test indent issue

Dutch lab results conventionally report HbA1c in mmol/mol rather than
DCCT/NGSP %. Adds gmi_mmol_per_mol() using the standard IFCC conversion
(Hoelzel et al. 2004) and shows it next to the existing % value on the
Samenvatting page and in the PDF summary.

Closes gitea #25.

Dockerfile

@@ -1,5 +1,12 @@
 FROM python:3.12-slim
 
+ENV HOME=/app \
+    PYTHONUNBUFFERED=1 \
+    STREAMLIT_SERVER_PORT=8501 \
+    STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
+    STREAMLIT_SERVER_HEADLESS=true \
+    STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
 WORKDIR /app
 
 # Systeem-dependency voor de HEALTHCHECK hieronder
@@ -11,8 +18,9 @@ RUN pip install --no-cache-dir -r requirements.txt
 
 COPY . .
 
+RUN chgrp -R 0 /app && chmod -R g=u /app
+
+USER 1001
 EXPOSE 8501
-
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1
-
-ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "app.py"]

Jenkinsfile

@@ -0,0 +1,103 @@
+pipeline {
+    agent {
+        label 'ssh-agent'
+    }
+
+
+    environment {
+        REGISTRY    = 'gitea.zb-kw.duckdns.org'
+        IMAGE       = "${REGISTRY}/willemjan/glucose-dashboard"
+        OC_SERVER   = 'https://api.rm1.0a51.p1.openshiftapps.com:6443'
+        OC_PROJECT  = 'wulsspaans-dev'
+    }
+
+    options {
+        buildDiscarder(logRotator(numToKeepStr: '20'))
+        timeout(time: 20, unit: 'MINUTES')
+    }
+
+    stages {
+        stage('Checkout') {
+            steps {
+                checkout scm
+                script {
+                    env.TAG = sh(script: 'git rev-parse --short HEAD',
+                                 returnStdout: true).trim()
+                }
+            }
+        }
+
+        stage('Test') {
+            steps {
+                sh '''
+                    pip install --break-system-packages --no-warn-script-location -q -r requirements.txt
+                    python -m pytest -q --junitxml=report.xml
+                '''
+            }
+            post {
+                always { junit allowEmptyResults: true, testResults: 'report.xml' }
+            }
+        }
+
+        stage('Build & push image') {
+            steps {
+                withCredentials([usernamePassword(
+                    credentialsId: 'gitea_user',
+                    usernameVariable: 'REG_USER',
+                    passwordVariable: 'REG_PASS')]) {
+                    sh '''
+                        echo "$REG_PASS" | docker login $REGISTRY -u "$REG_USER" --password-stdin
+                        docker build -t $IMAGE:$TAG -t $IMAGE:latest .
+                        docker push $IMAGE:$TAG
+                        docker push $IMAGE:latest
+                    '''
+                }
+            }
+        }
+
+        stage('Scan') {
+            steps {
+                sh '''
+                    docker run --rm \
+                      -v /var/run/docker.sock:/var/run/docker.sock \
+                      -v trivy-cache:/root/.cache/trivy \
+                      aquasec/trivy:latest image \
+                        --severity HIGH,CRITICAL \
+                        --ignore-unfixed \
+                        --exit-code 0 \
+                        $IMAGE:$TAG
+                '''
+            }
+        }
+
+        stage('Deploy to OpenShift') {
+            steps {
+                withCredentials([string(
+                    credentialsId: 'openshift-token',
+                    variable: 'OC_TOKEN')]) {
+                    sh '''
+                        oc login --token="$OC_TOKEN" --server="$OC_SERVER"
+                        oc project $OC_PROJECT
+
+                        oc apply -f openshift/service.yaml
+                        oc apply -f openshift/route.yaml
+                        oc apply -f openshift/configmap.yaml
+                        oc apply -f openshift/deployment.yaml
+
+                        oc set image deployment/glucose-dashboard \
+                            glucose-dashboard=$IMAGE:$TAG
+
+                        oc rollout status deployment/glucose-dashboard --timeout=5m
+                    '''
+                }
+            }
+            post {
+                always  { sh 'oc logout || true' }
+                success {
+                    sh 'echo "Deployed: $(oc get route glucose-dashboard -o jsonpath={.spec.host})" || true'
+                }
+            }
+        }
+    }
+    
+}

nightscout_client.py

@@ -176,29 +176,39 @@ def _entries_since(since: datetime, until: datetime | None = None) -> pd.DataFra
     return df[["timestamp", "mmol"]].sort_values("timestamp").reset_index(drop=True)
 
 
+_TREATMENT_COLUMNS = ["timestamp", "eventType", "insulin", "carbs", "type", "absolute", "duration"]
+
+
 def _treatments_since(since: datetime, until: datetime | None = None) -> pd.DataFrame:
-    """Haal treatments op sinds `since` (en optioneel tot `until`), lokaal gefilterd."""
+    """Haal treatments op sinds `since` (en optioneel tot `until`), lokaal gefilterd.
+
+    `type` en `absolute`/`duration` komen erbij naast de oorspronkelijke
+    kolommen: AndroidAPS registreert een SMB-microbolus niet als eigen
+    `eventType`, maar als `eventType == "Correction Bolus"` met
+    `type == "SMB"` (zie reporting.bolus_stats() / gitea #20) — en
+    `absolute` (E/uur) + `duration` (minuten) op Temp Basal-treatments maken
+    het mogelijk afgeleverde basaalinsuline te berekenen zonder het
+    pompprofiel te hoeven ophalen (zie reporting.basal_stats()).
+    """
     data = _paginated_get("/api/v1/treatments.json", since, until, TREATMENTS_CURSOR_FIELD)
 
     if not data:
-        return pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+        return pd.DataFrame(columns=_TREATMENT_COLUMNS)
 
     df = pd.DataFrame(data)
     df["timestamp"] = pd.to_datetime(df["created_at"], utc=True).dt.tz_localize(None)
-    for col in ("insulin", "carbs"):
+    for col in ("insulin", "carbs", "absolute", "duration"):
         if col not in df.columns:
             df[col] = pd.NA
         df[col] = pd.to_numeric(df[col], errors="coerce")
-    if "eventType" not in df.columns:
-        df["eventType"] = ""
+    for col in ("eventType", "type"):
+        if col not in df.columns:
+            df[col] = ""
+        df[col] = df[col].fillna("")
     df = df[df["timestamp"] >= since.replace(tzinfo=None)]
     if until is not None:
         df = df[df["timestamp"] <= until.replace(tzinfo=None)]
-    return (
-        df[["timestamp", "eventType", "insulin", "carbs"]]
-        .sort_values("timestamp")
-        .reset_index(drop=True)
-    )
+    return df[_TREATMENT_COLUMNS].sort_values("timestamp").reset_index(drop=True)
 
 
 def fetch_entries(hours: int = 24) -> pd.DataFrame:

openshift/configmap.yaml

@@ -0,0 +1,6 @@
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: glucose-dashboard-config
+data:
+  NIGHTSCOUT_URL: "https://zb-ns.duckdns.org"

openshift/deployment.yaml

@@ -0,0 +1,35 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: glucose-dashboard
+  labels:
+    app: glucose-dashboard
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: glucose-dashboard
+  template:
+    metadata:
+      labels:
+        app: glucose-dashboard
+    spec:
+      imagePullSecrets:
+        - name: gitea-registry
+      containers:
+        - name: glucose-dashboard
+          image: PLACEHOLDER
+          envFrom:
+            - configMapRef:
+                name: glucose-dashboard-config
+          ports:
+            - containerPort: 8501
+          resources:
+            requests: { cpu: 100m, memory: 256Mi }
+            limits:   { cpu: 500m, memory: 512Mi }
+          readinessProbe:
+            httpGet: { path: /_stcore/health, port: 8501 }
+            initialDelaySeconds: 10
+          livenessProbe:
+            httpGet: { path: /_stcore/health, port: 8501 }
+            initialDelaySeconds: 30

openshift/route.yaml

@@ -0,0 +1,13 @@
+apiVersion: route.openshift.io/v1
+kind: Route
+metadata:
+  name: glucose-dashboard
+spec:
+  to:
+    kind: Service
+    name: glucose-dashboard
+  port:
+    targetPort: 8501
+  tls:
+    termination: edge
+    insecureEdgeTerminationPolicy: Redirect

openshift/service.yaml

@@ -0,0 +1,10 @@
+apiVersion: v1
+kind: Service
+metadata:
+  name: glucose-dashboard
+spec:
+  selector:
+    app: glucose-dashboard
+  ports:
+    - port: 8501
+      targetPort: 8501

pages/2_Samenvatting.py

@@ -4,6 +4,7 @@ import streamlit as st
 from auth import require_auth
 from nightscout_client import NightscoutConfigError
 from reporting import (
+    gmi_mmol_per_mol,
     gmi_percent,
     percent_cv,
     sensor_active_percent,
@@ -43,6 +44,7 @@ active_pct = sensor_active_percent(glucose, start, end)
 
 mean_mmol = glucose["mmol"].mean()
 gmi = gmi_percent(mean_mmol)
+gmi_mmol_mol = gmi_mmol_per_mol(gmi)
 cv = percent_cv(glucose)
 tir = tir_breakdown_5band(glucose)
 
@@ -50,7 +52,11 @@ st.subheader("Kerncijfers")
 col1, col2, col3, col4 = st.columns(4)
 col1.metric("Sensordekking", f"{active_pct:.0f}%", help="Streefwaarde: ≥70%")
 col2.metric("Gemiddelde glucose", f"{mean_mmol:.1f} mmol/L")
-col3.metric("Geschat HbA1c (GMI)", f"{gmi:.1f}%", help="Bergenstal et al. 2018 — geen labwaarde")
+col3.metric(
+    "Geschat HbA1c (GMI)",
+    f"{gmi:.1f}% ({gmi_mmol_mol:.0f} mmol/mol)",
+    help="Bergenstal et al. 2018 — geen labwaarde",
+)
 col4.metric("Variabiliteit (%CV)", f"{cv:.0f}%", help="Streefwaarde: ≤36%")
 
 st.subheader("Tijd in bereik (5 klinische banden)")

pages/7_Trends.py

@@ -6,14 +6,16 @@ from auth import require_auth
 from data_processing import classify_treatments
 from nightscout_client import NightscoutConfigError
 from reporting import (
+    basal_stats,
     bolus_stats,
     day_of_week_pattern,
-    gmi_percent,
+    gmi_trend,
     overnight_daytime_split,
     site_interval_stats,
 )
 from ui_common import (
     get_colors,
+    hex_to_rgba,
     load_range_data,
     render_completeness_warning,
     select_period_days,
@@ -45,26 +47,70 @@ render_completeness_warning(glucose, start, end)
 colors = get_colors()
 events = classify_treatments(treatments)
 
-# --- FR7: geschat HbA1c (GMI) per maand ------------------------------------
-st.subheader("Geschat HbA1c (GMI) per maand")
-monthly = glucose.copy()
-monthly["month"] = monthly["timestamp"].dt.to_period("M").astype(str)
-monthly_gmi = monthly.groupby("month")["mmol"].mean().apply(gmi_percent)
+# --- FR7: geschat HbA1c (GMI) trend ----------------------------------------
+st.subheader("Geschat HbA1c (GMI) trend")
+trend = gmi_trend(glucose)
 
-if len(monthly_gmi) >= 1:
-    fig = go.Figure(
-        go.Bar(x=monthly_gmi.index, y=monthly_gmi.to_numpy(), marker_color=colors["glucose"])
+if len(trend) >= 2:
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"], y=trend["trend_upper"], mode="lines", line={"width": 0}, showlegend=False,
+            hoverinfo="skip",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["trend_lower"],
+            mode="lines",
+            line={"width": 0},
+            fill="tonexty",
+            fillcolor=hex_to_rgba(colors["muted"], 0.2),
+            name="95% betrouwbaarheidsband",
+            hoverinfo="skip",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["trend"],
+            mode="lines",
+            line={"color": colors["muted"], "width": 2, "dash": "dash"},
+            name="Trend (lineair)",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["rolling_avg"],
+            mode="lines",
+            line={"color": colors["glucose"], "width": 2},
+            name="Voortschrijdend gemiddelde (7d)",
+        )
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=trend["date"],
+            y=trend["gmi"],
+            mode="markers",
+            marker={"color": colors["glucose"], "size": 5, "opacity": 0.5},
+            name="Dag-GMI",
+        )
     )
     fig.update_layout(
         paper_bgcolor="rgba(0,0,0,0)",
         plot_bgcolor="rgba(0,0,0,0)",
         font_color=colors["text"],
-        height=280,
+        height=320,
         margin={"l": 10, "r": 10, "t": 10, "b": 10},
         yaxis={"title": "GMI (%)", "gridcolor": colors["grid"]},
-        xaxis={"title": "Maand", "gridcolor": colors["grid"]},
+        xaxis={"title": "Datum", "gridcolor": colors["grid"]},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "left", "x": 0},
     )
     st.plotly_chart(fig, width="stretch")
+else:
+    st.info("Niet genoeg dagen met data voor een trendlijn (minstens 2 nodig).")
 st.caption("GMI is een schatting (Bergenstal et al. 2018), geen labwaarde.")
 
 # --- FR8: nacht vs. overdag --------------------------------------------------
@@ -93,18 +139,25 @@ if not weekday_plot.empty:
     )
     st.plotly_chart(fig, width="stretch")
 
-# --- FR8: bolus-insuline --------------------------------------------------
-st.subheader("Bolus-insuline")
+# --- FR8: insuline (basaal + bolus-uitsplitsing) ---------------------------
+st.subheader("Insuline")
 st.caption(
-    "Alleen bolusinsuline (maaltijd/correctie/combo/SMB) — basaalinsuline vereist "
-    "profielgegevens en is hier niet in verwerkt (zie docs/design-reporting.md)."
+    "Basaal berekend uit Temp Basal-treatments (snelheid x duur), zonder pompprofiel "
+    "nodig — een ondergrens bij periodes zonder actieve temp basal. Boluses "
+    "opgesplitst naar voeding, handmatige correctie en automatische SMB-microbolus "
+    "(gitea #20)."
 )
 bolus = bolus_stats(treatments)
-col1, col2 = st.columns(2)
-col1.metric("Totaal bolus", f"{bolus['total_units']:.1f} E")
-col2.metric("Aantal boluses", bolus["count"])
-if bolus["by_type"]:
-    st.table({"type": list(bolus["by_type"].keys()), "eenheden": list(bolus["by_type"].values())})
+basal = basal_stats(treatments, days)
+total_units = bolus["total_units"] + basal["total_units"]
+col1, col2, col3 = st.columns(3)
+col1.metric("Totaal insuline", f"{total_units:.1f} E")
+col2.metric("Gem. per dag", f"{total_units / days:.1f} E")
+col3.metric("Aantal boluses", bolus["count"])
+
+breakdown = {"Basaal": basal["total_units"], **bolus["by_type"]}
+if any(breakdown.values()):
+    st.table({"Categorie": list(breakdown.keys()), "Eenheden": list(breakdown.values())})
 
 # --- FR8: pompsite-leeftijd vs. glucose/insuline ---------------------------
 st.subheader("Pompsite-leeftijd vs. glucose en insuline")

pdf_export.py

@@ -28,6 +28,7 @@ from reporting import (
     agp_percentile_bands,
     completeness_warning,
     detect_events,
+    gmi_mmol_per_mol,
     gmi_percent,
     percent_cv,
     sensor_active_percent,
@@ -178,7 +179,8 @@ 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"Geschat HbA1c (GMI): {gmi_percent(mean_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)
     pdf.ln(2)
     pdf.cell(0, 6, f"Zeer laag (<3.0 mmol/L): {tir['very_low']:.0f}%", **_LINE_KWARGS)

reporting.py

@@ -11,6 +11,7 @@ from __future__ import annotations
 
 from itertools import pairwise
 
+import numpy as np
 import pandas as pd
 
 from nightscout_client import INSULIN_EVENT_TYPES
@@ -35,6 +36,79 @@ def gmi_percent(mean_mmol: float) -> float:
     return round(3.31 + 0.4311 * mean_mmol, 1)
 
 
+def gmi_mmol_per_mol(gmi_pct: float) -> int:
+    """Zet een GMI-percentage (DCCT/NGSP) om naar IFCC mmol/mol.
+
+    mmol/mol = 10.929 x (% - 2.15) — de standaard IFCC-omrekening
+    (Hoelzel et al. 2004, Clin Chem 50(1):166-174), gangbare eenheid in
+    Nederlandse labuitslagen naast het %-getal (gitea #25). Bijv. 6.5% =
+    48 mmol/mol, de bekende diagnostische drempel.
+    """
+    return round(10.929 * (gmi_pct - 2.15))
+
+
+GMI_TREND_COLUMNS = ["date", "gmi", "rolling_avg", "trend", "trend_lower", "trend_upper"]
+
+
+def daily_gmi(glucose: pd.DataFrame) -> pd.DataFrame:
+    """Geschat HbA1c (GMI) per kalenderdag — scatterpunten voor de trendgrafiek (gitea #19)."""
+    if glucose.empty:
+        return pd.DataFrame(columns=["date", "gmi"])
+
+    df = glucose.copy()
+    df["date"] = df["timestamp"].dt.date
+    daily_mean = df.groupby("date")["mmol"].mean()
+    result = daily_mean.apply(gmi_percent).reset_index()
+    result.columns = ["date", "gmi"]
+    return result.sort_values("date").reset_index(drop=True)
+
+
+def gmi_trend(glucose: pd.DataFrame, rolling_window_days: int = 7) -> pd.DataFrame:
+    """Dagelijkse GMI-punten plus voortschrijdend gemiddelde en een lineaire
+    trendlijn met 95%-betrouwbaarheidsband (gitea #19).
+
+    Gebruikt een normaalbenadering (z=1.96) voor de betrouwbaarheidsband i.p.v.
+    een exacte t-verdeling — dat zou een scipy-dependency toevoegen aan een
+    verder bewust lichte container (dezelfde afweging als het niet gebruiken
+    van kaleido in pdf_export.py). Bij de hier gebruikelijke steekproefgroottes
+    (14-90 dagpunten) is het verschil met de exacte t-kritieke waarde
+    verwaarloosbaar voor een visuele trendindicatie.
+
+    Elke keer opnieuw berekend uit de al-opgehaalde periode-data (geen aparte
+    database) — de bestaande 1-uurs Streamlit-cache op load_range_data()
+    voorkomt al herhaalde Nightscout-calls voor dezelfde periode. Bij een
+    veel langere trend (jaren i.p.v. de huidige max. 90 dagen) zou een aparte
+    opslag wél de moeite waard worden.
+    """
+    daily = daily_gmi(glucose)
+    if len(daily) < 2:
+        return daily.reindex(columns=GMI_TREND_COLUMNS)
+
+    daily = daily.copy()
+    daily["rolling_avg"] = daily["gmi"].rolling(window=rolling_window_days, min_periods=1).mean()
+
+    x = np.arange(len(daily), dtype=float)
+    y = daily["gmi"].to_numpy()
+    slope, intercept = np.polyfit(x, y, 1)
+    fitted = intercept + slope * x
+
+    residuals = y - fitted
+    degrees_of_freedom = max(len(x) - 2, 1)
+    residual_std = np.sqrt(np.sum(residuals**2) / degrees_of_freedom)
+    x_mean = x.mean()
+    sum_sq_x = np.sum((x - x_mean) ** 2)
+    standard_error = (
+        np.zeros_like(x)
+        if sum_sq_x == 0
+        else residual_std * np.sqrt(1 / len(x) + (x - x_mean) ** 2 / sum_sq_x)
+    )
+    z_95 = 1.96
+    daily["trend"] = fitted
+    daily["trend_lower"] = fitted - z_95 * standard_error
+    daily["trend_upper"] = fitted + z_95 * standard_error
+    return daily[GMI_TREND_COLUMNS]
+
+
 def percent_cv(glucose: pd.DataFrame) -> float:
     """Glucosevariabiliteit als %CV (standaarddeviatie / gemiddelde * 100)."""
     if glucose.empty:
@@ -330,22 +404,35 @@ def day_of_week_pattern(glucose: pd.DataFrame) -> pd.DataFrame:
     return pd.DataFrame(rows, columns=columns)
 
 
+SMB_TYPE = "SMB"
+BASAL_EVENT_TYPE = "Temp Basal"
+
+
 def bolus_stats(treatments: pd.DataFrame) -> dict:
     """Samenvatting van bolus-insuline, opgesplitst naar type.
 
-    Let op: dit betreft alleen bolusinsuline (maaltijd/correctie/combo/SMB).
-    Basaalinsuline wordt hier niet in meegenomen — dat vereist profielgegevens
-    (zie docs/design-reporting.md, scoping-kanttekening) en levert dus geen
-    volledige totale-dagdosis op.
+    AndroidAPS in SMB-looping registreert een automatische micro-bolus niet
+    als eigen `eventType`, maar als `eventType == "Correction Bolus"` met
+    `type == "SMB"` — zonder dit onderscheid werden alle SMB's stilzwijgend
+    als "Correction Bolus" (handmatige correctie) meegeteld, wat gitea #20
+    meldde als "bolus stats empty/onduidelijk". Geverifieerd rechtstreeks
+    tegen de productiedata: bij deze gebruiker is vrijwel elke "Correction
+    Bolus" in werkelijkheid een automatische SMB.
+
+    Let op: dit betreft alleen bolusinsuline (voeding/correctie/combo/SMB).
+    Basaalinsuline zit hier niet in — zie basal_stats().
     """
     if treatments.empty:
         return {"total_units": 0.0, "count": 0, "by_type": {}}
 
-    bolus = treatments[treatments["eventType"].isin(INSULIN_EVENT_TYPES) & treatments["insulin"].notna()]
+    bolus = treatments[treatments["eventType"].isin(INSULIN_EVENT_TYPES) & treatments["insulin"].notna()].copy()
     if bolus.empty:
         return {"total_units": 0.0, "count": 0, "by_type": {}}
 
-    by_type = bolus.groupby("eventType")["insulin"].sum().round(1).to_dict()
+    is_smb = (bolus["eventType"] == "Correction Bolus") & (bolus.get("type", "") == SMB_TYPE)
+    bolus["category"] = bolus["eventType"].where(~is_smb, SMB_TYPE)
+
+    by_type = bolus.groupby("category")["insulin"].sum().round(1).to_dict()
     return {
         "total_units": round(bolus["insulin"].sum(), 1),
         "count": len(bolus),
@@ -353,6 +440,35 @@ def bolus_stats(treatments: pd.DataFrame) -> dict:
     }
 
 
+def basal_stats(treatments: pd.DataFrame, period_days: float) -> dict:
+    """Totale basaalinsuline uit Temp Basal-treatments (rate x duur), zonder pompprofiel.
+
+    AndroidAPS zet in gesloten-lus-modus vrijwel continu Temp Basal-treatments
+    met een `absolute` snelheid (E/uur) en `duration` (minuten); de som van
+    afgeleverde eenheden daaruit benadert de daadwerkelijke basaaldosis
+    nauwkeurig genoeg zonder het pompprofiel te hoeven ophalen (gitea #20).
+    Periodes zonder actieve temp basal (dan geldt het profiel-basaaltempo)
+    worden hierin gemist — dit is dus een ondergrens, geen exacte totale-
+    dagdosis.
+    """
+    if treatments.empty or period_days <= 0:
+        return {"total_units": 0.0, "avg_units_per_day": 0.0}
+
+    temp_basal = treatments[
+        (treatments["eventType"] == BASAL_EVENT_TYPE)
+        & treatments["absolute"].notna()
+        & treatments["duration"].notna()
+    ]
+    if temp_basal.empty:
+        return {"total_units": 0.0, "avg_units_per_day": 0.0}
+
+    total_units = (temp_basal["absolute"] * temp_basal["duration"] / 60).sum()
+    return {
+        "total_units": round(total_units, 1),
+        "avg_units_per_day": round(total_units / period_days, 1),
+    }
+
+
 SITE_INTERVAL_COLUMNS = [
     "start",
     "end",

tests/test_nightscout_client.py

@@ -90,6 +90,38 @@ def test_fetch_treatments_fills_missing_columns(monkeypatch):
     assert result.iloc[0]["eventType"] == "Site Change"
     assert pd_isna(result.iloc[0]["insulin"])
     assert pd_isna(result.iloc[0]["carbs"])
+    assert result.iloc[0]["type"] == ""
+    assert pd_isna(result.iloc[0]["absolute"])
+    assert pd_isna(result.iloc[0]["duration"])
+
+
+def test_fetch_treatments_passes_through_type_and_temp_basal_fields(monkeypatch):
+    """SMB-classificatie (reporting.bolus_stats) en basaalberekening
+    (reporting.basal_stats) hebben deze velden nodig — zie gitea #20."""
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    now = datetime.now(timezone.utc)
+    payload = [
+        {
+            "created_at": now.isoformat(),
+            "eventType": "Correction Bolus",
+            "insulin": 0.5,
+            "type": "SMB",
+        },
+        {
+            "created_at": now.isoformat(),
+            "eventType": "Temp Basal",
+            "absolute": 1.2,
+            "duration": 30,
+        },
+    ]
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse(payload))
+
+    result = ns.fetch_treatments(hours=24)
+    smb_row = result[result["eventType"] == "Correction Bolus"].iloc[0]
+    basal_row = result[result["eventType"] == "Temp Basal"].iloc[0]
+    assert smb_row["type"] == "SMB"
+    assert basal_row["absolute"] == pytest.approx(1.2)
+    assert basal_row["duration"] == pytest.approx(30)
 
 
 def pd_isna(value):

tests/test_reporting.py

@@ -4,12 +4,17 @@ import pandas as pd
 import pytest
 
 from reporting import (
+    GMI_TREND_COLUMNS,
     agp_percentile_bands,
+    basal_stats,
     bolus_stats,
     completeness_warning,
+    daily_gmi,
     day_of_week_pattern,
     detect_events,
+    gmi_mmol_per_mol,
     gmi_percent,
+    gmi_trend,
     minutes_per_day,
     overnight_daytime_split,
     percent_cv,
@@ -31,6 +36,63 @@ def test_gmi_percent_matches_bergenstal_formula():
     assert gmi_percent(7.0) == pytest.approx(6.3, abs=0.05)
 
 
+def test_gmi_mmol_per_mol_matches_known_ifcc_reference_values():
+    # 6.5% is de bekende diagnostische drempel = 48 mmol/mol (IFCC).
+    assert gmi_mmol_per_mol(6.5) == 48
+    assert gmi_mmol_per_mol(7.0) == 53
+
+
+def test_daily_gmi_aggregates_per_calendar_day():
+    glucose = _series(
+        [
+            ("2026-01-01 08:00", 6.0),
+            ("2026-01-01 20:00", 8.0),
+            ("2026-01-02 08:00", 7.0),
+        ]
+    )
+    result = daily_gmi(glucose)
+    assert list(result["date"].astype(str)) == ["2026-01-01", "2026-01-02"]
+    assert result["gmi"].iloc[0] == pytest.approx(gmi_percent(7.0))  # gemiddelde van 6.0 en 8.0
+    assert result["gmi"].iloc[1] == pytest.approx(gmi_percent(7.0))
+
+
+def test_daily_gmi_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    result = daily_gmi(empty)
+    assert result.empty
+    assert list(result.columns) == ["date", "gmi"]
+
+
+def test_gmi_trend_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    result = gmi_trend(empty)
+    assert result.empty
+    assert list(result.columns) == GMI_TREND_COLUMNS
+
+
+def test_gmi_trend_returns_no_line_for_a_single_day():
+    glucose = _series([("2026-01-01 08:00", 6.0)])
+    result = gmi_trend(glucose)
+    assert list(result.columns) == GMI_TREND_COLUMNS
+    assert len(result) == 1
+    assert pd.isna(result["trend"].iloc[0])
+
+
+def test_gmi_trend_fits_increasing_linear_trend_with_band_around_it():
+    pairs = [(f"2026-01-{i + 1:02d} 08:00", 6.0 + i * 0.2) for i in range(10)]
+    glucose = _series(pairs)
+    result = gmi_trend(glucose, rolling_window_days=3)
+
+    assert len(result) == 10
+    assert result["trend"].iloc[-1] > result["trend"].iloc[0]
+    assert (result["trend_lower"] <= result["trend"]).all()
+    assert (result["trend"] <= result["trend_upper"]).all()
+    # Vrijwel lineaire input (gmi_percent rondt af op 1 decimaal, dus een
+    # kleine restfout blijft over) -> smalle betrouwbaarheidsband.
+    assert (result["trend_upper"] - result["trend_lower"]).max() == pytest.approx(0, abs=0.15)
+    assert result["rolling_avg"].iloc[-1] > result["rolling_avg"].iloc[0]
+
+
 def test_percent_cv_handles_empty_input():
     empty = pd.DataFrame(columns=["timestamp", "mmol"])
     assert percent_cv(empty) == 0.0
@@ -339,6 +401,7 @@ def test_bolus_stats_splits_by_event_type():
             "eventType": ["Meal Bolus", "Correction Bolus", "Site Change"],
             "insulin": [4.0, 1.5, None],
             "carbs": [45.0, None, None],
+            "type": ["NORMAL", "NORMAL", ""],
         }
     )
     result = bolus_stats(treatments)
@@ -347,12 +410,76 @@ def test_bolus_stats_splits_by_event_type():
     assert result["by_type"] == {"Meal Bolus": 4.0, "Correction Bolus": 1.5}
 
 
+def test_bolus_stats_reclassifies_smb_correction_boluses_separately():
+    """AndroidAPS registreert een automatische SMB-microbolus als
+    eventType == "Correction Bolus" met type == "SMB" — dit moet apart van
+    een handmatige correctiebolus (type != "SMB") geteld worden (gitea #20)."""
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 12:00"]),
+            "eventType": ["Correction Bolus", "Correction Bolus"],
+            "insulin": [0.3, 0.9],
+            "carbs": [None, None],
+            "type": ["SMB", "NORMAL"],
+        }
+    )
+    result = bolus_stats(treatments)
+    assert result["by_type"] == {"SMB": 0.3, "Correction Bolus": 0.9}
+
+
+def test_bolus_stats_treats_missing_type_column_as_non_smb():
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00"]),
+            "eventType": ["Correction Bolus"],
+            "insulin": [0.9],
+            "carbs": [None],
+        }
+    )
+    result = bolus_stats(treatments)
+    assert result["by_type"] == {"Correction Bolus": 0.9}
+
+
 def test_bolus_stats_handles_empty_input():
-    empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+    empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs", "type"])
     result = bolus_stats(empty)
     assert result == {"total_units": 0.0, "count": 0, "by_type": {}}
 
 
+def test_basal_stats_sums_temp_basal_rate_times_duration():
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 08:30"]),
+            "eventType": ["Temp Basal", "Temp Basal"],
+            "absolute": [1.2, 0.6],
+            "duration": [30, 60],
+        }
+    )
+    # 1.2 E/u * 0.5u + 0.6 E/u * 1u = 0.6 + 0.6 = 1.2 E over 1 dag
+    result = basal_stats(treatments, period_days=1)
+    assert result["total_units"] == pytest.approx(1.2)
+    assert result["avg_units_per_day"] == pytest.approx(1.2)
+
+
+def test_basal_stats_ignores_non_temp_basal_rows_and_missing_fields():
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 08:30"]),
+            "eventType": ["Meal Bolus", "Temp Basal"],
+            "absolute": [None, None],
+            "duration": [None, 30],
+        }
+    )
+    result = basal_stats(treatments, period_days=1)
+    assert result == {"total_units": 0.0, "avg_units_per_day": 0.0}
+
+
+def test_basal_stats_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "eventType", "absolute", "duration"])
+    result = basal_stats(empty, period_days=1)
+    assert result == {"total_units": 0.0, "avg_units_per_day": 0.0}
+
+
 def test_site_interval_stats_splits_by_site_change():
     glucose = _series(
         [