ntp graphs

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.11-alpine3.21
+FROM python:3.12-alpine3.21
 
 ENV REDIS_URL=""
 ENV SECRET_KEY=""

debug-build-run.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+set -x
+
+IMAGE_NAME=numberimage
+
+docker build --progress=plain -t $IMAGE_NAME .
+
+
+. load-debug-env
+
+docker run \
+  -it \
+  --rm \
+  -e "REDIS_URL=$REDIS_URL" \
+  -e "SECRET_KEY=$SECRET_KEY" \
+  -e "OIDC_CLIENT_SECRETS=$OIDC_CLIENT_SECRETS" \
+  -e "PGHOST=$PGHOST" \
+  -e "PGDATABASE=$PGDATABASE" \
+  -e "PGSSLMODE=$PGSSLMODE" \
+  -e "PGUSER=$PGUSER" \
+  -e "PGPASSWORD=$PGPASSWORD" \
+  -p 8080:8080 \
+  $IMAGE_NAME
+

deployment/deploy-yml.tmpl

@@ -1,27 +1,27 @@
 apiVersion: apps/v1
 kind: Deployment
 metadata:
-  name: pv-stats
+  name: numbers
   labels:
-    app: pv-stats
+    app: numbers
   annotations:
-    secret.reloader.stakater.com/reload: pv-stats
+    secret.reloader.stakater.com/reload: numbers
 spec:
   replicas: 1
   selector:
     matchLabels:
-      app: pv-stats
+      app: numbers
   template:
     metadata:
       labels:
-        app: pv-stats
+        app: numbers
     spec:
       containers:
-        - name: pv-stats
+        - name: numbers
           image: %IMAGE%
           envFrom:
             - secretRef: 
-                name: pv-stats
+                name: numbers
           ports:
             - containerPort: 8080
               protocol: TCP
@@ -29,11 +29,11 @@ spec:
 apiVersion: v1
 kind: Service
 metadata:
-  name: pv-stats
+  name: numbers
 spec:
   type: ClusterIP
   selector:
-    app: pv-stats
+    app: numbers
   ports:
     - name: http
       targetPort: 8080
@@ -42,23 +42,23 @@ spec:
 apiVersion: networking.k8s.io/v1
 kind: Ingress
 metadata:
-  name: pv-stats
+  name: numbers
   annotations:
     cert-manager.io/cluster-issuer: letsencrypt-production-http
 spec:
   tls:
     - hosts:
-        - pv-stats.hottis.de
-      secretName: pv-stats-cert
+        - numbers.hottis.de
+      secretName: numbers-cert
   rules:
-    - host: pv-stats.hottis.de
+    - host: numbers.hottis.de
       http:
         paths:
           - path: /
             pathType: Prefix
             backend:
               service:
-                name: pv-stats
+                name: numbers
                 port:
                   number: 80
 

deployment/deploy.sh

@@ -9,7 +9,7 @@ if [ "$GPG_PASSPHRASE" == "" ]; then
   exit 1
 fi
 
-IMAGE_NAME=gitea.hottis.de/wn/pv-stats
+IMAGE_NAME=gitea.hottis.de/wn/numbers
 NAMESPACE=homea
 DEPLOYMENT_DIR=$PWD/deployment
 
@@ -25,7 +25,7 @@ kubectl create namespace $NAMESPACE \
 # rm $SECRETS_FILE
 eval "`cat secrets.asc | /usr/local/bin/decrypt-secrets.sh`"
 
-kubectl create secret generic pv-stats \
+kubectl create secret generic numbers \
   --dry-run=client \
   -o yaml \
   --save-config \

load-debug-env

@@ -0,0 +1,15 @@
+SECRETS=`mktemp`
+gpg --decrypt --passphrase $GPG_PASSPHRASE --yes --batch --output $SECRETS ./deployment/secrets.asc
+. $SECRETS
+rm $SECRETS
+
+DB_NAMESPACE=database1
+DB_DEPLOYNAME=database
+
+REDIS_NAMESPACE=redis
+REDIS_SERVICE_NAME=redis
+
+PGHOST=`kubectl get services $DB_DEPLOYNAME -n $DB_NAMESPACE -o jsonpath="{.status.loadBalancer.ingress[0].ip}"`
+REDISHOST=`kubectl get services $REDIS_SERVICE_NAME -n $REDIS_NAMESPACE -o jsonpath="{.status.loadBalancer.ingress[0].ip}"`
+
+REDIS_URL=redis://$REDISHOST:6379/4

src/app.py

@@ -0,0 +1,32 @@
+from flask import Flask, session, g, render_template_string
+from flask_session import Session
+from flask_oidc import OpenIDConnect
+from werkzeug.middleware.proxy_fix import ProxyFix
+from loguru import logger
+import redis
+import json
+import os
+
+try:
+  redis_url = os.environ['REDIS_URL']
+  oidc_client_secrets = os.environ['OIDC_CLIENT_SECRETS']
+  secret_key = os.environ['SECRET_KEY']
+except KeyError as e:
+  logger.error(f"Required environment variable not set ({e})")
+  raise e
+
+app = Flask(__name__)
+app.config.update({
+  'SECRET_KEY': secret_key,
+  'SESSION_TYPE': 'redis',
+  'SESSION_REDIS': redis.from_url(redis_url),
+  'OIDC_CLIENT_SECRETS': json.loads(oidc_client_secrets),
+  'OIDC_SCOPES': 'openid email',
+  'OIDC_USER_INFO_ENABLED': True,
+  'SESSION_USE_SIGNER': True,
+})
+Session(app)
+oidc = OpenIDConnect(app)
+
+
+

src/debug_routes.py

@@ -0,0 +1,16 @@
+from loguru import logger
+import json
+
+from app import app
+from app import oidc
+
+
+@app.route('/token_debug', methods=['GET'])
+@oidc.require_login
+def token_debug():
+    # Access Token vom Identity Provider abrufen
+    access_token = oidc.get_access_token()
+    return json.dumps({
+        "access_token": access_token
+    })
+

src/ntp_routes.py

@@ -0,0 +1,129 @@
+from flask import Flask, session, g, render_template_string, Response
+from loguru import logger
+import json
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from matplotlib.ticker import ScalarFormatter
+import pandas as pd
+import psycopg
+import sqlalchemy
+import time
+import io
+
+from app import app
+from app import oidc
+
+
+
+
+@app.route('/ntp/stratum-rootdisp.png')
+def stratum_rootdisp_png():
+    dbh = psycopg.connect()
+    engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
+    query = """
+      select   time_bucket('5 minutes', time) as bucket,
+        attributes->>'Label' as device,
+        avg(cast(values->'rootdisp'->>'value'  as float)) as rootdisp,
+        max(cast(values->'stratum'->>'value'  as int)) as stratum
+      from measurements
+      where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
+        application = 'SNMP' and attributes->>'Label' IN ('harrison', 'david')
+      group by bucket, attributes->>'Label'
+      order by bucket, attributes->>'Label'
+    """
+    df = pd.read_sql(query, con=engine)
+    
+    df['rootdisp'] = df['rootdisp'] / 1e6
+
+    
+    # Extract date for title
+    plot_date = df['bucket'].dt.date.iloc[0] if not df.empty else "Unknown Date"
+    
+    # Create figure with two side-by-side subplots
+    fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
+    
+    for i, device in enumerate(['harrison', 'david']):
+        ax1 = axes[i]
+        ax2 = ax1.twinx()
+        
+        device_df = df[df['device'] == device]
+        
+        ax1.plot(device_df['bucket'], device_df['rootdisp'], 'r-', label='Root Dispersion')
+        ax1.set_xlabel('Time')
+        ax1.set_ylabel('Root Dispersion (ms)', color='r')
+        ax1.tick_params(axis='y', labelcolor='r')
+        
+        ax2.plot(device_df['bucket'], device_df['stratum'], 'b-', label='Stratum')
+        ax2.set_ylabel('Stratum', color='b')
+        ax2.tick_params(axis='y', labelcolor='b')
+        ax2.set_yticks(range(int(device_df['stratum'].min()), int(device_df['stratum'].max()) + 1))
+        
+        ax1.set_title(f'{device.capitalize()}')
+        
+        ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
+        fig.autofmt_xdate(rotation=45)
+    
+    fig.suptitle(f'Stratum and Root Dispersion - {plot_date}')
+    fig.tight_layout()
+    
+    img_io = io.BytesIO()
+    plt.savefig(img_io, format='png')
+    img_io.seek(0)
+    plt.close(fig)
+
+    return Response(img_io, mimetype='image/png')
+
+
+@app.route('/ntp/packets-load.png')
+def packets_load_png():
+    dbh = psycopg.connect()
+    engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
+    query = """
+      select   time_bucket('5 minutes', time) as bucket,
+        attributes->>'Label' as device,
+        avg(cast(values->'load1'->>'value'  as float)) as load,
+        avg(cast(values->'processed-pkts'->>'value'  as int)) as packets
+      from measurements
+      where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
+        application = 'SNMP' and attributes->>'Label' IN ('harrison', 'david')
+      group by bucket, attributes->>'Label'
+      order by bucket, attributes->>'Label'
+    """
+    df = pd.read_sql(query, con=engine)
+    
+    
+    # Extract date for title
+    plot_date = df['bucket'].dt.date.iloc[0] if not df.empty else "Unknown Date"
+    
+    # Create figure with two side-by-side subplots
+    fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
+    
+    for i, device in enumerate(['harrison', 'david']):
+        ax1 = axes[i]
+        ax2 = ax1.twinx()
+        
+        device_df = df[df['device'] == device]
+        
+        ax1.plot(device_df['bucket'], device_df['load'], 'r-', label='CPU Load')
+        ax1.set_xlabel('Time')
+        ax1.set_ylabel('Load', color='r')
+        ax1.tick_params(axis='y', labelcolor='r')
+        
+        ax2.plot(device_df['bucket'], device_df['packets'], 'b-', label='Processed Packets')
+        ax2.set_ylabel('Packets', color='b')
+        ax2.tick_params(axis='y', labelcolor='b')
+        
+        ax1.set_title(f'{device.capitalize()}')
+        
+        ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
+        fig.autofmt_xdate(rotation=45)
+    
+    fig.suptitle(f'CPU Load and Processed Packets - {plot_date}')
+    fig.tight_layout()
+    
+    img_io = io.BytesIO()
+    plt.savefig(img_io, format='png')
+    img_io.seek(0)
+    plt.close(fig)
+
+    return Response(img_io, mimetype='image/png')

src/pv_routes.py

@@ -0,0 +1,117 @@
+from flask import Flask, session, g, render_template_string
+from loguru import logger
+import json
+import plotly.express as px
+import plotly.graph_objects as po
+import pandas as pd
+import psycopg
+import sqlalchemy
+import time
+
+from app import app
+from app import oidc
+
+
+@app.route('/pvstats')
+@oidc.require_login
+def pvstats():
+    try:
+        stepX_time = time.time()
+        dbh = psycopg.connect()
+        engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
+
+        step0_time = time.time()
+        df = pd.read_sql("SELECT month, cast(year AS varchar), current_energy AS value FROM pv_energy_by_month", con=engine)
+        step1_time = time.time()
+        duration1 = step1_time - step0_time
+        logger.info(f"{duration1=}")
+        fig_1 = px.bar(df, x='month', y='value', color='year', barmode='group')
+        step2_time = time.time()
+        duration2 = step2_time - step1_time
+        logger.info(f"{duration2=}")
+        fig_1.update_layout(
+            title=f"Jahreswerte Exportierte Energie {duration1:.3f}, {duration2:.3f}",
+            xaxis_title="",
+            yaxis_title="",
+            legend_title="Jahr",
+            xaxis=dict(
+                tickmode="array",
+                tickvals=list(range(1, 13)),  # Monate 1–12
+                ticktext=["Jan", "Feb", "Mär", "Apr", "Mai", "Jun", "Jul", "Aug", "Sep", "Okt", "Nov", "Dez"]
+            ),
+            yaxis=dict(ticksuffix=" kWh")
+        )
+        graph_html_1 = fig_1.to_html(full_html=False, default_height='30%')
+
+        step3_time = time.time()
+        df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) - '1 day'::interval AND time < date_trunc('day', now()) GROUP BY bucket ORDER BY bucket", con=engine)
+        step4_time = time.time()
+        duration3 = step4_time - step3_time
+        logger.info(f"{duration3=}")
+        fig_2 = px.line(df, x='bucket', y='avg_power')
+        step5_time = time.time()
+        duration4 = step5_time - step4_time
+        logger.info(f"{duration4=}")
+        fig_2.update_layout(
+            xaxis_title="",
+            yaxis_title="",
+            title=f"Export gestern {duration3:.3f}, {duration4:.3f}",
+            yaxis=dict(ticksuffix=" W")
+        )
+        graph_html_2 = fig_2.to_html(full_html=False, default_height='30%')
+
+        step6_time = time.time()
+        df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval GROUP BY bucket ORDER BY bucket", con=engine)
+        step7_time = time.time()
+        duration5 = step7_time - step6_time
+        logger.info(f"{duration5=}")
+        fig_3 = px.line(df, x='bucket', y='avg_power')
+        step8_time = time.time()
+        duration6 = step8_time - step7_time
+        logger.info(f"{duration6=}")
+        fig_3.update_layout(
+            xaxis_title="",
+            yaxis_title="",
+            title=f"Export heute {duration5:.3f}, {duration6:.3f}",
+            yaxis=dict(ticksuffix=" W")
+        )
+        graph_html_3 = fig_3.to_html(full_html=False, default_height='30%')
+        stepZ_time = time.time()
+        duration7 = stepZ_time - stepX_time
+        logger.info(f"{duration7=}")
+
+        return render_template_string(f"""
+            <html>
+                <head>
+                    <title>Jahreswerte PV-Energie</title>
+                </head>
+                <body>
+                    {graph_html_1}
+                    {graph_html_2}
+                    {graph_html_3}
+                    <div style="height:9vh; background-color:lightgrey; font-family: Courier, Consolas, monospace;">
+                      <table style="border-collapse: collapse;">
+                        <style>
+                          td.smallsep {{ padding-right: 10px }}
+                          td.largesep {{ padding-right: 30px }}
+                        </style> 
+                        <tr>
+                          <td class="smallsep">Query 1:</td><td class="largesep"> {duration1:.3f} s</td><td class="smallsep">Graph 1:</td><td> {duration2:.3f} s</td>
+                        </tr><tr>
+                          <td class="smallsep">Query 2:</td><td class="largesep"> {duration3:.3f} s</td><td class="smallsep">Graph 2:</td><td> {duration4:.3f} s</td>
+                        </tr><tr>
+                          <td class="smallsep">Query 3:</td><td class="largesep"> {duration5:.3f} s</td><td class="smallsep">Graph 3:</td><td> {duration6:.3f} s</td>
+                        </tr><tr>
+                          <td class="smallsep">Total:</td><td> {duration7:.3f} s</td><td></td><td></td>
+                        </tr>
+                      </table>   
+                    </div>
+                </body>
+            </html>
+        """)
+    except Exception as e:
+        raise Exception(f"Error when querying energy export values: {e}")
+    finally:
+        if dbh is not None:
+            dbh.close()
+

src/requirements.txt

@@ -38,3 +38,6 @@ tzdata==2025.1
 urllib3==2.3.0
 Werkzeug==3.1.3
 zipp==3.21.0
+pillow==11.1.0
+matplotlib==3.10.1
+

src/routes.py

@@ -0,0 +1,24 @@
+from flask import abort, Response
+from PIL import Image, ImageDraw
+import io
+from app import app
+from app import oidc
+
+
+@app.route('/')
+def index():
+    abort(404)
+
+@app.route('/generate_image')
+def generate_image():
+    img = Image.new('RGB', (200, 100), color=(255, 255, 255))
+    
+    draw = ImageDraw.Draw(img)
+    draw.text((50, 40), "Hello, Flask!", fill=(0, 0, 0))  # Schwarzer Text
+    
+    img_io = io.BytesIO()
+    img.save(img_io, 'PNG')
+    img_io.seek(0)  # Zeiger zurücksetzen
+
+    return Response(img_io, mimetype='image/png')
+

src/run.py

@@ -1,265 +1,16 @@
-from flask import Flask, session, g, render_template_string
-from flask_session import Session
-from flask_oidc import OpenIDConnect
 from werkzeug.middleware.proxy_fix import ProxyFix
 from loguru import logger
-import redis
-import json
-import os
-import plotly.express as px
-import plotly.graph_objects as po
-import pandas as pd
-import psycopg
-import sqlalchemy
-import time
-
-try:
-  redis_url = os.environ['REDIS_URL']
-  oidc_client_secrets = os.environ['OIDC_CLIENT_SECRETS']
-  secret_key = os.environ['SECRET_KEY']
-except KeyError as e:
-  logger.error(f"Required environment variable not set ({e})")
-  raise e
-  
-
-app = Flask(__name__)
-
-app.config.update({
-  'SECRET_KEY': secret_key,
-  'SESSION_TYPE': 'redis',
-  'SESSION_REDIS': redis.from_url(redis_url),
-  'OIDC_CLIENT_SECRETS': json.loads(oidc_client_secrets),
-  'OIDC_SCOPES': 'openid email',
-  'OIDC_USER_INFO_ENABLED': True,
-  'SESSION_USE_SIGNER': True,
-})
-Session(app)
-oidc = OpenIDConnect(app)
-
-@app.route('/token_debug', methods=['GET'])
-@oidc.require_login
-def token_debug():
-    # Access Token vom Identity Provider abrufen
-    access_token = oidc.get_access_token()
-    return json.dumps({
-        "access_token": access_token
-    })
-
-@app.route('/')
-@oidc.require_login
-def index():
-    try:
-        stepX_time = time.time()
-        dbh = psycopg.connect()
-        engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
-
-        step0_time = time.time()
-        df = pd.read_sql("SELECT month, cast(year AS varchar), current_energy AS value FROM pv_energy_by_month", con=engine)
-        step1_time = time.time()
-        duration1 = step1_time - step0_time
-        logger.info(f"{duration1=}")
-        fig_1 = px.bar(df, x='month', y='value', color='year', barmode='group')
-        step2_time = time.time()
-        duration2 = step2_time - step1_time
-        logger.info(f"{duration2=}")
-        fig_1.update_layout(
-            title=f"Jahreswerte Exportierte Energie {duration1:.3f}, {duration2:.3f}",
-            xaxis_title="",
-            yaxis_title="",
-            legend_title="Jahr",
-            xaxis=dict(
-                tickmode="array",
-                tickvals=list(range(1, 13)),  # Monate 1–12
-                ticktext=["Jan", "Feb", "Mär", "Apr", "Mai", "Jun", "Jul", "Aug", "Sep", "Okt", "Nov", "Dez"]
-            ),
-            yaxis=dict(ticksuffix=" kWh")
-        )
-        graph_html_1 = fig_1.to_html(full_html=False, default_height='30%')
-
-        step3_time = time.time()
-        df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) - '1 day'::interval AND time < date_trunc('day', now()) GROUP BY bucket ORDER BY bucket", con=engine)
-        step4_time = time.time()
-        duration3 = step4_time - step3_time
-        logger.info(f"{duration3=}")
-        fig_2 = px.line(df, x='bucket', y='avg_power')
-        step5_time = time.time()
-        duration4 = step5_time - step4_time
-        logger.info(f"{duration4=}")
-        fig_2.update_layout(
-            xaxis_title="",
-            yaxis_title="",
-            title=f"Export gestern {duration3:.3f}, {duration4:.3f}",
-            yaxis=dict(ticksuffix=" W")
-        )
-        graph_html_2 = fig_2.to_html(full_html=False, default_height='30%')
-
-        step6_time = time.time()
-        df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval GROUP BY bucket ORDER BY bucket", con=engine)
-        step7_time = time.time()
-        duration5 = step7_time - step6_time
-        logger.info(f"{duration5=}")
-        fig_3 = px.line(df, x='bucket', y='avg_power')
-        step8_time = time.time()
-        duration6 = step8_time - step7_time
-        logger.info(f"{duration6=}")
-        fig_3.update_layout(
-            xaxis_title="",
-            yaxis_title="",
-            title=f"Export heute {duration5:.3f}, {duration6:.3f}",
-            yaxis=dict(ticksuffix=" W")
-        )
-        graph_html_3 = fig_3.to_html(full_html=False, default_height='30%')
-        stepZ_time = time.time()
-        duration7 = stepZ_time - stepX_time
-        logger.info(f"{duration7=}")
-
-        return render_template_string(f"""
-            <html>
-                <head>
-                    <title>Jahreswerte PV-Energie</title>
-                </head>
-                <body>
-                    {graph_html_1}
-                    {graph_html_2}
-                    {graph_html_3}
-                    <div style="height:9vh; background-color:lightgrey; font-family: Courier, Consolas, monospace;">
-                      <table style="border-collapse: collapse;">
-                        <style>
-                          td.smallsep {{ padding-right: 10px }}
-                          td.largesep {{ padding-right: 30px }}
-                        </style> 
-                        <tr>
-                          <td class="smallsep">Query 1:</td><td class="largesep"> {duration1:.3f} s</td><td class="smallsep">Graph 1:</td><td> {duration2:.3f} s</td>
-                        </tr><tr>
-                          <td class="smallsep">Query 2:</td><td class="largesep"> {duration3:.3f} s</td><td class="smallsep">Graph 2:</td><td> {duration4:.3f} s</td>
-                        </tr><tr>
-                          <td class="smallsep">Query 3:</td><td class="largesep"> {duration5:.3f} s</td><td class="smallsep">Graph 3:</td><td> {duration6:.3f} s</td>
-                        </tr><tr>
-                          <td class="smallsep">Total:</td><td> {duration7:.3f} s</td><td></td><td></td>
-                        </tr>
-                      </table>   
-                    </div>
-                </body>
-            </html>
-        """)
-    except Exception as e:
-        raise Exception(f"Error when querying energy export values: {e}")
-    finally:
-        if dbh is not None:
-            dbh.close()
-
-
-@app.route('/ntpserver')
-def ntpserver():
-    try:
-        dbh = psycopg.connect()
-        engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
-
-        query = """
-          select   time_bucket('5 minutes', time) as bucket,
-            device,
-            avg(cast(values->'rootdisp'->>'value'  as float)) as rootdisp,
-            max(cast(values->'stratum'->>'value'  as int)) as stratum
-          from measurements
-          where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and 
-            application = 'TSM' and attributes->>'Label' = 'david'
-          group by bucket, device
-          order by bucket, device
-        """      
-
-        df = pd.read_sql(query, con=engine)
-
-        fig = po.Figure()
-        fig.add_trace(po.Scatter(x=df['bucket'], y=df['rootdisp'], mode='lines', name='Root Dispersion', yaxis='y1', line=dict(color='red')))
-        fig.add_trace(po.Scatter(x=df['bucket'], y=df['stratum'], mode='lines', name='Stratum', yaxis='y2', line=dict(color='blue')))
-
-        fig.update_layout(
-            title='NTP Server Numbers',
-            
-            # Linke Y-Achse
-            yaxis=dict(
-                title='Root Dispersion',
-                ticksuffix=' ms'
-            ),
-            
-            # Rechte Y-Achse
-            yaxis2=dict(
-                title='Stratum',
-                overlaying='y',  # Legt die zweite Y-Achse über die erste
-                side='right',  # Setzt sie auf die rechte Seite
-                tickmode='linear',  # Stellt sicher, dass die Ticks in festen Intervallen sind
-                dtick=1,  # Zeigt nur ganzzahlige Ticks
-            ),
-            
-            legend=dict(x=0.05, y=1)  # Position der Legende
-        )
-
-        graph_html_1 = fig.to_html(full_html=False, default_height='30%')
-
-        query = """
-            select   time_bucket('5 minutes', time) as bucket,
-              device,
-              avg(cast(values->'time-reqs-pkts'->>'value'  as float)) as packets
-            from measurements
-            where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and 
-              application = 'SNMP' and attributes->>'Label' = 'david'
-            group by bucket, device
-            order by bucket, device
-        """
-        df = pd.read_sql(query, con=engine)
-        fig_2 = px.line(df, x='bucket', y='packets')
-        fig_2.update_layout(
-            xaxis_title="",
-            yaxis_title="",
-            yaxis_ticksuffix="p/s",
-            title=f"Time Requests"
-        )
-        graph_html_2 = fig_2.to_html(full_html=False, default_height='30%')
-
-        query = """
-            select   time_bucket('5 minutes', time) as bucket,
-              device,
-              avg(cast(values->'load1'->>'value'  as float)) as loadaverage1min
-            from measurements
-            where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and 
-              application = 'SNMP' and attributes->>'Label' = 'david'
-            group by bucket, device
-            order by bucket, device
-        """
-        df = pd.read_sql(query, con=engine)
-        fig_3 = px.line(df, x='bucket', y='loadaverage1min')
-        fig_3.update_layout(
-            xaxis_title="",
-            yaxis_title="",
-            title=f"CPU Load"
-        )
-        graph_html_3 = fig_3.to_html(full_html=False, default_height='30%')
-
-        return render_template_string(f"""
-            <html>
-                <head>
-                    <title>NTP Server Numbers</title>
-                </head>
-                <body>
-                    {graph_html_1}
-                    {graph_html_2}
-                    {graph_html_3}
-                </body>
-            </html>
-        """)
-    except Exception as e:
-        raise Exception(f"Error when querying NTP server values: {e}")
-    finally:
-        if dbh is not None:
-            dbh.close()
-
-
 
+from app import app
 
+import routes
+import debug_routes
+import pv_routes
+import ntp_routes
 
 
 if __name__ == '__main__':
-    app.run(port=8080)
+    app.run(host='0.0.0.0', port=8080)
 else:
     exposed_app = ProxyFix(app, x_for=1, x_host=1)