Add basal/bolus/carb/target overlays to the Live and Dagoverzicht charts

Overlay the full insulin picture onto the interactive glucose charts:
basal rate as a semi-transparent step-area (profile schedule overridden
by active temp basal), bolus doses as size-scaled dots split into
food/correction/SMB categories (food and correction labeled with units,
SMB left unlabeled), carbs as bars, and the pump's target glucose as a
line — each with its own color, alpha, and legend entry. Shared trace-
building helpers live in ui_common.py so both pages (and the PDF report
in a follow-up PR) render identically.

Builds on the pump profile work in #29 (profile_data.py, load_profile()).

tests/test_reporting.py

@@ -8,10 +8,13 @@ from reporting import (
     agp_percentile_bands,
     basal_stats,
     bolus_stats,
+    classify_bolus,
     completeness_warning,
     daily_gmi,
     day_of_week_pattern,
     detect_events,
+    effective_basal_series,
+    glucose_min_max,
     gmi_mmol_per_mol,
     gmi_percent,
     gmi_trend,
@@ -21,6 +24,7 @@ from reporting import (
     sensor_active_percent,
     site_interval_stats,
     summarize_events,
+    target_glucose_series,
     tir_breakdown_5band,
 )
 
@@ -480,6 +484,140 @@ def test_basal_stats_handles_empty_input():
     assert result == {"total_units": 0.0, "avg_units_per_day": 0.0}
 
 
+def test_glucose_min_max_returns_min_and_max():
+    glucose = _series([("2026-01-01 00:00", 6.5), ("2026-01-01 01:00", 3.2), ("2026-01-01 02:00", 14.1)])
+    assert glucose_min_max(glucose) == (3.2, 14.1)
+
+
+def test_glucose_min_max_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    assert glucose_min_max(empty) == (0.0, 0.0)
+
+
+def _bolus_treatment(dt, event_type, insulin=1.0, carbs=None, bolus_type=""):
+    return {
+        "timestamp": pd.Timestamp(dt),
+        "eventType": event_type,
+        "insulin": insulin,
+        "carbs": carbs,
+        "type": bolus_type,
+    }
+
+
+def test_classify_bolus_recognizes_meal_bolus_as_food():
+    treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "Meal Bolus", insulin=4.0)])
+    result = classify_bolus(treatments)
+    assert result.iloc[0]["category"] == "food"
+
+
+def test_classify_bolus_recognizes_smb_via_type_field():
+    treatments = pd.DataFrame(
+        [_bolus_treatment("2026-01-01 08:00", "Correction Bolus", insulin=0.3, bolus_type="SMB")]
+    )
+    result = classify_bolus(treatments)
+    assert result.iloc[0]["category"] == "smb"
+
+
+def test_classify_bolus_recognizes_smb_via_event_type():
+    treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "SMB", insulin=0.3)])
+    result = classify_bolus(treatments)
+    assert result.iloc[0]["category"] == "smb"
+
+
+def test_classify_bolus_treats_correction_bolus_without_carbs_as_correction():
+    treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "Correction Bolus", insulin=1.5)])
+    result = classify_bolus(treatments)
+    assert result.iloc[0]["category"] == "correction"
+
+
+def test_classify_bolus_treats_generic_bolus_with_carbs_as_food():
+    treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "Bolus", insulin=3.0, carbs=40.0)])
+    result = classify_bolus(treatments)
+    assert result.iloc[0]["category"] == "food"
+
+
+def test_classify_bolus_ignores_non_bolus_rows():
+    treatments = pd.DataFrame(
+        [
+            _bolus_treatment("2026-01-01 08:00", "Meal Bolus", insulin=4.0),
+            {"timestamp": pd.Timestamp("2026-01-01 08:05"), "eventType": "Site Change", "insulin": None, "carbs": None, "type": ""},
+        ]
+    )
+    result = classify_bolus(treatments)
+    assert len(result) == 1
+
+
+def test_classify_bolus_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs", "type"])
+    result = classify_bolus(empty)
+    assert result.empty
+    assert list(result.columns) == ["timestamp", "eventType", "insulin", "carbs", "category"]
+
+
+def test_effective_basal_series_uses_profile_baseline_without_temp_basal():
+    profile = {"basal": [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]}
+    empty_treatments = pd.DataFrame(columns=["timestamp", "eventType", "absolute", "duration"])
+    result = effective_basal_series(
+        profile, empty_treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02")
+    )
+    assert result.iloc[0]["rate"] == pytest.approx(0.8)
+    assert result[result["timestamp"] == pd.Timestamp("2026-01-01 06:00")].iloc[0]["rate"] == pytest.approx(1.2)
+
+
+def test_effective_basal_series_temp_basal_overrides_profile_rate():
+    profile = {"basal": [{"minutes": 0, "value": 0.8}]}
+    treatments = pd.DataFrame(
+        [{"timestamp": pd.Timestamp("2026-01-01 08:00"), "eventType": "Temp Basal", "absolute": 2.0, "duration": 30}]
+    )
+    result = effective_basal_series(profile, treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
+    during_temp = result[result["timestamp"] == pd.Timestamp("2026-01-01 08:00")].iloc[0]
+    after_temp = result[result["timestamp"] == pd.Timestamp("2026-01-01 08:30")].iloc[0]
+    assert during_temp["rate"] == pytest.approx(2.0)
+    assert after_temp["rate"] == pytest.approx(0.8)  # terug naar het profieltempo
+
+
+def test_effective_basal_series_without_profile_defaults_to_zero_baseline():
+    empty_treatments = pd.DataFrame(columns=["timestamp", "eventType", "absolute", "duration"])
+    result = effective_basal_series(None, empty_treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
+    assert result.iloc[0]["rate"] == pytest.approx(0.0)
+
+
+def test_effective_basal_series_clips_temp_basal_to_day_boundaries():
+    profile = {"basal": [{"minutes": 0, "value": 0.8}]}
+    # Temp basal begint de dag ervoor en loopt door tot 01:00 van de dag zelf.
+    treatments = pd.DataFrame(
+        [{"timestamp": pd.Timestamp("2025-12-31 23:00"), "eventType": "Temp Basal", "absolute": 2.0, "duration": 120}]
+    )
+    result = effective_basal_series(profile, treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
+    assert result.iloc[0]["timestamp"] == pd.Timestamp("2026-01-01")
+    assert result.iloc[0]["rate"] == pytest.approx(2.0)
+
+
+def test_target_glucose_series_averages_low_and_high():
+    profile = {
+        "target_low": [{"minutes": 0, "value": 4.5}],
+        "target_high": [{"minutes": 0, "value": 8.5}],
+    }
+    result = target_glucose_series(profile, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
+    assert result.iloc[0]["target"] == pytest.approx(6.5)
+
+
+def test_target_glucose_series_falls_back_to_single_bound():
+    profile = {"target_low": [{"minutes": 0, "value": 5.0}], "target_high": []}
+    result = target_glucose_series(profile, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
+    assert result.iloc[0]["target"] == pytest.approx(5.0)
+
+
+def test_target_glucose_series_empty_without_targets_in_profile():
+    result = target_glucose_series({}, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
+    assert result.empty
+
+
+def test_target_glucose_series_handles_none_profile():
+    result = target_glucose_series(None, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
+    assert result.empty
+
+
 def test_site_interval_stats_splits_by_site_change():
     glucose = _series(
         [