diff --git a/backend/data_layer/history_overview_viz.py b/backend/data_layer/history_overview_viz.py
index 40627f9..4d278c2 100644
--- a/backend/data_layer/history_overview_viz.py
+++ b/backend/data_layer/history_overview_viz.py
@@ -13,6 +13,7 @@ from data_layer.correlations import calculate_lag_correlation, calculate_top_dri
from data_layer.fitness_viz import get_fitness_dashboard_viz_bundle
from data_layer.nutrition_viz import get_nutrition_history_viz_bundle
from data_layer.recovery_viz import get_recovery_dashboard_viz_bundle
+from data_layer.utils import safe_float
def _take_kpis(tiles: Any, max_n: int = 4) -> List[Dict[str, Any]]:
@@ -90,11 +91,9 @@ def get_history_overview_viz_bundle(profile_id: str, days: int) -> Dict[str, Any
c3_rhr = calculate_lag_correlation(profile_id, "load", "rhr", 14)
c3 = None
if c3_hrv and c3_rhr:
- c3 = (
- c3_hrv
- if abs(float(c3_hrv.get("correlation") or 0)) >= abs(float(c3_rhr.get("correlation") or 0))
- else c3_rhr
- )
+ a1 = abs(safe_float(c3_hrv.get("correlation"), 0.0))
+ a2 = abs(safe_float(c3_rhr.get("correlation"), 0.0))
+ c3 = c3_hrv if a1 >= a2 else c3_rhr
if c3 is c3_hrv:
c3 = dict(c3)
c3["metric"] = "HRV"
diff --git a/backend/data_layer/nutrition_body_merge.py b/backend/data_layer/nutrition_body_merge.py
index 3263c45..ac8768f 100644
--- a/backend/data_layer/nutrition_body_merge.py
+++ b/backend/data_layer/nutrition_body_merge.py
@@ -9,7 +9,7 @@ from __future__ import annotations
from typing import Any, Dict, List, Optional
from db import get_db, get_cursor, r2d
-from caliper_composition import compute_lean_fat_kg, nearest_weight_kg_from_map
+from caliper_composition import as_date, compute_lean_fat_kg, nearest_weight_kg_from_map
def build_merged_daily_nutrition_body_rows(profile_id: str) -> List[Dict[str, Any]]:
@@ -20,21 +20,42 @@ def build_merged_daily_nutrition_body_rows(profile_id: str) -> List[Dict[str, An
with get_db() as conn:
cur = get_cursor(conn)
cur.execute("SELECT * FROM nutrition_log WHERE profile_id=%s ORDER BY date", (profile_id,))
- nutr = {r["date"]: r2d(r) for r in cur.fetchall()}
+ nutr: Dict[Any, Dict[str, Any]] = {}
+ for r in cur.fetchall():
+ rd = r2d(r)
+ dk = as_date(rd.get("date"))
+ if dk is not None:
+ nutr[dk] = rd
cur.execute("SELECT date, weight FROM weight_log WHERE profile_id=%s ORDER BY date", (profile_id,))
- wlog = {r["date"]: r["weight"] for r in cur.fetchall()}
+ wlog: Dict[Any, Any] = {}
+ for r in cur.fetchall():
+ rd = r2d(r)
+ dk = as_date(rd.get("date"))
+ if dk is not None:
+ wlog[dk] = rd["weight"]
cur.execute(
"SELECT date, lean_mass, body_fat_pct FROM caliper_log WHERE profile_id=%s ORDER BY date",
(profile_id,),
)
- cals = sorted([r2d(r) for r in cur.fetchall()], key=lambda x: x["date"])
+ cals = [r2d(r) for r in cur.fetchall()]
+ cals = sorted(
+ [c for c in cals if as_date(c.get("date")) is not None],
+ key=lambda x: as_date(x["date"]),
+ )
- all_dates = sorted(set(list(nutr.keys()) + list(wlog.keys())))
+ # Alle Keys sind datetime.date — vermeidet TypeError bei Vergleichen (str vs date)
+ all_dates = sorted(set(nutr.keys()) | set(wlog.keys()))
mi = 0
last_cal: Dict[str, Any] = {}
cal_by_date: Dict[Any, Dict[str, Any]] = {}
for d in all_dates:
- while mi < len(cals) and cals[mi]["date"] <= d:
+ while mi < len(cals):
+ cd = as_date(cals[mi].get("date"))
+ if cd is None:
+ mi += 1
+ continue
+ if cd > d:
+ break
last_cal = cals[mi]
mi += 1
if last_cal:
diff --git a/frontend/src/pages/History.jsx b/frontend/src/pages/History.jsx
index 95b76db..d33d09c 100644
--- a/frontend/src/pages/History.jsx
+++ b/frontend/src/pages/History.jsx
@@ -1205,6 +1205,34 @@ function chartJsScatterPoints(payload) {
return raw.map((p) => ({ x: Number(p.x), y: Number(p.y) }))
}
+/** Backend metadata.lag_details: [{ lag, n_pairs, r }] — für Lag-Kurve L → r (C3: ggf. lag_details_hrv / lag_details_rhr) */
+function lagDetailsToCurve(meta) {
+ let ld = meta?.lag_details
+ if (!Array.isArray(ld) || ld.length === 0) {
+ const m = String(meta?.metric || '').toUpperCase()
+ if (m === 'HRV' && Array.isArray(meta?.lag_details_hrv)) ld = meta.lag_details_hrv
+ else if (m === 'RHR' && Array.isArray(meta?.lag_details_rhr)) ld = meta.lag_details_rhr
+ else {
+ const h = meta?.lag_details_hrv
+ const r = meta?.lag_details_rhr
+ const hl = Array.isArray(h) ? h.length : 0
+ const rl = Array.isArray(r) ? r.length : 0
+ if (hl >= rl && hl > 0) ld = h
+ else if (rl > 0) ld = r
+ else ld = []
+ }
+ }
+ if (!Array.isArray(ld) || ld.length === 0) return []
+ return ld
+ .map((d) => ({
+ lag: Number(d?.lag),
+ r: d?.r == null || d?.r === '' ? null : Number(d.r),
+ n_pairs: d?.n_pairs != null ? Number(d.n_pairs) : null,
+ }))
+ .filter((d) => Number.isFinite(d.lag) && d.r != null && Number.isFinite(d.r))
+ .sort((a, b) => a.lag - b.lag)
+}
+
function driverBarFromStatus(st) {
const s = String(st || '').toLowerCase()
if (s.includes('hinder')) return { v: -1, fill: 'var(--danger)' }
@@ -1240,10 +1268,13 @@ function chartJsBarRows(payload, fallbackDrivers) {
function CorrelationScatterTile({ title, accent, payload }) {
const meta = payload?.metadata || {}
const pts = chartJsScatterPoints(payload)
+ const curve = lagDetailsToCurve(meta)
const hasChart = pts.length > 0 && meta.correlation != null
const r = Number(meta.correlation)
const strength =
!Number.isFinite(r) ? 'bad' : Math.abs(r) >= 0.35 ? 'good' : Math.abs(r) >= 0.15 ? 'warn' : 'bad'
+ const bestLag = meta.best_lag_days != null ? Number(meta.best_lag_days) : null
+ const maxLagAxis = curve.length ? Math.max(14, ...curve.map((d) => d.lag), bestLag || 0) : 28
return (
{title}
r = {meta.correlation != null ? Number(meta.correlation).toFixed(3) : '—'}
- {meta.best_lag_days != null ? ` · Lag ${meta.best_lag_days} T` : ''}
+ {meta.best_lag_days != null ? ` · bestes Lag ${meta.best_lag_days} T` : ''}
{meta.metric ? ` · ${meta.metric}` : ''}
{meta.confidence ? ` · ${meta.confidence}` : ''}
{!hasChart ? (
- {meta.message || 'Keine Daten für diese Korrelation.'}
+ <>
+
+ {meta.message || 'Keine Daten für diese Korrelation.'}
+
+ {curve.length > 0 && (
+
+ Lag-Sweep (kein Lag mit ≥15 Paaren): r über Lags — nur zur Einordnung.
+
+ )}
+ {curve.length > 0 && (
+
+
+
+
+
+
+ [`r = ${Number(v).toFixed(3)}`, `Lag ${item?.payload?.lag} T · n = ${item?.payload?.n_pairs ?? '—'}`]}
+ />
+
+
+
+ )}
+
+ ) : curve.length >= 1 ? (
+ <>
+
+ Kurve: Pearson-r je Lag (Tage); starker Punkt = gewähltes bestes Lag.
+
+
+
+
+
+
+
+ [`r = ${Number(v).toFixed(3)}`, `Lag ${item?.payload?.lag} T · n = ${item?.payload?.n_pairs ?? '—'}`]}
+ />
+ {
+ const { cx, cy, payload: pl } = props
+ if (cx == null || cy == null || !pl) return null
+ const isBest = bestLag != null && Number(pl.lag) === bestLag
+ return (
+
+ )
+ }}
+ />
+
+
+
) : (