From c46e91d0f551560978981db6de03061080278f76 Mon Sep 17 00:00:00 2001
From: Davide Scaini <davide.scaini@alumni.cern>
Date: Tue, 12 May 2026 23:51:22 +0200
Subject: [PATCH] Compute NP from timeseries in frontend for activities missing
 np_power_w in JSON

---
 site/src/components/ActivityDetail.svelte | 40 +++++++++++++++++++++--
 1 file changed, 38 insertions(+), 2 deletions(-)

diff --git a/site/src/components/ActivityDetail.svelte b/site/src/components/ActivityDetail.svelte
index 3540dfb..3ec9b6f 100644
--- a/site/src/components/ActivityDetail.svelte
+++ b/site/src/components/ActivityDetail.svelte
@@ -112,6 +112,42 @@
   $: galleryImages = (detail?.custom as any)?.images as string[] ?? [];
 
 
+  // Coggan NP from timeseries — mirrors the Python implementation in metrics.py.
+  // Used as fallback for activities extracted before np_power_w was added to the JSON.
+  function computeNpFromTimeseries(ts: Timeseries): number | null {
+    const { t, power_w } = ts;
+    if (!power_w || !t || t.length < 30) return null;
+    if (!power_w.some(v => v != null)) return null;
+
+    const sparse = new Map<number, number>();
+    for (let i = 0; i < t.length; i++) {
+      if (power_w[i] != null) sparse.set(t[i], power_w[i]!);
+    }
+    if (sparse.size < 2) return null;
+
+    const tMin = Math.min(...sparse.keys());
+    const tMax = Math.max(...sparse.keys());
+    if (tMax - tMin > 7 * 24 * 3600) return null;
+
+    const dense: number[] = [];
+    for (let i = 0; i <= tMax - tMin; i++) dense.push(sparse.get(tMin + i) ?? 0);
+
+    const win = 30;
+    if (dense.length < win) return null;
+
+    const half = Math.floor(win / 2);
+    let windowSum = dense.slice(0, win).reduce((a, b) => a + b, 0);
+    const fourthPowers: number[] = [];
+    for (let i = half; i < dense.length - half; i++) {
+      fourthPowers.push((windowSum / win) ** 4);
+      if (i + half + 1 < dense.length) windowSum += dense[i + half + 1] - dense[i - half];
+    }
+    if (!fourthPowers.length) return null;
+    return Math.round((fourthPowers.reduce((a, b) => a + b, 0) / fourthPowers.length) ** 0.25);
+  }
+
+  $: npPower = detail?.np_power_w ?? (timeseries ? computeNpFromTimeseries(timeseries) : null);
+
   const stat = (label: string, value: string, key?: string) => ({ label, value, key });
   $: hiddenStats = new Set<string>((detail?.custom as any)?.hide_stats ?? []);
   $: stats = [
@@ -124,8 +160,8 @@
     stat('Max HR',      activity.max_hr_bpm ? `${activity.max_hr_bpm} bpm` : '—',     'heart_rate'),
     stat('Cadence',     activity.avg_cadence_rpm ? `${activity.avg_cadence_rpm} rpm` : '—', 'cadence'),
     ...(activity.avg_power_w != null ? [
-      stat('Avg power', `${activity.avg_power_w} W`,                               'power'),
-      stat('NP',        detail?.np_power_w != null ? `${detail.np_power_w} W` : '—', 'power'),
+      stat('Avg power', `${activity.avg_power_w} W`,                                   'power'),
+      stat('NP',        npPower != null ? `${npPower} W` : '—',                        'power'),
     ] : []),
   ].filter(s => !s.key || !hiddenStats.has(s.key));
 </script>