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21
Commits
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b8e01fa8c9 |
+11
-3
@@ -1,5 +1,12 @@
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FROM python:3.12-slim
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ENV HOME=/app \
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PYTHONUNBUFFERED=1 \
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STREAMLIT_SERVER_PORT=8501 \
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STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
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STREAMLIT_SERVER_HEADLESS=true \
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STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
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WORKDIR /app
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# Systeem-dependency voor de HEALTHCHECK hieronder
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@@ -11,8 +18,9 @@ RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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RUN chgrp -R 0 /app && chmod -R g=u /app
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USER 1001
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EXPOSE 8501
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HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1
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ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
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CMD ["streamlit", "run", "app.py"]
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Vendored
+103
@@ -0,0 +1,103 @@
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pipeline {
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agent {
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label 'ssh-agent'
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}
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environment {
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REGISTRY = 'gitea.zb-kw.duckdns.org'
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IMAGE = "${REGISTRY}/willemjan/glucose-dashboard"
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OC_SERVER = 'https://api.rm1.0a51.p1.openshiftapps.com:6443'
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OC_PROJECT = 'wulsspaans-dev'
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}
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options {
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buildDiscarder(logRotator(numToKeepStr: '20'))
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timeout(time: 20, unit: 'MINUTES')
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}
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stages {
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stage('Checkout') {
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steps {
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checkout scm
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script {
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env.TAG = sh(script: 'git rev-parse --short HEAD',
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returnStdout: true).trim()
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}
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}
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}
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stage('Test') {
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steps {
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sh '''
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pip install --break-system-packages --no-warn-script-location -q -r requirements.txt
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python -m pytest -q --junitxml=report.xml
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'''
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}
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post {
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always { junit allowEmptyResults: true, testResults: 'report.xml' }
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}
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}
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stage('Build & push image') {
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steps {
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withCredentials([usernamePassword(
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credentialsId: 'gitea_user',
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usernameVariable: 'REG_USER',
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passwordVariable: 'REG_PASS')]) {
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sh '''
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echo "$REG_PASS" | docker login $REGISTRY -u "$REG_USER" --password-stdin
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docker build -t $IMAGE:$TAG -t $IMAGE:latest .
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docker push $IMAGE:$TAG
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docker push $IMAGE:latest
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'''
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}
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}
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}
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stage('Scan') {
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steps {
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sh '''
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docker run --rm \
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-v /var/run/docker.sock:/var/run/docker.sock \
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-v trivy-cache:/root/.cache/trivy \
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aquasec/trivy:latest image \
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--severity HIGH,CRITICAL \
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--ignore-unfixed \
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--exit-code 0 \
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$IMAGE:$TAG
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'''
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}
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}
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stage('Deploy to OpenShift') {
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steps {
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withCredentials([string(
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credentialsId: 'openshift-token',
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variable: 'OC_TOKEN')]) {
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sh '''
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oc login --token="$OC_TOKEN" --server="$OC_SERVER"
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oc project $OC_PROJECT
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oc apply -f openshift/service.yaml
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oc apply -f openshift/route.yaml
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oc apply -f openshift/configmap.yaml
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oc apply -f openshift/deployment.yaml
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oc set image deployment/glucose-dashboard \
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glucose-dashboard=$IMAGE:$TAG
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oc rollout status deployment/glucose-dashboard --timeout=5m
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'''
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}
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}
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post {
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always { sh 'oc logout || true' }
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success {
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sh 'echo "Deployed: $(oc get route glucose-dashboard -o jsonpath={.spec.host})" || true'
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}
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}
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}
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}
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}
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@@ -0,0 +1,6 @@
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apiVersion: v1
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kind: ConfigMap
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metadata:
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name: glucose-dashboard-config
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data:
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NIGHTSCOUT_URL: "https://zb-ns.duckdns.org"
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@@ -0,0 +1,35 @@
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: glucose-dashboard
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labels:
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app: glucose-dashboard
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: glucose-dashboard
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template:
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metadata:
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labels:
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app: glucose-dashboard
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spec:
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imagePullSecrets:
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- name: gitea-registry
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containers:
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- name: glucose-dashboard
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image: PLACEHOLDER
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envFrom:
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- configMapRef:
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name: glucose-dashboard-config
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ports:
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- containerPort: 8501
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resources:
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requests: { cpu: 100m, memory: 256Mi }
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limits: { cpu: 500m, memory: 512Mi }
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readinessProbe:
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httpGet: { path: /_stcore/health, port: 8501 }
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initialDelaySeconds: 10
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livenessProbe:
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httpGet: { path: /_stcore/health, port: 8501 }
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initialDelaySeconds: 30
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@@ -0,0 +1,13 @@
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apiVersion: route.openshift.io/v1
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kind: Route
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metadata:
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name: glucose-dashboard
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spec:
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to:
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kind: Service
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name: glucose-dashboard
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port:
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targetPort: 8501
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tls:
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termination: edge
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insecureEdgeTerminationPolicy: Redirect
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@@ -0,0 +1,10 @@
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apiVersion: v1
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kind: Service
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metadata:
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name: glucose-dashboard
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spec:
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selector:
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app: glucose-dashboard
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ports:
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- port: 8501
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targetPort: 8501
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@@ -4,6 +4,7 @@ import streamlit as st
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from auth import require_auth
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from nightscout_client import NightscoutConfigError
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from reporting import (
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gmi_mmol_per_mol,
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gmi_percent,
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percent_cv,
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sensor_active_percent,
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@@ -43,6 +44,7 @@ active_pct = sensor_active_percent(glucose, start, end)
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mean_mmol = glucose["mmol"].mean()
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gmi = gmi_percent(mean_mmol)
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gmi_mmol_mol = gmi_mmol_per_mol(gmi)
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cv = percent_cv(glucose)
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tir = tir_breakdown_5band(glucose)
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@@ -50,7 +52,11 @@ st.subheader("Kerncijfers")
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col1, col2, col3, col4 = st.columns(4)
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col1.metric("Sensordekking", f"{active_pct:.0f}%", help="Streefwaarde: ≥70%")
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col2.metric("Gemiddelde glucose", f"{mean_mmol:.1f} mmol/L")
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col3.metric("Geschat HbA1c (GMI)", f"{gmi:.1f}%", help="Bergenstal et al. 2018 — geen labwaarde")
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col3.metric(
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"Geschat HbA1c (GMI)",
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f"{gmi:.1f}% ({gmi_mmol_mol:.0f} mmol/mol)",
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help="Bergenstal et al. 2018 — geen labwaarde",
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)
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col4.metric("Variabiliteit (%CV)", f"{cv:.0f}%", help="Streefwaarde: ≤36%")
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st.subheader("Tijd in bereik (5 klinische banden)")
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+3
-1
@@ -28,6 +28,7 @@ from reporting import (
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agp_percentile_bands,
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completeness_warning,
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detect_events,
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gmi_mmol_per_mol,
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gmi_percent,
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percent_cv,
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sensor_active_percent,
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@@ -178,7 +179,8 @@ def _add_summary_section(pdf: FPDF, glucose: pd.DataFrame, start, end) -> None:
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pdf.set_font("Helvetica", "", 11)
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pdf.cell(0, 6, f"Sensordekking: {active_pct:.0f}%", **_LINE_KWARGS)
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pdf.cell(0, 6, f"Gemiddelde glucose: {mean_mmol:.1f} mmol/L", **_LINE_KWARGS)
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pdf.cell(0, 6, f"Geschat HbA1c (GMI): {gmi_percent(mean_mmol):.1f}%", **_LINE_KWARGS)
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gmi = gmi_percent(mean_mmol)
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pdf.cell(0, 6, f"Geschat HbA1c (GMI): {gmi:.1f}% ({gmi_mmol_per_mol(gmi):.0f} mmol/mol)", **_LINE_KWARGS)
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pdf.cell(0, 6, f"Variabiliteit (%CV): {percent_cv(glucose):.0f}%", **_LINE_KWARGS)
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pdf.ln(2)
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pdf.cell(0, 6, f"Zeer laag (<3.0 mmol/L): {tir['very_low']:.0f}%", **_LINE_KWARGS)
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@@ -36,6 +36,17 @@ def gmi_percent(mean_mmol: float) -> float:
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return round(3.31 + 0.4311 * mean_mmol, 1)
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def gmi_mmol_per_mol(gmi_pct: float) -> int:
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"""Zet een GMI-percentage (DCCT/NGSP) om naar IFCC mmol/mol.
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mmol/mol = 10.929 x (% - 2.15) — de standaard IFCC-omrekening
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(Hoelzel et al. 2004, Clin Chem 50(1):166-174), gangbare eenheid in
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Nederlandse labuitslagen naast het %-getal (gitea #25). Bijv. 6.5% =
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48 mmol/mol, de bekende diagnostische drempel.
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"""
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return round(10.929 * (gmi_pct - 2.15))
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GMI_TREND_COLUMNS = ["date", "gmi", "rolling_avg", "trend", "trend_lower", "trend_upper"]
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@@ -12,6 +12,7 @@ from reporting import (
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daily_gmi,
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day_of_week_pattern,
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detect_events,
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gmi_mmol_per_mol,
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gmi_percent,
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gmi_trend,
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minutes_per_day,
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@@ -35,6 +36,12 @@ def test_gmi_percent_matches_bergenstal_formula():
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assert gmi_percent(7.0) == pytest.approx(6.3, abs=0.05)
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def test_gmi_mmol_per_mol_matches_known_ifcc_reference_values():
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# 6.5% is de bekende diagnostische drempel = 48 mmol/mol (IFCC).
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assert gmi_mmol_per_mol(6.5) == 48
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assert gmi_mmol_per_mol(7.0) == 53
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def test_daily_gmi_aggregates_per_calendar_day():
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glucose = _series(
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[
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Reference in New Issue
Block a user