refactor(insights): move the feature window arithmetic to a leaf module so signals-of-day no longer imports the extractor

src/__tests__/pulse-day-mean-reader-guard.test.ts

@@ -128,7 +128,7 @@ const TS_READERS: Record<string, Entry> = {
       /applyPayloadBudget\(\s*pulsePoints\s*,\s*\{[^}]*type\s*:\s*"PULSE"/,
   },
   "lib/insights/signals-of-day.ts": {
-    via: "lib/insights/features.ts",
+    via: "lib/insights/feature-window.ts",
     passes: /byType\(\s*"PULSE"\s*\)[\s\S]{0,80}type\s*:\s*"PULSE"/,
   },
   "lib/ai/coach/snapshot-blocks/core-metrics-block.ts": {

src/lib/insights/feature-window.ts

@@ -0,0 +1,58 @@
+/**
+ * The window arithmetic the feature extractor and the signals-of-day builder
+ * share: a reading list as trend points, a mean over a days-ago window, and
+ * a spread rounded to the payload's one decimal. A leaf module, so the
+ * signals-of-day builder does not import the extractor that re-exports it.
+ */
+import type { DataPoint } from "@/lib/analytics/trends";
+import { readingsMean } from "@/lib/measurements/day-mean";
+import { usesHourlyMeanDay } from "@/lib/measurements/day-statistic";
+import { standardDeviation, type VarianceKind } from "@/lib/stats/spread";
+
+/**
+ * A spread rounded to the payload's one decimal, `null` below two values: a
+ * single reading has no spread to report, not a spread of zero.
+ */
+export function roundedSpread(
+  values: readonly number[],
+  kind: VarianceKind,
+): number | null {
+  if (values.length < 2) return null;
+  const sd = standardDeviation(values, kind);
+  return sd === null ? null : Math.round(sd * 10) / 10;
+}
+
+export function toDataPoints(
+  records: Array<{ value: number; measuredAt: Date }>,
+): DataPoint[] {
+  return records.map((r) => ({ date: r.measuredAt, value: r.value }));
+}
+
+/**
+ * Compute average of values within a time window (days ago from now).
+ *
+ * `day` names the type and the zone its days are read in. For a type whose
+ * day is the mean of its hours' means (pulse, see `day-mean.ts`) the window
+ * is then the mean of its day values; every other type keeps the plain mean.
+ */
+export function avgInWindow(
+  records: Array<{ value: number; measuredAt: Date }>,
+  now: number,
+  fromDaysAgo: number,
+  toDaysAgo: number = 0,
+  day?: { type: string; tz: string },
+): number | null {
+  const fromMs = now - fromDaysAgo * 24 * 60 * 60 * 1000;
+  const toMs = now - toDaysAgo * 24 * 60 * 60 * 1000;
+  const filtered = records.filter((r) => {
+    const t = r.measuredAt.getTime();
+    return t >= fromMs && t <= toMs;
+  });
+  if (filtered.length === 0) return null;
+  if (day && usesHourlyMeanDay(day.type)) {
+    const mean = readingsMean(day.type, filtered, day.tz);
+    return mean === null ? null : Math.round(mean * 100) / 100;
+  }
+  const sum = filtered.reduce((s, r) => s + r.value, 0);
+  return Math.round((sum / filtered.length) * 100) / 100;
+}

src/lib/insights/features.ts

@@ -21,7 +21,6 @@ import {
 } from "@/lib/tz/format";
 import { readMoodDailyPoints } from "@/lib/insights/mood-daily-points";
 import { readingsMean } from "@/lib/measurements/day-mean";
-import { usesHourlyMeanDay } from "@/lib/measurements/day-statistic";
 import { isCurrentForTodayClaim } from "@/lib/insights/measurement-freshness";
 import { computeMoodDimensionSeries } from "@/lib/insights/mood-dimension-series";
 import { computeContextMoodComparison } from "@/lib/insights/mood-context-crosstab";
@@ -35,11 +34,12 @@ import {
 import { loadUserSourcePriority } from "@/lib/rollups/measurement-read";
 import { getBpTargets } from "@/lib/analytics/bp-targets";
 import { isBpReadingInTarget } from "@/lib/analytics/bp-in-target";
+import { READING_SPREAD } from "@/lib/stats/spread";
 import {
-  READING_SPREAD,
-  standardDeviation,
-  type VarianceKind,
-} from "@/lib/stats/spread";
+  avgInWindow,
+  roundedSpread,
+  toDataPoints,
+} from "@/lib/insights/feature-window";
 import {
   pairByTimestamp,
   significantPearsonCorrelation,
@@ -900,25 +900,6 @@ export class FeaturesPayloadTooLargeError extends Error {
   }
 }
 
-/**
- * A spread rounded to the payload's one decimal, `null` below two values: a
- * single reading has no spread to report, not a spread of zero.
- */
-export function roundedSpread(
-  values: readonly number[],
-  kind: VarianceKind,
-): number | null {
-  if (values.length < 2) return null;
-  const sd = standardDeviation(values, kind);
-  return sd === null ? null : Math.round(sd * 10) / 10;
-}
-
-export function toDataPoints(
-  records: Array<{ value: number; measuredAt: Date }>,
-): DataPoint[] {
-  return records.map((r) => ({ date: r.measuredAt, value: r.value }));
-}
-
 function computeCoverage(
   records: Array<{ measuredAt: Date }>,
   now: number,
@@ -948,35 +929,6 @@ function computeCoverage(
   };
 }
 
-/**
- * Compute average of values within a time window (days ago from now).
- *
- * `day` names the type and the zone its days are read in. For a type whose
- * day is the mean of its hours' means (pulse, see `day-mean.ts`) the window
- * is then the mean of its day values; every other type keeps the plain mean.
- */
-export function avgInWindow(
-  records: Array<{ value: number; measuredAt: Date }>,
-  now: number,
-  fromDaysAgo: number,
-  toDaysAgo: number = 0,
-  day?: { type: string; tz: string },
-): number | null {
-  const fromMs = now - fromDaysAgo * 24 * 60 * 60 * 1000;
-  const toMs = now - toDaysAgo * 24 * 60 * 60 * 1000;
-  const filtered = records.filter((r) => {
-    const t = r.measuredAt.getTime();
-    return t >= fromMs && t <= toMs;
-  });
-  if (filtered.length === 0) return null;
-  if (day && usesHourlyMeanDay(day.type)) {
-    const mean = readingsMean(day.type, filtered, day.tz);
-    return mean === null ? null : Math.round(mean * 100) / 100;
-  }
-  const sum = filtered.reduce((s, r) => s + r.value, 0);
-  return Math.round((sum / filtered.length) * 100) / 100;
-}
-
 // ─── v1.22 — cross-signal integration blocks (briefing-scoped) ────────────
 
 /**

src/lib/insights/signals-of-day.ts

@@ -13,7 +13,7 @@ import {
   avgInWindow,
   roundedSpread,
   toDataPoints,
-} from "@/lib/insights/features";
+} from "@/lib/insights/feature-window";
 import {
   SAME_HOURS_MIN_DAYS,
   SAME_HOURS_TYPES,