add Trends page: GMI trend, overnight/daytime, weekday pattern, bolus, site-age (FR7, FR8)

reporting.py gains overnight_daytime_split, day_of_week_pattern,
bolus_stats, and site_interval_stats. The last two are explicitly scoped
to bolus-only insulin (no basal reconstruction) since that needs the
Nightscout profile, which is the separate profile-tuning feature's
territory — documented as a caveat in both the UI and the design doc.

Co-Authored-By: Claude Sonnet 5 <[email protected]>

pages/7_Trends.py

@@ -0,0 +1,118 @@
+import plotly.graph_objects as go
+import requests
+import streamlit as st
+
+from auth import require_auth
+from data_processing import classify_treatments
+from nightscout_client import NightscoutConfigError
+from reporting import (
+    bolus_stats,
+    day_of_week_pattern,
+    gmi_percent,
+    overnight_daytime_split,
+    site_interval_stats,
+)
+from ui_common import get_colors, load_range_data, select_period_days
+
+st.set_page_config(page_title="Glucose dashboard — Trends", page_icon="📊", layout="wide")
+require_auth()
+
+st.title("Trends")
+st.caption("Geschat HbA1c (GMI) over tijd en overige statistieken (FR7, FR8).")
+
+start, end, days = select_period_days(default_label="90 dagen")
+
+try:
+    glucose, treatments = load_range_data(start, end)
+except NightscoutConfigError as exc:
+    st.error(str(exc))
+    st.stop()
+except (requests.RequestException, ValueError) as exc:
+    st.error(f"Kon geen data ophalen van Nightscout: {exc}")
+    st.stop()
+
+if glucose.empty:
+    st.info("Geen glucosemetingen gevonden in deze periode.")
+    st.stop()
+
+colors = get_colors()
+events = classify_treatments(treatments)
+
+# --- FR7: geschat HbA1c (GMI) per maand ------------------------------------
+st.subheader("Geschat HbA1c (GMI) per maand")
+monthly = glucose.copy()
+monthly["month"] = monthly["timestamp"].dt.to_period("M").astype(str)
+monthly_gmi = monthly.groupby("month")["mmol"].mean().apply(gmi_percent)
+
+if len(monthly_gmi) >= 1:
+    fig = go.Figure(
+        go.Bar(x=monthly_gmi.index, y=monthly_gmi.to_numpy(), marker_color=colors["glucose"])
+    )
+    fig.update_layout(
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color=colors["text"],
+        height=280,
+        margin={"l": 10, "r": 10, "t": 10, "b": 10},
+        yaxis={"title": "GMI (%)", "gridcolor": colors["grid"]},
+        xaxis={"title": "Maand", "gridcolor": colors["grid"]},
+    )
+    st.plotly_chart(fig, width="stretch")
+st.caption("GMI is een schatting (Bergenstal et al. 2018), geen labwaarde.")
+
+# --- FR8: nacht vs. overdag --------------------------------------------------
+st.subheader("Nacht (00:00-06:00) vs. overdag")
+split = overnight_daytime_split(glucose)
+col1, col2 = st.columns(2)
+col1.metric("Tijd in bereik 's nachts", f"{split['overnight']['in_range']:.0f}%")
+col2.metric("Tijd in bereik overdag", f"{split['daytime']['in_range']:.0f}%")
+
+# --- FR8: patroon per weekdag -------------------------------------------------
+st.subheader("Patroon per weekdag")
+weekday_df = day_of_week_pattern(glucose)
+weekday_plot = weekday_df.dropna(subset=["mean_mmol"])
+if not weekday_plot.empty:
+    fig = go.Figure(
+        go.Bar(x=weekday_plot["weekday"], y=weekday_plot["mean_mmol"], marker_color=colors["glucose"])
+    )
+    fig.update_layout(
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color=colors["text"],
+        height=280,
+        margin={"l": 10, "r": 10, "t": 10, "b": 10},
+        yaxis={"title": "Gem. glucose (mmol/L)", "gridcolor": colors["grid"]},
+        xaxis={"gridcolor": colors["grid"]},
+    )
+    st.plotly_chart(fig, width="stretch")
+
+# --- FR8: bolus-insuline --------------------------------------------------
+st.subheader("Bolus-insuline")
+st.caption(
+    "Alleen bolusinsuline (maaltijd/correctie/combo/SMB) — basaalinsuline vereist "
+    "profielgegevens en is hier niet in verwerkt (zie docs/design-reporting.md)."
+)
+bolus = bolus_stats(treatments)
+col1, col2 = st.columns(2)
+col1.metric("Totaal bolus", f"{bolus['total_units']:.1f} E")
+col2.metric("Aantal boluses", bolus["count"])
+if bolus["by_type"]:
+    st.table({"type": list(bolus["by_type"].keys()), "eenheden": list(bolus["by_type"].values())})
+
+# --- FR8: pompsite-leeftijd vs. glucose/insuline ---------------------------
+st.subheader("Pompsite-leeftijd vs. glucose en insuline")
+st.caption(
+    "Statistieken per interval tussen opeenvolgende pompwissels — een benadering "
+    "(zonder basaalinsuline) van of insulinebehoefte samenhangt met de leeftijd "
+    "van de pompsite."
+)
+site_stats = site_interval_stats(glucose, treatments, events["site_changes"])
+if site_stats.empty:
+    st.info("Geen pompwissels gevonden in deze periode.")
+else:
+    display = site_stats.copy()
+    display["start"] = display["start"].dt.strftime("%d-%m-%Y")
+    display["end"] = display["end"].dt.strftime("%d-%m-%Y")
+    st.dataframe(display, width="stretch", hide_index=True)
+
+st.caption(f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}")

reporting.py

@@ -9,8 +9,12 @@ definities waarop dit gebaseerd is (Battelino et al. 2019, Bergenstal et al.
 
 from __future__ import annotations
 
+from itertools import pairwise
+
 import pandas as pd
 
+from nightscout_client import INSULIN_EVENT_TYPES
+
 # Klinische drempels in mmol/L (Battelino et al. 2019, Diabetes Care 42(8):1593-1603).
 VERY_LOW = 3.0
 LOW = 3.9
@@ -226,3 +230,132 @@ def agp_percentile_bands(glucose: pd.DataFrame, bin_minutes: int = 30) -> pd.Dat
     grouped.columns = ["p5", "p25", "p50", "p75", "p95"]
     grouped = grouped.reset_index().rename(columns={"bin": "minute_of_day"})
     return grouped[AGP_PERCENTILE_COLUMNS].sort_values("minute_of_day").reset_index(drop=True)
+
+
+def overnight_daytime_split(
+    glucose: pd.DataFrame, overnight_start: int = 0, overnight_end: int = 6
+) -> dict[str, dict]:
+    """Tijd-in-bereik apart voor de nacht (overnight_start-overnight_end uur) en overdag."""
+    empty = tir_breakdown_5band(glucose)
+    if glucose.empty:
+        return {"overnight": empty, "daytime": empty}
+
+    hour = glucose["timestamp"].dt.hour
+    is_overnight = (hour >= overnight_start) & (hour < overnight_end)
+    return {
+        "overnight": tir_breakdown_5band(glucose[is_overnight]),
+        "daytime": tir_breakdown_5band(glucose[~is_overnight]),
+    }
+
+
+WEEKDAY_NAMES = ["Maandag", "Dinsdag", "Woensdag", "Donderdag", "Vrijdag", "Zaterdag", "Zondag"]
+
+
+def day_of_week_pattern(glucose: pd.DataFrame) -> pd.DataFrame:
+    """Gemiddelde glucose en tijd-in-bereik per weekdag."""
+    columns = ["weekday", "mean_mmol", "in_range_pct", "count"]
+    if glucose.empty:
+        return pd.DataFrame(columns=columns)
+
+    df = glucose.copy()
+    df["weekday"] = df["timestamp"].dt.dayofweek
+
+    rows = []
+    for weekday in range(7):
+        day_df = df[df["weekday"] == weekday]
+        if day_df.empty:
+            rows.append(
+                {"weekday": WEEKDAY_NAMES[weekday], "mean_mmol": None, "in_range_pct": None, "count": 0}
+            )
+            continue
+        tir = tir_breakdown_5band(day_df)
+        rows.append(
+            {
+                "weekday": WEEKDAY_NAMES[weekday],
+                "mean_mmol": round(day_df["mmol"].mean(), 1),
+                "in_range_pct": tir["in_range"],
+                "count": len(day_df),
+            }
+        )
+    return pd.DataFrame(rows, columns=columns)
+
+
+def bolus_stats(treatments: pd.DataFrame) -> dict:
+    """Samenvatting van bolus-insuline, opgesplitst naar type.
+
+    Let op: dit betreft alleen bolusinsuline (maaltijd/correctie/combo/SMB).
+    Basaalinsuline wordt hier niet in meegenomen — dat vereist profielgegevens
+    (zie docs/design-reporting.md, scoping-kanttekening) en levert dus geen
+    volledige totale-dagdosis op.
+    """
+    if treatments.empty:
+        return {"total_units": 0.0, "count": 0, "by_type": {}}
+
+    bolus = treatments[treatments["eventType"].isin(INSULIN_EVENT_TYPES) & treatments["insulin"].notna()]
+    if bolus.empty:
+        return {"total_units": 0.0, "count": 0, "by_type": {}}
+
+    by_type = bolus.groupby("eventType")["insulin"].sum().round(1).to_dict()
+    return {
+        "total_units": round(bolus["insulin"].sum(), 1),
+        "count": len(bolus),
+        "by_type": by_type,
+    }
+
+
+SITE_INTERVAL_COLUMNS = [
+    "start",
+    "end",
+    "duration_days",
+    "mean_mmol",
+    "in_range_pct",
+    "total_bolus_units",
+    "avg_bolus_units_per_day",
+    "total_carbs_g",
+]
+
+
+def _site_interval_row(glucose: pd.DataFrame, treatments: pd.DataFrame, start, end) -> dict:
+    duration_days = max((end - start).total_seconds() / 86400, 1e-9)
+    window_glucose = glucose[(glucose["timestamp"] >= start) & (glucose["timestamp"] < end)]
+    window_treatments = (
+        treatments[(treatments["timestamp"] >= start) & (treatments["timestamp"] < end)]
+        if not treatments.empty
+        else treatments
+    )
+    bolus = bolus_stats(window_treatments)
+    total_carbs = window_treatments["carbs"].fillna(0).sum() if not treatments.empty else 0.0
+    tir = tir_breakdown_5band(window_glucose)
+
+    return {
+        "start": start,
+        "end": end,
+        "duration_days": round(duration_days, 1),
+        "mean_mmol": round(window_glucose["mmol"].mean(), 1) if not window_glucose.empty else None,
+        "in_range_pct": tir["in_range"],
+        "total_bolus_units": bolus["total_units"],
+        "avg_bolus_units_per_day": round(bolus["total_units"] / duration_days, 1),
+        "total_carbs_g": round(total_carbs, 0),
+    }
+
+
+def site_interval_stats(
+    glucose: pd.DataFrame, treatments: pd.DataFrame, site_changes: pd.DataFrame
+) -> pd.DataFrame:
+    """Statistieken per interval tussen opeenvolgende pompwissels (FR8).
+
+    Voor elk interval sinds een pompwissel: duur in dagen ("leeftijd" van de
+    pompsite bij het einde van het interval), gemiddelde glucose,
+    tijd-in-bereik, en bolus-insuline/koolhydraten in dat interval — als
+    benadering van of insulinebehoefte samenhangt met de leeftijd van de
+    pompsite. Zie bolus_stats() voor de kanttekening dat basaalinsuline hier
+    niet in zit.
+    """
+    if site_changes.empty or glucose.empty:
+        return pd.DataFrame(columns=SITE_INTERVAL_COLUMNS)
+
+    change_times = sorted(site_changes["timestamp"])
+    boundaries = [*change_times, glucose["timestamp"].max()]
+
+    rows = [_site_interval_row(glucose, treatments, start, end) for start, end in pairwise(boundaries)]
+    return pd.DataFrame(rows, columns=SITE_INTERVAL_COLUMNS)

tests/test_reporting.py

@@ -3,11 +3,15 @@ import pytest
 
 from reporting import (
     agp_percentile_bands,
+    bolus_stats,
+    day_of_week_pattern,
     detect_events,
     gmi_percent,
     minutes_per_day,
+    overnight_daytime_split,
     percent_cv,
     sensor_active_percent,
+    site_interval_stats,
     summarize_events,
     tir_breakdown_5band,
 )
@@ -243,6 +247,114 @@ def test_agp_percentile_bands_handles_empty_input():
     assert list(result.columns) == ["minute_of_day", "p5", "p25", "p50", "p75", "p95"]
 
 
+def test_overnight_daytime_split_separates_by_hour():
+    glucose = _series(
+        [
+            ("2026-01-01 02:00", 3.0),  # nacht, laag
+            ("2026-01-01 03:00", 3.0),  # nacht, laag
+            ("2026-01-01 12:00", 6.0),  # dag, in bereik
+            ("2026-01-01 13:00", 6.0),  # dag, in bereik
+        ]
+    )
+    result = overnight_daytime_split(glucose, overnight_start=0, overnight_end=6)
+    assert result["overnight"]["low"] == pytest.approx(100.0)
+    assert result["daytime"]["in_range"] == pytest.approx(100.0)
+
+
+def test_overnight_daytime_split_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    result = overnight_daytime_split(empty)
+    assert result["overnight"] == result["daytime"]
+
+
+def test_day_of_week_pattern_computes_per_weekday_stats():
+    # 2026-01-05 is een maandag
+    glucose = _series(
+        [
+            ("2026-01-05 08:00", 6.0),
+            ("2026-01-05 09:00", 8.0),
+            ("2026-01-12 08:00", 7.0),  # ook een maandag
+        ]
+    )
+    result = day_of_week_pattern(glucose)
+    monday = result[result["weekday"] == "Maandag"].iloc[0]
+    assert monday["count"] == 3
+    assert monday["mean_mmol"] == pytest.approx(7.0)
+
+    tuesday = result[result["weekday"] == "Dinsdag"].iloc[0]
+    assert tuesday["count"] == 0
+    assert pd.isna(tuesday["mean_mmol"])
+
+
+def test_day_of_week_pattern_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    result = day_of_week_pattern(empty)
+    assert result.empty
+    assert list(result.columns) == ["weekday", "mean_mmol", "in_range_pct", "count"]
+
+
+def test_bolus_stats_splits_by_event_type():
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 12:00", "2026-01-01 20:00"]),
+            "eventType": ["Meal Bolus", "Correction Bolus", "Site Change"],
+            "insulin": [4.0, 1.5, None],
+            "carbs": [45.0, None, None],
+        }
+    )
+    result = bolus_stats(treatments)
+    assert result["total_units"] == pytest.approx(5.5)
+    assert result["count"] == 2
+    assert result["by_type"] == {"Meal Bolus": 4.0, "Correction Bolus": 1.5}
+
+
+def test_bolus_stats_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+    result = bolus_stats(empty)
+    assert result == {"total_units": 0.0, "count": 0, "by_type": {}}
+
+
+def test_site_interval_stats_splits_by_site_change():
+    glucose = _series(
+        [
+            ("2026-01-01 00:00", 6.0),
+            ("2026-01-05 00:00", 6.0),
+            ("2026-01-10 00:00", 8.0),
+        ]
+    )
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-02 00:00", "2026-01-06 00:00"]),
+            "eventType": ["Meal Bolus", "Meal Bolus"],
+            "insulin": [3.0, 4.0],
+            "carbs": [30.0, 40.0],
+        }
+    )
+    site_changes = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 00:00", "2026-01-06 00:00"]),
+            "eventType": ["Site Change", "Site Change"],
+            "insulin": [None, None],
+            "carbs": [None, None],
+        }
+    )
+
+    result = site_interval_stats(glucose, treatments, site_changes)
+    assert len(result) == 2
+    assert result.iloc[0]["duration_days"] == pytest.approx(5.0)
+    assert result.iloc[0]["total_bolus_units"] == pytest.approx(3.0)
+    # de bolus op 6 jan valt binnen het tweede interval ([6 jan, 10 jan))
+    assert result.iloc[1]["total_bolus_units"] == pytest.approx(4.0)
+
+
+def test_site_interval_stats_handles_no_site_changes():
+    glucose = _series([("2026-01-01 00:00", 6.0)])
+    treatments = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+    empty_site_changes = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+    result = site_interval_stats(glucose, treatments, empty_site_changes)
+    assert result.empty
+
+
 def test_summarize_events_handles_empty_input():
     empty = pd.DataFrame(columns=["start", "end", "duration_minutes"])
     result = summarize_events(empty, period_days=14)