear, 2, 1, 12));\n const peakWindowStart = new Date(Date.UTC(runoffYear, 3, 1, 12)),\n peakWindowEnd = new Date(Date.UTC(runoffYear, 6, 31, 23, 59));\n const recreationStart = new Date(Date.UTC(runoffYear, 4, 1, 12)),\n laborDay = firstMondayOfSeptember(runoffYear);\n laborDay.setUTCHours(23, 59, 59, 999);\n const waterYearDaily = dailyFlow.filter((p) =>\n p.date >= waterYearStart && p.date <= current.date\n ),\n springDaily = waterYearDaily.filter((p) => p.date >= springStart);\n const first400 = springDaily.find((p) => p.value >= 400) || null;\n const peakDaily = springDaily.filter((p) =>\n p.date >= peakWindowStart && p.date <= peakWindowEnd\n ).reduce(\n (best, p) => !best || p.value > best.value ? p : best,\n null,\n );\n const peakInstant = instantaneous.filter((p) =>\n p.date >= peakWindowStart && p.date <= peakWindowEnd &&\n p.date <= current.date\n ).reduce(\n (best, p) => !best || p.value > best.value ? p : 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((p) =>\n isoDay(p.date) === isoDay(peakDaily.date) && p.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 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((p) =>\n p.date > peakDaily.date && p.value >= 30 && p.value < 400\n )?.date || null\n : null;\n const tubingOpening =\n findTubingOpening(instantaneous, peakDaily?.date || null) ||\n dailyTubingFallback;\n const seasonDaily = waterYearDaily.filter((p) =>\n p.date >= recreationStart && p.date <= laborDay && p.date <= current.date\n );\n const counts = {\n p300: seasonDaily.filter((p) => p.value >= 300).length,\n p600: seasonDaily.filter((p) => p.value >= 600).length,\n p800: seasonDaily.filter((p) => p.value >= 800).length,\n tubing: seasonDaily.filter((p) => p.value >= 30 && p.value < 400).length,\n };\n const strength = average(\n waterYearDaily.filter((p) =>\n p.date >= recreationStart &&\n p.date <= new Date(Date.UTC(runoffYear, 5, 30, 23, 59))\n ).map((p) => p.value),\n );\n const snowDates = [\n ...new Set(\n Object.values(snotel).flatMap((station) =>\n (station.WTEQ || []).map((p) => isoDay(p.date))\n ),\n ),\n ].map((day) => new Date(`${day}T12:00:00Z`)).filter((date) =>\n date >= waterYearStart && date <= current.date\n ).sort((a, b) => +a - +b);\n const snowSeries = snowDates.map((date) => ({\n date,\n value: stationAverage(snotel, \"WTEQ\", date),\n })).filter((p): p is { date: Date; value: number } =>\n Number.isFinite(p.value)\n );\n let persistentSnow = null;\n for (let i = 0; i <= snowSeries.length - 7; i += 1) {\n const window = snowSeries.slice(i, i + 7);\n if (window[0].value >= 1 && window.every((p) => p.value >= 0.5)) {\n persistentSnow = window[0];\n break;\n }\n }\n const peakSwe = snowSeries.reduce<{ date: Date; value: number } | null>(\n (best, p) => !best || p.value > best.value ? p : best,\n null,\n );\n const daysSincePeakSwe = peakSwe\n ? Math.floor(\n (current.date.getTime() - peakSwe.date.getTime()) / 86_400_000,\n )\n : 0,\n 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\ntype YearData = { daily: FlowPoint[]; instant: FlowPoint[]; snotel: Snotel };\n\nfunction snapshotAt(data: YearData, cutoffAt: Date): BasinSnapshot {\n const dailyFlow = data.daily.filter((p) => p.date <= cutoffAt),\n instantaneous = data.instant.filter((p) => p.date <= cutoffAt),\n snotel = filteredSnotel(data.snotel, cutoffAt);\n const current = instantaneous.at(-1) || dailyFlow.at(-1);\n if (!current || !dailyFlow.length) {\n throw new Error(`No flow data at ${cutoffAt.toISOString()}`);\n }\n return {\n capturedAt: cutoffAt,\n cutoffAt,\n current,\n instantaneous,\n dailyFlow,\n snotel,\n features: buildFeatures(dailyFlow, current.value, snotel, current.date),\n milestones: deriveMilestones(dailyFlow, instantaneous, snotel, current),\n sourceStatus: {},\n };\n}\n\nasync function fetchYear(runoffYear: number): Promise {\n const start = `${runoffYear - 1}-08-15`, end = `${runoffYear}-09-12`;\n const site = CONFIG.usgsSite,\n stations = encodeURIComponent(CONFIG.snotelStations.join(\",\"));\n const dailyUrl =\n `https://waterservices.usgs.gov/nwis/dv/?format=json&sites=${site}&startDT=${start}&endDT=${end}¶meterCd=00060&statCd=00003&siteStatus=all`;\n const instantUrl =\n `https://waterservices.usgs.gov/nwis/iv/?format=json&sites=${site}&startDT=${start}&endDT=${end}¶meterCd=00060&siteStatus=all`;\n const snowUrl =\n `https://wcc.sc.egov.usda.gov/awdbRestApi/services/v1/data?stationTriplets=${stations}&elements=WTEQ%2CSNWD%2CPREC%2CTAVG&duration=DAILY&beginDate=${start}&endDate=${end}&periodRef=END¢ralTendencyType=MEDIAN&returnFlags=false&returnOriginalValues=false&returnSuspectData=false`;\n const [daily, instant, snow] = await Promise.all([\n fetchJson(dailyUrl),\n fetchJson(instantUrl),\n fetchJson(snowUrl),\n ]);\n return {\n daily: parseUsgs(daily),\n instant: parseUsgs(instant),\n snotel: parseSnotel(snow),\n };\n}\n\nasync function replayYear(runoffYear: number) {\n const data = await fetchYear(runoffYear),\n finalSnapshot = snapshotAt(data, mountainToUtc(runoffYear, 9, 10, 12));\n const eventSnapshots = new Map();\n // Event locks are day-scale rules. One noon-Mountain snapshot per day preserves\n // the historical cutoff while avoiding thousands of redundant full rebuilds.\n for (\n let t = mountainToUtc(runoffYear, 4, 1, 12).getTime();\n t <= mountainToUtc(runoffYear, 8, 1, 12).getTime();\n t += 24 * 3_600_000\n ) {\n const snapshot = snapshotAt(data, new Date(t));\n for (const q of QUESTIONS.filter((item) => item.issueKind === \"event\")) {\n if (!eventSnapshots.has(q.number) && q.trigger?.(snapshot)) {\n eventSnapshots.set(q.number, snapshotAt(data, q.trigger!(snapshot)!));\n }\n }\n }\n const cards = QUESTIONS.map((q) => {\n const issue = q.issueKind === \"fixed\"\n ? snapshotAt(data, fixedIssueAt(runoffYear, q.issueMonth!, q.issueDay!))\n : eventSnapshots.get(q.number);\n if (!issue) {\n return { number: q.number, title: q.title, status: \"not-issued\" };\n }\n const prediction = q.calculate(issue), actual = q.actual(finalSnapshot);\n return {\n number: q.number,\n title: q.title,\n issue_at: issue.cutoffAt.toISOString(),\n prediction,\n actual,\n result: actual\n ? (q.score(prediction, actual) ? \"HIT\" : \"MISS\")\n : \"NO-ACTUAL\",\n };\n });\n const hits = cards.filter((c: any) => c.result === \"HIT\").length,\n misses = cards.filter((c: any) => c.result === \"MISS\").length;\n return {\n water_year: runoffYear,\n score: `Black Hat ${hits} – Mother Nature ${misses}`,\n hits,\n misses,\n cards,\n };\n}\n\nexport async function runHistoricalBacktest() {\n const results = [];\n for (const year of [2022, 2023, 2024]) results.push(await replayYear(year));\n console.log(\n JSON.stringify(\n {\n method: \"historical-cutoff replay using the current 0.5.0 model\",\n results,\n },\n null,\n 2,\n ),\n );\n}\n\nexport default runHistoricalBacktest;\n\n// Val Town's Run button evaluates the module; execute the read-only replay at module load.\nawait runHistoricalBacktest();\nimport { CONFIG } from \"./config.ts\";\nimport {\n addDays,\n firstMondayOfSeptember,\n fixedIssueAt,\n isoDay,\n mountainToUtc,\n} from \"./dates.ts\";\nimport { QUESTIONS } from \"./questions.ts\";\nimport type { BasinSnapshot, FlowPoint, Milestones, Snotel } from \"./types.ts\";\n\nconst average = (values: unknown[]): number | null => {\n const valid = values.map(Number).filter(Number.isFinite);\n return valid.length\n ? valid.reduce((sum, value) => sum + value, 0) / valid.length\n : null;\n};\n\nasync function fetchJson(url: string): Promise {\n const response = await fetch(url, {\n headers: { Accept: \"application/json\" },\n });\n if (!response.ok) {\n throw new Error(`${new URL(url).hostname} returned ${response.status}`);\n }\n return response.json();\n}\n\nfunction parseUsgs(payload: any): FlowPoint[] {\n const series = payload?.value?.timeSeries?.[0]?.values?.[0]?.value || [];\n return series.map((item: any) => ({\n date: new Date(item.dateTime),\n value: Number(item.value),\n }))\n .filter((item: FlowPoint) =>\n !Number.isNaN(item.date.getTime()) && Number.isFinite(item.value) &&\n item.value >= 0\n )\n .sort((a: FlowPoint, b: FlowPoint) => a.date.getTime() - b.date.getTime());\n}\n\nfunction parseSnotel(payload: any): Snotel {\n const output: Snotel = {};\n for (const station of payload || []) {\n const stationId = String(station.stationTriplet);\n output[stationId] ||= {};\n for (const series of station.data || []) {\n const code = series.stationElement?.elementCode;\n if (!code) continue;\n output[stationId][code] = (series.values || []).map((item: any) => ({\n date: new Date(`${item.date}T12:00:00Z`),\n value: Number(item.value),\n median: item.median == null ? null : Number(item.median),\n })).filter((item: any) =>\n !Number.isNaN(item.date.getTime()) && 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 filteredSnotel(snotel: Snotel, cutoff: Date): Snotel {\n return Object.fromEntries(\n Object.entries(snotel).map((\n [id, station],\n ) => [\n id,\n Object.fromEntries(\n Object.entries(station).map((\n [code, points],\n ) => [\n code,\n points.filter((point) => point.date <= cutoff),\n ]),\n ),\n ]),\n );\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 startYear = 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(startYear, 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), date30 = addDays(now, -30);\n const recent7 = flowDaily.filter((p) => p.date >= date7 && p.date <= now).map(\n (p) => p.value,\n );\n const recent30 = flowDaily.filter((p) => p.date >= date30 && p.date <= now)\n .map((p) => p.value);\n const flow7Ago = latestAtOrBefore(flowDaily, date7)?.value ?? currentFlow;\n const flowMean7 = average(recent7) ?? currentFlow,\n flowMean30 = average(recent30) ?? flowMean7;\n const swe = stationAverage(snotel, \"WTEQ\", now) ?? 0,\n 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), 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 = (start: Date) =>\n average(stationIds.map((id) => {\n const current = latestAtOrBefore(snotel[id].PREC || [], now)?.value;\n const earlier = latestAtOrBefore(snotel[id].PREC || [], start)?.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 precip30 = precipitationChange(date30),\n 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\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 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 (\n window.length >= 4 &&\n window.at(-1)!.date.getTime() - window[0].date.getTime() >=\n 23 * 3_600_000 &&\n window.every((item) => item.value >= 30 && item.value < 400)\n ) return endPoint.date;\n }\n return null;\n}\n\nfunction deriveMilestones(\n dailyFlow: FlowPoint[],\n instantaneous: FlowPoint[],\n snotel: Snotel,\n current: FlowPoint,\n): Milestones {\n const runoffYear = current.date.getUTCMonth() >= 9\n ? current.date.getUTCFullYear() + 1\n : current.date.getUTCFullYear();\n const waterYearStart = new Date(Date.UTC(runoffYear - 1, 9, 1, 12)),\n springStart = new Date(Date.UTC(runoffYear, 2, 1, 12));\n const peakWindowStart = new Date(Date.UTC(runoffYear, 3, 1, 12)),\n peakWindowEnd = new Date(Date.UTC(runoffYear, 6, 31, 23, 59));\n const recreationStart = new Date(Date.UTC(runoffYear, 4, 1, 12)),\n laborDay = firstMondayOfSeptember(runoffYear);\n laborDay.setUTCHours(23, 59, 59, 999);\n const waterYearDaily = dailyFlow.filter((p) =>\n p.date >= waterYearStart && p.date <= current.date\n ),\n springDaily = waterYearDaily.filter((p) => p.date >= springStart);\n const first400 = springDaily.find((p) => p.value >= 400) || null;\n const peakDaily = springDaily.filter((p) =>\n p.date >= peakWindowStart && p.date <= peakWindowEnd\n ).reduce(\n (best, p) => !best || p.value > best.value ? p : best,\n null,\n );\n const peakInstant = instantaneous.filter((p) =>\n p.date >= peakWindowStart && p.date <= peakWindowEnd &&\n p.date <= current.date\n ).reduce(\n (best, p) => !best || p.value > best.value ? p : 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((p) =>\n isoDay(p.date) === isoDay(peakDaily.date) && p.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 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((p) =>\n p.date > peakDaily.date && p.value >= 30 && p.value < 400\n )?.date || null\n : null;\n const tubingOpening =\n findTubingOpening(instantaneous, peakDaily?.date || null) ||\n dailyTubingFallback;\n const seasonDaily = waterYearDaily.filter((p) =>\n p.date >= recreationStart && p.date <= laborDay && p.date <= current.date\n );\n const counts = {\n p300: seasonDaily.filter((p) => p.value >= 300).length,\n p600: seasonDaily.filter((p) => p.value >= 600).length,\n p800: seasonDaily.filter((p) => p.value >= 800).length,\n tubing: seasonDaily.filter((p) => p.value >= 30 && p.value < 400).length,\n };\n const strength = average(\n waterYearDaily.filter((p) =>\n p.date >= recreationStart &&\n p.date <= new Date(Date.UTC(runoffYear, 5, 30, 23, 59))\n ).map((p) => p.value),\n );\n const snowDates = [\n ...new Set(\n Object.values(snotel).flatMap((station) =>\n (station.WTEQ || []).map((p) => isoDay(p.date))\n ),\n ),\n ].map((day) => new Date(`${day}T12:00:00Z`)).filter((date) =>\n date >= waterYearStart && date <= current.date\n ).sort((a, b) => +a - +b);\n const snowSeries = snowDates.map((date) => ({\n date,\n value: stationAverage(snotel, \"WTEQ\", date),\n })).filter((p): p is { date: Date; value: number } =>\n Number.isFinite(p.value)\n );\n let persistentSnow = null;\n for (let i = 0; i <= snowSeries.length - 7; i += 1) {\n const window = snowSeries.slice(i, i + 7);\n if (window[0].value >= 1 && window.every((p) => p.value >= 0.5)) {\n persistentSnow = window[0];\n break;\n }\n }\n const peakSwe = snowSeries.reduce<{ date: Date; value: number } | null>(\n (best, p) => !best || p.value > best.value ? p : best,\n null,\n );\n const daysSincePeakSwe = peakSwe\n ? Math.floor(\n (current.date.getTime() - peakSwe.date.getTime()) / 86_400_000,\n )\n : 0,\n 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\ntype YearData = { daily: FlowPoint[]; instant: FlowPoint[]; snotel: Snotel };\n\nfunction snapshotAt(data: YearData, cutoffAt: Date): BasinSnapshot {\n const dailyFlow = data.daily.filter((p) => p.date <= cutoffAt),\n instantaneous = data.instant.filter((p) => p.date <= cutoffAt),\n snotel = filteredSnotel(data.snotel, cutoffAt);\n const current = instantaneous.at(-1) || dailyFlow.at(-1);\n if (!current || !dailyFlow.length) {\n throw new Error(`No flow data at ${cutoffAt.toISOString()}`);\n }\n return {\n capturedAt: cutoffAt,\n cutoffAt,\n current,\n instantaneous,\n dailyFlow,\n snotel,\n features: buildFeatures(dailyFlow, current.value, snotel, current.date),\n milestones: deriveMilestones(dailyFlow, instantaneous, snotel, current),\n sourceStatus: {},\n };\n}\n\nasync function fetchYear(runoffYear: number): Promise {\n const start = `${runoffYear - 1}-08-15`, end = `${runoffYear}-09-12`;\n const site = CONFIG.usgsSite,\n stations = encodeURIComponent(CONFIG.snotelStations.join(\",\"));\n const dailyUrl =\n `https://waterservices.usgs.gov/nwis/dv/?format=json&sites=${site}&startDT=${start}&endDT=${end}¶meterCd=00060&statCd=00003&siteStatus=all`;\n const instantUrl =\n `https://waterservices.usgs.gov/nwis/iv/?format=json&sites=${site}&startDT=${start}&endDT=${end}¶meterCd=00060&siteStatus=all`;\n const snowUrl =\n `https://wcc.sc.egov.usda.gov/awdbRestApi/services/v1/data?stationTriplets=${stations}&elements=WTEQ%2CSNWD%2CPREC%2CTAVG&duration=DAILY&beginDate=${start}&endDate=${end}&periodRef=END¢ralTendencyType=MEDIAN&returnFlags=false&returnOriginalValues=false&returnSuspectData=false`;\n const [daily, instant, snow] = await Promise.all([\n fetchJson(dailyUrl),\n fetchJson(instantUrl),\n fetchJson(snowUrl),\n ]);\n return {\n daily: parseUsgs(daily),\n instant: parseUsgs(instant),\n snotel: parseSnotel(snow),\n };\n}\n\nasync function replayYear(runoffYear: number) {\n const data = await fetchYear(runoffYear),\n finalSnapshot = snapshotAt(data, mountainToUtc(runoffYear, 9, 10, 12));\n const eventSnapshots = new Map();\n for (\n let t = mountainToUtc(runoffYear, 4, 1, 0).getTime();\n t <= mountainToUtc(runoffYear, 8, 1, 0).getTime();\n t += 3 * 3_600_000\n ) {\n const snapshot = snapshotAt(data, new Date(t));\n for (const q of QUESTIONS.filter((item) => item.issueKind === \"event\")) {\n if (!eventSnapshots.has(q.number) && q.trigger?.(snapshot)) {\n eventSnapshots.set(q.number, snapshotAt(data, q.trigger!(snapshot)!));\n }\n }\n }\n const cards = QUESTIONS.map((q) => {\n const issue = q.issueKind === \"fixed\"\n ? snapshotAt(data, fixedIssueAt(runoffYear, q.issueMonth!, q.issueDay!))\n : eventSnapshots.get(q.number);\n if (!issue) {\n return { number: q.number, title: q.title, status: \"not-issued\" };\n }\n const prediction = q.calculate(issue), actual = q.actual(finalSnapshot);\n return {\n number: q.number,\n title: q.title,\n issue_at: issue.cutoffAt.toISOString(),\n prediction,\n actual,\n result: actual\n ? (q.score(prediction, actual) ? \"HIT\" : \"MISS\")\n : \"NO-ACTUAL\",\n };\n });\n const hits = cards.filter((c: any) => c.result === \"HIT\").length,\n misses = cards.filter((c: any) => c.result === \"MISS\").length;\n return {\n water_year: runoffYear,\n score: `Black Hat ${hits} – Mother Nature ${misses}`,\n hits,\n misses,\n cards,\n };\n}\n\nexport async function runHistoricalBacktest() {\n const results = [];\n for (const year of [2022, 2023, 2024]) results.push(await replayYear(year));\n console.log(\n JSON.stringify(\n {\n method: \"historical-cutoff replay using the current 0.5.0 model\",\n results,\n },\n null,\n 2,\n ),\n );\n}\n\nexport default runHistoricalBacktest;\n\n// Val Town's Run button evaluates the module; execute the read-only replay at module load.\nawait runHistoricalBacktest();","import { CONFIG } from \"./config.ts\";\nimport {\n addDays,\n firstMondayOfSeptember,\n fixedIssueAt,\n isoDay,\n mountainToUtc,\n} from \"./dates.ts\";\nimport { QUESTIONS } from \"./questions.ts\";\nimport type { BasinSnapshot, FlowPoint, Milestones, Snotel } from \"./types.ts\";\n\nconst average = (values: unknown[]): number | null => {\n const valid = values.map(Number).filter(Number.isFinite);\n return valid.length\n ? valid.reduce((sum, value) => sum + value, 0) / valid.length\n : null;\n};\n\nasync function fetchJson(url: string): Promise {\n const response = await fetch(url, {\n headers: { Accept: \"application/json\" },\n });\n if (!response.ok) {\n throw new Error(`${new URL(url).hostname} returned ${response.status}`);\n }\n return response.json();\n}\n\nfunction parseUsgs(payload: any): FlowPoint[] {\n const series = payload?.value?.timeSeries?.[0]?.values?.[0]?.value || [];\n return series.map((item: any) => ({\n date: new Date(item.dateTime),\n value: Number(item.value),\n }))\n .filter((item: FlowPoint) =>\n !Number.isNaN(item.date.getTime()) && Number.isFinite(item.value) &&\n item.value >= 0\n )\n .sort((a: FlowPoint, b: FlowPoint) => a.date.getTime() - b.date.getTime());\n}\n\nfunction parseSnotel(payload: any): Snotel {\n const output: Snotel = {};\n for (const station of payload || []) {\n const stationId = String(station.stationTriplet);\n output[stationId] ||= {};\n for (const series of station.data || []) {\n const code = series.stationElement?.elementCode;\n if (!code) continue;\n output[stationId][code] = (series.values || []).map((item: any) => ({\n date: new Date(`${item.date}T12:00:00Z`),\n value: Number(item.value),\n median: item.median == null ? null : Number(item.median),\n })).filter((item: any) =>\n !Number.isNaN(item.date.getTime()) && 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 filteredSnotel(snotel: Snotel, cutoff: Date): Snotel {\n return Object.fromEntries(\n Object.entries(snotel).map((\n [id, station],\n ) => [\n id,\n Object.fromEntries(\n Object.entries(station).map((\n [code, points],\n ) => [\n code,\n points.filter((point) => point.date <= cutoff),\n ]),\n ),\n ]),\n );\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 startYear = 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(startYear, 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), date30 = addDays(now, -30);\n const recent7 = flowDaily.filter((p) => p.date >= date7 && p.date <= now).map(\n (p) => p.value\n );\n const recent30 = flowDaily.filter((p) => p.date >= date30 && p.date <= now)\n .map((p) => p.value);\n const flow7Ago = latestAtOrBefore(flowDaily, date7)?.value ?? currentFlow;\n const flowMean7 = average(recent7) ?? currentFlow,\n flowMean30 = average(recent30) ?? flowMean7;\n const swe = stationAverage(snotel, \"WTEQ\", now) ?? 0,\n 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), 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 = (start: Date) =>\n average(stationIds.map((id) => {\n const current = latestAtOrBefore(snotel[id].PREC || [], now)?.value;\n const earlier = latestAtOrBefore(snotel[id].PREC || [], start)?.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 precip30 = precipitationChange(date30),\n 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\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 let left = 0, outOfRange = 0;\n for (let index = 0; index < ordered.length; index += 1) {\n const endPoint = ordered[index],\n startTime = endPoint.date.getTime() - 24 * 3_600_000;\n if (endPoint.value < 30 || endPoint.value >= 400) outOfRange += 1;\n while (left <= index && ordered[left].date.getTime() < startTime) {\n if (ordered[left].value < 30 || ordered[left].value >= 400) {\n outOfRange -= 1;\n }\n left += 1;\n }\n if (\n index - left + 1 >= 4 &&\n endPoint.date.getTime() - ordered[left].date.getTime() >=\n 23 * 3_600_000 &&\n outOfRange === 0\n ) return endPoint.date;\n }\n return null;\n}\n\nfunction deriveMilestones(\n dailyFlow: FlowPoint[],\n instantaneous: FlowPoint[],\n snotel: Snotel,\n current: FlowPoint,\n): Milestones {\n const runoffYear = current.date.getUTCMonth() >= 9\n ? current.date.getUTCFullYear() + 1\n : current.date.getUTCFullYear();\n const waterYearStart = new Date(Date.UTC(runoffYear - 1, 9, 1, 12)),\n springStart = new Date(Date.UTC(runoffYear, 2, 1, 12));\n const peakWindowStart = new Date(Date.UTC(runoffYear, 3, 1, 12)),\n peakWindowEnd = new Date(Date.UTC(runoffYear, 6, 31, 23, 59));\n const recreationStart = new Date(Date.UTC(runoffYear, 4, 1, 12)),\n laborDay = firstMondayOfSeptember(runoffYear);\n laborDay.setUTCHours(23, 59, 59, 999);\n const waterYearDaily = dailyFlow.filter((p) =>\n p.date >= waterYearStart && p.date <= current.date\n ),\n springDaily = waterYearDaily.filter((p) => p.date >= springStart);\n const first400 = springDaily.find((p) => p.value >= 400) || null;\n const peakDaily = springDaily.filter((p) =>\n p.date >= peakWindowStart && p.date <= peakWindowEnd\n ).reduce(\n (best, p) => !best || p.value > best.value ? p : best,\n null,\n );\n const peakInstant = instantaneous.filter((p) =>\n p.date >= peakWindowStart && p.date <= peakWindowEnd &&\n p.date <= current.date\n ).reduce(\n (best, p) => !best || p.value > best.value ? p : 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((p) =>\n isoDay(p.date) === isoDay(peakDaily.date) && p.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 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((p) =>\n p.date > peakDaily.date && p.value >= 30 && p.value < 400\n )?.date || null\n : null;\n const tubingOpening =\n findTubingOpening(instantaneous, peakDaily?.date || null) ||\n dailyTubingFallback;\n const seasonDaily = waterYearDaily.filter((p) =>\n p.date >= recreationStart && p.date <= laborDay && p.date <= current.date\n );\n const counts = {\n p300: seasonDaily.filter((p) => p.value >= 300).length,\n p600: seasonDaily.filter((p) => p.value >= 600).length,\n p800: seasonDaily.filter((p) => p.value >= 800).length,\n tubing: seasonDaily.filter((p) => p.value >= 30 && p.value < 400).length,\n };\n const strength = average(\n waterYearDaily.filter((p) =>\n p.date >= recreationStart &&\n p.date <= new Date(Date.UTC(runoffYear, 5, 30, 23, 59))\n ).map((p) => p.value),\n );\n const snowDates = [\n ...new Set(\n Object.values(snotel).flatMap((station) =>\n (station.WTEQ || []).map((p) => isoDay(p.date))\n ),\n ),\n ].map((day) => new Date(`${day}T12:00:00Z`)).filter((date) =>\n date >= waterYearStart && date <= current.date\n ).sort((a, b) => +a - +b);\n const snowSeries = snowDates.map((date) => ({\n date,\n value: stationAverage(snotel, \"WTEQ\", date),\n })).filter((p): p is { date: Date; value: number } =>\n Number.isFinite(p.value)\n );\n let persistentSnow = null;\n for (let i = 0; i <= snowSeries.length - 7; i += 1) {\n const window = snowSeries.slice(i, i + 7);\n if (window[0].value >= 1 && window.every((p) => p.value >= 0.5)) {\n persistentSnow = window[0];\n break;\n }\n }\n const peakSwe = snowSeries.reduce<{ date: Date; value: number } | null>(\n (best, p) => !best || p.value > best.value ? p : best,\n null,\n );\n const daysSincePeakSwe = peakSwe\n ? 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One noon-Mountain snapshot per day preserves\n // the historical cutoff while avoiding thousands of redundant full rebuilds.\n for (\n let t = mountainToUtc(runoffYear, 4, 1, 12).getTime();\n t <= mountainToUtc(runoffYear, 8, 1, 12).getTime();\n t += 24 * 3_600_000\n ) {\n const snapshot = snapshotAt(data, new Date(t));\n for (const q of QUESTIONS.filter((item) => item.issueKind === \"event\")) {\n if (!eventSnapshots.has(q.number) && q.trigger?.(snapshot)) {\n eventSnapshots.set(q.number, snapshotAt(data, q.trigger!(snapshot)!));\n }\n }\n }\n const cards = QUESTIONS.map((q) => {\n const issue = q.issueKind === \"fixed\"\n ? snapshotAt(data, fixedIssueAt(runoffYear, q.issueMonth!, q.issueDay!))\n : eventSnapshots.get(q.number);\n if (!issue) {\n return { number: q.number, title: q.title, status: \"not-issued\" };\n }\n const prediction = q.calculate(issue), actual = q.actual(finalSnapshot);\n return {\n number: q.number,\n title: q.title,\n issue_at: issue.cutoffAt.toISOString(),\n prediction,\n actual,\n result: actual\n ? (q.score(prediction, actual) ? \"HIT\" : \"MISS\")\n : \"NO-ACTUAL\",\n };\n });\n const hits = cards.filter((c: any) => c.result === \"HIT\").length,\n misses = cards.filter((c: any) => c.result === \"MISS\").length;\n return {\n water_year: runoffYear,\n score: `Black Hat ${hits} – Mother Nature ${misses}`,\n hits,\n misses,\n cards,\n };\n}\n\nexport async function runHistoricalBacktest() {\n const results = [];\n for (const year of [2022, 2023, 2024]) results.push(await replayYear(year));\n console.log(\n JSON.stringify(\n {\n method: \"historical-cutoff replay using the current 0.5.0 model\",\n results,\n },\n null,\n 2,\n ),\n );\n}\n\nexport default runHistoricalBacktest;\n\n// Val Town's Run button evaluates the module; execute the read-only replay at module load.\nawait 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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",[200],"21c090ef-62be-4050-8d72-5a81612ee9c3","storage.ts","cf66eefc-ab9e-11f1-91ac-1607ee4eb77e",145,["D",1788883402631],"// 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}",[211],"1cee68bb-501e-42c3-b4e0-e9c6869d0c41","storage-old.ts","a3869a0c-ab9a-11f1-b55c-1607ee4eb77e",82,["D",1788882997593],4,"// 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}",[218],"e3291864-af50-4867-a46d-1dcd8a938dc0","sources.ts","d2a4f780-ab9e-11f1-9744-1607ee4eb77e",149,["D",1788883407435],"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}",[226],"1ee23f82-db4b-49ad-88bb-3f3080ab3ce9","sources-old.ts","9fe9133e-ab9a-11f1-b085-1607ee4eb77e",86,["D",1788883009046],7,[233],"b4cc03aa-1ca6-49eb-a14f-817c4692eb3d","questions.ts","43b22c18-ab9f-11f1-b79c-1607ee4eb77e",252,["D",1788897595357],"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];\n\n// Val Town dependency refresh 2026-09-08-4.",[240],"9f38b5cc-9f34-480e-b8bb-e5988ca6b6ef","questions-old.ts","9c517f2c-ab9a-11f1-94dd-1607ee4eb77e",90,["D",1788883014695],6,"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];",[247],"d6d43ff6-1e5d-4104-bffc-1e27225c5925","models.ts","c5bf5060-ab9e-11f1-a03b-1607ee4eb77e",250,["D",1788897588383],5,"import {\n MODEL_BUNDLE,\n SMALL_RIGHT_NOW_MODELS,\n TUBING_OPEN_LIVE_MODEL,\n} from \"./models-core.ts\";\nimport { FIRST400_CHUNK_1 } from \"./first400-chunk-1.ts\";\nimport { FIRST400_CHUNK_2 } from \"./first400-chunk-2.ts\";\nimport { FIRST400_CHUNK_3 } from \"./first400-chunk-3.ts\";\nimport { FIRST400_CHUNK_4 } from \"./first400-chunk-4.ts\";\nimport { FIRST400_CHUNK_5 } from \"./first400-chunk-5.ts\";\nimport { FIRST400_CHUNK_6 } from \"./first400-chunk-6.ts\";\nimport { FIRST400_CHUNK_7 } from \"./first400-chunk-7.ts\";\nimport { FIRST400_CHUNK_8 } from \"./first400-chunk-8.ts\";\nimport { FIRST400_CHUNK_9 } from \"./first400-chunk-9.ts\";\nimport { FIRST400_CHUNK_10 } from \"./first400-chunk-10.ts\";\nimport { FIRST400_CHUNK_11 } from \"./first400-chunk-11.ts\";\nimport { FIRST400_CHUNK_12 } from \"./first400-chunk-12.ts\";\nimport { FIRST400_CHUNK_13 } from \"./first400-chunk-13.ts\";\nimport { FIRST400_CHUNK_14 } from \"./first400-chunk-14.ts\";\nimport { FIRST400_CHUNK_15 } from \"./first400-chunk-15.ts\";\nimport { FIRST400_CHUNK_16 } from \"./first400-chunk-16.ts\";\nimport { FIRST400_CHUNK_17 } from \"./first400-chunk-17.ts\";\nimport { FIRST400_CHUNK_18 } from \"./first400-chunk-18.ts\";\nimport { FIRST400_CHUNK_19 } from \"./first400-chunk-19.ts\";\nimport { FIRST400_CHUNK_20 } from \"./first400-chunk-20.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_CHUNK_1,\n ...FIRST400_CHUNK_2,\n ...FIRST400_CHUNK_3,\n ...FIRST400_CHUNK_4,\n ...FIRST400_CHUNK_5,\n ...FIRST400_CHUNK_6,\n ...FIRST400_CHUNK_7,\n ...FIRST400_CHUNK_8,\n ...FIRST400_CHUNK_9,\n ...FIRST400_CHUNK_10,\n ...FIRST400_CHUNK_11,\n ...FIRST400_CHUNK_12,\n ...FIRST400_CHUNK_13,\n ...FIRST400_CHUNK_14,\n ...FIRST400_CHUNK_15,\n ...FIRST400_CHUNK_16,\n ...FIRST400_CHUNK_17,\n ...FIRST400_CHUNK_18,\n ...FIRST400_CHUNK_19,\n ...FIRST400_CHUNK_20,\n ],\n },\n} as const;\n\n// Val Town dependency refresh 2026-09-08-2.",[255],"02e519d6-7291-4c89-ad7b-f36645e8af30","models-meta.ts","c6a0a148-abbf-11f1-9b80-1607ee4eb77e",248,["D",1788897562686],"export const MODEL_BUNDLE = {\n \"source_model_name\": \"Black Hat Hydrology Lab state-of-basin model\",\n \"source_model_version\": \"0.5.0-2027-game\",\n \"trained_through\": \"2026-08-21\",\n \"seasonal_peak_time_minutes_p50\": 1462.5,\n \"sha256\": \"cc6c199f4072ee4d337c16d4c6c752aff5d5ced0ca9c722e5ba59c4da97be8be\",\n} as const;\nexport const TUBING_OPEN_LIVE_MODEL = {\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 \"mean\": [\n 6.97585162,\n 7.04091227,\n 7.06515894,\n -0.10721759,\n 9.4434669,\n 2.07765016,\n -3.48222997,\n 0.67177586,\n 0.43832753,\n 2.48188153,\n 0.69860627,\n -0.84680816,\n -0.4498932,\n 0.70533016,\n -0.46311098,\n 0.80854985,\n 0.07682927,\n ],\n \"scale\": [\n 0.54855494,\n 0.48301989,\n 0.41855368,\n 0.42025083,\n 11.67654279,\n 1.37283372,\n 3.0764911,\n 0.31783321,\n 0.63600834,\n 1.94856624,\n 0.45886332,\n 0.12029728,\n 0.25698369,\n 0.36115282,\n 0.39699649,\n 1.04467709,\n 0.24157162,\n ],\n \"coef\": [\n 1.93888677,\n 1.61252652,\n 3.08113436,\n 0.54679868,\n 5.64390861,\n 0.87673807,\n -0.09552357,\n -2.72070232,\n 2.22824366,\n 2.87020041,\n -0.64809595,\n 4.13682288,\n -2.05678695,\n -3.20135015,\n 0.88416863,\n 0.77469563,\n -0.76800089,\n ],\n \"intercept\": 24.88675958,\n \"audit\": { \"hitRate\": 0.7940074906367042, \"mae\": 4.15837482833963, \"n\": 267 },\n} as const;\nexport const OTHER_RIGHT_NOW_MODELS = {\n \"peak_swe\": {\n \"type\": \"ridge\",\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 \"mean\": [\n 4.80257144,\n 4.75536123,\n 4.57659407,\n 0.1117378,\n 25.43399302,\n 3,\n 1.22776484,\n -0.47571707,\n 1.27456345,\n 5.35459837,\n 1,\n 0.38949448,\n -0.84033095,\n -0.56880157,\n 0.45649879,\n 0.042433,\n 0,\n ],\n \"scale\": [\n 0.88175638,\n 0.8566741,\n 0.7376566,\n 0.34527298,\n 10.33041822,\n 1,\n 1.45845067,\n 0.3115649,\n 1.22072314,\n 3.05725236,\n 1,\n 0.34647461,\n 0.14863811,\n 0.49711646,\n 0.47005198,\n 0.21819705,\n 1,\n ],\n \"coef\": [\n -0.14778794,\n 0.01109569,\n -0.41813397,\n -0.02396197,\n 10.4386483,\n 0,\n 0.0604768,\n -0.05447049,\n -0.04218131,\n 0.60621624,\n 0,\n 0.9867118,\n 1.21382416,\n 0.29597339,\n -1.12837899,\n 0.33867877,\n 0,\n ],\n \"intercept\": 30.95430733,\n \"audit\": {\n \"model\": \"ridge\",\n \"hit_rate\": 0.8377659574468085,\n \"mae\": 3.075304904908431,\n \"n\": 376,\n },\n },\n \"river_strength\": {\n \"type\": \"ridge\",\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 \"mean\": [\n 6.47142856,\n 6.48877591,\n 6.4442825,\n -0.00241695,\n 15.48074664,\n 2.28912166,\n -2.08758389,\n 0.4177563,\n 0.54932886,\n 2.73649329,\n 0.77097315,\n -0.62276623,\n -0.64887054,\n 0.63301723,\n 0.02620673,\n 0.58244018,\n 0.06342282,\n ],\n \"scale\": [\n 0.87604822,\n 0.8388446,\n 0.78277847,\n 0.48095271,\n 14.51757326,\n 1.26547667,\n 2.88482727,\n 0.44480798,\n 0.72357211,\n 2.1891286,\n 0.42020656,\n 0.31473617,\n 0.30343102,\n 0.31162842,\n 0.70815967,\n 0.88047602,\n 0.26461514,\n ],\n \"coef\": [\n 64.5830231,\n 53.73314191,\n 206.9711545,\n 30.39462673,\n 414.06011917,\n -36.76494284,\n -42.39904483,\n -126.71451116,\n 14.07218489,\n 22.09756441,\n 33.47428291,\n -78.48761483,\n 140.89985696,\n -172.85944542,\n -92.27130526,\n -21.53680249,\n -30.17698587,\n ],\n \"intercept\": 1036.20152657,\n \"audit\": {\n \"model\": \"ridge\",\n \"hit_rate\": 0.8242753623188406,\n \"mae\": 164.78891395264745,\n \"n\": 552,\n },\n },\n \"spring_peak_flow\": {\n \"type\": \"ridge\",\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 \"mean\": [\n 6.79047791,\n 6.71592949,\n 6.45389461,\n 0.18499767,\n 23.70379747,\n 3,\n -2.31381857,\n 0.19869726,\n 0.74778481,\n 3.51413502,\n 1,\n -0.53207989,\n -0.81249603,\n 0.81212244,\n 0.36103025,\n 0.89703251,\n 0,\n ],\n \"scale\": [\n 0.5993387,\n 0.55251757,\n 0.5218272,\n 0.47174647,\n 12.28037637,\n 1,\n 2.75444481,\n 0.2618179,\n 0.79795384,\n 2.15399016,\n 1,\n 0.19072457,\n 0.14270711,\n 0.17192752,\n 0.42491791,\n 0.97572158,\n 1,\n ],\n \"coef\": [\n 192.84377069,\n 115.0473917,\n 67.5405791,\n 83.88042029,\n 595.24003434,\n 0,\n -17.5693668,\n -228.01927293,\n 120.11889258,\n -7.93152056,\n 0,\n -70.4721202,\n 151.19004473,\n -133.18606929,\n -103.26115542,\n 10.2003656,\n 0,\n ],\n \"intercept\": 2527.77637131,\n \"audit\": {\n \"model\": \"ridge\",\n \"hit_rate\": 0.8214285714285714,\n \"mae\": 468.7018486469332,\n \"n\": 224,\n },\n },\n \"tubing_open\": {\n \"type\": \"ridge\",\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 \"mean\": [\n 6.97585162,\n 7.04091227,\n 7.06515894,\n -0.10721759,\n 9.4434669,\n 2.07765016,\n -3.48222997,\n 0.67177586,\n 0.43832753,\n 2.48188153,\n 0.69860627,\n -0.84680816,\n -0.4498932,\n 0.70533016,\n -0.46311098,\n 0.80854985,\n 0.07682927,\n ],\n \"scale\": [\n 0.54855494,\n 0.48301989,\n 0.41855368,\n 0.42025083,\n 11.67654279,\n 1.37283372,\n 3.0764911,\n 0.31783321,\n 0.63600834,\n 1.94856624,\n 0.45886332,\n 0.12029728,\n 0.25698369,\n 0.36115282,\n 0.39699649,\n 1.04467709,\n 0.24157162,\n ],\n \"coef\": [\n 1.93888677,\n 1.61252652,\n 3.08113436,\n 0.54679868,\n 5.64390861,\n 0.87673807,\n -0.09552357,\n -2.72070232,\n 2.22824366,\n 2.87020041,\n -0.64809595,\n 4.13682288,\n -2.05678695,\n -3.20135015,\n 0.88416863,\n 0.77469563,\n -0.76800089,\n ],\n \"intercept\": 24.88675958,\n \"audit\": {\n \"model\": \"ridge\",\n \"hit_rate\": 0.7940074906367042,\n \"mae\": 4.15837482833963,\n \"n\": 267,\n },\n },\n \"tubing_days\": {\n \"type\": \"ridge\",\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 \"countTubing\",\n \"daysLeft\",\n ],\n \"mean\": [\n 5.4863663,\n 5.57776318,\n 5.88980334,\n -0.15637848,\n 2.88577886,\n 0.71053711,\n -1.27430459,\n 0.93869875,\n 0.49228095,\n 2.20354659,\n 0.2433936,\n -0.84091831,\n 0.10878574,\n -0.11745633,\n -0.45822477,\n 0.27303038,\n 0.3788943,\n 24.32267038,\n 56.75938804,\n ],\n \"scale\": [\n 1.36015,\n 1.34211888,\n 1.22884367,\n 0.4229029,\n 7.5139639,\n 1.26710489,\n 2.5121344,\n 0.28092182,\n 0.62802775,\n 1.72226016,\n 0.4291307,\n 0.14821872,\n 0.50897277,\n 0.75045189,\n 0.46157992,\n 0.72490092,\n 0.57565162,\n 24.45384345,\n 32.8420245,\n ],\n \"coef\": [\n 2.20867236,\n -0.33671746,\n 5.57641569,\n 0.07024543,\n -4.48248068,\n 2.53048435,\n 2.33099726,\n 0.96805372,\n -1.86081968,\n -0.46945575,\n -1.77696344,\n -0.94946085,\n -6.24863491,\n -2.97149597,\n 1.91053745,\n -0.9666171,\n 2.83764918,\n 17.9989254,\n 11.58818477,\n ],\n \"intercept\": 65.527121,\n \"audit\": {\n \"target\": \"tubing_days\",\n \"model\": \"ridge\",\n \"n\": 678,\n \"hit_rate\": 0.799410029498525,\n \"mae\": 7.067015163645627,\n },\n },\n \"days_ge_300\": {\n \"type\": \"ridge\",\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 \"count300\",\n \"daysLeft\",\n ],\n \"mean\": [\n 5.63005703,\n 5.70722697,\n 5.95820011,\n -0.12560406,\n 4.97756173,\n 0.96767471,\n -1.39549383,\n 0.8598531,\n 0.5220679,\n 2.3008642,\n 0.32839506,\n -0.81087996,\n 0.00486163,\n 0.00042612,\n -0.36903073,\n 0.34974604,\n 0.33632716,\n 43.2345679,\n 63.85555556,\n ],\n \"scale\": [\n 1.35918317,\n 1.32944104,\n 1.18456241,\n 0.436904,\n 10.16729588,\n 1.3955662,\n 2.55060476,\n 0.35366809,\n 0.65182487,\n 1.76197119,\n 0.46962937,\n 0.16428344,\n 0.56165915,\n 0.78099583,\n 0.50384685,\n 0.77735314,\n 0.55539422,\n 23.27459358,\n 36.84495543,\n ],\n \"coef\": [\n 3.19270349,\n 0.87533222,\n 5.28846421,\n 0.57487294,\n 10.15363325,\n 0.01108983,\n -2.65633276,\n 0.29786571,\n 4.4963263,\n 0.49403388,\n 0.70489521,\n 4.22163753,\n -1.78447805,\n -6.49501994,\n -2.7965965,\n -1.24781443,\n -4.21443896,\n 18.17805522,\n 1.90604527,\n ],\n \"intercept\": 60.21851852,\n \"audit\": {\n \"target\": \"days_ge_300\",\n \"model\": \"ridge\",\n \"n\": 762,\n \"hit_rate\": 0.8083989501312336,\n \"mae\": 6.118018403171732,\n },\n },\n \"days_ge_600\": {\n \"type\": \"ridge\",\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 \"count600\",\n \"daysLeft\",\n ],\n \"mean\": [\n 5.63005703,\n 5.70722697,\n 5.95820011,\n -0.12560406,\n 4.97756173,\n 0.96767471,\n -1.39549383,\n 0.8598531,\n 0.5220679,\n 2.3008642,\n 0.32839506,\n -0.81087996,\n 0.00486163,\n 0.00042612,\n -0.36903073,\n 0.34974604,\n 0.33632716,\n 33.79506173,\n 63.85555556,\n ],\n \"scale\": [\n 1.35918317,\n 1.32944104,\n 1.18456241,\n 0.436904,\n 10.16729588,\n 1.3955662,\n 2.55060476,\n 0.35366809,\n 0.65182487,\n 1.76197119,\n 0.46962937,\n 0.16428344,\n 0.56165915,\n 0.78099583,\n 0.50384685,\n 0.77735314,\n 0.55539422,\n 20.36157663,\n 36.84495543,\n ],\n \"coef\": [\n 1.4607615,\n -0.13211631,\n 2.0257731,\n 0.3656029,\n 10.03039827,\n 0.35205036,\n -2.0233072,\n 0.27525037,\n 3.63255311,\n 0.07973701,\n -0.01223367,\n 3.16812436,\n 0.09775501,\n -4.00334481,\n -1.71314792,\n -0.11403465,\n -3.3697709,\n 18.50612383,\n 3.51594179,\n ],\n \"intercept\": 43.70802469,\n \"audit\": {\n \"target\": \"days_ge_600\",\n \"model\": \"ridge\",\n \"n\": 762,\n \"hit_rate\": 0.9750656167979003,\n \"mae\": 2.7186457806129627,\n },\n },\n \"days_ge_800\": {\n \"type\": \"ridge\",\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 \"count800\",\n \"daysLeft\",\n ],\n \"mean\": [\n 5.63005703,\n 5.70722697,\n 5.95820011,\n -0.12560406,\n 4.97756173,\n 0.96767471,\n -1.39549383,\n 0.8598531,\n 0.5220679,\n 2.3008642,\n 0.32839506,\n -0.81087996,\n 0.00486163,\n 0.00042612,\n -0.36903073,\n 0.34974604,\n 0.33632716,\n 27.89753086,\n 63.85555556,\n ],\n \"scale\": [\n 1.35918317,\n 1.32944104,\n 1.18456241,\n 0.436904,\n 10.16729588,\n 1.3955662,\n 2.55060476,\n 0.35366809,\n 0.65182487,\n 1.76197119,\n 0.46962937,\n 0.16428344,\n 0.56165915,\n 0.78099583,\n 0.50384685,\n 0.77735314,\n 0.55539422,\n 19.82153384,\n 36.84495543,\n ],\n \"coef\": [\n 1.09737349,\n -0.05873298,\n 0.92616667,\n 0.45463773,\n 11.35979725,\n -0.79291601,\n -2.27841774,\n 0.34937781,\n 3.62388113,\n -0.07843303,\n -0.50053604,\n 1.96927797,\n 0.3661438,\n -2.1335638,\n -1.34112476,\n -0.53126482,\n -3.38215588,\n 18.9275063,\n 1.78100505,\n ],\n \"intercept\": 35.61296296,\n \"audit\": {\n \"target\": \"days_ge_800\",\n \"model\": \"ridge\",\n \"n\": 762,\n \"hit_rate\": 0.9540682414698163,\n \"mae\": 2.793165394032372,\n },\n },\n} as const;",[263],"e606d5c4-9c9d-4419-92de-0ee0d46dda2e","models-full-old.ts","c5f98036-ab9a-11f1-8a31-1607ee4eb77e",70,["D",1788882784046],"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 const;",[270],"5626fd38-bb58-4cbd-aa01-4c2718cac318","models-core.ts","7547205e-ab9e-11f1-9265-1607ee4eb77e",249,["D",1788897582926],"import { PERSISTENT_MODEL } from \"./model-persistent.ts\";\nimport { SPRING_PEAK_DAYS_MODEL } from \"./model-spring-peak-days.ts\";\nimport {\n MODEL_BUNDLE,\n OTHER_RIGHT_NOW_MODELS,\n TUBING_OPEN_LIVE_MODEL,\n} from \"./models-meta.ts\";\n\nexport { MODEL_BUNDLE, TUBING_OPEN_LIVE_MODEL };\nexport const SMALL_RIGHT_NOW_MODELS = {\n ...OTHER_RIGHT_NOW_MODELS,\n persistent: PERSISTENT_MODEL,\n spring_peak_days: SPRING_PEAK_DAYS_MODEL,\n} as const;\n\n// Val Town dependency refresh 2026-09-08-1.",[277],"151e3b74-7a5e-48d2-bc9a-ab1d85d8941e","model-spring-peak-days.ts","c1f3c0ee-abbf-11f1-93e9-1607ee4eb77e",245,["D",1788897553887],"export const SPRING_PEAK_DAYS_MODEL = {\n ...{\n \"type\": \"gradient_boost\",\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 \"init\": 19,\n \"learningRate\": 0.03,\n \"audit\": {\n \"model\": \"gradient_boost\",\n \"hit_rate\": 0.39732142857142855,\n \"mae\": 11.384463681343822,\n \"n\": 224,\n },\n },\n trees: [\n {\n \"feature\": [11, 7, -2, -2, 4, -2, -2],\n \"threshold\": [-0.56456754, 0.37803571, -2, -2, 29.72500038, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.08227848,\n -7.06666667,\n -2.61538462,\n -11.55660377,\n 7.56439394,\n 4.57763975,\n 14.66990291,\n ],\n },\n {\n \"feature\": [12, 4, -2, -2, 7, -2, -2],\n \"threshold\": [-0.84427366, 29.65000057, -2, -2, 0.48196428, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.05263186,\n 7.97306151,\n 5.15051675,\n 15.03358712,\n -6.16609218,\n -3.4324024,\n -13.53473919,\n ],\n },\n {\n \"feature\": [12, 4, -2, -2, 7, -2, -2],\n \"threshold\": [-0.84427366, 29.65000057, -2, -2, 0.48196428, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.02315567,\n 7.73645935,\n 4.99074562,\n 14.58257951,\n -5.97951456,\n -3.33035006,\n -13.13593993,\n ],\n },\n {\n \"feature\": [12, 6, -2, -2, 7, -2, -2],\n \"threshold\": [-0.82534203, -0.625, -2, -2, 0.37803571, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.99466109,\n 6.93079847,\n 3.94520753,\n 12.82685074,\n -6.46791162,\n -2.3340405,\n -10.65598668,\n ],\n },\n {\n \"feature\": [12, 4, -2, -2, 7, -2, -2],\n \"threshold\": [-0.84427366, 24.84999943, -2, -2, 0.48196428, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.9500238,\n 7.26798937,\n 3.53084226,\n 12.54057043,\n -5.64026788,\n -3.13516217,\n -12.44194196,\n ],\n },\n {\n \"feature\": [12, 6, -2, -2, 7, -2, -2],\n \"threshold\": [-0.82534203, -0.625, -2, -2, 0.37803571, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.89837207,\n 6.45947356,\n 3.59236069,\n 12.18064099,\n -6.09272694,\n -2.16996442,\n -10.0989764,\n ],\n },\n {\n \"feature\": [14, 7, -2, -2, 6, -2, -2],\n \"threshold\": [0.42563848, 0.48196428, -2, -2, -1.875, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.84591576,\n -5.319577,\n -2.95600783,\n -11.80047861,\n 6.75663608,\n 1.42765965,\n 10.81325155,\n ],\n },\n {\n \"feature\": [12, 4, -2, -2, 7, -2, -2],\n \"threshold\": [-0.84427366, 29.65000057, -2, -2, 0.48196428, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.80110718,\n 6.51368376,\n 3.92543936,\n 13.00220787,\n -5.15770115,\n -2.87118947,\n -11.46167039,\n ],\n },\n {\n \"feature\": [7, 11, -2, -2, 14, -2, -2],\n \"threshold\": [0.36660714, -0.50648479, -2, -2, 0.77231434, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.76158242,\n 4.27722941,\n -1.07308614,\n 9.27056649,\n -8.44350858,\n -9.15647551,\n 14.25591768,\n ],\n },\n {\n \"feature\": [7, 12, -2, -2, 7, -2, -2],\n \"threshold\": [0.36660714, -0.86220598, -2, -2, 0.58625001, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.7552619,\n 4.16833953,\n 8.99361517,\n -1.04054723,\n -8.18126962,\n -6.50129861,\n -13.12187245,\n ],\n },\n {\n \"feature\": [7, 11, -2, -2, 14, -2, -2],\n \"threshold\": [0.36660714, -0.50648479, -2, -2, 0.77231434, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.7172257,\n 4.02121635,\n -1.01128595,\n 8.71660509,\n -7.93368113,\n -8.6304263,\n 14.0232791,\n ],\n },\n {\n \"feature\": [7, 11, -2, -2, 7, -2, -2],\n \"threshold\": [0.36660714, -0.50648479, -2, -2, 0.58625001, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.68165722,\n 3.87865833,\n -0.98284228,\n 8.44977872,\n -7.68911668,\n -6.10137206,\n -12.46930349,\n ],\n },\n {\n \"feature\": [11, 7, -2, -2, 4, -2, -2],\n \"threshold\": [-0.59262362, 0.37803571, -2, -2, 29.625, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.64625654,\n -5.53951894,\n -2.13526993,\n -8.6973825,\n 4.71243062,\n 2.13772196,\n 10.65528015,\n ],\n },\n {\n \"feature\": [7, 12, -2, -2, 7, -2, -2],\n \"threshold\": [0.36660714, -0.86220598, -2, -2, 0.58625001, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 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const;",[284],"a44363db-c449-453e-b117-83caf6d8e701","model-runtime.ts","40793528-ab9f-11f1-b45e-1607ee4eb77e",251,["D",1788897591900],"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}\n\n// Val Town dependency refresh 2026-09-08-3.",[291],"3cf0fa99-c590-4138-8f42-0c3314a043cf","model-runtime-old.ts","9e38bf58-ab9a-11f1-9797-1607ee4eb77e",78,["D",1788882986481],"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}",[298],"f114a9fe-50fc-435b-bcc5-ff7b9725f521","model-persistent.ts","bd9ef054-abbf-11f1-9df7-1607ee4eb77e",242,["D",1788897546380],"export const PERSISTENT_MODEL = {\n ...{\n \"type\": \"gradient_boost\",\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 \"init\": 14,\n \"learningRate\": 0.03,\n \"audit\": {\n \"model\": \"gradient_boost\",\n \"hit_rate\": 0.7960526315789473,\n \"mae\": 9.150493212134046,\n \"n\": 152,\n },\n },\n trees: [\n {\n \"feature\": [4, 2, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 3.68571115, -2, -2, 5.74831676, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [2.56451613, 4.16, -7.9375, 5.22007722, -9.97142857, -11.3, -2],\n },\n {\n \"feature\": [4, 16, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 0.125, -2, -2, 5.58066177, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 2.47974194,\n 4.02636364,\n 1.68845783,\n 8.92402268,\n -9.67228571,\n -11.10927586,\n -2.72683333,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 3.68571115, -2, -2, 5.74831676, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 2.38984417,\n 3.88809379,\n -7.87212889,\n 4.90816803,\n -9.38211714,\n -10.63610417,\n -1.858195,\n ],\n },\n {\n \"feature\": [4, 16, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 0.125, -2, -2, 5.58066177, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 2.31079055,\n 3.76315618,\n 1.47975009,\n 8.50624497,\n -9.10065363,\n -10.45691446,\n -2.54539294,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 5.7319088, -2, -2, 5.74831676, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 2.22675675,\n 3.63367921,\n 4.72203165,\n -6.67003831,\n -8.82763402,\n -10.01122513,\n -1.72608736,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 3.68571115, -2, -2, 5.58066177, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 2.15303899,\n 3.51687368,\n -7.94059058,\n 4.48688691,\n -8.562805,\n -9.84287027,\n -2.37582284,\n ],\n },\n {\n \"feature\": [4, 16, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 0.075, -2, -2, 5.74831676, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 2.0818755,\n 3.40395867,\n 0.89526695,\n 7.43148528,\n -8.30592085,\n -9.42306931,\n -1.60303006,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 0, -2, -2],\n \"threshold\": [0.57500002, 5.7319088, -2, -2, 5.58066177, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 2.00711565,\n 3.28797041,\n 4.32338063,\n -6.7098364,\n -8.05674322,\n -9.26489209,\n -2.21735706,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 4, -2, -2],\n \"threshold\": [0.57500002, 3.68571115, -2, -2, 0.97499999, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.94070152,\n 3.18234147,\n -7.96923097,\n 4.10611017,\n -7.81504093,\n -6.02713831,\n -11.71591937,\n ],\n },\n {\n \"feature\": [16, 4, -2, -2, 7, -2, -2],\n \"threshold\": [0.075, 0.57500002, -2, -2, 0.23517857, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.87659361,\n -0.76954089,\n 0.62659692,\n -7.5805897,\n 6.24158471,\n 25.40230988,\n 3.39740702,\n ],\n },\n {\n \"feature\": [16, 4, -2, -2, 7, -2, -2],\n \"threshold\": [0.075, 0.57500002, -2, -2, 0.23517857, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.80574759,\n -0.75927593,\n 0.58280853,\n -7.35317201,\n 6.03694022,\n 24.67613599,\n 3.27054694,\n ],\n },\n {\n \"feature\": [16, 4, -2, -2, 7, -2, -2],\n \"threshold\": [0.075, 0.57500002, -2, -2, 0.23517857, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.7648291,\n -0.73778297,\n 0.55697836,\n -7.13257685,\n 5.89306953,\n 23.94562499,\n 3.16478603,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 2, -2, -2],\n \"threshold\": [0.375, 5.7319088, -2, -2, 5.11960173, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.66192463,\n 3.20641141,\n 4.29512235,\n -7.14566573,\n -4.64261975,\n -8.05907372,\n 0.46895456,\n ],\n },\n {\n \"feature\": [16, 4, -2, -2, 7, -2, -2],\n \"threshold\": [0.075, 0.57500002, -2, -2, 0.23517857, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.60472995,\n -0.77335845,\n 0.43202382,\n -6.76454419,\n 5.52755953,\n 23.17542279,\n 2.90023914,\n ],\n },\n {\n \"feature\": [16, 4, -2, -2, 7, -2, -2],\n \"threshold\": [0.075, 0.57500002, -2, -2, 0.23517857, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.56096337,\n -0.7540827,\n 0.40579429,\n -6.56160787,\n 5.37980004,\n 22.49482076,\n 2.80134912,\n ],\n },\n {\n \"feature\": [14, 1, -2, -2, 2, -2, -2],\n \"threshold\": [0.86924917, 3.95685935, -2, -2, 5.7180233, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.5255853,\n -1.69027274,\n -8.79002502,\n 0.13570494,\n 4.17393898,\n 6.01083157,\n -7.7272962,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 2, -2, -2],\n \"threshold\": [0.375, 3.66590118, -2, -2, 5.11960173, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.46787189,\n 2.88415915,\n -7.9176922,\n 3.85776121,\n -4.31336626,\n -7.59823887,\n 0.57823252,\n ],\n },\n {\n \"feature\": [16, 4, -2, -2, 7, -2, -2],\n \"threshold\": [0.075, 0.57500002, -2, -2, 0.23517857, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.41770845,\n -0.78326375,\n 0.30988263,\n -6.23557945,\n 5.04837198,\n 21.60654196,\n 2.49251619,\n ],\n },\n {\n \"feature\": [13, 2, -2, -2, 1, -2, -2],\n \"threshold\": [0.4931761, 5.7180233, -2, -2, 3.95685935, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.38525937,\n 3.9030463,\n 5.64989659,\n -7.69038624,\n -1.67205333,\n -8.55188719,\n 0.08414989,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 1, -2, -2],\n \"threshold\": [0.025, 5.48689866, -2, -2, 4.66440558, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.33291355,\n 3.3233241,\n 4.72527185,\n -5.21325536,\n -2.38989136,\n -5.06574158,\n 0.51892717,\n ],\n },\n {\n \"feature\": [16, 4, -2, -2, 7, -2, -2],\n \"threshold\": [0.075, 0.57500002, -2, -2, 0.23517857, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.28639603,\n -0.80052496,\n 0.2019459,\n -5.98016148,\n 4.72892381,\n 20.74107364,\n 2.19691239,\n ],\n },\n {\n \"feature\": [11, 2, -2, -2, 1, -2, -2],\n \"threshold\": [0.25514466, 3.72697401, -2, -2, 3.95685935, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 1.23996795,\n 3.62267978,\n -7.88215241,\n 5.31204305,\n -1.65332499,\n -8.34845146,\n 0.03451641,\n ],\n },\n {\n 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\"threshold\": [0.43224345, 3.87846148, -2, -2, 3.95685935, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.33057078,\n 1.81671447,\n 10.32483601,\n 0.67044271,\n -1.1365198,\n -5.87603827,\n -0.08621313,\n ],\n },\n {\n \"feature\": [2, 9, -2, -2, 9, -2, -2],\n \"threshold\": [5.46594763, 5.125, -2, -2, 4.70000005, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.32506365,\n 0.90467714,\n -0.06984229,\n 13.49241301,\n -3.37342242,\n -0.07339216,\n -5.617443,\n ],\n },\n {\n \"feature\": [2, 1, -2, -2, 12, -2, -2],\n \"threshold\": [3.78184938, 3.72683394, -2, -2, 0.97495425, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.28677484,\n -5.10109802,\n -3.39492927,\n -6.97788364,\n 0.67828117,\n -1.30085441,\n 2.7419423,\n ],\n },\n {\n \"feature\": [4, 2, -2, -2, 2, -2, -2],\n \"threshold\": [0.375, 5.46594763, -2, -2, 5.27346468, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.27736975,\n 0.98439777,\n 1.6540425,\n -3.34344166,\n -2.60869546,\n -4.50907295,\n 1.41272917,\n ],\n },\n {\n \"feature\": [2, 16, -2, -2, 16, -2, -2],\n \"threshold\": [3.78184938, 0.72499999, -2, -2, 0.325, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.26703394,\n -4.96246855,\n -3.7288592,\n -7.42968725,\n 0.64703239,\n -0.38332229,\n 4.77482583,\n ],\n },\n {\n \"feature\": [2, 9, -2, -2, 9, -2, -2],\n \"threshold\": [5.67069054, 4.625, -2, -2, 6.92499995, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.25840286,\n 0.67130265,\n -0.26030486,\n 6.43979444,\n -4.44865479,\n -2.05066465,\n -7.04647744,\n ],\n },\n {\n \"feature\": [4, 8, -2, -2, 8, -2, -2],\n \"threshold\": [0.375, 1.80000001, -2, -2, 0.375, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.25628005,\n 0.93864362,\n 0.46183739,\n 9.88883374,\n -2.52910568,\n 0.99572432,\n -4.45605004,\n ],\n },\n {\n \"feature\": [2, 9, -2, -2, 9, -2, -2],\n \"threshold\": [5.46594763, 5.125, -2, -2, 4.70000005, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.25425354,\n 0.79557391,\n -0.11402841,\n 12.80198183,\n -3.19988599,\n 0.02362619,\n -5.39187427,\n ],\n },\n {\n \"feature\": [2, 16, -2, -2, 14, -2, -2],\n \"threshold\": [3.72697401, 0.72499999, -2, -2, 0.95187858, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.25517353,\n -4.93245761,\n -3.70639999,\n -7.20942176,\n 0.61294119,\n -0.88891806,\n 3.01143052,\n ],\n },\n {\n \"feature\": [2, 9, -2, -2, 6, -2, -2],\n \"threshold\": [5.46594763, 5.125, -2, -2, -0.39999999, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.24516117,\n 0.77369205,\n -0.11395485,\n 12.37271206,\n -3.12736921,\n -10.21023826,\n -2.17022474,\n ],\n },\n {\n \"feature\": [4, 7, -2, -2, 8, -2, -2],\n \"threshold\": [0.025, 0.24107143, -2, -2, 0.025, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.24628313,\n 1.22337124,\n 5.79573113,\n 0.02820456,\n -1.58123353,\n 13.85306288,\n -2.23915359,\n ],\n },\n {\n \"feature\": [14, 7, -2, -2, 2, -2, -2],\n \"threshold\": [0.99408948, 0.55482143, -2, -2, 5.42908168, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.24318515,\n -0.333643,\n 0.16814626,\n -6.0310654,\n 3.31567796,\n 5.6729008,\n -6.15696802,\n ],\n },\n {\n \"feature\": [1, 1, -2, -2, 2, -2, -2],\n \"threshold\": [3.78759265, 3.69046354, -2, -2, 5.46594763, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.23102313,\n -4.31394285,\n -1.58362175,\n -8.86447801,\n 0.61241888,\n 1.13480875,\n -2.98830178,\n ],\n },\n {\n \"feature\": [11, 7, -2, -2, 6, -2, -2],\n \"threshold\": [0.67396972, -0.22053571, -2, -2, -0.025, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.2329817,\n 0.56848893,\n 24.81890282,\n 0.14221916,\n -4.90557652,\n -7.09526209,\n -4.12354596,\n ],\n },\n {\n \"feature\": [2, 16, -2, -2, 16, -2, -2],\n \"threshold\": [3.68571115, 0.72499999, -2, -2, 0.325, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.23882234,\n -4.9026949,\n -3.65312184,\n -7.04482013,\n 0.57452278,\n -0.37783071,\n 4.33555472,\n ],\n },\n {\n \"feature\": [4, 9, -2, -2, 9, -2, -2],\n \"threshold\": [0.77500001, 0.875, -2, -2, 5.7249999, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.24852904,\n 0.60620083,\n -2.19941214,\n 1.51573188,\n -4.4337199,\n -5.39575415,\n -1.16280344,\n ],\n },\n {\n \"feature\": [2, 9, -2, -2, 9, -2, -2],\n \"threshold\": [5.67069054, 4.625, -2, -2, 6.92499995, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.24511785,\n 0.62388033,\n -0.22707987,\n 5.84020679,\n -4.07277441,\n -1.7618974,\n -6.5762245,\n ],\n },\n {\n \"feature\": [4, 7, -2, -2, 8, -2, -2],\n \"threshold\": [0.025, 0.24107143, -2, -2, 0.025, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.22767851,\n 1.15700409,\n 5.55314936,\n -0.02344756,\n -1.51050452,\n 13.36679113,\n -2.14973709,\n ],\n },\n {\n \"feature\": [12, 7, -2, -2, 2, -2, -2],\n \"threshold\": [0.99852088, 0.55482143, -2, -2, 5.42908168, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.21045652,\n -0.33755934,\n 0.14475577,\n -5.85820993,\n 3.12947979,\n 5.37494479,\n -5.98658992,\n ],\n },\n {\n \"feature\": [2, 2, -2, -2, 12, -2, -2],\n \"threshold\": [3.68571115, 3.6230768, -2, -2, 0.98789284, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.2081314,\n -4.74092934,\n -4.16222302,\n -9.659933,\n 0.53126595,\n -0.84366087,\n 2.80092396,\n ],\n },\n {\n \"feature\": [11, 7, -2, -2, 6, -2, -2],\n \"threshold\": [0.67396972, -0.22053571, -2, -2, -0.025, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.19901448,\n 0.51698387,\n 23.80417038,\n 0.10719443,\n -4.67093765,\n -6.8259052,\n -3.90130639,\n ],\n },\n {\n \"feature\": [2, 2, -2, -2, 16, -2, -2],\n \"threshold\": [3.68571115, 3.6230768, -2, -2, 0.425, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.20462574,\n -4.60191729,\n -4.04057217,\n -9.37335085,\n 0.51845501,\n -0.29781518,\n 4.48688719,\n ],\n },\n {\n \"feature\": [2, 9, -2, -2, 9, -2, -2],\n \"threshold\": [5.7319088, 5.2249999, -2, -2, 6.92499995, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.19126159,\n 0.52317001,\n -0.11494049,\n 5.94405168,\n -4.37643052,\n -1.64679158,\n -6.42365972,\n ],\n },\n {\n \"feature\": [4, 7, -2, -2, 1, -2, -2],\n \"threshold\": [0.77500001, -0.22053571, -2, -2, 5.56212854, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.17576711,\n 0.51116351,\n 16.16493381,\n 0.12946486,\n -4.21487657,\n -5.08294855,\n -1.26343186,\n ],\n },\n {\n \"feature\": [11, 7, -2, -2, 6, -2, -2],\n \"threshold\": [0.67396972, -0.22053571, -2, -2, -0.025, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.17652707,\n 0.48613402,\n 22.48735559,\n 0.07575964,\n -4.56534778,\n -6.61262932,\n -3.8341758,\n ],\n },\n {\n \"feature\": [2, 9, -2, -2, 7, -2, -2],\n \"threshold\": [5.46594763, 4.625, -2, -2, 0.52321427, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.18281726,\n 0.66162803,\n -0.3391011,\n 8.04335798,\n -2.8724515,\n -2.16374678,\n -12.08561285,\n ],\n },\n {\n \"feature\": [3, 0, -2, -2, 2, -2, -2],\n \"threshold\": [-1.0392516, 5.40856743, -2, -2, 5.49278665, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.17995914,\n 12.34348841,\n 18.02293976,\n 6.45113184,\n 0.06109729,\n 0.33262757,\n -3.33190802,\n ],\n },\n {\n \"feature\": [1, 1, -2, -2, 1, -2, -2],\n \"threshold\": [3.78759265, 3.69046354, -2, -2, 3.88815773, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.18021713,\n -3.97603952,\n -1.26797231,\n -8.48948486,\n 0.52899391,\n 7.67595053,\n -0.01223024,\n ],\n },\n {\n \"feature\": [4, 8, -2, -2, 3, -2, -2],\n \"threshold\": [0.375, 1.80000001, -2, -2, -0.17529399, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.17936933,\n 0.77091956,\n 0.34996367,\n 8.32597348,\n -2.23531934,\n 1.62474975,\n -3.69520085,\n ],\n },\n {\n \"feature\": [9, 12, -2, -2, 7, -2, -2],\n \"threshold\": [0.875, 0.99261174, -2, -2, -0.22053571, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.17947108,\n -1.9995416,\n -3.04208209,\n 5.28593323,\n 0.82679284,\n 16.79312502,\n 0.46271199,\n ],\n },\n {\n \"feature\": [2, 16, -2, -2, 13, -2, -2],\n \"threshold\": [3.68571115, 0.72499999, -2, -2, 0.30453891, -2, -2],\n \"left\": [1, 2, -1, -1, 5, -1, -1],\n \"right\": [4, 3, -1, -1, 6, -1, -1],\n \"value\": [\n 0.182109,\n -4.43669073,\n -3.12289801,\n -6.68890683,\n 0.48368011,\n 2.60743419,\n -0.78821019,\n ],\n },\n ],\n} as const;",[305],"f7447733-2614-4ce8-a4b3-a792bfac2fa0","main.ts",255,["D",1788897606453],"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}\n\n// Val Town dependency refresh 2026-09-08-7.",[312],"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",[319],"19594cc2-2ba7-4c93-8e16-d28a7175a17f","first400-trees-5.ts","b88b8cec-ab9e-11f1-bc0e-1607ee4eb77e",179,["D",1788884101306],3,"export const FIRST400_TREES_5 = [\n {\n \"feature\": [\n 14,\n 0,\n 1,\n 4,\n 2,\n 14,\n 9,\n 14,\n -2,\n -2,\n 2,\n -2,\n -2,\n 13,\n 8,\n 11,\n -2,\n -2,\n -2,\n 12,\n 14,\n -2,\n -2,\n -2,\n -2,\n 2,\n 3,\n -2,\n -2,\n 12,\n -2,\n -2,\n 12,\n 7,\n -2,\n -2,\n -2,\n 0,\n 6,\n 4,\n 2,\n 4,\n 2,\n -2,\n -2,\n -2,\n -2,\n 6,\n -2,\n -2,\n 4,\n -2,\n -2,\n 14,\n -2,\n 6,\n -2,\n 0,\n -2,\n 0,\n 9,\n -2,\n 0,\n -2,\n -2,\n -2,\n 11,\n 1,\n 13,\n -2,\n 6,\n 2,\n -2,\n -2,\n 2,\n -2,\n -2,\n 0,\n 7,\n 7,\n -2,\n 3,\n -2,\n 0,\n -2,\n -2,\n 1,\n -2,\n -2,\n -2,\n 4,\n 1,\n 2,\n 14,\n 8,\n -2,\n 1,\n -2,\n -2,\n 3,\n 3,\n -2,\n -2,\n 1,\n -2,\n -2,\n 6,\n -2,\n -2,\n 0,\n 1,\n -2,\n -2,\n -2,\n 3,\n -2,\n -2,\n ],\n \"threshold\": [\n 0.87653768,\n 4.7921112,\n 4.47136484,\n 29.61801654,\n 4.223203,\n 0.57383543,\n 5.50258583,\n 0.50977228,\n -2,\n -2,\n 3.96963597,\n -2,\n -2,\n -0.76439375,\n 0.99306143,\n 0.44283203,\n -2,\n -2,\n -2,\n -0.92495302,\n 0.74932933,\n -2,\n -2,\n -2,\n -2,\n 4.07671249,\n 0.12518032,\n -2,\n -2,\n -0.93490488,\n -2,\n -2,\n -0.90856806,\n -0.55129933,\n -2,\n -2,\n -2,\n 5.1377155,\n 1.82529989,\n 30.33469698,\n 4.75285668,\n 26.12560913,\n 4.26493311,\n -2,\n -2,\n -2,\n -2,\n 0.57489474,\n -2,\n -2,\n 34.72836655,\n -2,\n -2,\n 0.62829844,\n -2,\n -0.18041943,\n -2,\n 5.2231028,\n -2,\n 5.73374065,\n 3.49311037,\n -2,\n 5.32793707,\n -2,\n -2,\n -2,\n 0.0732917,\n 5.09879957,\n -0.07308395,\n -2,\n 0.20847607,\n 4.75141927,\n -2,\n -2,\n 4.69025697,\n -2,\n -2,\n 5.91145953,\n -0.0895072,\n -0.42800683,\n -2,\n -0.09346358,\n -2,\n 5.48172494,\n -2,\n -2,\n 5.40868963,\n -2,\n -2,\n -2,\n 41.46949465,\n 5.25805397,\n 4.63920488,\n 0.92861266,\n 1.03453858,\n -2,\n 4.52892494,\n -2,\n -2,\n 0.21529649,\n -0.05518611,\n -2,\n -2,\n 4.74634324,\n -2,\n -2,\n 0.43917273,\n -2,\n -2,\n 5.80985199,\n 5.44109142,\n -2,\n -2,\n -2,\n 0.28136886,\n -2,\n -2,\n ],\n \"left\": [\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n -1,\n -1,\n 11,\n -1,\n -1,\n 14,\n 15,\n 16,\n -1,\n -1,\n -1,\n 20,\n 21,\n -1,\n -1,\n -1,\n -1,\n 26,\n 27,\n -1,\n -1,\n 30,\n -1,\n -1,\n 33,\n 34,\n -1,\n -1,\n -1,\n 38,\n 39,\n 40,\n 41,\n 42,\n 43,\n -1,\n -1,\n -1,\n -1,\n 48,\n -1,\n -1,\n 51,\n -1,\n -1,\n 54,\n -1,\n 56,\n -1,\n 58,\n -1,\n 60,\n 61,\n -1,\n 63,\n -1,\n -1,\n -1,\n 67,\n 68,\n 69,\n -1,\n 71,\n 72,\n -1,\n -1,\n 75,\n -1,\n -1,\n 78,\n 79,\n 80,\n -1,\n 82,\n -1,\n 84,\n -1,\n -1,\n 87,\n -1,\n -1,\n -1,\n 91,\n 92,\n 93,\n 94,\n 95,\n -1,\n 97,\n -1,\n -1,\n 100,\n 101,\n -1,\n -1,\n 104,\n -1,\n -1,\n 107,\n -1,\n -1,\n 110,\n 111,\n -1,\n -1,\n -1,\n 115,\n -1,\n -1,\n ],\n \"right\": [\n 66,\n 37,\n 32,\n 25,\n 24,\n 13,\n 10,\n 9,\n -1,\n -1,\n 12,\n -1,\n -1,\n 19,\n 18,\n 17,\n -1,\n -1,\n -1,\n 23,\n 22,\n -1,\n -1,\n -1,\n -1,\n 29,\n 28,\n -1,\n -1,\n 31,\n -1,\n -1,\n 36,\n 35,\n -1,\n -1,\n -1,\n 53,\n 50,\n 47,\n 46,\n 45,\n 44,\n -1,\n -1,\n -1,\n -1,\n 49,\n -1,\n -1,\n 52,\n -1,\n -1,\n 55,\n -1,\n 57,\n -1,\n 59,\n -1,\n 65,\n 62,\n -1,\n 64,\n -1,\n -1,\n -1,\n 90,\n 77,\n 70,\n -1,\n 74,\n 73,\n -1,\n -1,\n 76,\n -1,\n -1,\n 89,\n 86,\n 81,\n -1,\n 83,\n -1,\n 85,\n -1,\n -1,\n 88,\n -1,\n -1,\n -1,\n 114,\n 109,\n 106,\n 99,\n 96,\n -1,\n 98,\n -1,\n -1,\n 103,\n 102,\n -1,\n -1,\n 105,\n -1,\n -1,\n 108,\n -1,\n -1,\n 113,\n 112,\n -1,\n -1,\n -1,\n 116,\n -1,\n -1,\n ],\n \"value\": [\n 17.20971867,\n 22.17647059,\n 27.96,\n 30.47524752,\n 32.42666667,\n 33.55223881,\n 36.95833333,\n 40.8,\n 41.8,\n 39.8,\n 34.21428571,\n 36.33333333,\n 30.4,\n 31.65116279,\n 34.77777778,\n 37.09090909,\n 35.66666667,\n 38.8,\n 31.14285714,\n 29.4,\n 28.375,\n 30.125,\n 26.625,\n 31.22222222,\n 23,\n 24.84615385,\n 21.64285714,\n 16.57142857,\n 26.71428571,\n 28.58333333,\n 23.16666667,\n 34,\n 17.375,\n 20.1875,\n 15.42857143,\n 23.88888889,\n 11.75,\n 14.64583333,\n 19.21276596,\n 16.90909091,\n 20.43478261,\n 23.88888889,\n 27.16666667,\n 32.16666667,\n 22.16666667,\n 17.33333333,\n 8,\n 8.8,\n 10,\n 7.6,\n 24.64285714,\n 30.5,\n 16.83333333,\n 10.26530612,\n 18.14285714,\n 8.95238095,\n 3.6,\n 10.625,\n 15.71428571,\n 9.2,\n 10.63157895,\n 12.2,\n 10.07142857,\n 15.33333333,\n 6.125,\n 4.66666667,\n 10.75294118,\n 7.3373494,\n 10.0625,\n 13.22222222,\n 8.82608696,\n 10.36363636,\n 12.83333333,\n 7.4,\n 7.41666667,\n 7.5,\n 7.33333333,\n 5.62745098,\n 6.97560976,\n 5.2,\n 3.5,\n 5.625,\n 7.33333333,\n 4.6,\n 6,\n 3,\n 11.81818182,\n 11,\n 12.8,\n 0.1,\n 14.01149425,\n 15.10810811,\n 17.85714286,\n 19.09302326,\n 20.89473684,\n 22.5,\n 20.15384615,\n 19,\n 20.875,\n 17.66666667,\n 20.5,\n 21.83333333,\n 19.16666667,\n 14.83333333,\n 14.6,\n 15,\n 13.76923077,\n 10.8,\n 15.625,\n 6.55555556,\n 10.8,\n 12.8,\n 8.8,\n 1.25,\n 7.76923077,\n 11,\n 2.6,\n ],\n },\n {\n \"feature\": [\n 1,\n 2,\n 2,\n 12,\n 9,\n 3,\n 9,\n -2,\n -2,\n -2,\n 11,\n 7,\n -2,\n -2,\n 6,\n 2,\n -2,\n -2,\n -2,\n 6,\n 1,\n 9,\n -2,\n 8,\n -2,\n -2,\n 6,\n -2,\n -2,\n -2,\n 11,\n 6,\n 13,\n -2,\n -2,\n 9,\n -2,\n -2,\n 2,\n 0,\n -2,\n 0,\n -2,\n 1,\n -2,\n -2,\n 7,\n 12,\n -2,\n -2,\n 13,\n 7,\n -2,\n 12,\n 13,\n -2,\n -2,\n -2,\n 9,\n 11,\n 4,\n -2,\n -2,\n -2,\n -2,\n 6,\n 3,\n 7,\n -2,\n -2,\n 12,\n -2,\n -2,\n 8,\n -2,\n -2,\n 11,\n 1,\n 12,\n 4,\n 11,\n 13,\n -2,\n -2,\n 2,\n -2,\n -2,\n 9,\n 0,\n -2,\n -2,\n -2,\n 8,\n 6,\n 12,\n -2,\n -2,\n -2,\n -2,\n 8,\n 15,\n 4,\n 11,\n -2,\n -2,\n 3,\n -2,\n -2,\n -2,\n 0,\n 6,\n -2,\n -2,\n -2,\n 11,\n -2,\n -2,\n ],\n \"threshold\": [\n 4.96953351,\n 4.67424476,\n 4.18658069,\n -0.90987793,\n 7.21978858,\n 0.21117504,\n 4.66245662,\n -2,\n -2,\n -2,\n 0.29275145,\n -0.56567302,\n -2,\n -2,\n 2.7880715,\n 4.0390711,\n -2,\n -2,\n -2,\n 2.54948265,\n 4.08319376,\n 4.8561877,\n -2,\n 0.49307963,\n -2,\n -2,\n 1.165021,\n -2,\n -2,\n -2,\n 0.1542491,\n 0.74756455,\n -0.17549853,\n -2,\n -2,\n 7.0611442,\n -2,\n -2,\n 4.27497317,\n 4.48732228,\n -2,\n 4.62344905,\n -2,\n 4.56716697,\n -2,\n -2,\n -0.658682,\n -0.91652657,\n -2,\n -2,\n -0.53067453,\n -0.53771837,\n -2,\n -0.9073169,\n -0.63455214,\n -2,\n -2,\n -2,\n 6.82821708,\n 0.23157051,\n 23.5439384,\n -2,\n -2,\n -2,\n -2,\n 1.01410498,\n 0.03531685,\n -0.40455731,\n -2,\n -2,\n -0.98283671,\n -2,\n -2,\n 1.81403702,\n -2,\n -2,\n 0.4026086,\n 5.39091675,\n -0.98236879,\n 28.15765089,\n 0.02781586,\n 0.20276649,\n -2,\n -2,\n 4.72306373,\n -2,\n -2,\n 11.33162872,\n 5.31025234,\n -2,\n -2,\n -2,\n 0.84070838,\n 0.49990008,\n -0.95897831,\n -2,\n -2,\n -2,\n -2,\n 0.65614304,\n 0.17563638,\n 29.11369816,\n 0.22784427,\n -2,\n -2,\n 0.48334911,\n -2,\n -2,\n -2,\n 5.54908902,\n 0.92045728,\n -2,\n -2,\n -2,\n 0.46111999,\n -2,\n -2,\n ],\n \"left\": [\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n -1,\n -1,\n -1,\n 11,\n 12,\n -1,\n -1,\n 15,\n 16,\n -1,\n -1,\n -1,\n 20,\n 21,\n 22,\n -1,\n 24,\n -1,\n -1,\n 27,\n -1,\n -1,\n -1,\n 31,\n 32,\n 33,\n -1,\n -1,\n 36,\n -1,\n -1,\n 39,\n 40,\n -1,\n 42,\n -1,\n 44,\n -1,\n -1,\n 47,\n 48,\n -1,\n -1,\n 51,\n 52,\n -1,\n 54,\n 55,\n -1,\n -1,\n -1,\n 59,\n 60,\n 61,\n -1,\n -1,\n -1,\n -1,\n 66,\n 67,\n 68,\n -1,\n -1,\n 71,\n -1,\n -1,\n 74,\n -1,\n -1,\n 77,\n 78,\n 79,\n 80,\n 81,\n 82,\n -1,\n -1,\n 85,\n -1,\n -1,\n 88,\n 89,\n -1,\n -1,\n -1,\n 93,\n 94,\n 95,\n -1,\n -1,\n -1,\n -1,\n 100,\n 101,\n 102,\n 103,\n -1,\n -1,\n 106,\n -1,\n -1,\n -1,\n 110,\n 111,\n -1,\n -1,\n -1,\n 115,\n -1,\n -1,\n ],\n \"right\": [\n 76,\n 65,\n 30,\n 19,\n 10,\n 9,\n 8,\n -1,\n -1,\n -1,\n 14,\n 13,\n -1,\n -1,\n 18,\n 17,\n -1,\n -1,\n -1,\n 29,\n 26,\n 23,\n -1,\n 25,\n -1,\n -1,\n 28,\n -1,\n -1,\n -1,\n 38,\n 35,\n 34,\n -1,\n -1,\n 37,\n -1,\n -1,\n 46,\n 41,\n -1,\n 43,\n -1,\n 45,\n -1,\n -1,\n 50,\n 49,\n -1,\n -1,\n 58,\n 53,\n -1,\n 57,\n 56,\n -1,\n -1,\n -1,\n 64,\n 63,\n 62,\n -1,\n -1,\n -1,\n -1,\n 73,\n 70,\n 69,\n -1,\n -1,\n 72,\n -1,\n -1,\n 75,\n -1,\n -1,\n 114,\n 99,\n 92,\n 87,\n 84,\n 83,\n -1,\n -1,\n 86,\n -1,\n -1,\n 91,\n 90,\n -1,\n -1,\n -1,\n 98,\n 97,\n 96,\n -1,\n -1,\n -1,\n -1,\n 109,\n 108,\n 105,\n 104,\n -1,\n -1,\n 107,\n -1,\n -1,\n -1,\n 113,\n 112,\n -1,\n -1,\n -1,\n 116,\n -1,\n -1,\n ],\n \"value\": [\n 17.20971867,\n 22.20647773,\n 24.28301887,\n 29.84946237,\n 25.22222222,\n 29.05555556,\n 26.72727273,\n 25.4,\n 27.83333333,\n 32.71428571,\n 22.66666667,\n 19.8,\n 18.8,\n 20.8,\n 24.35294118,\n 26.90909091,\n 29.66666667,\n 23.6,\n 19.66666667,\n 34.1875,\n 35.74358974,\n 37.08695652,\n 42.2,\n 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16.14285714,\n 6.27777778,\n 9,\n 7,\n 10.66666667,\n 2,\n 9.57615894,\n 13.23076923,\n 16.74193548,\n 20.33333333,\n 15.16666667,\n 23.77777778,\n 13.375,\n 15.16666667,\n 12.3,\n 14.4,\n 10.2,\n 10.91489362,\n 11.53658537,\n 10.53846154,\n 11.89473684,\n 16.57142857,\n 9.16666667,\n 8.66666667,\n 9.66666667,\n 6.85714286,\n 13.26666667,\n 11.2,\n 14.3,\n 6.66666667,\n 5.67123288,\n 7.25,\n 5.15,\n 5.71428571,\n 4.84615385,\n 3.375,\n 7.2,\n 12.5,\n 4.68888889,\n 6.59375,\n 8.77777778,\n 4.42857143,\n 11.54545455,\n 3.78571429,\n 8.4,\n 1.22222222,\n 0,\n ],\n },\n] as const;",[325],"195a0b3b-7e8a-4c1a-816f-3a3a36b2479c","first400-trees-4.ts","b597b498-ab9e-11f1-b55f-1607ee4eb77e",129,["D",1788883357811],1,[333],"9c421bfb-ec62-4bdc-b4ae-75e68a5a66fb","first400-trees-3.ts","b2a13f7a-ab9e-11f1-b382-1607ee4eb77e",126,["D",1788883352897],[340],"aaa5d3d9-75e9-4054-946c-6a9bb551e66c","first400-trees-2.ts","af64a842-ab9e-11f1-aea1-1607ee4eb77e",123,["D",1788883348337],[346],"aada16c2-aee7-40c8-903c-0832dce5584d","first400-trees-1.ts","78758680-ab9e-11f1-8cca-1607ee4eb77e",120,["D",1788883302327],[352],"9d199274-6b3b-478c-8ef6-d79dc36f0185","first400-chunk-9.ts","41152ab2-aba1-11f1-be43-1607ee4eb77e",206,["D",1788884453395],"export const FIRST400_CHUNK_9 = [\n {\n \"feature\": [\n 11,\n 11,\n 3,\n 9,\n -2,\n -2,\n 9,\n 13,\n -2,\n 13,\n -2,\n 1,\n -2,\n -2,\n -2,\n 0,\n 11,\n 11,\n 1,\n -2,\n -2,\n 1,\n -2,\n 12,\n -2,\n -2,\n 7,\n 4,\n 14,\n 2,\n -2,\n -2,\n 11,\n 3,\n -2,\n -2,\n -2,\n 2,\n 13,\n 0,\n -2,\n -2,\n -2,\n 6,\n 1,\n 6,\n -2,\n -2,\n -2,\n 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8.45,\n 5.75,\n 10.25,\n 13.66666667,\n 6.83333333,\n 0.95238095,\n 0.18181818,\n 0.33333333,\n 0,\n 1.8,\n ],\n },\n] as const;",[358],"bba9c62d-9bfc-4882-8013-b8c6e647ac5d","first400-chunk-8.ts","3cc65cb0-aba1-11f1-b658-1607ee4eb77e",203,["D",1788884446368],"export const FIRST400_CHUNK_8 = [\n {\n \"feature\": [\n 1,\n 12,\n 11,\n 13,\n 8,\n 8,\n 6,\n -2,\n 14,\n -2,\n -2,\n -2,\n 12,\n -2,\n -2,\n 0,\n -2,\n -2,\n 9,\n 3,\n -2,\n 2,\n 6,\n -2,\n 4,\n -2,\n -2,\n -2,\n -2,\n 4,\n 2,\n 8,\n 12,\n -2,\n 4,\n 11,\n 8,\n 13,\n -2,\n -2,\n -2,\n -2,\n -2,\n 4,\n 11,\n 2,\n -2,\n -2,\n 12,\n -2,\n -2,\n -2,\n 2,\n 3,\n 14,\n 7,\n 9,\n -2,\n -2,\n -2,\n 9,\n 0,\n -2,\n -2,\n -2,\n -2,\n 8,\n 1,\n 8,\n 12,\n -2,\n -2,\n -2,\n 0,\n -2,\n 14,\n -2,\n -2,\n -2,\n 2,\n 7,\n -2,\n -2,\n 4,\n -2,\n -2,\n 11,\n 3,\n 0,\n 13,\n 7,\n -2,\n -2,\n 13,\n -2,\n 14,\n -2,\n -2,\n 0,\n 4,\n 3,\n -2,\n -2,\n 13,\n -2,\n -2,\n 15,\n 14,\n 3,\n -2,\n -2,\n 0,\n -2,\n -2,\n -2,\n -2,\n -2,\n ],\n 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18.66666667,\n 7.05333333,\n 7.36923077,\n 6.87719298,\n 5.94444444,\n 7,\n 5.68965517,\n 6.42857143,\n 8.66666667,\n 4.75,\n 6.5,\n 3,\n 3.75,\n 8.47619048,\n 9.25,\n 11.71428571,\n 7.33333333,\n 6,\n 10.875,\n 5,\n 6.6,\n 3.4,\n 2.5,\n 9.6,\n 0.95652174,\n 1.57142857,\n 1,\n 2.6,\n 0,\n ],\n },\n] as const;",[379],"52913067-e3f5-43bd-8f58-39820699f8a6","first400-chunk-5.ts","2fecb250-aba1-11f1-a89a-1607ee4eb77e",194,["D",1788884424248],"export const FIRST400_CHUNK_5 = [\n {\n \"feature\": [\n 2,\n 11,\n 11,\n 8,\n -2,\n -2,\n 4,\n 4,\n 9,\n -2,\n -2,\n -2,\n -2,\n 13,\n 7,\n 12,\n -2,\n -2,\n 7,\n -2,\n 4,\n 0,\n -2,\n -2,\n -2,\n 11,\n -2,\n 4,\n 4,\n -2,\n -2,\n 12,\n -2,\n -2,\n 11,\n 2,\n 6,\n 2,\n 8,\n -2,\n -2,\n 7,\n -2,\n -2,\n 11,\n -2,\n 4,\n -2,\n -2,\n 8,\n 0,\n 11,\n 4,\n -2,\n 9,\n -2,\n 11,\n 2,\n -2,\n -2,\n -2,\n 4,\n 6,\n -2,\n -2,\n -2,\n 3,\n -2,\n 13,\n -2,\n -2,\n -2,\n 2,\n 13,\n 2,\n -2,\n -2,\n 14,\n -2,\n -2,\n 2,\n 8,\n 8,\n 14,\n -2,\n 4,\n -2,\n 2,\n 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const;",[414],"ac8cecb6-9924-4821-b3cf-f90420a75e40","first400-chunk-19.ts","b3bcbeae-abbf-11f1-ab2d-1607ee4eb77e",236,["D",1788897530738],"export const FIRST400_CHUNK_19 = [\n {\n \"feature\": [\n 1,\n 11,\n -2,\n 9,\n 8,\n -2,\n 8,\n -2,\n -2,\n 1,\n -2,\n 4,\n 8,\n -2,\n -2,\n 3,\n -2,\n -2,\n 1,\n 12,\n 2,\n 1,\n -2,\n 1,\n 11,\n 8,\n -2,\n 3,\n -2,\n -2,\n 4,\n 4,\n -2,\n 6,\n -2,\n -2,\n -2,\n 12,\n -2,\n -2,\n 9,\n -2,\n -2,\n 4,\n 2,\n 2,\n 11,\n 1,\n -2,\n -2,\n 4,\n -2,\n 4,\n -2,\n -2,\n 1,\n -2,\n 9,\n -2,\n -2,\n 7,\n 4,\n -2,\n 8,\n 13,\n -2,\n -2,\n -2,\n 0,\n -2,\n -2,\n 2,\n 0,\n 4,\n 12,\n -2,\n 2,\n -2,\n -2,\n -2,\n 9,\n 12,\n 7,\n -2,\n -2,\n -2,\n -2,\n 2,\n 13,\n -2,\n -2,\n 8,\n -2,\n -2,\n 14,\n -2,\n 3,\n 15,\n 1,\n 12,\n 13,\n -2,\n -2,\n -2,\n 2,\n 4,\n -2,\n -2,\n -2,\n -2,\n 8,\n -2,\n -2,\n ],\n \"threshold\": [\n 4.25800802,\n 0.343173,\n -2,\n 5.90370762,\n 0.29449117,\n -2,\n 0.96754128,\n -2,\n -2,\n 3.7472409,\n -2,\n 28.21369928,\n 1.70584208,\n 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],\n },\n] as const;",[421],"0b98619c-6530-48e9-840c-6ed13c207fa3","first400-chunk-18.ts","966d44b8-abbf-11f1-bf76-1607ee4eb77e",233,["D",1788897499878],"export const FIRST400_CHUNK_18 = [\n {\n \"feature\": [\n 0,\n 12,\n 14,\n 2,\n -2,\n 9,\n -2,\n 12,\n -2,\n -2,\n 11,\n -2,\n -2,\n 0,\n 9,\n 8,\n -2,\n -2,\n -2,\n 7,\n -2,\n 8,\n 4,\n 9,\n 9,\n 0,\n -2,\n -2,\n -2,\n -2,\n 4,\n -2,\n 12,\n 13,\n -2,\n -2,\n 2,\n -2,\n -2,\n -2,\n 13,\n 0,\n 13,\n 4,\n 1,\n 3,\n 2,\n -2,\n 3,\n 8,\n -2,\n -2,\n -2,\n -2,\n 2,\n -2,\n -2,\n 0,\n 2,\n 14,\n 13,\n -2,\n -2,\n 7,\n 1,\n -2,\n -2,\n -2,\n 13,\n -2,\n -2,\n 12,\n 1,\n -2,\n -2,\n -2,\n 2,\n 0,\n 11,\n 12,\n -2,\n 3,\n -2,\n -2,\n -2,\n 9,\n 8,\n 2,\n -2,\n -2,\n -2,\n 11,\n -2,\n 4,\n 0,\n -2,\n -2,\n -2,\n 1,\n 6,\n -2,\n -2,\n 1,\n -2,\n -2,\n -2,\n 9,\n 8,\n 11,\n 11,\n -2,\n -2,\n -2,\n -2,\n 3,\n -2,\n -2,\n ],\n \"threshold\": [\n 4.70862108,\n -0.93543364,\n 0.97209378,\n 4.07295971,\n -2,\n 4.0405081,\n -2,\n -0.95801505,\n -2,\n 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const;",[456],"4bc20bd5-855d-4752-a383-7ca8a512c279","first400-chunk-13.ts","700ba60c-aba1-11f1-9e55-1607ee4eb77e",218,["D",1788884532502],"export const FIRST400_CHUNK_13 = [\n {\n \"feature\": [\n 1,\n 0,\n 11,\n 13,\n 12,\n -2,\n -2,\n 4,\n -2,\n -2,\n 0,\n -2,\n 7,\n 14,\n 8,\n -2,\n 12,\n -2,\n -2,\n -2,\n 4,\n 11,\n -2,\n -2,\n 9,\n 9,\n 4,\n 14,\n -2,\n -2,\n -2,\n -2,\n -2,\n 13,\n 4,\n -2,\n 7,\n -2,\n 11,\n 7,\n 3,\n 2,\n -2,\n -2,\n 4,\n -2,\n 6,\n 1,\n -2,\n -2,\n -2,\n -2,\n -2,\n 11,\n 3,\n -2,\n -2,\n -2,\n 1,\n 4,\n 6,\n 0,\n 13,\n 4,\n 7,\n -2,\n -2,\n -2,\n 2,\n -2,\n -2,\n -2,\n -2,\n 2,\n 7,\n -2,\n -2,\n 0,\n 0,\n -2,\n 11,\n -2,\n -2,\n 4,\n 14,\n -2,\n -2,\n -2,\n 9,\n 11,\n 7,\n 0,\n -2,\n -2,\n -2,\n -2,\n 11,\n 1,\n 0,\n 1,\n -2,\n -2,\n 2,\n -2,\n -2,\n -2,\n 0,\n 4,\n 3,\n 9,\n -2,\n -2,\n -2,\n -2,\n 0,\n 0,\n -2,\n 6,\n -2,\n -2,\n -2,\n ],\n \"threshold\": [\n 4.82790619,\n 4.52066053,\n 0.34834368,\n -0.55281946,\n -0.95123236,\n -2,\n -2,\n 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const;",[463],"8c97387b-f688-4cc0-b865-7878afe224c5","first400-chunk-12.ts","5284ff34-aba1-11f1-8fa5-1607ee4eb77e",215,["D",1788884501795],"export const FIRST400_CHUNK_12 = [\n {\n \"feature\": [\n 14,\n 12,\n 1,\n 2,\n 6,\n 13,\n -2,\n 8,\n -2,\n 4,\n 0,\n -2,\n -2,\n -2,\n -2,\n 2,\n -2,\n -2,\n 2,\n 14,\n 11,\n -2,\n -2,\n 11,\n -2,\n -2,\n 4,\n -2,\n -2,\n 1,\n 2,\n 11,\n -2,\n -2,\n -2,\n 2,\n 1,\n 8,\n -2,\n -2,\n 14,\n -2,\n -2,\n 8,\n 0,\n -2,\n 1,\n -2,\n -2,\n -2,\n 0,\n 13,\n 4,\n 11,\n 8,\n -2,\n 4,\n -2,\n -2,\n 11,\n 8,\n -2,\n 12,\n 8,\n -2,\n -2,\n -2,\n 9,\n 4,\n -2,\n -2,\n 6,\n -2,\n -2,\n 14,\n 1,\n 2,\n -2,\n -2,\n 11,\n -2,\n -2,\n 1,\n 11,\n -2,\n -2,\n -2,\n 1,\n 9,\n -2,\n -2,\n 3,\n -2,\n -2,\n 4,\n 0,\n 15,\n 4,\n 0,\n 11,\n -2,\n -2,\n 0,\n 13,\n -2,\n -2,\n 12,\n -2,\n -2,\n -2,\n 14,\n -2,\n -2,\n 13,\n -2,\n 0,\n -2,\n -2,\n -2,\n ],\n \"threshold\": [\n 0.78678665,\n -0.89870933,\n 4.65624165,\n 4.27009722,\n 3.46615987,\n -0.76972939,\n -2,\n 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const;",[484],"5ffa47c3-3cb4-494d-b28b-b4ac917a3ad6","first400-chunk-1.ts","03641e62-aba1-11f1-bbeb-1607ee4eb77e",182,["D",1788884349225],"export const FIRST400_CHUNK_1 = [\n {\n \"feature\": [\n 1,\n 4,\n 13,\n 6,\n -2,\n 8,\n 9,\n 4,\n -2,\n -2,\n 11,\n -2,\n -2,\n -2,\n -2,\n 14,\n 0,\n -2,\n -2,\n -2,\n 2,\n 12,\n 13,\n 12,\n 1,\n -2,\n -2,\n -2,\n 12,\n -2,\n 4,\n 7,\n 2,\n 8,\n -2,\n -2,\n -2,\n -2,\n 13,\n -2,\n -2,\n -2,\n 0,\n 4,\n 8,\n 2,\n 14,\n -2,\n 2,\n -2,\n -2,\n 2,\n -2,\n -2,\n 9,\n 13,\n -2,\n -2,\n -2,\n 2,\n -2,\n 0,\n -2,\n 2,\n 2,\n 11,\n -2,\n -2,\n -2,\n 8,\n -2,\n -2,\n 2,\n 3,\n 3,\n -2,\n 11,\n -2,\n -2,\n -2,\n 4,\n 3,\n 11,\n 0,\n 4,\n 11,\n 4,\n -2,\n 6,\n -2,\n 3,\n -2,\n -2,\n -2,\n -2,\n -2,\n -2,\n -2,\n 3,\n 4,\n 11,\n 13,\n 4,\n -2,\n -2,\n -2,\n 3,\n 2,\n -2,\n -2,\n -2,\n -2,\n 1,\n -2,\n -2,\n ],\n \"threshold\": [\n 4.24322988,\n 25.62981519,\n -0.56886559,\n 0.16125773,\n -2,\n 2.23625429,\n 4.8034631,\n 12.73380333,\n -2,\n -2,\n 0.41801147,\n 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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 ${hit ? \"HIT\" : \"MISS\"}`);\n }\n }\n }\n const message = actions.length\n ? actions.join(\"; \")\n : \"Data refreshed; no lock or score was due.\";\n await recordRun(true, mode, message, current.sourceStatus);\n await refreshPublicCache(runoffYear);\n return {\n ok: true,\n mode,\n water_year: runoffYear,\n actions,\n source_status: current.sourceStatus,\n };\n}\n\n// Val Town dependency refresh 2026-09-08-5.",[498],"62109e24-6ec6-45e4-9c7f-6c3eaaf8e9fb","engine-old.ts","a6362ff6-ab9a-11f1-9797-1607ee4eb77e",94,["D",1788883026203],"import { CONFIG, IS_LIVE } from \"./config.ts\";\nimport { addDays, fixedIssueAt, mountainToUtc, waterYear } from \"./dates.ts\";\nimport { MODEL_BUNDLE } from \"./models.ts\";\nimport { QUESTIONS } from \"./questions.ts\";\nimport { fetchBasinSnapshot } from \"./sources.ts\";\nimport {\n cachePublicFeed,\n ensureSeason,\n finalizePrediction,\n getPrediction,\n initDb,\n listPredictions,\n lockPrediction,\n recentRuns,\n recordEvent,\n recordRun,\n} from \"./storage.ts\";\nimport type { BasinSnapshot, QuestionDefinition } from \"./types.ts\";\n\nfunction canonical(value: any): string {\n if (Array.isArray(value)) return `[${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}",[505],"7a8908c5-6892-4e95-9214-8dda1b63db29","dates.ts","682186ee-ab9e-11f1-b382-1607ee4eb77e",114,["D",1788883228591],"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}",[512],"5c5fb06d-0c56-490b-8d6c-f0b6e727cedf","dates-old.ts","8d25a690-ab9a-11f1-ade0-1607ee4eb77e",74,["D",1788882955145],[519],"bdcd7640-969b-4f8c-b30f-83d878d6dc3c","cron.ts","a7fd641c-ab9a-11f1-83fc-1607ee4eb77e",254,["D",1788897602617],13,"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}\n\n// Val Town dependency refresh 2026-09-08-6.",[525],"2105a0ef-fb80-4e08-8cc6-64a92f17070e","config.ts","62a61c66-ab9e-11f1-997f-1607ee4eb77e",106,["D",1788883219504],"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\";",[534],"9a7e7f73-2658-4ed0-bd43-5e8892206351","backup4-engine.ts","dec756f2-ab9e-11f1-91ac-1607ee4eb77e",167,["D",1788883599607],[541],"3d1be9d2-ec95-4ff2-8919-4a1a168d7815","backup3-questions.ts","d589ba8a-ab9e-11f1-a65b-1607ee4eb77e",163,["D",1788883594370],[547],"8d7c8b26-21d2-49fe-8c72-f933035487ec","backup3-model-runtime.ts","c85e49c0-ab9e-11f1-bfd4-1607ee4eb77e",159,["D",1788883588396],[553],"3046b22a-146f-470e-8157-ee14be23555d","backup3-engine.ts","520ed0e6-ab9e-11f1-84ac-1607ee4eb77e",154,["D",1788883424583],[559],"8bf1d3fe-d7d6-423b-9132-ebd97d5addd9","backup2-types.ts","9a8b01ea-ab9a-11f1-9797-1607ee4eb77e",107,["D",1788883221085],[565],"df81ab01-bb46-4f81-9cee-81ddf782b407","backup2-storage.ts","e05769d6-ab9d-11f1-8b5e-1607ee4eb77e",142,["D",1788883399233],"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}\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}",[571],"104cfb6c-a8d3-47d4-b366-972a8871d8c6","backup2-sources.ts","e7307cca-ab9d-11f1-ba80-1607ee4eb77e",146,["D",1788883404303],"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];\nimport { 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}\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}",[578],"a45f853e-943d-4ac8-8017-a88e6fc127f0","backup2-questions.ts","ea85e900-ab9d-11f1-bc76-1607ee4eb77e",150,["D",1788883409099],"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}\nimport { 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: 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 = 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];\nimport { 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}\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 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