test indent issue

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

@@ -1,11 +1,103 @@
 pipeline {
-    agent any
+    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('Do nothing') {
+        stage('Checkout') {
             steps {
-                sh '/bin/true'
+                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

@@ -252,21 +252,3 @@ 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_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]

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/1_Live.py

@@ -1,8 +1,6 @@
-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 (
@@ -12,15 +10,7 @@ from data_processing import (
     summarize_time_in_range,
 )
 from nightscout_client import NightscoutConfigError, fetch_entries, fetch_treatments
-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,
-)
+from ui_common import MMOL_HOVERTEMPLATE, get_colors
 
 st.set_page_config(page_title="Glucose dashboard — Live", page_icon="📊", layout="wide")
 require_auth()
@@ -37,28 +27,7 @@ def load_nightscout_data(hours: int):
     return glucose, treatments
 
 
-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:
+def build_chart(glucose, insulin, carbs, 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
@@ -67,7 +36,7 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
     insulin_lane_y = lane_base + 1.5
     carbs_lane_y = lane_base + 0.7
 
-    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    fig = go.Figure()
 
     # Streefbereik als achtergrondband, geen losse legenda-entry nodig.
     fig.add_hrect(
@@ -76,13 +45,8 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
         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"],
@@ -91,9 +55,7 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
             name="Glucose",
             line={"color": colors["glucose"], "width": 2, "shape": "spline"},
             hovertemplate=MMOL_HOVERTEMPLATE,
-        ),
-        row=1,
-        col=1,
+        )
     )
 
     low_points = glucose[glucose["mmol"] < TARGET_LOW]
@@ -107,9 +69,7 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
                 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(
@@ -120,12 +80,21 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
                 name=f"Hoog (>{TARGET_HIGH})",
                 marker={"color": colors["high"], "size": 6},
                 hovertemplate=MMOL_HOVERTEMPLATE,
-            ),
-            row=1,
-            col=1,
+            )
         )
 
-    add_bolus_marker_traces(fig, bolus, colors, insulin_lane_y, row=1)
+    if not insulin.empty:
+        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(
@@ -137,16 +106,14 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
                 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, row=1, col=1)
+        fig.add_vline(x=row["timestamp"], line_dash="dot", line_color=colors["muted"], opacity=0.6)
         fig.add_annotation(
             x=row["timestamp"],
             y=y_top,
@@ -154,11 +121,9 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
             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, row=1, col=1)
+        fig.add_vline(x=row["timestamp"], line_dash="dash", line_color=colors["muted"], opacity=0.6)
         fig.add_annotation(
             x=row["timestamp"],
             y=y_top,
@@ -166,8 +131,6 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
             text="Sensor",
             showarrow=False,
             font={"color": colors["muted"], "size": 10},
-            row=1,
-            col=1,
         )
 
     fig.update_layout(
@@ -177,18 +140,14 @@ def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_chang
         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
 
 
@@ -201,7 +160,6 @@ with st.sidebar:
 
 try:
     glucose, treatments = load_nightscout_data(hours)
-    profile = load_profile()
 except NightscoutConfigError as exc:
     st.error(str(exc))
     st.stop()
@@ -214,9 +172,7 @@ 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
@@ -236,17 +192,14 @@ colors = get_colors()
 
 fig = build_chart(
     glucose,
-    bolus,
+    events["insulin"],
     events["carbs"],
-    basal,
-    target,
     events["site_changes"],
     events["sensor_changes"],
     colors,
 )
 st.plotly_chart(fig, width="stretch")
 st.caption(
-    "Stippen = bolusinsuline (voeding/correctie/SMB, grootte naar dosis) · cirkels = koolhydraten · "
-    "vlak = basaal · streepstippellijn = streefwaarde uit pompprofiel · "
-    "stippellijn = pompwissel · streepjeslijn = sensorwissel"
+    "Driehoekjes = insuline · cirkels = koolhydraten · stippellijn = pompwissel · "
+    "streepjeslijn = sensorwissel"
 )

pages/4_Dagoverzicht.py

@@ -1,21 +1,14 @@
-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, classify_treatments
+from data_processing import TARGET_HIGH, TARGET_LOW
 from nightscout_client import NightscoutConfigError
-from reporting import classify_bolus, effective_basal_series, target_glucose_series
 from ui_common import (
-    add_basal_area_trace,
-    add_bolus_marker_traces,
-    add_carb_bars_trace,
-    add_target_line_trace,
     add_threshold_lines,
     get_colors,
-    load_profile,
     load_range_data,
     render_completeness_warning,
     select_period_days,
@@ -25,7 +18,7 @@ st.set_page_config(page_title="Glucose dashboard — Dagoverzicht", page_icon="
 require_auth()
 
 st.title("Dagoverzicht")
-st.caption("Eén glucosegrafiek per kalenderdag, met basaal/bolus/koolhydraten-overlay (FR4).")
+st.caption("Eén glucosegrafiek per kalenderdag (FR4).")
 
 start, end, days = select_period_days(default_label="14 dagen")
 if days > 31:
@@ -35,8 +28,7 @@ if days > 31:
     )
 
 try:
-    glucose, treatments = load_range_data(start, end)
-    profile = load_profile()
+    glucose, _treatments = load_range_data(start, end)
 except NightscoutConfigError as exc:
     st.error(str(exc))
     st.stop()
@@ -55,122 +47,44 @@ glucose = glucose.copy()
 glucose["date"] = glucose["timestamp"].dt.date
 glucose["hour"] = glucose["timestamp"].dt.hour + glucose["timestamp"].dt.minute / 60
 
-carbs_events = classify_treatments(treatments)["carbs"].copy()
-if not carbs_events.empty:
-    carbs_events["date"] = carbs_events["timestamp"].dt.date
-    carbs_events["hour"] = carbs_events["timestamp"].dt.hour + carbs_events["timestamp"].dt.minute / 60
-
-bolus_events = classify_bolus(treatments)
-if not bolus_events.empty:
-    bolus_events["date"] = bolus_events["timestamp"].dt.date
-    bolus_events["hour"] = bolus_events["timestamp"].dt.hour + bolus_events["timestamp"].dt.minute / 60
-
-
-def _close_step_to_day_end(df: pd.DataFrame, value_col: str) -> pd.DataFrame:
-    """Voeg een laatste punt op hour=24 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([{"hour": 24.0, value_col: last_value}])
-    return pd.concat([df, closing_row], ignore_index=True)
-
-
 dates = sorted(glucose["date"].unique())
-row_height = 190
+row_height = 150
 fig = make_subplots(
     rows=len(dates),
     cols=1,
     shared_xaxes=True,
+    subplot_titles=[d.strftime("%A %d-%m-%Y") for d in dates],
     vertical_spacing=min(0.4 / max(len(dates) - 1, 1), 0.05),
-    specs=[[{"secondary_y": True}]] * len(dates),
 )
 
 for i, day in enumerate(dates, start=1):
     day_df = glucose[glucose["date"] == day]
-    day_start = pd.Timestamp(day)
-    day_end = day_start + pd.Timedelta(1, unit="D")
-
     fig.add_hrect(
         y0=TARGET_LOW, y1=TARGET_HIGH, fillcolor=colors["band"], line_width=0, layer="below", row=i, col=1
     )
-    # Handmatige titel-annotatie t.o.v. het domein van déze rij i.p.v.
-    # make_subplots' subplot_titles (die t.o.v. de hele figuur positioneert en
-    # daardoor bij veel dagen de gedeelde legenda bovenaan kan overlappen).
-    fig.add_annotation(
-        text=day.strftime("%A %d-%m-%Y"),
-        xref="x domain",
-        yref="y domain",
-        x=0.0,
-        y=1.15,
-        xanchor="left",
-        yanchor="bottom",
-        showarrow=False,
-        font={"size": 13, "color": colors["text"]},
-        row=i,
-        col=1,
-    )
-
-    basal_day = effective_basal_series(profile, treatments, day_start, day_end)
-    if not basal_day.empty:
-        basal_day["hour"] = (basal_day["timestamp"] - day_start).dt.total_seconds() / 3600
-        basal_day = _close_step_to_day_end(basal_day, "rate")
-    add_basal_area_trace(fig, basal_day, colors, row=i, secondary_y=True, x_col="hour")
-
-    target_day = target_glucose_series(profile, day_start, day_end)
-    if not target_day.empty:
-        target_day["hour"] = (target_day["timestamp"] - day_start).dt.total_seconds() / 3600
-        target_day = _close_step_to_day_end(target_day, "target")
-    add_target_line_trace(fig, target_day, colors, row=i, x_col="hour")
-
-    add_carb_bars_trace(fig, carbs_events[carbs_events["date"] == day], colors, row=i, secondary_y=True, x_col="hour")
-
-    lane_y = day_df["mmol"].min() - 1.0 if not day_df.empty else TARGET_LOW - 1.0
-    add_bolus_marker_traces(fig, bolus_events[bolus_events["date"] == day], colors, lane_y, row=i, x_col="hour")
-
     fig.add_trace(
         go.Scatter(
             x=day_df["hour"],
             y=day_df["mmol"],
             mode="lines",
             line={"color": colors["glucose"], "width": 2},
-            name="Glucose",
+            showlegend=False,
             hovertemplate="%{y:.1f} mmol/L<extra></extra>",
         ),
         row=i,
         col=1,
-        secondary_y=False,
-    )
-    fig.update_yaxes(title_text="mmol/L", gridcolor=colors["grid"], row=i, col=1, secondary_y=False)
-    fig.update_yaxes(
-        title_text="Basaal (E/uur) · KH (g)", showgrid=False, row=i, col=1, secondary_y=True
     )
+    fig.update_yaxes(title_text="mmol/L", gridcolor=colors["grid"], row=i, col=1)
     add_threshold_lines(fig, colors, row=i)
 
-# Elke dag herhaalt dezelfde spoornamen (Glucose/Basaal/Koolhydraten/...) — zonder
-# deduplicatie zou de legenda 7x "Glucose" etc. tonen. Alleen de eerste keer per
-# naam blijft in de legenda staan.
-seen_names = set()
-for trace in fig.data:
-    if trace.name in seen_names:
-        trace.showlegend = False
-    else:
-        seen_names.add(trace.name)
-
 fig.update_xaxes(title_text="Uur van de dag", range=[0, 24], dtick=4, 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=row_height * len(dates) + 40,
-    showlegend=True,
-    legend={"orientation": "h", "yanchor": "bottom", "y": 1.0, "xanchor": "left", "x": 0},
-    margin={"l": 10, "r": 10, "t": 40, "b": 10},
+    height=row_height * len(dates),
+    showlegend=False,
+    margin={"l": 10, "r": 10, "t": 30, "b": 10},
 )
 st.plotly_chart(fig, width="stretch")
-st.caption(
-    f"{len(dates)} dagen · {len(glucose)} metingen · stippen = bolusinsuline "
-    "(voeding/correctie/SMB, grootte naar dosis) · vlak = basaal · staafjes = koolhydraten · "
-    "streepstippellijn = streefwaarde uit pompprofiel"
-)
+st.caption(f"{len(dates)} dagen · {len(glucose)} metingen")

pages/9_Insulineprofiel.py

@@ -1,98 +0,0 @@
-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, 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=colors["basal"],
-        opacity=0.7,
-        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)."
-)

profile_data.py

@@ -1,146 +0,0 @@
-"""
-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

reporting.py

@@ -15,7 +15,6 @@ 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
@@ -441,166 +440,6 @@ 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 basal_stats(treatments: pd.DataFrame, period_days: float) -> dict:
     """Totale basaalinsuline uit Temp Basal-treatments (rate x duur), zonder pompprofiel.
 

tests/test_nightscout_client.py

@@ -357,29 +357,6 @@ 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_profile_data.py

@@ -1,117 +0,0 @@
-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

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

tests/test_ui_common.py

@@ -1,13 +1,8 @@
-import pandas as pd
 import plotly.graph_objects as go
 
 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,
     hex_to_rgba,
@@ -41,99 +36,3 @@ 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_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_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_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

@@ -14,8 +14,7 @@ import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
 
-from nightscout_client import fetch_entries_range, fetch_profile, fetch_treatments_range
-from profile_data import parse_profile
+from nightscout_client import fetch_entries_range, fetch_treatments_range
 from reporting import (
     HIGH,
     LOW,
@@ -58,11 +57,6 @@ 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",
@@ -82,11 +76,6 @@ 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",
     },
 }
 
@@ -140,15 +129,6 @@ def load_range_data(start: date, end: date):
     return glucose, treatments
 
 
-@st.cache_data(ttl=21600, show_spinner="Profiel ophalen van Nightscout...")
-def load_profile() -> dict:
-    """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."""
-    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.
 
@@ -209,160 +189,6 @@ def build_tir_stacked_bar_figure(tir: dict, colors: dict) -> go.Figure:
     return fig
 
 
-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:
     """Toon reporting.completeness_warning()'s oordeel als st.warning, indien nodig.