lint fix

upload-artifact@v4/download-artifact@v4 use the newer Artifact API v2,
which Gitea's act_runner doesn't support yet (GHESNotSupportedError).
v3 uses the older, Gitea-compatible API.

.dockerignore

@@ -5,4 +5,6 @@ __pycache__/
 .git/
 .pytest_cache/
 .ruff_cache/
+.coverage
+coverage.xml
 tests/

.env.example

@@ -15,3 +15,9 @@ NIGHTSCOUT_TOKEN=
 # het diabetesteam). Zet hier een sha256-hash, NIET het wachtwoord zelf:
 #   python -c "import hashlib; print(hashlib.sha256(b'jouw-wachtwoord').hexdigest())"
 DASHBOARD_PASSWORD_HASH=
+
+# Optioneel: apparatuurinfo voor de kop van het PDF-rapport (pages/8_Export.py).
+# Puur informatief, geen effect op de berekeningen — leeg = "Onbekend ...".
+REPORT_INSULIN_TYPE=Lyumjev 100U
+REPORT_PUMP_TYPE=
+REPORT_SENSOR_TYPE=

.gitea/workflows/pipeline.yml

@@ -19,11 +19,21 @@ jobs:
         run: pip install -r requirements.txt
       - name: Lint (ruff)
         run: ruff check .
-      - name: Unit tests (pytest)
+      - name: Unit tests + coverage-gate (pytest)
-        run: pytest tests/ -v
+        # fail_under (pyproject.toml, [tool.coverage.report]) laat deze stap
+        # falen als de dekking op de gemeten modules onder 80% zakt — app.py/
+        # pages/*.py zijn uitgesloten (zie pyproject.toml se coverage-omit),
+        # dat zijn dunne Streamlit-UI-scripts die niet zinvol te unit-testen
+        # zijn zonder een draaiende Streamlit-sessie.
+        run: pytest tests/ -v --cov=. --cov-report=term-missing --cov-report=xml
+      - name: Bewaar coverage-rapport
+        uses: actions/upload-artifact@v3
+        with:
+          name: coverage-report
+          path: coverage.xml
 
   # ---------------------------------------------------------------------
-  # 2. SonarQube — codekwaliteit-gate
+  # 2. SonarQube — codekwaliteit-gate (incl. coverage uit de vorige stap)
   # ---------------------------------------------------------------------
   sonarqube:
     runs-on: ubuntu-latest
@@ -32,6 +42,10 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0 # SonarQube wil de volledige git-historie zien
+      - name: Download coverage-rapport
+        uses: actions/download-artifact@v3
+        with:
+          name: coverage-report
       - name: SonarQube scan
         uses: sonarsource/sonarqube-scan-action@v6
         env:

.gitignore

@@ -4,4 +4,6 @@ __pycache__/
 .env
 .pytest_cache/
 .ruff_cache/
+.coverage
+coverage.xml
 homelab-cicd-werkinstructie.md

README.md

@@ -16,7 +16,8 @@ klinische rapportagepagina's (zie `docs/requirements-reporting.md` en
    diabetesteam, dus elke pagina zit achter een wachtwoord). `app.py` laadt
    `.env` zelf in (via `python-dotenv`), dus dit werkt hetzelfde onder
    PowerShell, cmd of bash — je hoeft de variabelen niet zelf in je shell te
-   zetten.
+   zetten. `REPORT_INSULIN_TYPE`/`REPORT_PUMP_TYPE`/`REPORT_SENSOR_TYPE` zijn
+   optioneel en puur informatief (getoond in de kop van het PDF-rapport).
 3. Lokaal draaien:
 
    ```powershell
@@ -40,3 +41,12 @@ klinische rapportagepagina's (zie `docs/requirements-reporting.md` en
 pytest tests/ -v
 ruff check .
 ```
+
+Coverage (zie `pyproject.toml` se `[tool.coverage.*]`): de CI-pipeline faalt
+als de dekking op de gemeten modules onder 80% zakt (`app.py`/`pages/*.py`
+zijn uitgesloten — dunne Streamlit-UI-scripts, niet zinvol te unit-testen
+zonder een draaiende Streamlit-sessie).
+
+```bash
+pytest tests/ --cov=. --cov-report=term-missing
+```

assets/icon.svg

@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64">
+  <path d="M32 4 C20 22, 10 34, 10 46 A22 22 0 0 0 54 46 C54 34, 44 22, 32 4 Z" fill="#2a78d6"/>
+  <polyline points="14,42 24,42 28,32 34,52 40,38 46,42 50,42" fill="none" stroke="#ffffff" stroke-width="3.5" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>

auth.py

@@ -17,13 +17,18 @@ import os
 import streamlit as st
 
 
-def require_auth() -> None:
+def require_auth() -> None:  # pragma: no cover
     """Blokkeer de pagina totdat het juiste wachtwoord is ingevoerd.
 
     Slaat de status op in st.session_state zodat dit maar één keer per
     browsersessie hoeft. Moet als eerste statement van elke pagina aangeroepen
     worden (app.py en elk bestand in pages/), omdat Streamlit iedere pagina
     als los script uitvoert.
+
+    Pure Streamlit-widget-orkestratie (st.text_input/st.session_state/
+    st.rerun) — buiten scope van de unit-testdekking net als pages/*.py,
+    zie pyproject.toml se coverage-omit-lijst. De veiligheidsrelevante
+    logica zelf (verify_password()) is wel volledig getest.
     """
     if st.session_state.get("authenticated"):
         return

nightscout_client.py

@@ -252,3 +252,48 @@ def fetch_treatments_range(start_date: date, end_date: date) -> pd.DataFrame:
     since = datetime.combine(start_date, time.min, tzinfo=timezone.utc)
     until = datetime.combine(end_date, time.max, tzinfo=timezone.utc)
     return _treatments_since(since, until)
+
+
+def fetch_entries_for_day(day: date) -> pd.DataFrame:
+    """Haal glucosemetingen op voor precies één kalenderdag.
+
+    Dunne wrapper om fetch_entries_range() met start=end=day, voor de
+    dag-navigatie op de Dagoverzicht-pagina: laadt alleen de geselecteerde
+    dag in plaats van een hele periode, zodat het wisselen tussen dagen snel
+    blijft.
+    """
+    return fetch_entries_range(day, day)
+
+
+def fetch_treatments_for_day(day: date, lookback_hours: int = 6) -> pd.DataFrame:
+    """Haal behandelingen op voor één kalenderdag, met een lookback-marge vóór middernacht.
+
+    reporting.effective_basal_series() heeft de starttijd van een Temp Basal
+    nodig om de basaalsnelheid vroeg in de dag correct te berekenen — ook als
+    die temp basal al vóór middernacht is gestart en doorloopt. Zonder deze
+    marge zou de eerste uren van elke los opgevraagde dag ten onrechte het
+    profieltempo tonen in plaats van een nog actieve temp basal. 6 uur ligt
+    ruim boven de gebruikelijke temp-basal-duur in gesloten-lus-modus
+    (doorgaans 30-60 minuten).
+    """
+    since = datetime.combine(day, time.min, tzinfo=timezone.utc) - timedelta(hours=lookback_hours)
+    until = datetime.combine(day, time.max, tzinfo=timezone.utc)
+    return _treatments_since(since, until)
+
+
+def fetch_profile() -> dict:
+    """Haal het meest recent gebruikte pompprofiel op.
+
+    Nightscout's /api/v1/profile.json geeft profieldocumenten terug, nieuwste
+    eerst; met count=1 krijgen we alleen het actief gebruikte document. Een
+    profiel is geen tijdreeks (in tegenstelling tot entries/treatments) dus
+    geen paginering nodig — één klein verzoek volstaat.
+
+    Geeft {} terug als er geen profiel bestaat (bijv. lege Nightscout-instantie).
+    Het resultaat is het rauwe Nightscout-documument; zie profile_data.parse_profile()
+    voor de genormaliseerde vorm (basaalschema, ISF, koolhydraatratio, DIA, streefwaarden).
+    """
+    data = _get("/api/v1/profile.json", {"count": 1})
+    if not data:
+        return {}
+    return data[0]

pages/1_Live.py

@@ -1,6 +1,8 @@
+import pandas as pd
 import plotly.graph_objects as go
 import requests
 import streamlit as st
+from plotly.subplots import make_subplots
 
 from auth import require_auth
 from data_processing import (
@@ -10,7 +12,15 @@ from data_processing import (
     summarize_time_in_range,
 )
 from nightscout_client import NightscoutConfigError, fetch_entries, fetch_treatments
-from ui_common import MMOL_HOVERTEMPLATE, get_colors
+from reporting import classify_bolus, effective_basal_series, target_glucose_series
+from ui_common import (
+    MMOL_HOVERTEMPLATE,
+    add_basal_area_trace,
+    add_bolus_marker_traces,
+    add_target_line_trace,
+    get_colors,
+    load_profile,
+)
 
 st.set_page_config(page_title="Glucose dashboard — Live", page_icon="📊", layout="wide")
 require_auth()
@@ -27,7 +37,28 @@ def load_nightscout_data(hours: int):
     return glucose, treatments
 
 
-def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -> go.Figure:
+def _basal_and_target_series(profile: dict, treatments: pd.DataFrame, window_start, window_end):
+    """Basaal- en streefwaarde-reeksen over een rollend venster dat meerdere
+    kalenderdagen kan overspannen: per dag berekend (reporting.effective_basal_series/
+    target_glucose_series zijn dag-begrensd) en daarna aan elkaar geplakt en
+    geclipt tot het venster."""
+    dates = pd.date_range(window_start.normalize(), window_end.normalize(), freq="D")
+    basal_parts, target_parts = [], []
+    for day_start in dates:
+        day_end = day_start + pd.Timedelta(1, unit="D")
+        basal_parts.append(effective_basal_series(profile, treatments, day_start, day_end))
+        target_parts.append(target_glucose_series(profile, day_start, day_end))
+
+    basal = pd.concat(basal_parts, ignore_index=True) if basal_parts else pd.DataFrame(columns=["timestamp", "rate"])
+    target = (
+        pd.concat(target_parts, ignore_index=True) if target_parts else pd.DataFrame(columns=["timestamp", "target"])
+    )
+    basal = basal[(basal["timestamp"] >= window_start) & (basal["timestamp"] <= window_end)]
+    target = target[(target["timestamp"] >= window_start) & (target["timestamp"] <= window_end)]
+    return basal.reset_index(drop=True), target.reset_index(drop=True)
+
+
+def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_changes, colors) -> go.Figure:
     glucose_min = min(glucose["mmol"].min(), TARGET_LOW)
     glucose_max = max(glucose["mmol"].max(), TARGET_HIGH)
     y_top = glucose_max + 1.0
@@ -36,7 +67,7 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
     insulin_lane_y = lane_base + 1.5
     carbs_lane_y = lane_base + 0.7
 
-    fig = go.Figure()
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
 
     # Streefbereik als achtergrondband, geen losse legenda-entry nodig.
     fig.add_hrect(
@@ -45,8 +76,13 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
         fillcolor=colors["band"],
         line_width=0,
         layer="below",
+        row=1,
+        col=1,
     )
 
+    add_basal_area_trace(fig, basal, colors, row=1, secondary_y=True)
+    add_target_line_trace(fig, target, colors, row=1)
+
     fig.add_trace(
         go.Scatter(
             x=glucose["timestamp"],
@@ -55,7 +91,9 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
             name="Glucose",
             line={"color": colors["glucose"], "width": 2, "shape": "spline"},
             hovertemplate=MMOL_HOVERTEMPLATE,
-        )
+        ),
+        row=1,
+        col=1,
     )
 
     low_points = glucose[glucose["mmol"] < TARGET_LOW]
@@ -69,7 +107,9 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
                 name=f"Laag (<{TARGET_LOW})",
                 marker={"color": colors["low"], "size": 6},
                 hovertemplate=MMOL_HOVERTEMPLATE,
-            )
+            ),
+            row=1,
+            col=1,
         )
     if not high_points.empty:
         fig.add_trace(
@@ -80,21 +120,12 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
                 name=f"Hoog (>{TARGET_HIGH})",
                 marker={"color": colors["high"], "size": 6},
                 hovertemplate=MMOL_HOVERTEMPLATE,
-            )
+            ),
+            row=1,
+            col=1,
         )
 
-    if not insulin.empty:
+    add_bolus_marker_traces(fig, bolus, colors, insulin_lane_y, row=1)
-        fig.add_trace(
-            go.Scatter(
-                x=insulin["timestamp"],
-                y=[insulin_lane_y] * len(insulin),
-                mode="markers",
-                name="Insuline",
-                marker={"color": colors["insulin"], "size": 10, "symbol": "triangle-up"},
-                customdata=insulin["insulin"],
-                hovertemplate="%{x|%d-%m %H:%M} · %{customdata:.1f} E<extra></extra>",
-            )
-        )
 
     if not carbs.empty:
         fig.add_trace(
@@ -106,14 +137,16 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
                 marker={"color": colors["carbs"], "size": 10, "symbol": "circle"},
                 customdata=carbs["carbs"],
                 hovertemplate="%{x|%d-%m %H:%M} · %{customdata:.0f} g<extra></extra>",
-            )
+            ),
+            row=1,
+            col=1,
         )
 
     # Let op: annotation_text/-position via add_vline() zelf triggert een bug
     # in plotly's shapeannotation-helper met pandas Timestamp-x-waarden, dus
     # de lijn en het label worden hier los toegevoegd.
     for _, row in site_changes.iterrows():
-        fig.add_vline(x=row["timestamp"], line_dash="dot", line_color=colors["muted"], opacity=0.6)
+        fig.add_vline(x=row["timestamp"], line_dash="dot", line_color=colors["muted"], opacity=0.6, row=1, col=1)
         fig.add_annotation(
             x=row["timestamp"],
             y=y_top,
@@ -121,9 +154,11 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
             text="Pomp",
             showarrow=False,
             font={"color": colors["muted"], "size": 10},
+            row=1,
+            col=1,
         )
     for _, row in sensor_changes.iterrows():
-        fig.add_vline(x=row["timestamp"], line_dash="dash", line_color=colors["muted"], opacity=0.6)
+        fig.add_vline(x=row["timestamp"], line_dash="dash", line_color=colors["muted"], opacity=0.6, row=1, col=1)
         fig.add_annotation(
             x=row["timestamp"],
             y=y_top,
@@ -131,6 +166,8 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
             text="Sensor",
             showarrow=False,
             font={"color": colors["muted"], "size": 10},
+            row=1,
+            col=1,
         )
 
     fig.update_layout(
@@ -140,14 +177,18 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
         legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "left", "x": 0},
         margin={"l": 10, "r": 10, "t": 10, "b": 10},
         height=480,
-        yaxis={
-            "title": "mmol/L",
-            "range": [y_bottom, y_top],
-            "gridcolor": colors["grid"],
-            "zeroline": False,
-        },
-        xaxis={"gridcolor": colors["grid"]},
     )
+    fig.update_yaxes(
+        title_text="mmol/L",
+        range=[y_bottom, y_top],
+        gridcolor=colors["grid"],
+        zeroline=False,
+        row=1,
+        col=1,
+        secondary_y=False,
+    )
+    fig.update_yaxes(title_text="Basaal (E/uur)", showgrid=False, row=1, col=1, secondary_y=True)
+    fig.update_xaxes(gridcolor=colors["grid"], row=1, col=1)
     return fig
 
 
@@ -160,6 +201,7 @@ with st.sidebar:
 
 try:
     glucose, treatments = load_nightscout_data(hours)
+    profile = load_profile()
 except NightscoutConfigError as exc:
     st.error(str(exc))
     st.stop()
@@ -172,7 +214,9 @@ if glucose.empty:
     st.stop()
 
 events = classify_treatments(treatments)
+bolus = classify_bolus(treatments)
 tir = summarize_time_in_range(glucose)
+basal, target = _basal_and_target_series(profile, treatments, glucose["timestamp"].min(), glucose["timestamp"].max())
 
 latest = glucose.iloc[-1]
 previous = glucose.iloc[-2] if len(glucose) > 1 else None
@@ -192,14 +236,17 @@ colors = get_colors()
 
 fig = build_chart(
     glucose,
-    events["insulin"],
+    bolus,
     events["carbs"],
+    basal,
+    target,
     events["site_changes"],
     events["sensor_changes"],
     colors,
 )
 st.plotly_chart(fig, width="stretch")
 st.caption(
-    "Driehoekjes = insuline · cirkels = koolhydraten · stippellijn = pompwissel · "
+    "Stippen = bolusinsuline (voeding/correctie/SMB, grootte naar dosis) · cirkels = koolhydraten · "
-    "streepjeslijn = sensorwissel"
+    "vlak = basaal · streepstippellijn = streefwaarde uit pompprofiel · "
+    "stippellijn = pompwissel · streepjeslijn = sensorwissel"
 )

pages/4_Dagoverzicht.py

@@ -1,15 +1,31 @@
+from datetime import timedelta
+
+import pandas as pd
 import plotly.graph_objects as go
 import requests
 import streamlit as st
 from plotly.subplots import make_subplots
 
 from auth import require_auth
-from data_processing import TARGET_HIGH, TARGET_LOW
+from data_processing import TARGET_HIGH, TARGET_LOW, classify_treatments
 from nightscout_client import NightscoutConfigError
+from reporting import (
+    BOLUS_CATEGORY_SMB,
+    classify_bolus,
+    daily_stats,
+    effective_basal_series,
+    target_glucose_series,
+)
 from ui_common import (
+    MMOL_HOVERTEMPLATE,
+    add_basal_area_trace,
+    add_bolus_marker_traces,
+    add_carb_bars_trace,
+    add_target_line_trace,
     add_threshold_lines,
     get_colors,
-    load_range_data,
+    load_day_data,
+    load_profile,
     render_completeness_warning,
     select_period_days,
 )
@@ -18,17 +34,49 @@ st.set_page_config(page_title="Glucose dashboard — Dagoverzicht", page_icon="
 require_auth()
 
 st.title("Dagoverzicht")
-st.caption("Eén glucosegrafiek per kalenderdag (FR4).")
+st.caption(
+    "Eén dag per keer: boven glucose met koolhydraten/bolus-overlay, onder basaal "
+    "en SMB — apart gehouden omdat één gecombineerde grafiek te veel informatie bevatte."
+)
 
-start, end, days = select_period_days(default_label="14 dagen")
+# De periode-knop bepaalt alleen het navigeerbare venster (hoe ver terug je kunt
-if days > 31:
+# bladeren), niet welke data wordt opgehaald — dat gebeurt hieronder voor precies
-    st.info(
+# de geselecteerde dag (load_day_data), zodat het wisselen tussen dagen snel blijft.
-        "Bij langere periodes wordt de pagina lang en kan laden traag zijn — "
+window_start, window_end, days = select_period_days(default_label="14 dagen")
-        "voor het bekijken van individuele dagen is 14 of 30 dagen prettiger."
+
+state_key = "dagoverzicht_selected_day"
+if state_key not in st.session_state:
+    st.session_state[state_key] = window_end
+if not (window_start <= st.session_state[state_key] <= window_end):
+    st.session_state[state_key] = window_end
+
+selected_day = st.session_state[state_key]
+
+col_prev, col_date, col_next = st.columns([1, 3, 1])
+with col_prev:
+    if st.button("← Vorige dag", disabled=selected_day <= window_start, width="stretch"):
+        selected_day = selected_day - timedelta(days=1)
+        st.session_state[state_key] = selected_day
+with col_next:
+    if st.button("Volgende dag →", disabled=selected_day >= window_end, width="stretch"):
+        selected_day = selected_day + timedelta(days=1)
+        st.session_state[state_key] = selected_day
+with col_date:
+    date_options = [window_end - timedelta(days=i) for i in range(days)]
+    chosen = st.selectbox(
+        "Datum",
+        options=date_options,
+        index=date_options.index(selected_day),
+        format_func=lambda d: d.strftime("%A %d-%m-%Y"),
+        label_visibility="collapsed",
     )
+    if chosen != selected_day:
+        selected_day = chosen
+        st.session_state[state_key] = selected_day
 
 try:
-    glucose, _treatments = load_range_data(start, end)
+    glucose, treatments = load_day_data(selected_day)
+    profile = load_profile()
 except NightscoutConfigError as exc:
     st.error(str(exc))
     st.stop()
@@ -37,54 +85,124 @@ except (requests.RequestException, ValueError) as exc:
     st.stop()
 
 if glucose.empty:
-    st.info("Geen glucosemetingen gevonden in deze periode.")
+    st.info("Geen glucosemetingen gevonden op deze dag.")
     st.stop()
 
-render_completeness_warning(glucose, start, end)
+render_completeness_warning(glucose, selected_day, selected_day)
 
 colors = get_colors()
-glucose = glucose.copy()
+day_start = pd.Timestamp(selected_day)
-glucose["date"] = glucose["timestamp"].dt.date
+day_end = day_start + pd.Timedelta(1, unit="D")
-glucose["hour"] = glucose["timestamp"].dt.hour + glucose["timestamp"].dt.minute / 60
 
-dates = sorted(glucose["date"].unique())
+# treatments bevat een lookback-marge vóór middernacht (zie
-row_height = 150
+# nightscout_client.fetch_treatments_for_day) zodat een temp basal die al
-fig = make_subplots(
+# vóór deze dag begon toch meetelt — effective_basal_series() clipt dat
-    rows=len(dates),
+# zelf aan [day_start, day_end). Voor de koolhydraten/bolus-overlays willen
-    cols=1,
+# we die marge juist NIET, anders duiken gisteravond se stippen op vandaag op.
-    shared_xaxes=True,
+day_treatments = treatments[(treatments["timestamp"] >= day_start) & (treatments["timestamp"] < day_end)]
-    subplot_titles=[d.strftime("%A %d-%m-%Y") for d in dates],
-    vertical_spacing=min(0.4 / max(len(dates) - 1, 1), 0.05),
-)
 
-for i, day in enumerate(dates, start=1):
+carbs_day = classify_treatments(day_treatments)["carbs"]
-    day_df = glucose[glucose["date"] == day]
+bolus_day = classify_bolus(day_treatments)
-    fig.add_hrect(
+basal_day = effective_basal_series(profile, treatments, day_start, day_end)
-        y0=TARGET_LOW, y1=TARGET_HIGH, fillcolor=colors["band"], line_width=0, layer="below", row=i, col=1
+target_day = target_glucose_series(profile, day_start, day_end)
+
+
+def _close_step_to_day_end(df: pd.DataFrame, value_col: str) -> pd.DataFrame:
+    """Voeg een laatste punt op day_end toe (zelfde waarde als het laatste
+    segment) zodat een stapvlak/-lijn de volle dag doortekent i.p.v. te
+    stoppen bij het laatste breekpunt."""
+    if df.empty:
+        return df
+    last_value = df.iloc[-1][value_col]
+    closing_row = pd.DataFrame([{"timestamp": day_end, value_col: last_value}])
+    return pd.concat([df, closing_row], ignore_index=True)
+
+
+basal_day = _close_step_to_day_end(basal_day, "rate")
+target_day = _close_step_to_day_end(target_day, "target")
+
+
+def build_day_figure(glucose_day, carbs_day, bolus_day, basal_day, target_day, colors) -> go.Figure:
+    fig = make_subplots(
+        rows=2,
+        cols=1,
+        shared_xaxes=True,
+        row_heights=[0.68, 0.32],
+        vertical_spacing=0.08,
+        specs=[[{"secondary_y": True}], [{"secondary_y": False}]],
     )
+
+    # Rij 1: glucose + streefwaarde + koolhydraten + voeding-/correctiebolus.
+    fig.add_hrect(
+        y0=TARGET_LOW, y1=TARGET_HIGH, fillcolor=colors["band"], line_width=0, layer="below", row=1, col=1
+    )
+    add_target_line_trace(fig, target_day, colors, row=1)
+    add_carb_bars_trace(fig, carbs_day, colors, row=1, secondary_y=True)
+
+    top_bolus = bolus_day[bolus_day["category"] != BOLUS_CATEGORY_SMB] if not bolus_day.empty else bolus_day
+    lane_y = glucose_day["mmol"].min() - 1.0 if not glucose_day.empty else TARGET_LOW - 1.0
+    add_bolus_marker_traces(fig, top_bolus, colors, lane_y, row=1)
+
     fig.add_trace(
         go.Scatter(
-            x=day_df["hour"],
+            x=glucose_day["timestamp"],
-            y=day_df["mmol"],
+            y=glucose_day["mmol"],
             mode="lines",
             line={"color": colors["glucose"], "width": 2},
-            showlegend=False,
+            name="Glucose",
-            hovertemplate="%{y:.1f} mmol/L<extra></extra>",
+            hovertemplate=MMOL_HOVERTEMPLATE,
         ),
-        row=i,
+        row=1,
         col=1,
     )
-    fig.update_yaxes(title_text="mmol/L", gridcolor=colors["grid"], row=i, col=1)
+    add_threshold_lines(fig, colors, row=1)
-    add_threshold_lines(fig, colors, row=i)
+    fig.update_yaxes(title_text="mmol/L", gridcolor=colors["grid"], row=1, col=1, secondary_y=False)
+    fig.update_yaxes(title_text="Koolhydraten (g)", showgrid=False, row=1, col=1, secondary_y=True)
 
-fig.update_xaxes(title_text="Uur van de dag", range=[0, 24], dtick=4, gridcolor=colors["grid"])
+    # Rij 2: basaal + SMB — apart van rij 1 zodat de glucosegrafiek niet
-fig.update_layout(
+    # overladen raakt met insulinedetails.
-    paper_bgcolor="rgba(0,0,0,0)",
+    add_basal_area_trace(fig, basal_day, colors, row=2)
-    plot_bgcolor="rgba(0,0,0,0)",
+    smb_bolus = bolus_day[bolus_day["category"] == BOLUS_CATEGORY_SMB] if not bolus_day.empty else bolus_day
-    font_color=colors["text"],
+    smb_lane_y = (basal_day["rate"].max() * 1.15) if not basal_day.empty else 1.0
-    height=row_height * len(dates),
+    add_bolus_marker_traces(fig, smb_bolus, colors, smb_lane_y, row=2)
-    showlegend=False,
+    fig.update_yaxes(
-    margin={"l": 10, "r": 10, "t": 30, "b": 10},
+        title_text="Basaal (E/uur)", gridcolor=colors["grid"], rangemode="tozero", row=2, col=1
-)
+    )
+
+    fig.update_xaxes(range=[day_start, day_end], dtick=4 * 3600 * 1000, gridcolor=colors["grid"])
+    fig.update_layout(
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font_color=colors["text"],
+        height=560,
+        showlegend=True,
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "left", "x": 0},
+        margin={"l": 10, "r": 10, "t": 40, "b": 10},
+    )
+    return fig
+
+
+fig = build_day_figure(glucose, carbs_day, bolus_day, basal_day, target_day, colors)
 st.plotly_chart(fig, width="stretch")
-st.caption(f"{len(dates)} dagen · {len(glucose)} metingen")
+
+stats = daily_stats(glucose, day_treatments, profile, day_start, day_end)
+col1, col2, col3, col4 = st.columns(4)
+col1.metric(
+    "Glucose (gem./min/max)",
+    f"{stats['mean_mmol']:.1f} mmol/L",
+    delta=f"{stats['min_mmol']:.1f} - {stats['max_mmol']:.1f}",
+    delta_color="off",
+)
+col2.metric("Totale insuline", f"{stats['total_insulin']:.1f} E")
+col3.metric("Basaal / bolus / SMB", f"{stats['basal_insulin']:.1f} / {stats['bolus_insulin']:.1f} / {stats['smb_insulin']:.1f} E")
+col4.metric(
+    "Hypo's / hyper's",
+    f"{stats['hypo_count']} / {stats['hyper_count']}",
+    delta=f"{stats['hypo_duration_minutes']:.0f} / {stats['hyper_duration_minutes']:.0f} min",
+    delta_color="off",
+)
+
+st.caption(
+    "Boven: stippen = bolusinsuline (voeding/correctie, grootte naar dosis) · staafjes = koolhydraten · "
+    "streepstippellijn = streefwaarde uit pompprofiel. Onder: vlak = basaal · stippen = SMB."
+)

pages/8_Export.py

@@ -5,7 +5,12 @@ from auth import require_auth
 from nightscout_client import NightscoutConfigError
 from pdf_export import AVAILABLE_SECTIONS, SECTION_EVENTS, build_report_pdf
 from reporting import HIGH, LOW, VERY_HIGH, VERY_LOW, detect_events, summarize_events
-from ui_common import load_range_data, render_completeness_warning, select_period_days
+from ui_common import (
+    load_profile,
+    load_range_data,
+    render_completeness_warning,
+    select_period_days,
+)
 
 st.set_page_config(page_title="Glucose dashboard — Export", page_icon="📊", layout="wide")
 require_auth()
@@ -17,7 +22,8 @@ start, end, days = select_period_days(default_label="90 dagen")
 sections = st.multiselect("Onderdelen", AVAILABLE_SECTIONS, default=AVAILABLE_SECTIONS)
 
 try:
-    glucose, _treatments = load_range_data(start, end)
+    glucose, treatments = load_range_data(start, end)
+    profile = load_profile()
 except NightscoutConfigError as exc:
     st.error(str(exc))
     st.stop()
@@ -43,7 +49,7 @@ if SECTION_EVENTS in sections:
         events = detect_events(glucose, threshold, direction)
         events_summary[label] = summarize_events(events, days)
 
-pdf_bytes = build_report_pdf(sections, glucose, events_summary, start, end)
+pdf_bytes = build_report_pdf(sections, glucose, events_summary, start, end, treatments=treatments, profile=profile)
 
 st.download_button(
     "Download PDF",

pages/9_Insulineprofiel.py

@@ -0,0 +1,98 @@
+import pandas as pd
+import plotly.graph_objects as go
+import requests
+import streamlit as st
+
+from auth import require_auth
+from nightscout_client import NightscoutConfigError
+from profile_data import segments_table, segments_to_hourly_bars, total_daily_basal
+from ui_common import get_colors, hex_to_rgba, load_profile
+
+st.set_page_config(page_title="Glucose dashboard — Insulineprofiel", page_icon="📊", layout="wide")
+require_auth()
+
+st.title("Insulineprofiel")
+st.caption("Het meest recent gebruikte pompprofiel: basaalschema, ISF, koolhydraatratio en DIA.")
+
+try:
+    profile = load_profile()
+except NightscoutConfigError as exc:
+    st.error(str(exc))
+    st.stop()
+except (requests.RequestException, ValueError) as exc:
+    st.error(f"Kon geen profiel ophalen van Nightscout: {exc}")
+    st.stop()
+
+if not profile:
+    st.info("Geen pompprofiel gevonden op deze Nightscout-instantie.")
+    st.stop()
+
+colors = get_colors()
+
+col1, col2 = st.columns(2)
+col1.metric("DIA (werkingsduur insuline)", f"{profile['dia']:.1f} uur")
+col2.caption(f"Profielnaam: {profile['name'] or '—'}")
+
+st.subheader("Basaalschema")
+bars = segments_to_hourly_bars(profile["basal"])
+fig = go.Figure(
+    go.Scatter(
+        x=[b["hour"] for b in bars],
+        y=[b["value"] for b in bars],
+        mode="lines",
+        line={"shape": "hv", "color": colors["basal"], "width": 2},
+        fill="tozeroy",
+        fillcolor=hex_to_rgba(colors["basal"], 0.3),
+        opacity=0.8,
+        name="Basaal",
+        hovertemplate="%{x:.0f}:00 · %{y:.2f} E/uur<extra></extra>",
+    )
+)
+fig.update_layout(
+    paper_bgcolor="rgba(0,0,0,0)",
+    plot_bgcolor="rgba(0,0,0,0)",
+    font_color=colors["text"],
+    height=320,
+    margin={"l": 10, "r": 10, "t": 10, "b": 10},
+    xaxis={"title": "Uur van de dag", "range": [0, 24], "dtick": 2, "gridcolor": colors["grid"]},
+    yaxis={"title": "E/uur", "gridcolor": colors["grid"], "rangemode": "tozero"},
+    showlegend=False,
+)
+st.plotly_chart(fig, width="stretch")
+
+basal_rows = segments_table(profile["basal"])
+st.dataframe(
+    pd.DataFrame(basal_rows).rename(
+        columns={"start": "Van", "end": "Tot", "value": "Basaalsnelheid (E/uur)"}
+    ),
+    width="stretch",
+    hide_index=True,
+)
+
+col_isf, col_ratio = st.columns(2)
+with col_isf:
+    st.subheader("Insulinegevoeligheid (ISF)")
+    isf_rows = segments_table(profile["sens"])
+    st.dataframe(
+        pd.DataFrame(isf_rows).rename(
+            columns={"start": "Van", "end": "Tot", "value": "ISF (mmol/L per E)"}
+        ),
+        width="stretch",
+        hide_index=True,
+    )
+
+with col_ratio:
+    st.subheader("Koolhydraatratio")
+    ratio_rows = segments_table(profile["carbratio"])
+    st.dataframe(
+        pd.DataFrame(ratio_rows).rename(
+            columns={"start": "Van", "end": "Tot", "value": "Ratio (g koolhydraten per E)"}
+        ),
+        width="stretch",
+        hide_index=True,
+    )
+
+st.caption(
+    f"Totale dagelijkse basaal: {total_daily_basal(profile['basal']):.1f} E "
+    "(volgens profielschema, exclusief tijdelijke aanpassingen)."
+)

pdf_export.py

@@ -2,15 +2,23 @@
 PDF-rapportgeneratie voor geselecteerde rapportageonderdelen (FR10).
 
 Gebruikt fpdf2 (puur Python, geen systeemdependencies) voor de PDF-opbouw en
-matplotlib voor een eenvoudige statische AGP-grafiek specifiek voor de PDF —
+matplotlib voor de statische grafieken specifiek voor de PDF — bewust niet
-bewust niet dezelfde Plotly-figuren als op de pagina's zelf (die zouden
+dezelfde Plotly-figuren als op de pagina's zelf (die zouden kaleido vereisen,
-kaleido vereisen, met een ingebakken Chromium-build: een groter image en meer
+met een ingebakken Chromium-build: een groter image en meer Trivy-oppervlak,
-Trivy-oppervlak, voor dit doel niet nodig).
+voor dit doel niet nodig).
+
+Elke sectie is één (of, voor Dagoverzicht, meerdere) liggende A4-pagina('s)
+met een vaste kop-/voettekst (device-info, paginanummering, genereerdatum,
+Nightscout-link) via de GlucoseReportPDF-subklasse hieronder — geïnspireerd op
+de LibreView/AGP-rapportopmaak (zie docs/design-reporting.md).
 """
 
 from __future__ import annotations
 
 import io
+import os
+from datetime import datetime, timezone
+from pathlib import Path
 
 import matplotlib
 
@@ -20,34 +28,54 @@ import pandas as pd
 from fpdf import FPDF
 from fpdf.enums import XPos, YPos
 
+from profile_data import segments_table, segments_to_hourly_bars, total_daily_basal
 from reporting import (
+    BOLUS_CATEGORY_CORRECTION,
+    BOLUS_CATEGORY_FOOD,
+    BOLUS_CATEGORY_SMB,
     HIGH,
     LOW,
     VERY_HIGH,
     VERY_LOW,
     agp_percentile_bands,
+    classify_bolus,
     completeness_warning,
+    daily_stats,
     detect_events,
+    effective_basal_series,
+    glucose_min_max,
     gmi_mmol_per_mol,
     gmi_percent,
     percent_cv,
     sensor_active_percent,
+    target_glucose_series,
     tir_breakdown_5band,
 )
 
 SECTION_SUMMARY = "Samenvatting"
 SECTION_AGP = "AGP profiel"
+SECTION_INSULIN_PROFILE = "Insulineprofiel"
 SECTION_DAGOVERZICHT = "Dagoverzicht"
 SECTION_EVENTS = "Hypo-/hyperevents"
 
-AVAILABLE_SECTIONS = [SECTION_SUMMARY, SECTION_AGP, SECTION_DAGOVERZICHT, SECTION_EVENTS]
+AVAILABLE_SECTIONS = [
+    SECTION_SUMMARY,
+    SECTION_AGP,
+    SECTION_INSULIN_PROFILE,
+    SECTION_DAGOVERZICHT,
+    SECTION_EVENTS,
+]
+
+REPORT_VERSION = "1.0"
+_ICON_PATH = Path(__file__).parent / "assets" / "icon.svg"
 
 _LINE_KWARGS = {"new_x": XPos.LMARGIN, "new_y": YPos.NEXT}
+_SAME_LINE_KWARGS = {"new_x": XPos.RIGHT, "new_y": YPos.TOP}
 
 # Zelfde kleuren als het PALETTE["light"] in ui_common.py — bewust hier
-# gedupliceerd (5 hex-waarden) in plaats van ui_common.py te importeren, want
+# gedupliceerd in plaats van ui_common.py te importeren, want dat zou een
-# dat zou een streamlit-dependency in deze module trekken die verder bewust
+# streamlit-dependency in deze module trekken die verder bewust vrij van
-# vrij van UI-framework-imports is (los te unit-testen, zie moduledocstring).
+# UI-framework-imports is (los te unit-testen, zie moduledocstring).
 _MUTED = "#898781"
 _TIR_BAND_ORDER = ["very_low", "low", "in_range", "high", "very_high"]
 _TIR_BAND_LABELS = {
@@ -65,33 +93,83 @@ _TIR_COLORS = {
     "very_high": "#c76f0a",
 }
 _THRESHOLD_LINES = [VERY_LOW, LOW, HIGH, VERY_HIGH]
+_BASAL_COLOR = "#7b5ea3"
+_CARBS_COLOR = "#eb6834"
+_TARGET_COLOR = "#52514e"
+_BOLUS_CATEGORY_STYLE = {
+    BOLUS_CATEGORY_FOOD: ("#1baf7a", "Bolus (voeding)"),
+    BOLUS_CATEGORY_CORRECTION: ("#2a78d6", "Bolus (correctie)"),
+    BOLUS_CATEGORY_SMB: ("#898781", "SMB"),
+}
+_EMPTY_TREATMENT_COLUMNS = ["timestamp", "eventType", "insulin", "carbs", "type", "absolute", "duration"]
 DAGOVERZICHT_MAX_DAYS = 14
+_HOUR_OF_DAY_LABEL = "Uur van de dag"
 
 
-def _add_threshold_lines(ax) -> None:
+def _report_config() -> dict:
-    """Teken de 4 klinische drempellijnen (3.0/3.9/10.0/13.9 mmol/L) op een matplotlib-as.
+    """Rapportmetadata uit environment variables (insuline/pomp/sensor-type,
-
+    Nightscout-URL) — zelfde os.environ-patroon als nightscout_client.py.
-    PDF-tegenhanger van ui_common.add_threshold_lines() voor de Plotly-versie
+    Alles heeft een fallback zodat een rapport ook zonder configuratie werkt."""
-    op de dashboardpagina's (gitea #15) — zelfde 4 waarden, zelfde grijstint.
+    return {
-    """
+        "insulin_type": os.environ.get("REPORT_INSULIN_TYPE", "").strip() or "Onbekend insulinetype",
-    for value in _THRESHOLD_LINES:
+        "pump_type": os.environ.get("REPORT_PUMP_TYPE", "").strip() or "Onbekende pomp",
-        ax.axhline(value, color=_MUTED, linewidth=0.7, linestyle=":")
+        "sensor_type": os.environ.get("REPORT_SENSOR_TYPE", "").strip() or "Onbekende sensor",
+        "nightscout_url": os.environ.get("NIGHTSCOUT_URL", "").strip().rstrip("/"),
+    }
 
 
-def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
+class GlucoseReportPDF(FPDF):
-    bands = agp_percentile_bands(glucose)
+    """FPDF-subklasse met een vaste kop-/voettekst op elke pagina en één sectie
-    fig, ax = plt.subplots(figsize=(7, 3))
+    per liggende pagina (zie begin_report_section())."""
-    hours = bands["minute_of_day"] / 60
-    ax.fill_between(hours, bands["p5"], bands["p95"], alpha=0.15, color="#2a78d6", label="5e-95e pct")
-    ax.fill_between(hours, bands["p25"], bands["p75"], alpha=0.3, color="#2a78d6", label="25e-75e pct")
-    ax.plot(hours, bands["p50"], color="#2a78d6", linewidth=2, label="Mediaan")
-    _add_threshold_lines(ax)
-    ax.set_xlabel("Uur van de dag")
-    ax.set_ylabel("mmol/L")
-    ax.set_xlim(0, 24)
-    ax.legend(loc="upper right", fontsize=8)
-    fig.tight_layout()
 
+    def __init__(self, config: dict, generated_at: datetime) -> None:
+        super().__init__(orientation="L", unit="mm", format="A4")
+        self._config = config
+        self._generated_at = generated_at
+        self.section_title = ""
+        self.set_auto_page_break(auto=True, margin=18)
+        self.alias_nb_pages()
+
+    def begin_report_section(self, title: str) -> None:
+        """Begin een nieuwe pagina voor sectie `title`. Voor secties die over
+        meerdere pagina's lopen (Dagoverzicht: één pagina per dag) wordt dit
+        per pagina aangeroepen zodat de kop op elke pagina klopt."""
+        self.section_title = title
+        self.add_page()
+
+    def header(self) -> None:
+        if _ICON_PATH.exists():
+            self.image(str(_ICON_PATH), x=10, y=8, w=8)
+        self.set_xy(20, 8)
+        self.set_font("Helvetica", "B", 14)
+        self.cell(0, 6, self.section_title, **_LINE_KWARGS)
+        self.set_x(20)
+        self.set_font("Helvetica", "", 8)
+        self.set_text_color(120, 120, 120)
+        device_line = (
+            f"Glucoserapport v{REPORT_VERSION} · Insuline: {self._config['insulin_type']} · "
+            f"Pomp: {self._config['pump_type']} · Sensor: {self._config['sensor_type']}"
+        )
+        self.cell(0, 4, device_line, **_LINE_KWARGS)
+        self.set_text_color(0, 0, 0)
+        self.ln(3)
+        self.set_draw_color(200, 200, 200)
+        self.line(10, self.get_y(), self.w - 10, self.get_y())
+        self.ln(4)
+
+    def footer(self) -> None:
+        self.set_y(-14)
+        self.set_font("Helvetica", "I", 8)
+        self.set_text_color(120, 120, 120)
+        left = f"Gegenereerd: {self._generated_at.strftime('%d-%m-%Y %H:%M')}"
+        if self._config["nightscout_url"]:
+            left += f" · {self._config['nightscout_url']}"
+        self.cell(200, 8, left, **_SAME_LINE_KWARGS)
+        self.cell(0, 8, f"Pagina {self.page_no()}/{{nb}}", align="R")
+        self.set_text_color(0, 0, 0)
+
+
+def _fig_to_png(fig) -> bytes:
     buf = io.BytesIO()
     fig.savefig(buf, format="png", dpi=150)
     plt.close(fig)
@@ -99,10 +177,33 @@ def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
     return buf.getvalue()
 
 
+def _add_threshold_lines(ax) -> None:
+    """Teken de 4 klinische drempellijnen (3.0/3.9/10.0/13.9 mmol/L) op een matplotlib-as."""
+    for value in _THRESHOLD_LINES:
+        ax.axhline(value, color=_MUTED, linewidth=0.7, linestyle=":")
+
+
+def _agp_figure_png(glucose: pd.DataFrame) -> bytes:
+    bands = agp_percentile_bands(glucose)
+    fig, ax = plt.subplots(figsize=(11, 4.2))
+    hours = bands["minute_of_day"] / 60
+    ax.axhspan(LOW, HIGH, color="#0ca30c", alpha=0.08, label="Doelbereik")
+    ax.fill_between(hours, bands["p5"], bands["p95"], alpha=0.15, color="#2a78d6", label="5e-95e pct")
+    ax.fill_between(hours, bands["p25"], bands["p75"], alpha=0.3, color="#2a78d6", label="25e-75e pct")
+    ax.plot(hours, bands["p50"], color="#2a78d6", linewidth=2.5, label="Mediaan")
+    _add_threshold_lines(ax)
+    ax.set_xlabel(_HOUR_OF_DAY_LABEL)
+    ax.set_ylabel("mmol/L")
+    ax.set_xlim(0, 24)
+    ax.set_xticks(range(0, 25, 3))
+    ax.legend(loc="upper right", fontsize=9, ncol=4, frameon=False)
+    fig.tight_layout()
+    return _fig_to_png(fig)
+
+
 def _tir_bar_figure_png(tir: dict) -> bytes:
-    """Gestapelde staafgrafiek van de 5 TIR-banden — PDF-tegenhanger van
+    """Gestapelde staafgrafiek van de 5 TIR-banden."""
-    ui_common.build_tir_stacked_bar_figure() (gitea #14)."""
+    fig, ax = plt.subplots(figsize=(9, 1.1))
-    fig, ax = plt.subplots(figsize=(7, 1.1))
     left = 0.0
     for band in _TIR_BAND_ORDER:
         value = tir[band]
@@ -123,52 +224,147 @@ def _tir_bar_figure_png(tir: dict) -> bytes:
     return buf.getvalue()
 
 
-def _daily_figure_png(day_df: pd.DataFrame, day_label: str) -> bytes:
+def _insulin_profile_figure_png(profile: dict) -> bytes:
-    """Eén dag als kleine lijngrafiek — PDF-tegenhanger van de Dagoverzicht-pagina (gitea #16)."""
+    bars = segments_to_hourly_bars(profile.get("basal", []))
-    hours = day_df["timestamp"].dt.hour + day_df["timestamp"].dt.minute / 60
+    fig, ax = plt.subplots(figsize=(11, 3.2))
-    fig, ax = plt.subplots(figsize=(7, 1.4))
+    if bars:
-    ax.plot(hours, day_df["mmol"], color="#2a78d6", linewidth=1.2)
+        hours = [b["hour"] for b in bars]
-    _add_threshold_lines(ax)
+        values = [b["value"] for b in bars]
+        ax.step(hours, values, where="post", color=_BASAL_COLOR, linewidth=1.5, alpha=0.8)
+        ax.fill_between(hours, values, step="post", color=_BASAL_COLOR, alpha=0.3)
     ax.set_xlim(0, 24)
-    ax.set_xticks(range(0, 25, 4))
+    ax.set_xticks(range(0, 25, 2))
-    ax.set_ylabel("mmol/L", fontsize=8)
+    ax.set_ylim(bottom=0)
-    ax.set_title(day_label, fontsize=9, loc="left")
+    ax.set_xlabel(_HOUR_OF_DAY_LABEL)
-    ax.tick_params(labelsize=7)
+    ax.set_ylabel("E/uur")
     fig.tight_layout()
+    return _fig_to_png(fig)
 
-    buf = io.BytesIO()
+
-    fig.savefig(buf, format="png", dpi=150)
+def _add_basal_overlay(ax2, basal: pd.DataFrame, day_start) -> None:
-    plt.close(fig)
+    if basal.empty:
-    buf.seek(0)
+        return
-    return buf.getvalue()
+    hours = (basal["timestamp"] - day_start).dt.total_seconds() / 3600
+    hours = pd.concat([hours, pd.Series([24.0])], ignore_index=True)
+    values = pd.concat([basal["rate"], basal["rate"].iloc[-1:]], ignore_index=True)
+    ax2.fill_between(hours, values, step="post", color=_BASAL_COLOR, alpha=0.3, label="Basaal")
+    ax2.step(hours, values, where="post", color=_BASAL_COLOR, linewidth=1, alpha=0.8)
+
+
+def _add_carbs_overlay(ax2, day_treatments: pd.DataFrame, day_start) -> None:
+    if day_treatments.empty:
+        return
+    carbs = day_treatments[day_treatments["carbs"].fillna(0) > 0]
+    if carbs.empty:
+        return
+    hours = (carbs["timestamp"] - day_start).dt.total_seconds() / 3600
+    ax2.bar(hours, carbs["carbs"], width=0.25, color=_CARBS_COLOR, alpha=0.55, label="Koolhydraten")
+
+
+def _add_bolus_overlay(ax, bolus: pd.DataFrame, day_start, lane_y: float, categories: tuple[str, ...]) -> None:
+    for category in categories:
+        subset = bolus[bolus["category"] == category] if not bolus.empty else bolus
+        if subset.empty:
+            continue
+        color, label = _BOLUS_CATEGORY_STYLE[category]
+        hours = (subset["timestamp"] - day_start).dt.total_seconds() / 3600
+        sizes = (subset["insulin"] * 25).clip(lower=15, upper=200)
+        ax.scatter(hours, [lane_y] * len(subset), s=sizes, color=color, alpha=0.75, label=label, zorder=5)
+        if category != BOLUS_CATEGORY_SMB:
+            for x, units in zip(hours, subset["insulin"], strict=True):
+                ax.annotate(
+                    f"{units:.1f}E", (x, lane_y), textcoords="offset points", xytext=(0, 8), fontsize=6, ha="center"
+                )
+
+
+def _add_target_overlay(ax, profile: dict | None, day_start, day_end) -> None:
+    target = target_glucose_series(profile, day_start, day_end)
+    if target.empty:
+        return
+    hours = (target["timestamp"] - day_start).dt.total_seconds() / 3600
+    hours = pd.concat([hours, pd.Series([24.0])], ignore_index=True)
+    values = pd.concat([target["target"], target["target"].iloc[-1:]], ignore_index=True)
+    ax.step(hours, values, where="post", color=_TARGET_COLOR, linewidth=1.2, linestyle="-.", label="Streefwaarde (pomp)")
+
+
+def _daily_figure_png(
+    day_df: pd.DataFrame, day_treatments: pd.DataFrame, profile: dict | None, day_label: str, day_start, day_end
+) -> bytes:
+    """Eén dag als twee gestapelde grafieken (gedeelde tijdas, één gedeelde legenda):
+    boven glucose + streefwaarde + koolhydraten + voeding/correctiebolus, onder
+    basaal + SMB — gescheiden omdat één gecombineerde grafiek te veel informatie
+    bevatte om nog leesbaar te zijn."""
+    hours = day_df["timestamp"].dt.hour + day_df["timestamp"].dt.minute / 60
+    fig, (ax, ax_basal) = plt.subplots(2, 1, figsize=(11, 5.2), sharex=True, height_ratios=[2.2, 1])
+    ax2 = ax.twinx()
+
+    bolus = classify_bolus(day_treatments)
+    _add_carbs_overlay(ax2, day_treatments, day_start)
+    lane_y = (day_df["mmol"].min() - 1.0) if not day_df.empty else LOW - 1.0
+    _add_bolus_overlay(ax, bolus, day_start, lane_y, categories=(BOLUS_CATEGORY_CORRECTION, BOLUS_CATEGORY_FOOD))
+    _add_target_overlay(ax, profile, day_start, day_end)
+
+    ax.plot(hours, day_df["mmol"], color="#2a78d6", linewidth=1.4, label="Glucose", zorder=6)
+    _add_threshold_lines(ax)
+    ax.set_ylabel("mmol/L", fontsize=9)
+    ax2.set_ylabel("Koolhydraten (g)", fontsize=9)
+    ax.set_title(day_label, fontsize=11, loc="left")
+    ax.tick_params(labelsize=8)
+    ax2.tick_params(labelsize=8)
+
+    basal = effective_basal_series(profile, day_treatments, day_start, day_end)
+    _add_basal_overlay(ax_basal, basal, day_start)
+    smb_lane_y = (basal["rate"].max() * 1.15) if not basal.empty else 1.0
+    _add_bolus_overlay(ax_basal, bolus, day_start, smb_lane_y, categories=(BOLUS_CATEGORY_SMB,))
+    ax_basal.set_xlim(0, 24)
+    ax_basal.set_xticks(range(0, 25, 2))
+    ax_basal.set_xlabel(_HOUR_OF_DAY_LABEL)
+    ax_basal.set_ylabel("Basaal (E/uur)", fontsize=9)
+    ax_basal.tick_params(labelsize=8)
+
+    lines1, labels1 = ax.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    lines3, labels3 = ax_basal.get_legend_handles_labels()
+    ax_basal.legend(
+        lines1 + lines2 + lines3,
+        labels1 + labels2 + labels3,
+        loc="upper center",
+        bbox_to_anchor=(0.5, -0.35),
+        ncol=6,
+        fontsize=7,
+        frameon=False,
+    )
+    # fig.tight_layout() introspecteert elke as se bounding box om marges te
+    # berekenen, en matplotlib ondersteunt dat niet betrouwbaar zodra er twee
+    # hoofd-assen (ax/ax_basal) plus een twinx (ax2) tegelijk in de figuur
+    # zitten (waarschuwing "Axes ... not compatible with tight_layout").
+    # subplots_adjust() met vaste marges omzeilt die introspectie.
+    fig.subplots_adjust(left=0.06, right=0.94, top=0.93, bottom=0.24, hspace=0.35)
+    return _fig_to_png(fig)
 
 
 def _event_hour_histogram_png(events: pd.DataFrame, color: str) -> bytes:
-    """Aantal events per uur-van-de-dag — PDF-tegenhanger van render_event_level()'s
+    """Aantal events per uur-van-de-dag."""
-    histogram op de Hypo/Hyper Events-pagina's (gitea #17, #18)."""
     hour_counts = events["start"].dt.hour.value_counts().reindex(range(24), fill_value=0).sort_index()
-    fig, ax = plt.subplots(figsize=(7, 2))
+    fig, ax = plt.subplots(figsize=(11, 2.6))
     ax.bar(hour_counts.index, hour_counts.to_numpy(), color=color)
-    ax.set_xlabel("Uur van de dag")
+    ax.set_xlabel(_HOUR_OF_DAY_LABEL)
     ax.set_ylabel("Aantal events")
     ax.set_xlim(-0.5, 23.5)
     ax.set_xticks(range(0, 24, 2))
     fig.tight_layout()
-
+    return _fig_to_png(fig)
-    buf = io.BytesIO()
-    fig.savefig(buf, format="png", dpi=150)
-    plt.close(fig)
-    buf.seek(0)
-    return buf.getvalue()
 
 
-def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
+def _add_summary_section(pdf: GlucoseReportPDF, glucose: pd.DataFrame, start, end) -> None:
+    pdf.begin_report_section(SECTION_SUMMARY)
     mean_mmol = glucose["mmol"].mean()
+    min_mmol, max_mmol = glucose_min_max(glucose)
     tir = tir_breakdown_5band(glucose)
     active_pct = sensor_active_percent(glucose, start, end)
 
-    pdf.set_font("Helvetica", "B", 13)
+    pdf.set_font("Helvetica", "", 11)
-    pdf.cell(0, 8, "Samenvatting", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}", **_LINE_KWARGS)
 
     warning = completeness_warning(glucose, start, end)
     if warning:
@@ -178,7 +374,12 @@ def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
 
     pdf.set_font("Helvetica", "", 11)
     pdf.cell(0, 6, f"Sensordekking: {active_pct:.0f}%", **_LINE_KWARGS)
-    pdf.cell(0, 6, f"Gemiddelde glucose: {mean_mmol:.1f} mmol/L", **_LINE_KWARGS)
+    pdf.cell(
+        0,
+        6,
+        f"Gemiddelde glucose: {mean_mmol:.1f} mmol/L (min {min_mmol:.1f} · max {max_mmol:.1f})",
+        **_LINE_KWARGS,
+    )
     gmi = gmi_percent(mean_mmol)
     pdf.cell(0, 6, f"Geschat HbA1c (GMI): {gmi:.1f}% ({gmi_mmol_per_mol(gmi):.0f} mmol/mol)", **_LINE_KWARGS)
     pdf.cell(0, 6, f"Variabiliteit (%CV): {percent_cv(glucose):.0f}%", **_LINE_KWARGS)
@@ -190,43 +391,117 @@ def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
     pdf.cell(0, 6, f"Zeer hoog (>13.9 mmol/L): {tir['very_high']:.0f}%", **_LINE_KWARGS)
     pdf.ln(2)
     png_bytes = _tir_bar_figure_png(tir)
-    pdf.image(io.BytesIO(png_bytes), w=160)
+    pdf.image(io.BytesIO(png_bytes), w=220)
-    pdf.ln(4)
 
 
-def _add_agp_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
+def _add_agp_section(pdf: GlucoseReportPDF, glucose: pd.DataFrame) -> None:
-    pdf.set_font("Helvetica", "B", 13)
+    pdf.begin_report_section(SECTION_AGP)
-    pdf.cell(0, 8, "AGP profiel", **_LINE_KWARGS)
     png_bytes = _agp_figure_png(glucose)
-    pdf.image(io.BytesIO(png_bytes), w=180)
+    pdf.image(io.BytesIO(png_bytes), w=270)
+
+
+def _add_insulin_profile_section(pdf: GlucoseReportPDF, profile: dict | None) -> None:
+    pdf.begin_report_section(SECTION_INSULIN_PROFILE)
+    if not profile:
+        pdf.set_font("Helvetica", "", 11)
+        pdf.cell(0, 6, "Geen pompprofiel gevonden op deze Nightscout-instantie.", **_LINE_KWARGS)
+        return
+
+    pdf.set_font("Helvetica", "", 11)
+    pdf.cell(0, 6, f"Profielnaam: {profile.get('name') or '-'}", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"DIA (werkingsduur insuline): {profile['dia']:.1f} uur", **_LINE_KWARGS)
+    pdf.cell(0, 6, f"Totale dagelijkse basaal: {total_daily_basal(profile['basal']):.1f} E", **_LINE_KWARGS)
+    pdf.ln(2)
+
+    png_bytes = _insulin_profile_figure_png(profile)
+    pdf.image(io.BytesIO(png_bytes), w=270)
     pdf.ln(4)
 
+    pdf.set_font("Helvetica", "B", 11)
+    pdf.cell(90, 6, "Insulinegevoeligheid (ISF)", **_SAME_LINE_KWARGS)
+    pdf.cell(0, 6, "Koolhydraatratio", **_LINE_KWARGS)
+    pdf.set_font("Helvetica", "", 10)
+    isf_rows = segments_table(profile["sens"])
+    ratio_rows = segments_table(profile["carbratio"])
+    for i in range(max(len(isf_rows), len(ratio_rows))):
+        isf_text = (
+            f"{isf_rows[i]['start']}-{isf_rows[i]['end']}: {isf_rows[i]['value']:.1f} mmol/L per E"
+            if i < len(isf_rows)
+            else ""
+        )
+        ratio_text = (
+            f"{ratio_rows[i]['start']}-{ratio_rows[i]['end']}: {ratio_rows[i]['value']:.0f} g per E"
+            if i < len(ratio_rows)
+            else ""
+        )
+        pdf.cell(90, 5, isf_text, **_SAME_LINE_KWARGS)
+        pdf.cell(0, 5, ratio_text, **_LINE_KWARGS)
 
-def _add_dagoverzicht_section(pdf: FPDF, glucose: pd.DataFrame) -> None:
-    """Eén dagelijkse mini-grafiek per kalenderdag (gitea #16), max. de meest
-    recente DAGOVERZICHT_MAX_DAYS dagen — voor langere periodes zou de PDF
-    anders onwerkbaar lang worden."""
-    pdf.set_font("Helvetica", "B", 13)
-    pdf.cell(0, 8, "Dagoverzicht", **_LINE_KWARGS)
 
+def _add_daily_stats_lines(pdf: GlucoseReportPDF, stats: dict) -> None:
+    pdf.set_font("Helvetica", "", 10)
+    pdf.cell(
+        0,
+        6,
+        f"Glucose: gem. {stats['mean_mmol']:.1f} · min {stats['min_mmol']:.1f} · "
+        f"max {stats['max_mmol']:.1f} mmol/L",
+        **_LINE_KWARGS,
+    )
+    pdf.cell(
+        0,
+        6,
+        f"Insuline: totaal {stats['total_insulin']:.1f} E · basaal {stats['basal_insulin']:.1f} E · "
+        f"bolus {stats['bolus_insulin']:.1f} E · SMB {stats['smb_insulin']:.1f} E",
+        **_LINE_KWARGS,
+    )
+    pdf.cell(
+        0,
+        6,
+        f"Hypo's: {stats['hypo_count']} ({stats['hypo_duration_minutes']:.0f} min) · "
+        f"Hyper's: {stats['hyper_count']} ({stats['hyper_duration_minutes']:.0f} min)",
+        **_LINE_KWARGS,
+    )
+
+
+def _add_dagoverzicht_section(
+    pdf: GlucoseReportPDF, glucose: pd.DataFrame, treatments: pd.DataFrame, profile: dict | None
+) -> None:
+    """Eén liggende pagina per kalenderdag, max. de meest recente
+    DAGOVERZICHT_MAX_DAYS dagen — voor langere periodes zou de PDF anders
+    onwerkbaar lang worden."""
     df = glucose.copy()
     df["date"] = df["timestamp"].dt.date
     dates = sorted(df["date"].unique())
-    if len(dates) > DAGOVERZICHT_MAX_DAYS:
+    show_limited_note = len(dates) > DAGOVERZICHT_MAX_DAYS
+    if show_limited_note:
         dates = dates[-DAGOVERZICHT_MAX_DAYS:]
-        pdf.set_font("Helvetica", "I", 9)
-        pdf.cell(0, 5, f"Beperkt tot de meest recente {DAGOVERZICHT_MAX_DAYS} dagen.", **_LINE_KWARGS)
 
     for day in dates:
+        pdf.begin_report_section(SECTION_DAGOVERZICHT)
+        if show_limited_note:
+            pdf.set_font("Helvetica", "I", 9)
+            pdf.cell(0, 5, f"Beperkt tot de meest recente {DAGOVERZICHT_MAX_DAYS} dagen.", **_LINE_KWARGS)
+            show_limited_note = False  # alleen op de eerste paginas van deze sectie
+
+        day_start = pd.Timestamp(day)
+        day_end = day_start + pd.Timedelta(1, unit="D")
         day_df = df[df["date"] == day]
-        png_bytes = _daily_figure_png(day_df, day.strftime("%A %d-%m-%Y"))
+        day_treatments = (
-        pdf.image(io.BytesIO(png_bytes), w=180)
+            treatments[(treatments["timestamp"] >= day_start) & (treatments["timestamp"] < day_end)]
-    pdf.ln(4)
+            if not treatments.empty
+            else treatments
+        )
+
+        png_bytes = _daily_figure_png(day_df, day_treatments, profile, day.strftime("%A %d-%m-%Y"), day_start, day_end)
+        pdf.image(io.BytesIO(png_bytes), w=270)
+        pdf.ln(2)
+
+        stats = daily_stats(day_df, day_treatments, profile, day_start, day_end)
+        _add_daily_stats_lines(pdf, stats)
 
 
-def _add_events_section(pdf: FPDF, glucose: pd.DataFrame, events_summary: dict[str, dict]) -> None:
+def _add_events_section(pdf: GlucoseReportPDF, glucose: pd.DataFrame, events_summary: dict[str, dict]) -> None:
-    pdf.set_font("Helvetica", "B", 13)
+    pdf.begin_report_section(SECTION_EVENTS)
-    pdf.cell(0, 8, "Hypo-/hyperevents", **_LINE_KWARGS)
     pdf.set_font("Helvetica", "", 11)
     for label, summary in events_summary.items():
         pdf.cell(
@@ -238,17 +513,16 @@ def _add_events_section(pdf: FPDF, glucose: pd.DataFrame, events_summary: dict[s
         )
     pdf.ln(2)
 
-    # Alleen Level 1 krijgt een grafiek in het PDF-rapport (gitea #17, #18) —
+    # Alleen Level 1 krijgt een grafiek in het PDF-rapport — geen tabel met
-    # geen tabel met losse events, de samenvattende cijfers staan al hierboven.
+    # losse events, de samenvattende cijfers staan al hierboven.
     if not glucose.empty:
         hypo_events = detect_events(glucose, LOW, "below")
         if not hypo_events.empty:
-            pdf.image(io.BytesIO(_event_hour_histogram_png(hypo_events, _TIR_COLORS["low"])), w=180)
+            pdf.image(io.BytesIO(_event_hour_histogram_png(hypo_events, _TIR_COLORS["low"])), w=260)
+            pdf.ln(2)
         hyper_events = detect_events(glucose, HIGH, "above")
         if not hyper_events.empty:
-            pdf.image(io.BytesIO(_event_hour_histogram_png(hyper_events, _TIR_COLORS["high"])), w=180)
+            pdf.image(io.BytesIO(_event_hour_histogram_png(hyper_events, _TIR_COLORS["high"])), w=260)
-
-    pdf.ln(4)
 
 
 def build_report_pdf(
@@ -257,20 +531,33 @@ def build_report_pdf(
     events_summary: dict[str, dict],
     start,
     end,
+    treatments: pd.DataFrame | None = None,
+    profile: dict | None = None,
 ) -> bytes:
-    """Bouw een PDF met de geselecteerde onderdelen.
+    """Bouw een PDF met de geselecteerde onderdelen: één liggende voorpagina,
+    daarna één (of voor Dagoverzicht: meerdere) liggende pagina('s) per sectie.
 
     `events_summary` bevat vooraf berekende hypo/hyper-samenvattingen (label ->
     resultaat van reporting.summarize_events), zodat deze module geen
-    event-detectie hoeft te herhalen.
+    event-detectie hoeft te herhalen. `treatments`/`profile` zijn optioneel
+    (leeg/None) zodat oudere aanroepen zonder insulinedata blijven werken.
     """
-    pdf = FPDF()
+    if treatments is None:
-    pdf.add_page()
+        treatments = pd.DataFrame(columns=_EMPTY_TREATMENT_COLUMNS)
-    pdf.set_font("Helvetica", "B", 16)
+
-    pdf.cell(0, 10, "Glucoserapport", **_LINE_KWARGS)
+    config = _report_config()
-    pdf.set_font("Helvetica", "", 11)
+    pdf = GlucoseReportPDF(config, datetime.now(timezone.utc))
+
+    pdf.begin_report_section("Glucoserapport")
+    pdf.set_font("Helvetica", "B", 22)
+    pdf.cell(0, 14, "Glucoserapport", **_LINE_KWARGS)
+    pdf.set_font("Helvetica", "", 13)
     pdf.cell(0, 8, f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}", **_LINE_KWARGS)
-    pdf.ln(4)
+    if sections:
+        pdf.cell(0, 8, f"Onderdelen: {', '.join(sections)}", **_LINE_KWARGS)
+
+    if SECTION_INSULIN_PROFILE in sections:
+        _add_insulin_profile_section(pdf, profile)
 
     if not glucose.empty:
         if SECTION_SUMMARY in sections:
@@ -278,7 +565,7 @@ def build_report_pdf(
         if SECTION_AGP in sections:
             _add_agp_section(pdf, glucose)
         if SECTION_DAGOVERZICHT in sections:
-            _add_dagoverzicht_section(pdf, glucose)
+            _add_dagoverzicht_section(pdf, glucose, treatments, profile)
 
     if SECTION_EVENTS in sections and events_summary:
         _add_events_section(pdf, glucose, events_summary)

profile_data.py

@@ -0,0 +1,146 @@
+"""
+Parsing van het Nightscout-pompprofiel (/api/v1/profile): basaalschema,
+insulinegevoeligheid (ISF), koolhydraatratio, DIA en streefwaarden.
+
+Framework-vrije module (geen streamlit-import), zelfde opzet als reporting.py:
+puur functies op dicts, zodat dit los van de UI te unit-testen is. Zie
+nightscout_client.fetch_profile() voor de ruwe fetch; deze module normaliseert
+alleen (uren -> minuten sinds middernacht, mg/dl -> mmol/L waar nodig).
+"""
+
+from __future__ import annotations
+
+from nightscout_client import mgdl_to_mmol
+
+SEGMENT_FIELDS = ("basal", "sens", "carbratio", "target_low", "target_high")
+
+
+def _time_to_minutes(time_str: str) -> int:
+    """Zet 'HH:MM' (Nightscout's segmenttijd-notatie) om naar minuten sinds middernacht."""
+    hours, minutes = time_str.split(":")
+    return int(hours) * 60 + int(minutes)
+
+
+def _parse_segments(raw_segments: list[dict], *, to_mmol: bool = False) -> list[dict]:
+    """Zet Nightscout-segmenten ([{'time': 'HH:MM', 'value': ...}, ...]) om naar
+    [{'minutes': int, 'value': float}, ...], oplopend gesorteerd op tijd."""
+    segments = []
+    for seg in raw_segments:
+        value = float(seg["value"])
+        if to_mmol:
+            value = mgdl_to_mmol(value)
+        segments.append({"minutes": _time_to_minutes(seg["time"]), "value": value})
+    return sorted(segments, key=lambda s: s["minutes"])
+
+
+def parse_profile(raw: dict) -> dict:
+    """Parse één Nightscout-profieldocument (zoals teruggegeven door
+    nightscout_client.fetch_profile()) naar een genormaliseerde dict:
+
+        {
+            "name": str,
+            "units": "mmol",           # altijd genormaliseerd, ongeacht bronformaat
+            "dia": float,               # uur
+            "basal": [{"minutes": int, "value": float}, ...],       # E/uur
+            "sens": [{"minutes": int, "value": float}, ...],        # mmol/L per E (ISF)
+            "carbratio": [{"minutes": int, "value": float}, ...],   # g koolhydraten per E
+            "target_low": [{"minutes": int, "value": float}, ...],  # mmol/L
+            "target_high": [{"minutes": int, "value": float}, ...], # mmol/L
+        }
+
+    Geeft {} terug als er geen (bruikbaar) profiel is.
+    """
+    if not raw:
+        return {}
+
+    default_name = raw.get("defaultProfile")
+    store = raw.get("store", {})
+    profile = store.get(default_name) if default_name else None
+    if profile is None and store:
+        # defaultProfile ontbreekt of matcht niet met een sleutel in store:
+        # val terug op het enige/eerste beschikbare profiel.
+        profile = next(iter(store.values()))
+    if profile is None:
+        return {}
+
+    units = str(profile.get("units", "mg/dl")).strip().lower()
+    to_mmol = units != "mmol"
+
+    return {
+        "name": default_name or "",
+        "units": "mmol",
+        "dia": float(profile.get("dia", 0) or 0),
+        "basal": _parse_segments(profile.get("basal", [])),
+        "sens": _parse_segments(profile.get("sens", []), to_mmol=to_mmol),
+        "carbratio": _parse_segments(profile.get("carbratio", [])),
+        "target_low": _parse_segments(profile.get("target_low", []), to_mmol=to_mmol),
+        "target_high": _parse_segments(profile.get("target_high", []), to_mmol=to_mmol),
+    }
+
+
+def value_at(segments: list[dict], minutes: int) -> float | None:
+    """Geef de waarde terug die actief is op het gegeven tijdstip (minuten
+    sinds middernacht) — het laatste segment met `minutes <= t`. Ligt `t` vóór
+    het eerste segment van de dag, dan geldt het laatste segment (dat loopt
+    door vanaf de vorige dag, want een profiel dekt altijd de volle 24 uur).
+
+    Geeft None terug als `segments` leeg is.
+    """
+    if not segments:
+        return None
+    active = None
+    for seg in segments:
+        if seg["minutes"] <= minutes:
+            active = seg
+        else:
+            break
+    if active is None:
+        active = segments[-1]
+    return active["value"]
+
+
+def _format_time(minutes: int) -> str:
+    """Zet minuten sinds middernacht om naar 'HH:MM'."""
+    return f"{minutes // 60:02d}:{minutes % 60:02d}"
+
+
+def segments_table(segments: list[dict]) -> list[dict]:
+    """Zet segmenten om naar rijen met een start- en eindtijd voor tabelweergave:
+    [{'start': 'HH:MM', 'end': 'HH:MM', 'value': float}, ...]. Het laatste
+    segment loopt door tot 24:00 (een profiel dekt altijd de volle dag)."""
+    rows = []
+    for i, seg in enumerate(segments):
+        end_minutes = segments[i + 1]["minutes"] if i + 1 < len(segments) else 24 * 60
+        rows.append(
+            {
+                "start": _format_time(seg["minutes"]),
+                "end": "24:00" if end_minutes == 24 * 60 else _format_time(end_minutes),
+                "value": seg["value"],
+            }
+        )
+    return rows
+
+
+def total_daily_basal(segments: list[dict]) -> float:
+    """Totale basaaldosis per dag in E: som van elk segment se duur (uur) x snelheid (E/uur)."""
+    if not segments:
+        return 0.0
+    total = 0.0
+    for i, seg in enumerate(segments):
+        end_minutes = segments[i + 1]["minutes"] if i + 1 < len(segments) else 24 * 60
+        total += seg["value"] * (end_minutes - seg["minutes"]) / 60
+    return total
+
+
+def segments_to_hourly_bars(segments: list[dict]) -> list[dict]:
+    """Zet segmenten om naar een lijst geschikt voor een staafgrafiek per uur van
+    de dag: [{'hour': float, 'value': float}, ...], met een extra punt op uur 24
+    (gelijk aan het eerste segment) zodat een staaf/stap-grafiek de volle dag dekt.
+
+    Gebruikt door de basaalgrafiek op de Insulineprofiel-pagina.
+    """
+    if not segments:
+        return []
+    bars = [{"hour": seg["minutes"] / 60, "value": seg["value"]} for seg in segments]
+    bars.append({"hour": 24.0, "value": segments[0]["value"]})
+    return bars

pyproject.toml

@@ -1,2 +1,15 @@
 [tool.pytest.ini_options]
 pythonpath = ["."]
+
+[tool.coverage.run]
+# app.py en pages/*.py zijn dunne Streamlit-UI-scripts (net als de
+# functies in ui_common.py/auth.py die zelf al met "# pragma: no cover"
+# gemarkeerd zijn) — niet zinvol te unit-testen zonder een draaiende
+# Streamlit-sessie, en buiten scope van de coverage-eis. De logica die ze
+# aanroepen (reporting.py, nightscout_client.py, data_processing.py,
+# pdf_export.py, profile_data.py, ui_common.py) wordt wél volledig gemeten.
+omit = ["app.py", "pages/*", "tests/*", ".venv/*"]
+
+[tool.coverage.report]
+fail_under = 80
+show_missing = true

reporting.py

@@ -15,6 +15,7 @@ import numpy as np
 import pandas as pd
 
 from nightscout_client import INSULIN_EVENT_TYPES
+from profile_data import value_at
 
 # Klinische drempels in mmol/L (Battelino et al. 2019, Diabetes Care 42(8):1593-1603).
 VERY_LOW = 3.0
@@ -440,6 +441,233 @@ def bolus_stats(treatments: pd.DataFrame) -> dict:
     }
 
 
+BOLUS_CATEGORY_FOOD = "food"
+BOLUS_CATEGORY_CORRECTION = "correction"
+BOLUS_CATEGORY_SMB = "smb"
+
+BOLUS_CATEGORY_COLUMNS = ["timestamp", "eventType", "insulin", "carbs", "category"]
+
+
+def classify_bolus(treatments: pd.DataFrame) -> pd.DataFrame:
+    """Splits bolus-insuline op in 'food'/'correction'/'smb', voor grafiekoverlays
+    (gitea insulineprofiel-werk, vervolg op bolus_stats()'s SMB-detectie).
+
+    Herkenningsregels, in volgorde:
+    - SMB: `eventType == "SMB"`, of `eventType == "Correction Bolus"` met
+      `type == "SMB"` (zie bolus_stats()) — automatische AndroidAPS-microbolus.
+    - food: `eventType == "Meal Bolus"`, of een andere bolus-eventType
+      (`Bolus`/`Combo Bolus`) met koolhydraten op dezelfde treatment-rij (een
+      sterke aanwijzing dat het om een maaltijdbolus gaat).
+    - correction: alle overige bolus-rijen (typisch `Correction Bolus` zonder
+      SMB-markering, of een generieke `Bolus`/`Combo Bolus` zonder koolhydraten).
+
+    Geeft een DataFrame terug met kolommen 'timestamp', 'eventType', 'insulin',
+    'carbs', 'category' — alleen de rijen die daadwerkelijk bolusinsuline zijn.
+    """
+    if treatments.empty:
+        return pd.DataFrame(columns=BOLUS_CATEGORY_COLUMNS)
+
+    bolus = treatments[treatments["eventType"].isin(INSULIN_EVENT_TYPES) & treatments["insulin"].notna()].copy()
+    if bolus.empty:
+        return pd.DataFrame(columns=BOLUS_CATEGORY_COLUMNS)
+
+    is_smb = (bolus["eventType"] == SMB_TYPE) | (
+        (bolus["eventType"] == "Correction Bolus") & (bolus.get("type", "") == SMB_TYPE)
+    )
+    has_carbs = pd.to_numeric(bolus["carbs"], errors="coerce").fillna(0) > 0
+    is_food = ~is_smb & ((bolus["eventType"] == "Meal Bolus") | has_carbs)
+
+    bolus["category"] = BOLUS_CATEGORY_CORRECTION
+    bolus.loc[is_food, "category"] = BOLUS_CATEGORY_FOOD
+    bolus.loc[is_smb, "category"] = BOLUS_CATEGORY_SMB
+    return bolus[BOLUS_CATEGORY_COLUMNS].sort_values("timestamp").reset_index(drop=True)
+
+
+def glucose_min_max(glucose: pd.DataFrame) -> tuple[float, float]:
+    """Minimum en maximum glucosewaarde (mmol/L) in de gegeven data."""
+    if glucose.empty:
+        return (0.0, 0.0)
+    return (round(glucose["mmol"].min(), 1), round(glucose["mmol"].max(), 1))
+
+
+def _temp_basal_intervals(treatments: pd.DataFrame, day_start, day_end) -> list[tuple]:
+    """Temp Basal-intervallen (start, eind, rate) die (deels) binnen [day_start, day_end) vallen,
+    geclipt aan de daggrenzen."""
+    if treatments is None or treatments.empty:
+        return []
+    temp_basal = treatments[
+        (treatments["eventType"] == BASAL_EVENT_TYPE)
+        & treatments["absolute"].notna()
+        & treatments["duration"].notna()
+    ]
+    intervals = []
+    for _, row in temp_basal.iterrows():
+        start = row["timestamp"]
+        end = start + pd.Timedelta(float(row["duration"]), unit="min")
+        if end <= day_start or start >= day_end:
+            continue
+        intervals.append((max(start, day_start), min(end, day_end), row["absolute"]))
+    return intervals
+
+
+def _basal_breakpoints(basal_segments: list[dict], temp_intervals: list[tuple], day_start, day_end) -> list:
+    """Alle tijdstippen waarop de effectieve basaalsnelheid kan wijzigen: daggrens,
+    profielsegment-grenzen, en temp-basal start/eind — oplopend gesorteerd."""
+    breakpoints = {day_start}
+    for seg in basal_segments:
+        ts = day_start + pd.Timedelta(float(seg["minutes"]), unit="min")
+        if day_start <= ts < day_end:
+            breakpoints.add(ts)
+    for start, end, _ in temp_intervals:
+        breakpoints.add(start)
+        breakpoints.add(end)
+    return sorted(t for t in breakpoints if day_start <= t < day_end)
+
+
+def _rate_at(ts, basal_segments: list[dict], temp_intervals: list[tuple], day_start) -> float:
+    """Effectieve basaalsnelheid op tijdstip `ts`: een actieve Temp Basal wint van het profieltempo."""
+    for start, end, rate in temp_intervals:
+        if start <= ts < end:
+            return rate
+    minutes_of_day = int((ts - day_start).total_seconds() / 60) % MINUTES_PER_DAY
+    value = value_at(basal_segments, minutes_of_day)
+    return value if value is not None else 0.0
+
+
+def effective_basal_series(profile: dict | None, treatments: pd.DataFrame, day_start, day_end) -> pd.DataFrame:
+    """Basaalsnelheid (E/uur) als stapfunctie over [day_start, day_end).
+
+    Basislijn is het pompprofiel se basaalschema (`profile["basal"]`); waar een
+    Temp Basal-treatment actief is (rate/duur uit de treatment) overschrijft
+    die het profieltempo voor de duur van die tijdelijke aanpassing — dit is de
+    daadwerkelijk afgeleverde basaal, in tegenstelling tot basal_stats() (die
+    alleen temp-basal-periodes optelt en periodes zonder actieve temp basal
+    mist omdat het profiel daar niet bij betrokken wordt).
+
+    Zonder profiel (`profile` is None/leeg) is de basislijn 0 — dan toont dit
+    alleen de temp-basal-periodes, net als basal_stats().
+
+    Geeft een DataFrame terug met kolommen 'timestamp' en 'rate': elke rij
+    geldt tot de volgende rij (stapfunctie), geschikt voor een
+    step-area-plot (line_shape='hv', fill='tozeroy').
+    """
+    day_start = pd.Timestamp(day_start)
+    day_end = pd.Timestamp(day_end)
+    basal_segments = (profile or {}).get("basal", [])
+    temp_intervals = _temp_basal_intervals(treatments, day_start, day_end)
+    breakpoints = _basal_breakpoints(basal_segments, temp_intervals, day_start, day_end)
+
+    rows = [
+        {"timestamp": ts, "rate": _rate_at(ts, basal_segments, temp_intervals, day_start)} for ts in breakpoints
+    ]
+    return pd.DataFrame(rows, columns=["timestamp", "rate"])
+
+
+def _target_midpoint_at(minutes_of_day: int, target_low: list[dict], target_high: list[dict]) -> float | None:
+    low = value_at(target_low, minutes_of_day) if target_low else None
+    high = value_at(target_high, minutes_of_day) if target_high else None
+    if low is not None and high is not None:
+        return (low + high) / 2
+    return low if low is not None else high
+
+
+def target_glucose_series(profile: dict | None, day_start, day_end) -> pd.DataFrame:
+    """Streefglucose (mmol/L) uit het profiel als stapfunctie over [day_start, day_end) —
+    het midden van target_low/target_high per tijdsegment (de pomp kent vaak één
+    streefwaarde per segment; met losse low/high-segmenten is dat gewoon het
+    gemiddelde). Geeft een lege DataFrame terug als het profiel geen streefwaarden
+    bevat.
+
+    Zelfde stapfunctie-vorm als effective_basal_series(): elke rij geldt tot de
+    volgende rij.
+    """
+    day_start = pd.Timestamp(day_start)
+    day_end = pd.Timestamp(day_end)
+    target_low = (profile or {}).get("target_low", [])
+    target_high = (profile or {}).get("target_high", [])
+    if not target_low and not target_high:
+        return pd.DataFrame(columns=["timestamp", "target"])
+
+    breakpoints = _basal_breakpoints(target_low + target_high, [], day_start, day_end)
+    rows = [
+        {
+            "timestamp": ts,
+            "target": _target_midpoint_at(
+                int((ts - day_start).total_seconds() / 60) % MINUTES_PER_DAY, target_low, target_high
+            ),
+        }
+        for ts in breakpoints
+    ]
+    return pd.DataFrame(rows, columns=["timestamp", "target"])
+
+
+def _integrate_step_series(series: pd.DataFrame, value_col: str, day_end) -> float:
+    """Oppervlakte onder een stapfunctie-DataFrame (zoals effective_basal_series()):
+    som van elke rij se waarde x duur (in uur) tot de volgende rij, of tot
+    `day_end` voor de laatste rij. Gebruikt om een E/uur-stapfunctie om te
+    rekenen naar een totaal aantal eenheden."""
+    if series.empty:
+        return 0.0
+    rows = series.reset_index(drop=True)
+    total = 0.0
+    for i in range(len(rows)):
+        start = rows.loc[i, "timestamp"]
+        end = rows.loc[i + 1, "timestamp"] if i + 1 < len(rows) else day_end
+        total += rows.loc[i, value_col] * (end - start).total_seconds() / 3600
+    return total
+
+
+DAILY_STATS_KEYS = [
+    "mean_mmol",
+    "min_mmol",
+    "max_mmol",
+    "total_insulin",
+    "basal_insulin",
+    "bolus_insulin",
+    "smb_insulin",
+    "hypo_count",
+    "hypo_duration_minutes",
+    "hyper_count",
+    "hyper_duration_minutes",
+]
+
+
+def daily_stats(day_glucose: pd.DataFrame, day_treatments: pd.DataFrame, profile: dict | None, day_start, day_end) -> dict:
+    """Samenvattende statistieken voor één kalenderdag, voor naast de dagelijkse
+    grafiek in het PDF-rapport: gemiddelde/min/max glucose, totale/basaal-/bolus-/
+    SMB-insuline (SMB apart gerapporteerd omdat dit zowel als basaal- als
+    bolus-vervanging kan gelden, zie classify_bolus()), en het aantal + de totale
+    duur van hypo- en hyperevents die dag.
+    """
+    min_mmol, max_mmol = glucose_min_max(day_glucose)
+    mean_mmol = round(day_glucose["mmol"].mean(), 1) if not day_glucose.empty else 0.0
+
+    bolus = classify_bolus(day_treatments)
+    by_category = bolus.groupby("category")["insulin"].sum().to_dict() if not bolus.empty else {}
+    bolus_units = by_category.get(BOLUS_CATEGORY_FOOD, 0.0) + by_category.get(BOLUS_CATEGORY_CORRECTION, 0.0)
+    smb_units = by_category.get(BOLUS_CATEGORY_SMB, 0.0)
+
+    basal_series = effective_basal_series(profile, day_treatments, day_start, day_end)
+    basal_units = _integrate_step_series(basal_series, "rate", day_end)
+
+    hypo_summary = summarize_events(detect_events(day_glucose, LOW, "below"), period_days=1)
+    hyper_summary = summarize_events(detect_events(day_glucose, HIGH, "above"), period_days=1)
+
+    return {
+        "mean_mmol": mean_mmol,
+        "min_mmol": min_mmol,
+        "max_mmol": max_mmol,
+        "total_insulin": round(bolus_units + smb_units + basal_units, 1),
+        "basal_insulin": round(basal_units, 1),
+        "bolus_insulin": round(bolus_units, 1),
+        "smb_insulin": round(smb_units, 1),
+        "hypo_count": hypo_summary["count"],
+        "hypo_duration_minutes": hypo_summary["total_minutes"],
+        "hyper_count": hyper_summary["count"],
+        "hyper_duration_minutes": hyper_summary["total_minutes"],
+    }
+
+
 def basal_stats(treatments: pd.DataFrame, period_days: float) -> dict:
     """Totale basaalinsuline uit Temp Basal-treatments (rate x duur), zonder pompprofiel.
 

requirements.txt

@@ -6,4 +6,5 @@ python-dotenv>=1.0,<2.0
 matplotlib>=3.9,<4.0
 fpdf2>=2.7,<3.0
 pytest>=8.0,<9.0
+pytest-cov>=5.0,<6.0
 ruff>=0.6,<1.0

sonar-project.properties

@@ -4,3 +4,4 @@ sonar.sources=.
 sonar.exclusions=data/**,tests/**
 sonar.tests=tests
 sonar.python.version=3.12
+sonar.python.coverage.reportPaths=coverage.xml

tests/test_nightscout_client.py

@@ -168,6 +168,50 @@ def test_fetch_treatments_range_filters_to_date_boundaries(monkeypatch):
     assert result.iloc[0]["insulin"] == pytest.approx(4.0)
 
 
+def test_fetch_entries_for_day_scopes_to_a_single_calendar_day(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    monkeypatch.delenv("NIGHTSCOUT_TOKEN", raising=False)
+    monkeypatch.delenv("NIGHTSCOUT_API_SECRET", raising=False)
+
+    payload = [
+        {"date": int(datetime(2026, 1, 15, 12, 0, tzinfo=timezone.utc).timestamp() * 1000), "sgv": 126},
+        {"date": int(datetime(2026, 1, 16, 12, 0, tzinfo=timezone.utc).timestamp() * 1000), "sgv": 100},
+    ]
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse(payload))
+
+    result = ns.fetch_entries_for_day(date(2026, 1, 15))
+    assert len(result) == 1
+    assert result.iloc[0]["timestamp"].date() == date(2026, 1, 15)
+
+
+def test_fetch_treatments_for_day_includes_lookback_before_midnight(monkeypatch):
+    """Een Temp Basal die net vóór middernacht is gestart moet toch meekomen
+    (zie de docstring bij fetch_treatments_for_day) — zonder lookback-marge
+    zou effective_basal_series() de vroege ochtenduren fout berekenen."""
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    payload = [
+        {"created_at": "2026-01-14T22:00:00Z", "eventType": "Temp Basal", "absolute": 1.2, "duration": 480},
+        {"created_at": "2026-01-15T12:00:00Z", "eventType": "Meal Bolus", "insulin": 4.0, "carbs": 40},
+        {"created_at": "2026-01-10T08:00:00Z", "eventType": "Meal Bolus", "insulin": 3.0, "carbs": 30},
+    ]
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse(payload))
+
+    result = ns.fetch_treatments_for_day(date(2026, 1, 15), lookback_hours=6)
+    assert len(result) == 2  # de 10 januari-bolus valt buiten de lookback-marge
+    assert set(result["eventType"]) == {"Temp Basal", "Meal Bolus"}
+
+
+def test_fetch_treatments_for_day_excludes_treatments_before_the_lookback_window(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    payload = [
+        {"created_at": "2026-01-14T12:00:00Z", "eventType": "Meal Bolus", "insulin": 4.0, "carbs": 40},
+    ]
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse(payload))
+
+    result = ns.fetch_treatments_for_day(date(2026, 1, 15), lookback_hours=6)
+    assert result.empty
+
+
 def _entry(dt, sgv=100):
     return {"date": int(dt.timestamp() * 1000), "sgv": sgv, "dateString": dt.isoformat()}
 
@@ -357,6 +401,29 @@ def test_fetch_treatments_range_retrieves_full_history_even_with_a_hidden_server
     assert result["timestamp"].max().date() == end_date
 
 
+def test_fetch_profile_returns_first_document(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    payload = [{"defaultProfile": "Default", "store": {"Default": {"dia": "5"}}}]
+
+    seen_params = []
+    monkeypatch.setattr(
+        ns.requests,
+        "get",
+        lambda *a, **k: (seen_params.append(k["params"]), FakeResponse(payload))[1],
+    )
+
+    result = ns.fetch_profile()
+    assert result == payload[0]
+    assert seen_params[0]["count"] == 1
+
+
+def test_fetch_profile_handles_empty_response(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse([]))
+
+    assert ns.fetch_profile() == {}
+
+
 def test_paginated_get_uses_created_at_field_for_treatments_cursor(monkeypatch):
     """Losstaande, gerichte assertie op het daadwerkelijke queryparameter-veld
     — moet `find[created_at][$lte]` zijn, nooit `find[date][$lte]`, voor het

tests/test_pdf_export.py

@@ -1,17 +1,32 @@
+import re
 from datetime import date
+from pathlib import Path
 
 import pandas as pd
+import pytest
 
 from pdf_export import (
     DAGOVERZICHT_MAX_DAYS,
     SECTION_AGP,
     SECTION_DAGOVERZICHT,
     SECTION_EVENTS,
+    SECTION_INSULIN_PROFILE,
     SECTION_SUMMARY,
+    _report_config,
     build_report_pdf,
 )
 
 
+def _page_count(pdf_bytes: bytes) -> int:
+    """Grof-maar-betrouwbaar paginatelling op de ruwe PDF-bytes: elke
+    paginaobject bevat een '/Type /Page' (zonder de 's' van '/Type /Pages')
+    in zijn objectdictionary, wat altijd onleesbaar-platte tekst blijft ook
+    al staat contentstream-compressie aan — geen losse PDF-parser nodig voor
+    deze structurele check (zelfde black-box-filosofie als de rest van deze
+    testset: geen pixel-vergelijkingen, wel structuur)."""
+    return len(re.findall(rb"/Type\s*/Page[^s]", pdf_bytes))
+
+
 def _sample_glucose():
     timestamps = pd.date_range("2026-01-01", periods=48, freq="30min")
     return pd.DataFrame({"timestamp": timestamps, "mmol": [6.0 + (i % 5) for i in range(48)]})
@@ -90,3 +105,185 @@ def test_build_report_pdf_draws_hypo_and_hyper_charts_when_events_present():
     )
     assert pdf_bytes.startswith(b"%PDF")
     assert len(pdf_bytes) > 500
+
+
+def test_build_report_pdf_always_has_at_least_a_cover_page():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    pdf_bytes = build_report_pdf([], empty, {}, date(2026, 1, 1), date(2026, 1, 2))
+    assert _page_count(pdf_bytes) == 1
+
+
+def test_build_report_pdf_one_page_per_single_page_section():
+    glucose = _sample_glucose()
+    pdf_bytes = build_report_pdf(
+        [SECTION_SUMMARY, SECTION_AGP], glucose, {}, date(2026, 1, 1), date(2026, 1, 2)
+    )
+    # cover + summary + agp = 3
+    assert _page_count(pdf_bytes) == 3
+
+
+def test_build_report_pdf_one_page_per_day_for_dagoverzicht():
+    timestamps = pd.date_range("2026-01-01", periods=3 * 24 * 12, freq="5min")  # 3 dagen
+    glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [7.0] * len(timestamps)})
+    pdf_bytes = build_report_pdf(
+        [SECTION_DAGOVERZICHT], glucose, {}, date(2026, 1, 1), date(2026, 1, 3)
+    )
+    # cover + 3 dagen
+    assert _page_count(pdf_bytes) == 4
+
+
+def test_build_report_pdf_caps_dagoverzicht_page_count_at_max_days():
+    timestamps = pd.date_range("2026-01-01", periods=(DAGOVERZICHT_MAX_DAYS + 10) * 24, freq="1h")
+    glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [7.0] * len(timestamps)})
+    pdf_bytes = build_report_pdf(
+        [SECTION_DAGOVERZICHT], glucose, {}, timestamps[0].date(), timestamps[-1].date()
+    )
+    # cover + max. DAGOVERZICHT_MAX_DAYS dagpagina's, ondanks (DAGOVERZICHT_MAX_DAYS + 10) dagen aan data
+    assert _page_count(pdf_bytes) == 1 + DAGOVERZICHT_MAX_DAYS
+
+
+def test_build_report_pdf_includes_insulin_profile_section_page():
+    glucose = _sample_glucose()
+    profile = {
+        "name": "Default",
+        "dia": 5.0,
+        "basal": [{"minutes": 0, "value": 0.8}, {"minutes": 720, "value": 1.1}],
+        "sens": [{"minutes": 0, "value": 2.8}],
+        "carbratio": [{"minutes": 0, "value": 10.0}],
+        "target_low": [{"minutes": 0, "value": 4.5}],
+        "target_high": [{"minutes": 0, "value": 8.0}],
+    }
+    pdf_bytes = build_report_pdf(
+        [SECTION_INSULIN_PROFILE], glucose, {}, date(2026, 1, 1), date(2026, 1, 2), profile=profile
+    )
+    # cover + insulineprofiel
+    assert _page_count(pdf_bytes) == 2
+    assert pdf_bytes.startswith(b"%PDF")
+
+
+def test_build_report_pdf_insulin_profile_section_handles_missing_profile():
+    glucose = _sample_glucose()
+    pdf_bytes = build_report_pdf(
+        [SECTION_INSULIN_PROFILE], glucose, {}, date(2026, 1, 1), date(2026, 1, 2), profile=None
+    )
+    assert _page_count(pdf_bytes) == 2
+    assert pdf_bytes.startswith(b"%PDF")
+
+
+def test_build_report_pdf_insulin_profile_section_renders_without_glucose():
+    """De Insulineprofiel-sectie gaat niet over glucosedata en moet dus ook
+    renderen als er geen glucosemetingen zijn (in tegenstelling tot
+    Samenvatting/AGP/Dagoverzicht, die wel glucosedata vereisen)."""
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    profile = {
+        "name": "Default",
+        "dia": 5.0,
+        "basal": [{"minutes": 0, "value": 0.8}],
+        "sens": [{"minutes": 0, "value": 2.8}],
+        "carbratio": [{"minutes": 0, "value": 10.0}],
+        "target_low": [],
+        "target_high": [],
+    }
+    pdf_bytes = build_report_pdf(
+        [SECTION_INSULIN_PROFILE], empty, {}, date(2026, 1, 1), date(2026, 1, 2), profile=profile
+    )
+    assert _page_count(pdf_bytes) == 2
+
+
+def test_build_report_pdf_dagoverzicht_uses_treatments_and_profile_without_error():
+    """Regressietest: de dagpagina moet basaal/bolus/koolhydraten/streefwaarde-
+    overlays tekenen zonder te crashen als er echte treatments/profile worden
+    meegegeven (in plaats van de lege defaults)."""
+    timestamps = pd.date_range("2026-01-01", periods=24 * 12, freq="5min")  # 1 dag
+    glucose = pd.DataFrame({"timestamp": timestamps, "mmol": [7.0] * len(timestamps)})
+    treatments = pd.DataFrame(
+        [
+            {
+                "timestamp": pd.Timestamp("2026-01-01 08:00"),
+                "eventType": "Meal Bolus",
+                "insulin": 4.0,
+                "carbs": 40.0,
+                "type": "",
+                "absolute": None,
+                "duration": None,
+            },
+            {
+                "timestamp": pd.Timestamp("2026-01-01 12:00"),
+                "eventType": "Correction Bolus",
+                "insulin": 0.3,
+                "carbs": None,
+                "type": "SMB",
+                "absolute": None,
+                "duration": None,
+            },
+            {
+                "timestamp": pd.Timestamp("2026-01-01 06:00"),
+                "eventType": "Temp Basal",
+                "insulin": None,
+                "carbs": None,
+                "type": "",
+                "absolute": 2.0,
+                "duration": 60,
+            },
+        ]
+    )
+    profile = {
+        "name": "Default",
+        "dia": 5.0,
+        "basal": [{"minutes": 0, "value": 0.8}],
+        "sens": [{"minutes": 0, "value": 2.8}],
+        "carbratio": [{"minutes": 0, "value": 10.0}],
+        "target_low": [{"minutes": 0, "value": 4.5}],
+        "target_high": [{"minutes": 0, "value": 8.0}],
+    }
+    pdf_bytes = build_report_pdf(
+        [SECTION_DAGOVERZICHT],
+        glucose,
+        {},
+        date(2026, 1, 1),
+        date(2026, 1, 1),
+        treatments=treatments,
+        profile=profile,
+    )
+    assert pdf_bytes.startswith(b"%PDF")
+    assert _page_count(pdf_bytes) == 2  # cover + 1 dag
+
+
+def test_report_config_uses_defaults_when_env_vars_unset(monkeypatch):
+    for var in ("REPORT_INSULIN_TYPE", "REPORT_PUMP_TYPE", "REPORT_SENSOR_TYPE", "NIGHTSCOUT_URL"):
+        monkeypatch.delenv(var, raising=False)
+    config = _report_config()
+    assert config["insulin_type"] == "Onbekend insulinetype"
+    assert config["pump_type"] == "Onbekende pomp"
+    assert config["sensor_type"] == "Onbekende sensor"
+    assert config["nightscout_url"] == ""
+
+
+def test_report_config_reads_env_vars_when_set(monkeypatch):
+    monkeypatch.setenv("REPORT_INSULIN_TYPE", "Lyumjev 100U")
+    monkeypatch.setenv("REPORT_PUMP_TYPE", "Omnipod DASH")
+    monkeypatch.setenv("REPORT_SENSOR_TYPE", "Dexcom G7")
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com/")
+    config = _report_config()
+    assert config["insulin_type"] == "Lyumjev 100U"
+    assert config["pump_type"] == "Omnipod DASH"
+    assert config["sensor_type"] == "Dexcom G7"
+    assert config["nightscout_url"] == "https://example.com"  # trailing slash gestript
+
+
+def test_icon_asset_exists():
+    icon_path = Path(__file__).parent.parent / "assets" / "icon.svg"
+    assert icon_path.exists()
+    assert icon_path.read_text(encoding="utf-8").strip().startswith("<svg")
+
+
+@pytest.mark.parametrize("section", [SECTION_SUMMARY, SECTION_AGP, SECTION_DAGOVERZICHT, SECTION_EVENTS])
+def test_all_sections_still_work_without_treatments_or_profile_args(section):
+    """Achterwaartse compatibiliteit: treatments/profile zijn optioneel — een
+    aanroep zoals vóór deze PR (zonder die argumenten) moet blijven werken."""
+    glucose = _glucose_with_hypo_and_hyper_events() if section == SECTION_EVENTS else _sample_glucose()
+    events_summary = {"Hypo Level 1": {"count": 1, "avg_duration_minutes": 25.0, "events_per_week": 2.3}}
+    pdf_bytes = build_report_pdf(
+        [section], glucose, events_summary if section == SECTION_EVENTS else {}, date(2026, 1, 1), date(2026, 1, 4)
+    )
+    assert pdf_bytes.startswith(b"%PDF")

tests/test_profile_data.py

@@ -0,0 +1,117 @@
+import pytest
+
+import profile_data as pd_
+
+
+def _raw_profile(units="mg/dl", default_name="Default"):
+    return {
+        "defaultProfile": default_name,
+        "store": {
+            "Default": {
+                "units": units,
+                "dia": "5",
+                "basal": [
+                    {"time": "00:00", "value": "0.8"},
+                    {"time": "06:00", "value": "1.2"},
+                    {"time": "22:00", "value": "0.7"},
+                ],
+                "sens": [{"time": "00:00", "value": "50"}],
+                "carbratio": [{"time": "00:00", "value": "10"}],
+                "target_low": [{"time": "00:00", "value": "90"}],
+                "target_high": [{"time": "00:00", "value": "120"}],
+            }
+        },
+    }
+
+
+def test_parse_profile_returns_empty_dict_for_empty_input():
+    assert pd_.parse_profile({}) == {}
+
+
+def test_parse_profile_converts_mgdl_to_mmol():
+    profile = pd_.parse_profile(_raw_profile(units="mg/dl"))
+    assert profile["units"] == "mmol"
+    # 90 mg/dl -> ~5.0 mmol/L, 120 mg/dl -> ~6.7 mmol/L, 50 mg/dl (ISF) -> ~2.8 mmol/L
+    assert profile["target_low"][0]["value"] == pytest.approx(5.0, abs=0.05)
+    assert profile["target_high"][0]["value"] == pytest.approx(6.7, abs=0.05)
+    assert profile["sens"][0]["value"] == pytest.approx(2.8, abs=0.05)
+
+
+def test_parse_profile_leaves_mmol_profiles_unconverted():
+    profile = pd_.parse_profile(_raw_profile(units="mmol"))
+    assert profile["target_low"][0]["value"] == pytest.approx(90.0)
+
+
+def test_parse_profile_does_not_convert_basal_or_carbratio():
+    profile = pd_.parse_profile(_raw_profile(units="mg/dl"))
+    assert profile["basal"][0]["value"] == pytest.approx(0.8)
+    assert profile["carbratio"][0]["value"] == pytest.approx(10.0)
+
+
+def test_parse_profile_sorts_basal_segments_by_time():
+    raw = _raw_profile()
+    raw["store"]["Default"]["basal"] = [
+        {"time": "22:00", "value": "0.7"},
+        {"time": "00:00", "value": "0.8"},
+        {"time": "06:00", "value": "1.2"},
+    ]
+    profile = pd_.parse_profile(raw)
+    assert [seg["minutes"] for seg in profile["basal"]] == [0, 360, 1320]
+
+
+def test_parse_profile_falls_back_to_first_profile_when_default_name_missing():
+    raw = _raw_profile(default_name="DoesNotExist")
+    profile = pd_.parse_profile(raw)
+    assert profile["dia"] == pytest.approx(5.0)
+
+
+def test_parse_profile_dia_and_name():
+    profile = pd_.parse_profile(_raw_profile())
+    assert profile["dia"] == pytest.approx(5.0)
+    assert profile["name"] == "Default"
+
+
+def test_value_at_returns_active_segment():
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}, {"minutes": 1320, "value": 0.7}]
+    assert pd_.value_at(segments, 100) == pytest.approx(0.8)
+    assert pd_.value_at(segments, 360) == pytest.approx(1.2)
+    assert pd_.value_at(segments, 1000) == pytest.approx(1.2)
+    assert pd_.value_at(segments, 1350) == pytest.approx(0.7)
+
+
+def test_value_at_wraps_to_last_segment_before_first():
+    segments = [{"minutes": 360, "value": 1.2}, {"minutes": 1320, "value": 0.7}]
+    assert pd_.value_at(segments, 100) == pytest.approx(0.7)
+
+
+def test_value_at_empty_segments_returns_none():
+    assert pd_.value_at([], 100) is None
+
+
+def test_segments_table_computes_end_times_from_next_segment():
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]
+    rows = pd_.segments_table(segments)
+    assert rows == [
+        {"start": "00:00", "end": "06:00", "value": 0.8},
+        {"start": "06:00", "end": "24:00", "value": 1.2},
+    ]
+
+
+def test_segments_to_hourly_bars_closes_the_24h_loop():
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]
+    bars = pd_.segments_to_hourly_bars(segments)
+    assert bars[-1] == {"hour": 24.0, "value": 0.8}
+
+
+def test_segments_to_hourly_bars_empty_input():
+    assert pd_.segments_to_hourly_bars([]) == []
+
+
+def test_total_daily_basal_weights_by_segment_duration():
+    # 6h @ 0.8 + 18h @ 1.2 = 4.8 + 21.6 = 26.4
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]
+    assert pd_.total_daily_basal(segments) == pytest.approx(26.4)
+
+
+def test_total_daily_basal_empty_input():
+    assert pd_.total_daily_basal([]) == 0.0

tests/test_reporting.py

@@ -8,10 +8,14 @@ from reporting import (
     agp_percentile_bands,
     basal_stats,
     bolus_stats,
+    classify_bolus,
     completeness_warning,
     daily_gmi,
+    daily_stats,
     day_of_week_pattern,
     detect_events,
+    effective_basal_series,
+    glucose_min_max,
     gmi_mmol_per_mol,
     gmi_percent,
     gmi_trend,
@@ -21,6 +25,7 @@ from reporting import (
     sensor_active_percent,
     site_interval_stats,
     summarize_events,
+    target_glucose_series,
     tir_breakdown_5band,
 )
 
@@ -480,6 +485,209 @@ 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_daily_stats_computes_glucose_and_insulin_breakdown():
+    day_start = pd.Timestamp("2026-01-01")
+    day_end = pd.Timestamp("2026-01-02")
+    glucose = _series(
+        [("2026-01-01 08:00", 3.2), ("2026-01-01 12:00", 14.1), ("2026-01-01 20:00", 6.5)]
+    )
+    treatments = pd.DataFrame(
+        [
+            {
+                "timestamp": pd.Timestamp("2026-01-01 08:00"),
+                "eventType": "Meal Bolus",
+                "insulin": 4.0,
+                "carbs": 40.0,
+                "type": "",
+                "absolute": None,
+                "duration": None,
+            },
+            {
+                "timestamp": pd.Timestamp("2026-01-01 12:00"),
+                "eventType": "Correction Bolus",
+                "insulin": 0.3,
+                "carbs": None,
+                "type": "SMB",
+                "absolute": None,
+                "duration": None,
+            },
+            {
+                "timestamp": pd.Timestamp("2026-01-01 06:00"),
+                "eventType": "Temp Basal",
+                "insulin": None,
+                "carbs": None,
+                "type": "",
+                "absolute": 2.0,
+                "duration": 60,
+            },
+        ]
+    )
+    profile = {"basal": [{"minutes": 0, "value": 0.8}]}
+
+    result = daily_stats(glucose, treatments, profile, day_start, day_end)
+
+    assert result["mean_mmol"] == pytest.approx((3.2 + 14.1 + 6.5) / 3, abs=0.05)
+    assert result["min_mmol"] == pytest.approx(3.2)
+    assert result["max_mmol"] == pytest.approx(14.1)
+    assert result["bolus_insulin"] == pytest.approx(4.0)
+    assert result["smb_insulin"] == pytest.approx(0.3)
+    # basaal: 23h @ 0.8 profieltempo + 1h @ 2.0 (temp basal 06:00-07:00) = 18.4 + 2.0 = 20.4
+    assert result["basal_insulin"] == pytest.approx(20.4, abs=0.05)
+    assert result["total_insulin"] == pytest.approx(4.0 + 0.3 + 20.4, abs=0.05)
+    assert result["hypo_count"] == 1
+    assert result["hyper_count"] == 1
+
+
+def test_daily_stats_handles_empty_glucose_and_treatments():
+    day_start = pd.Timestamp("2026-01-01")
+    day_end = pd.Timestamp("2026-01-02")
+    empty_glucose = pd.DataFrame(columns=["timestamp", "mmol"])
+    empty_treatments = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs", "type", "absolute", "duration"])
+
+    result = daily_stats(empty_glucose, empty_treatments, None, day_start, day_end)
+
+    assert result["mean_mmol"] == 0.0
+    assert result["min_mmol"] == 0.0
+    assert result["max_mmol"] == 0.0
+    assert result["total_insulin"] == 0.0
+    assert result["hypo_count"] == 0
+    assert result["hyper_count"] == 0
+
+
 def test_site_interval_stats_splits_by_site_change():
     glucose = _series(
         [

tests/test_ui_common.py

@@ -1,14 +1,38 @@
+from types import SimpleNamespace
+
+import pandas as pd
 import plotly.graph_objects as go
+import streamlit as st
 
 from ui_common import (
     PALETTE,
     THRESHOLD_LINES,
+    add_basal_area_trace,
+    add_bolus_marker_traces,
+    add_carb_bars_trace,
+    add_target_line_trace,
     add_threshold_lines,
     build_tir_stacked_bar_figure,
+    get_colors,
     hex_to_rgba,
 )
 
 
+def test_get_colors_returns_dark_palette_when_theme_is_dark(monkeypatch):
+    monkeypatch.setattr(st, "context", SimpleNamespace(theme=SimpleNamespace(type="dark")))
+    assert get_colors() == PALETTE["dark"]
+
+
+def test_get_colors_returns_light_palette_when_theme_is_light(monkeypatch):
+    monkeypatch.setattr(st, "context", SimpleNamespace(theme=SimpleNamespace(type="light")))
+    assert get_colors() == PALETTE["light"]
+
+
+def test_get_colors_falls_back_to_light_when_theme_context_unavailable(monkeypatch):
+    monkeypatch.setattr(st, "context", SimpleNamespace())  # geen .theme-attribuut
+    assert get_colors() == PALETTE["light"]
+
+
 def test_hex_to_rgba_converts_correctly():
     assert hex_to_rgba("#2a78d6", 0.3) == "rgba(42,120,214,0.3)"
 
@@ -36,3 +60,127 @@ def test_build_tir_stacked_bar_figure_has_one_bar_trace_per_band():
     assert len(fig.data) == 5
     assert [trace.y[0] for trace in fig.data] == [1.0, 3.0, 80.0, 12.0, 4.0]
     assert fig.layout.barmode == "stack"
+
+
+def test_add_basal_area_trace_adds_one_filled_step_trace():
+    basal = pd.DataFrame(
+        {"timestamp": pd.to_datetime(["2026-01-01 00:00", "2026-01-01 06:00"]), "rate": [0.8, 1.2]}
+    )
+    fig = go.Figure()
+    add_basal_area_trace(fig, basal, PALETTE["light"])
+    assert len(fig.data) == 1
+    assert fig.data[0].name == "Basaal"
+    assert fig.data[0].fill == "tozeroy"
+    assert fig.data[0].line.shape == "hv"
+
+
+def test_add_basal_area_trace_supports_secondary_y_on_a_subplot_figure():
+    from plotly.subplots import make_subplots
+
+    basal = pd.DataFrame(
+        {"timestamp": pd.to_datetime(["2026-01-01 00:00", "2026-01-01 06:00"]), "rate": [0.8, 1.2]}
+    )
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    add_basal_area_trace(fig, basal, PALETTE["light"], row=1, secondary_y=True)
+    assert len(fig.data) == 1
+    assert fig.data[0].yaxis == "y2"
+
+
+def test_add_basal_area_trace_skips_empty_input():
+    fig = go.Figure()
+    add_basal_area_trace(fig, pd.DataFrame(columns=["timestamp", "rate"]), PALETTE["light"])
+    assert len(fig.data) == 0
+
+
+def test_add_carb_bars_trace_supports_secondary_y_on_a_subplot_figure():
+    from plotly.subplots import make_subplots
+
+    carbs = pd.DataFrame({"timestamp": pd.to_datetime(["2026-01-01 08:00"]), "carbs": [40.0]})
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    add_carb_bars_trace(fig, carbs, PALETTE["light"], row=1, secondary_y=True)
+    assert len(fig.data) == 1
+    assert fig.data[0].yaxis == "y2"
+
+
+def test_add_carb_bars_trace_adds_one_bar_trace():
+    carbs = pd.DataFrame({"timestamp": pd.to_datetime(["2026-01-01 08:00"]), "carbs": [40.0]})
+    fig = go.Figure()
+    add_carb_bars_trace(fig, carbs, PALETTE["light"])
+    assert len(fig.data) == 1
+    assert fig.data[0].name == "Koolhydraten"
+    assert isinstance(fig.data[0], go.Bar)
+
+
+def test_add_carb_bars_trace_skips_empty_input():
+    fig = go.Figure()
+    add_carb_bars_trace(fig, pd.DataFrame(columns=["timestamp", "carbs"]), PALETTE["light"])
+    assert len(fig.data) == 0
+
+
+def test_add_bolus_marker_traces_adds_one_trace_per_category_present():
+    bolus = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 12:00"]),
+            "insulin": [4.0, 0.3],
+            "category": ["food", "smb"],
+        }
+    )
+    fig = go.Figure()
+    add_bolus_marker_traces(fig, bolus, PALETTE["light"], lane_y=2.0)
+    names = {trace.name for trace in fig.data}
+    assert names == {"Bolus (voeding)", "SMB"}
+
+
+def test_add_bolus_marker_traces_labels_food_and_correction_but_not_smb():
+    bolus = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 12:00"]),
+            "insulin": [4.0, 0.3],
+            "category": ["food", "smb"],
+        }
+    )
+    fig = go.Figure()
+    add_bolus_marker_traces(fig, bolus, PALETTE["light"], lane_y=2.0)
+    by_name = {trace.name: trace for trace in fig.data}
+    assert by_name["Bolus (voeding)"].mode == "markers+text"
+    assert by_name["SMB"].mode == "markers"
+
+
+def test_add_bolus_marker_traces_skips_empty_input():
+    fig = go.Figure()
+    add_bolus_marker_traces(fig, pd.DataFrame(columns=["timestamp", "insulin", "category"]), PALETTE["light"], 2.0)
+    assert len(fig.data) == 0
+
+
+def test_add_target_line_trace_adds_one_line_trace():
+    target = pd.DataFrame({"timestamp": pd.to_datetime(["2026-01-01 00:00"]), "target": [6.5]})
+    fig = go.Figure()
+    add_target_line_trace(fig, target, PALETTE["light"])
+    assert len(fig.data) == 1
+    assert fig.data[0].name == "Streefwaarde (pomp)"
+
+
+def test_add_target_line_trace_skips_empty_input():
+    fig = go.Figure()
+    add_target_line_trace(fig, pd.DataFrame(columns=["timestamp", "target"]), PALETTE["light"])
+    assert len(fig.data) == 0
+
+
+def test_overlay_traces_support_an_hour_based_x_column_for_dagoverzicht():
+    """Dagoverzicht plot elke dag op een gedeelde 0-24u-as ('hour') i.p.v. echte
+    datums; de hover mag daar dan ook geen datumformattering tonen."""
+    basal = pd.DataFrame({"hour": [0.0, 6.0], "rate": [0.8, 1.2]})
+    carbs = pd.DataFrame({"hour": [8.0], "carbs": [40.0]})
+    bolus = pd.DataFrame({"hour": [8.0], "insulin": [4.0], "category": ["food"]})
+    target = pd.DataFrame({"hour": [0.0], "target": [6.5]})
+
+    fig = go.Figure()
+    add_basal_area_trace(fig, basal, PALETTE["light"], x_col="hour")
+    add_carb_bars_trace(fig, carbs, PALETTE["light"], x_col="hour")
+    add_bolus_marker_traces(fig, bolus, PALETTE["light"], lane_y=2.0, x_col="hour")
+    add_target_line_trace(fig, target, PALETTE["light"], x_col="hour")
+
+    assert len(fig.data) == 4
+    bolus_trace = next(t for t in fig.data if t.name == "Bolus (voeding)")
+    assert list(bolus_trace.x) == [8.0]
+    assert "%d-%m" not in bolus_trace.hovertemplate

ui_common.py

@@ -14,7 +14,14 @@ import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
 
-from nightscout_client import fetch_entries_range, fetch_treatments_range
+from nightscout_client import (
+    fetch_entries_for_day,
+    fetch_entries_range,
+    fetch_profile,
+    fetch_treatments_for_day,
+    fetch_treatments_range,
+)
+from profile_data import parse_profile
 from reporting import (
     HIGH,
     LOW,
@@ -57,6 +64,11 @@ PALETTE = {
         "tir_in_range": "#0ca30c",
         "tir_high": "#fab219",
         "tir_very_high": "#c76f0a",
+        "basal": "#7b5ea3",
+        "bolus_food": "#1baf7a",
+        "bolus_correction": "#2a78d6",
+        "bolus_smb": "#898781",
+        "target": "#52514e",
     },
     "dark": {
         "glucose": "#3987e5",
@@ -76,6 +88,11 @@ PALETTE = {
         "tir_in_range": "#0ca30c",
         "tir_high": "#fab219",
         "tir_very_high": "#d9852e",
+        "basal": "#9b7ec2",
+        "bolus_food": "#199e70",
+        "bolus_correction": "#3987e5",
+        "bolus_smb": "#898781",
+        "target": "#c3c2b7",
     },
 }
 
@@ -107,8 +124,12 @@ def get_colors() -> dict:
     return PALETTE["dark" if theme_type == "dark" else "light"]
 
 
-def select_period_days(default_label: str) -> tuple[date, date, int]:
+def select_period_days(default_label: str) -> tuple[date, date, int]:  # pragma: no cover
-    """Periodekiezer voor rapportagepagina's; geeft (start, eind, aantal_dagen) terug."""
+    """Periodekiezer voor rapportagepagina's; geeft (start, eind, aantal_dagen) terug.
+
+    Pure Streamlit-widget-orkestratie (st.radio) — buiten scope van de
+    unit-testdekking net als pages/*.py, zie pyproject.toml se coverage-omit-lijst.
+    """
     labels = list(PERIOD_OPTIONS_DAYS.keys())
     label = st.radio("Periode", labels, index=labels.index(default_label), horizontal=True)
     days = PERIOD_OPTIONS_DAYS[label]
@@ -118,17 +139,53 @@ def select_period_days(default_label: str) -> tuple[date, date, int]:
 
 
 @st.cache_data(ttl=3600, show_spinner="Data ophalen van Nightscout...")
-def load_range_data(start: date, end: date):
+def load_range_data(start: date, end: date):  # pragma: no cover
     """Gecachete Nightscout-fetch voor een vaste periode.
 
     Lange TTL (in tegenstelling tot de 60s van de live-pagina): historische
     data van een afgeronde periode verandert niet.
+
+    Dunne st.cache_data-wrapper rond al-geteste fetch_entries_range()/
+    fetch_treatments_range() — buiten scope van de unit-testdekking net als
+    pages/*.py, zie pyproject.toml se coverage-omit-lijst.
     """
     glucose = fetch_entries_range(start, end)
     treatments = fetch_treatments_range(start, end)
     return glucose, treatments
 
 
+@st.cache_data(ttl=300, show_spinner="Data ophalen van Nightscout...")
+def load_day_data(day: date):  # pragma: no cover
+    """Gecachete Nightscout-fetch voor precies één kalenderdag (Dagoverzicht-navigatie).
+
+    Kortere TTL dan load_range_data (5 min i.p.v. 1 uur): in tegenstelling tot
+    een afgeronde periode kan de geselecteerde dag "vandaag" zijn, waar
+    nieuwe metingen blijven binnenkomen. fetch_treatments_for_day() haalt
+    zelf een lookback-marge vóór middernacht op zodat effective_basal_series()
+    de vroege ochtenduren correct kan berekenen.
+
+    Dunne st.cache_data-wrapper rond al-geteste fetch_entries_for_day()/
+    fetch_treatments_for_day() — buiten scope van de unit-testdekking net als
+    pages/*.py, zie pyproject.toml se coverage-omit-lijst.
+    """
+    glucose = fetch_entries_for_day(day)
+    treatments = fetch_treatments_for_day(day)
+    return glucose, treatments
+
+
+@st.cache_data(ttl=21600, show_spinner="Profiel ophalen van Nightscout...")
+def load_profile() -> dict:  # pragma: no cover
+    """Gecachete profiel-fetch. Langere TTL dan load_range_data (6 uur i.p.v.
+    1 uur): een pompprofiel wijzigt zelden, in tegenstelling tot glucose-/
+    behandeldata van een lopende periode.
+
+    Dunne st.cache_data-wrapper rond al-geteste fetch_profile()/parse_profile()
+    — buiten scope van de unit-testdekking net als pages/*.py.
+    """
+    raw = fetch_profile()
+    return parse_profile(raw)
+
+
 def add_threshold_lines(fig: go.Figure, colors: dict, row: int | None = None) -> None:
     """Teken de 4 klinische drempellijnen (3.0/3.9/10.0/13.9 mmol/L) op een grafiek.
 
@@ -189,19 +246,175 @@ def build_tir_stacked_bar_figure(tir: dict, colors: dict) -> go.Figure:
     return fig
 
 
-def render_completeness_warning(glucose: pd.DataFrame, start: date, end: date) -> None:
+BOLUS_CATEGORY_LABELS = {
+    "food": "Bolus (voeding)",
+    "correction": "Bolus (correctie)",
+    "smb": "SMB",
+}
+BOLUS_CATEGORY_COLOR_KEYS = {
+    "food": "bolus_food",
+    "correction": "bolus_correction",
+    "smb": "bolus_smb",
+}
+# Tekenvolgorde van bolus-categorieën: SMB eerst (onderop, meestal talrijker en
+# kleiner), voeding/correctie erbovenop (grotere, gelabelde stippen blijven
+# zichtbaar in plaats van bedolven te raken onder de SMB-stippen).
+BOLUS_CATEGORY_DRAW_ORDER = ["smb", "correction", "food"]
+
+
+def add_basal_area_trace(
+    fig: go.Figure,
+    basal: pd.DataFrame,
+    colors: dict,
+    *,
+    row: int | None = None,
+    secondary_y: bool = False,
+    x_col: str = "timestamp",
+) -> None:
+    """Basaal-insuline als semi-transparant stapvlak (line_shape='hv'), met alpha
+    zodat onderliggende data zichtbaar blijft. Verwacht kolommen 'rate' en
+    `x_col` (standaard 'timestamp'; de Dagoverzicht-pagina geeft hier 'hour'
+    aan omdat elke dag daar op een gedeelde 0-24u-as staat i.p.v. echte
+    datums — zie reporting.effective_basal_series). Bedoeld voor een
+    secundaire y-as (E/uur past niet op de mmol/L-schaal van de glucoselijn).
+    """
+    if basal.empty:
+        return
+    kwargs: dict = {"row": row, "col": 1} if row is not None else {}
+    if secondary_y:
+        kwargs["secondary_y"] = True
+    fig.add_trace(
+        go.Scatter(
+            x=basal[x_col],
+            y=basal["rate"],
+            mode="lines",
+            line={"shape": "hv", "color": colors["basal"], "width": 1},
+            fill="tozeroy",
+            fillcolor=hex_to_rgba(colors["basal"], 0.3),
+            opacity=0.8,
+            name="Basaal",
+            hovertemplate="%{y:.2f} E/uur<extra></extra>",
+        ),
+        **kwargs,
+    )
+
+
+def add_carb_bars_trace(
+    fig: go.Figure,
+    carbs: pd.DataFrame,
+    colors: dict,
+    *,
+    row: int | None = None,
+    secondary_y: bool = False,
+    x_col: str = "timestamp",
+) -> None:
+    """Koolhydraten als staafjes op het moment van invoer. Verwacht kolommen
+    'carbs' en `x_col` (zie add_basal_area_trace voor waarom dit configureerbaar
+    is). Zelfde secundaire as als de basaal-area (allebei geen mmol/L-grootheid),
+    met een eigen kleur en alpha zodat beide onderscheidbaar blijven."""
+    if carbs.empty:
+        return
+    kwargs: dict = {"row": row, "col": 1} if row is not None else {}
+    if secondary_y:
+        kwargs["secondary_y"] = True
+    fig.add_trace(
+        go.Bar(
+            x=carbs[x_col],
+            y=carbs["carbs"],
+            marker_color=colors["carbs"],
+            opacity=0.55,
+            name="Koolhydraten",
+            hovertemplate="%{y:.0f} g<extra></extra>",
+        ),
+        **kwargs,
+    )
+
+
+def add_bolus_marker_traces(
+    fig: go.Figure,
+    bolus: pd.DataFrame,
+    colors: dict,
+    lane_y: float,
+    *,
+    row: int | None = None,
+    x_col: str = "timestamp",
+) -> None:
+    """Eén Scatter-trace per boluscategorie (food/correction/smb), gepositioneerd
+    op een vaste 'lane'-hoogte op de primaire (mmol/L-)as — net als de bestaande
+    insuline/carbs-lanes op de Live-pagina. De hoogte zelf draagt geen
+    kwantitatieve betekenis; de markergrootte (geschaald naar toegediende
+    eenheden) en hoverwaarde wel. Voeding/correctie krijgen een tekstlabel met
+    de dosis; SMB's niet (te talrijk/te klein voor leesbare labels). Verwacht
+    kolommen 'insulin'/'category' en `x_col` (zie reporting.classify_bolus).
+    """
+    # Datumformattering in de hover werkt alleen op een echte datetime-as; de
+    # Dagoverzicht-pagina geeft hier 'hour' (een kaal getal 0-24) aan, dus
+    # daar laten we het tijdstip weg en toont de hover alleen de dosis.
+    hover_prefix = "%{x|%d-%m %H:%M} · " if x_col == "timestamp" else ""
+    kwargs: dict = {"row": row, "col": 1} if row is not None else {}
+    for category in BOLUS_CATEGORY_DRAW_ORDER:
+        subset = bolus[bolus["category"] == category] if not bolus.empty else bolus
+        if subset.empty:
+            continue
+        show_label = category != "smb"
+        fig.add_trace(
+            go.Scatter(
+                x=subset[x_col],
+                y=[lane_y] * len(subset),
+                mode="markers+text" if show_label else "markers",
+                marker={
+                    "size": (subset["insulin"] * 8).clip(lower=6, upper=30),
+                    "color": colors[BOLUS_CATEGORY_COLOR_KEYS[category]],
+                    "opacity": 0.75,
+                },
+                text=[f"{v:.1f}E" for v in subset["insulin"]] if show_label else None,
+                textposition="top center",
+                textfont={"size": 9, "color": colors["text"]},
+                name=BOLUS_CATEGORY_LABELS[category],
+                customdata=subset["insulin"],
+                hovertemplate=f"{hover_prefix}%{{customdata:.2f}} E<extra></extra>",
+            ),
+            **kwargs,
+        )
+
+
+def add_target_line_trace(
+    fig: go.Figure, target: pd.DataFrame, colors: dict, *, row: int | None = None, x_col: str = "timestamp"
+) -> None:
+    """Streefglucose uit het pompprofiel als lijn op de primaire (mmol/L-)as.
+    Verwacht kolommen 'target' en `x_col` (zie reporting.target_glucose_series)."""
+    if target.empty:
+        return
+    kwargs: dict = {"row": row, "col": 1} if row is not None else {}
+    fig.add_trace(
+        go.Scatter(
+            x=target[x_col],
+            y=target["target"],
+            mode="lines",
+            line={"shape": "hv", "color": colors["target"], "width": 1.5, "dash": "dashdot"},
+            opacity=0.9,
+            name="Streefwaarde (pomp)",
+            hovertemplate="%{y:.1f} mmol/L<extra></extra>",
+        ),
+        **kwargs,
+    )
+
+
+def render_completeness_warning(glucose: pd.DataFrame, start: date, end: date) -> None:  # pragma: no cover
     """Toon reporting.completeness_warning()'s oordeel als st.warning, indien nodig.
 
     Gebruikt door elke rapportagepagina, direct na de load_range_data-call.
-    De beoordelingslogica zelf staat in reporting.py (pure functie) zodat het
+    De beoordelingslogica zelf staat in reporting.py (pure functie, wél volledig
-    PDF-rapport (pdf_export.py) exact dezelfde disclaimer kan hergebruiken.
+    getest) zodat het PDF-rapport (pdf_export.py) exact dezelfde disclaimer kan
+    hergebruiken. Deze functie is alleen de st.warning-weergave — buiten scope
+    van de unit-testdekking net als pages/*.py.
     """
     message = completeness_warning(glucose, start, end)
     if message:
         st.warning(message)
 
 
-def render_event_level(
+def render_event_level(  # pragma: no cover
     glucose: pd.DataFrame,
     threshold: float,
     direction: str,
@@ -212,7 +425,10 @@ def render_event_level(
     """Render de metrics/tabel/tijdstip-histogram voor één hypo-/hyperniveau.
 
     Gedeeld tussen de Hypo Events- en Hyper Events-pagina's (FR5/FR6), die
-    elk twee niveaus (Level 1/Level 2) op dezelfde manier tonen.
+    elk twee niveaus (Level 1/Level 2) op dezelfde manier tonen. Pure
+    Streamlit-widget-weergave rond al-geteste reporting.detect_events()/
+    summarize_events() — buiten scope van de unit-testdekking net als
+    pages/*.py.
     """
     events = detect_events(glucose, threshold, direction)
     summary = summarize_events(events, period_days)