Replace monthly GMI bar chart with daily scatter + rolling avg + linear trend/CI

Adds daily_gmi() and gmi_trend(): per-day GMI as scatter points, a 7-day
rolling average line, and a linear regression trend line with a 95%
confidence band (normal approximation, no scipy dependency, consistent
with the existing preference for a lean container image).

Recomputed from the already-fetched period data on each page load; the
existing 1-hour Streamlit cache on load_range_data() already avoids
repeated Nightscout calls for the same period, so no separate storage
layer is needed at the current max. 90-day report window.

Closes gitea #19.

pages/7_Trends.py

@@ -9,12 +9,13 @@ from reporting import (
     basal_stats,
     bolus_stats,
     day_of_week_pattern,
-    gmi_percent,
+    gmi_trend,
     overnight_daytime_split,
     site_interval_stats,
 )
 from ui_common import (
     get_colors,
+    hex_to_rgba,
     load_range_data,
     render_completeness_warning,
     select_period_days,
@@ -46,26 +47,70 @@ render_completeness_warning(glucose, start, end)
 colors = get_colors()
 events = classify_treatments(treatments)
 
-# --- FR7: geschat HbA1c (GMI) per maand ------------------------------------
+# --- FR7: geschat HbA1c (GMI) trend ----------------------------------------
-st.subheader("Geschat HbA1c (GMI) per maand")
+st.subheader("Geschat HbA1c (GMI) trend")
-monthly = glucose.copy()
+trend = gmi_trend(glucose)
-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:
+if len(trend) >= 2:
-    fig = go.Figure(
+    fig = go.Figure()
-        go.Bar(x=monthly_gmi.index, y=monthly_gmi.to_numpy(), marker_color=colors["glucose"])
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"], y=trend["trend_upper"], mode="lines", line={"width": 0}, showlegend=False,
+            hoverinfo="skip",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["trend_lower"],
+            mode="lines",
+            line={"width": 0},
+            fill="tonexty",
+            fillcolor=hex_to_rgba(colors["muted"], 0.2),
+            name="95% betrouwbaarheidsband",
+            hoverinfo="skip",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["trend"],
+            mode="lines",
+            line={"color": colors["muted"], "width": 2, "dash": "dash"},
+            name="Trend (lineair)",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["rolling_avg"],
+            mode="lines",
+            line={"color": colors["glucose"], "width": 2},
+            name="Voortschrijdend gemiddelde (7d)",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["gmi"],
+            mode="markers",
+            marker={"color": colors["glucose"], "size": 5, "opacity": 0.5},
+            name="Dag-GMI",
+        )
     )
     fig.update_layout(
         paper_bgcolor="rgba(0,0,0,0)",
         plot_bgcolor="rgba(0,0,0,0)",
         font_color=colors["text"],
-        height=280,
+        height=320,
         margin={"l": 10, "r": 10, "t": 10, "b": 10},
         yaxis={"title": "GMI (%)", "gridcolor": colors["grid"]},
-        xaxis={"title": "Maand", "gridcolor": colors["grid"]},
+        xaxis={"title": "Datum", "gridcolor": colors["grid"]},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "left", "x": 0},
     )
     st.plotly_chart(fig, width="stretch")
+else:
+    st.info("Niet genoeg dagen met data voor een trendlijn (minstens 2 nodig).")
 st.caption("GMI is een schatting (Bergenstal et al. 2018), geen labwaarde.")
 
 # --- FR8: nacht vs. overdag --------------------------------------------------

reporting.py

@@ -11,6 +11,7 @@ from __future__ import annotations
 
 from itertools import pairwise
 
+import numpy as np
 import pandas as pd
 
 from nightscout_client import INSULIN_EVENT_TYPES
@@ -35,6 +36,68 @@ def gmi_percent(mean_mmol: float) -> float:
     return round(3.31 + 0.4311 * mean_mmol, 1)
 
 
+GMI_TREND_COLUMNS = ["date", "gmi", "rolling_avg", "trend", "trend_lower", "trend_upper"]
+
+
+def daily_gmi(glucose: pd.DataFrame) -> pd.DataFrame:
+    """Geschat HbA1c (GMI) per kalenderdag — scatterpunten voor de trendgrafiek (gitea #19)."""
+    if glucose.empty:
+        return pd.DataFrame(columns=["date", "gmi"])
+
+    df = glucose.copy()
+    df["date"] = df["timestamp"].dt.date
+    daily_mean = df.groupby("date")["mmol"].mean()
+    result = daily_mean.apply(gmi_percent).reset_index()
+    result.columns = ["date", "gmi"]
+    return result.sort_values("date").reset_index(drop=True)
+
+
+def gmi_trend(glucose: pd.DataFrame, rolling_window_days: int = 7) -> pd.DataFrame:
+    """Dagelijkse GMI-punten plus voortschrijdend gemiddelde en een lineaire
+    trendlijn met 95%-betrouwbaarheidsband (gitea #19).
+
+    Gebruikt een normaalbenadering (z=1.96) voor de betrouwbaarheidsband i.p.v.
+    een exacte t-verdeling — dat zou een scipy-dependency toevoegen aan een
+    verder bewust lichte container (dezelfde afweging als het niet gebruiken
+    van kaleido in pdf_export.py). Bij de hier gebruikelijke steekproefgroottes
+    (14-90 dagpunten) is het verschil met de exacte t-kritieke waarde
+    verwaarloosbaar voor een visuele trendindicatie.
+
+    Elke keer opnieuw berekend uit de al-opgehaalde periode-data (geen aparte
+    database) — de bestaande 1-uurs Streamlit-cache op load_range_data()
+    voorkomt al herhaalde Nightscout-calls voor dezelfde periode. Bij een
+    veel langere trend (jaren i.p.v. de huidige max. 90 dagen) zou een aparte
+    opslag wél de moeite waard worden.
+    """
+    daily = daily_gmi(glucose)
+    if len(daily) < 2:
+        return daily.reindex(columns=GMI_TREND_COLUMNS)
+
+    daily = daily.copy()
+    daily["rolling_avg"] = daily["gmi"].rolling(window=rolling_window_days, min_periods=1).mean()
+
+    x = np.arange(len(daily), dtype=float)
+    y = daily["gmi"].to_numpy()
+    slope, intercept = np.polyfit(x, y, 1)
+    fitted = intercept + slope * x
+
+    residuals = y - fitted
+    degrees_of_freedom = max(len(x) - 2, 1)
+    residual_std = np.sqrt(np.sum(residuals**2) / degrees_of_freedom)
+    x_mean = x.mean()
+    sum_sq_x = np.sum((x - x_mean) ** 2)
+    standard_error = (
+        np.zeros_like(x)
+        if sum_sq_x == 0
+        else residual_std * np.sqrt(1 / len(x) + (x - x_mean) ** 2 / sum_sq_x)
+    )
+    z_95 = 1.96
+    daily["trend"] = fitted
+    daily["trend_lower"] = fitted - z_95 * standard_error
+    daily["trend_upper"] = fitted + z_95 * standard_error
+    return daily[GMI_TREND_COLUMNS]
+
+
 def percent_cv(glucose: pd.DataFrame) -> float:
     """Glucosevariabiliteit als %CV (standaarddeviatie / gemiddelde * 100)."""
     if glucose.empty:

tests/test_reporting.py

@@ -4,13 +4,16 @@ import pandas as pd
 import pytest
 
 from reporting import (
+    GMI_TREND_COLUMNS,
     agp_percentile_bands,
     basal_stats,
     bolus_stats,
     completeness_warning,
+    daily_gmi,
     day_of_week_pattern,
     detect_events,
     gmi_percent,
+    gmi_trend,
     minutes_per_day,
     overnight_daytime_split,
     percent_cv,
@@ -32,6 +35,57 @@ def test_gmi_percent_matches_bergenstal_formula():
     assert gmi_percent(7.0) == pytest.approx(6.3, abs=0.05)
 
 
+def test_daily_gmi_aggregates_per_calendar_day():
+    glucose = _series(
+        [
+            ("2026-01-01 08:00", 6.0),
+            ("2026-01-01 20:00", 8.0),
+            ("2026-01-02 08:00", 7.0),
+        ]
+    )
+    result = daily_gmi(glucose)
+    assert list(result["date"].astype(str)) == ["2026-01-01", "2026-01-02"]
+    assert result["gmi"].iloc[0] == pytest.approx(gmi_percent(7.0))  # gemiddelde van 6.0 en 8.0
+    assert result["gmi"].iloc[1] == pytest.approx(gmi_percent(7.0))
+
+
+def test_daily_gmi_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    result = daily_gmi(empty)
+    assert result.empty
+    assert list(result.columns) == ["date", "gmi"]
+
+
+def test_gmi_trend_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    result = gmi_trend(empty)
+    assert result.empty
+    assert list(result.columns) == GMI_TREND_COLUMNS
+
+
+def test_gmi_trend_returns_no_line_for_a_single_day():
+    glucose = _series([("2026-01-01 08:00", 6.0)])
+    result = gmi_trend(glucose)
+    assert list(result.columns) == GMI_TREND_COLUMNS
+    assert len(result) == 1
+    assert pd.isna(result["trend"].iloc[0])
+
+
+def test_gmi_trend_fits_increasing_linear_trend_with_band_around_it():
+    pairs = [(f"2026-01-{i + 1:02d} 08:00", 6.0 + i * 0.2) for i in range(10)]
+    glucose = _series(pairs)
+    result = gmi_trend(glucose, rolling_window_days=3)
+
+    assert len(result) == 10
+    assert result["trend"].iloc[-1] > result["trend"].iloc[0]
+    assert (result["trend_lower"] <= result["trend"]).all()
+    assert (result["trend"] <= result["trend_upper"]).all()
+    # Vrijwel lineaire input (gmi_percent rondt af op 1 decimaal, dus een
+    # kleine restfout blijft over) -> smalle betrouwbaarheidsband.
+    assert (result["trend_upper"] - result["trend_lower"]).max() == pytest.approx(0, abs=0.15)
+    assert result["rolling_avg"].iloc[-1] > result["rolling_avg"].iloc[0]
+
+
 def test_percent_cv_handles_empty_input():
     empty = pd.DataFrame(columns=["timestamp", "mmol"])
     assert percent_cv(empty) == 0.0