feat(frontend): add computeNumericSeries helper

frontend/src/lib/csvAnalysis.js

@@ -230,3 +230,36 @@ export function extractNumber(str) {
   const m = String(str).match(/-?\d+(\.\d+)?/);
   return m ? parseFloat(m[0]) : null;
 }
+
+/**
+ * Build a numeric-vs-x series for a numerical bucket.
+ *
+ * @param {object[]} rows       - Parsed CSV rows
+ * @param {string}   xColName   - Column to plot on the x-axis (frame col, or timestamp as fallback)
+ * @param {string}   noteCol    - Column containing note values
+ * @param {object}   bucket     - { name, type:'numerical', notes: string[] }
+ * @returns {{points: {x:number,value:number,raw:string}[], avg:number|null, skipped:number, total:number, xLabel:string}}
+ */
+export function computeNumericSeries(rows, xColName, noteCol, bucket) {
+  const noteSet = new Set(bucket.notes);
+  const points = [];
+  let skipped = 0;
+  let total = 0;
+
+  for (const r of rows) {
+    const note = String(r[noteCol] ?? '').trim();
+    if (!noteSet.has(note)) continue;
+    total++;
+    const value = extractNumber(note);
+    const x = parseFloat(r[xColName]);
+    if (value == null || Number.isNaN(x)) { skipped++; continue; }
+    points.push({ x, value, raw: note });
+  }
+
+  points.sort((a, b) => a.x - b.x);
+  const avg = points.length === 0
+    ? null
+    : points.reduce((s, p) => s + p.value, 0) / points.length;
+
+  return { points, avg, skipped, total, xLabel: xColName };
+}

frontend/tests/csvAnalysis.test.js

@@ -4,6 +4,7 @@ import {
   getUniqueNoteValues,
   runAnalysis,
   extractNumber,
+  computeNumericSeries,
 } from '../src/lib/csvAnalysis';
 
 // ── Fixtures ───────────────────────────────────────────────────────────────────
@@ -306,3 +307,55 @@ describe('extractNumber', () => {
     expect(extractNumber(undefined)).toBeNull();
   });
 });
+
+describe('computeNumericSeries', () => {
+  const rows = [
+    { frame: '1', timestamp: '0.0', note: 'L25' },
+    { frame: '3', timestamp: '2.0', note: 'L30.5' },
+    { frame: '2', timestamp: '1.0', note: 'L40' },
+    { frame: '4', timestamp: '3.0', note: 'F' },
+    { frame: '5', timestamp: '4.0', note: 'abc' },   // bucket-member but no number
+    { frame: 'x', timestamp: '5.0', note: 'L99' },   // bad x
+    { frame: '6', timestamp: '6.0', note: '' },      // not in bucket
+  ];
+  const bucket = { name: 'Lick latency', type: 'numerical', notes: ['L25', 'L30.5', 'L40', 'abc', 'L99'] };
+
+  test('returns sorted points with average and counts', () => {
+    const out = computeNumericSeries(rows, 'frame', 'note', bucket);
+    expect(out.points).toEqual([
+      { x: 1, value: 25,   raw: 'L25' },
+      { x: 2, value: 40,   raw: 'L40' },
+      { x: 3, value: 30.5, raw: 'L30.5' },
+    ]);
+    expect(out.avg).toBeCloseTo((25 + 40 + 30.5) / 3);
+    expect(out.skipped).toBe(2);   // 'abc' (no number) + bad x
+    expect(out.total).toBe(5);     // bucket-member rows
+    expect(out.xLabel).toBe('frame');
+  });
+
+  test('falls back to timestamp x-column when frame is empty string', () => {
+    const out = computeNumericSeries(rows, 'timestamp', 'note', bucket);
+    expect(out.xLabel).toBe('timestamp');
+    expect(out.points[0].x).toBe(0);
+  });
+
+  test('returns avg=null and empty points when nothing plottable', () => {
+    const onlyBad = [
+      { frame: '1', note: 'abc' },
+    ];
+    const b = { name: 'Z', type: 'numerical', notes: ['abc'] };
+    const out = computeNumericSeries(onlyBad, 'frame', 'note', b);
+    expect(out.points).toEqual([]);
+    expect(out.avg).toBeNull();
+    expect(out.skipped).toBe(1);
+    expect(out.total).toBe(1);
+  });
+
+  test('returns total=0 when bucket notes match nothing in rows', () => {
+    const b = { name: 'Empty', type: 'numerical', notes: ['nope'] };
+    const out = computeNumericSeries(rows, 'frame', 'note', b);
+    expect(out.total).toBe(0);
+    expect(out.points).toEqual([]);
+    expect(out.avg).toBeNull();
+  });
+});