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.

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