debug start script and separate routes into separate files

debug-build-run.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+set -x
+
+IMAGE_NAME=numberimage
+
+docker build --progress=plain -t $IMAGE_NAME .
+
+
+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
+
+
+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
+

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 +1,119 @@
+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('/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-req-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()
+
+

src/pv_routes.py

@@ -0,0 +1,116 @@
+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('/')
+@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()
+

src/run.py

@@ -1,261 +1,10 @@
-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-req-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
 
 
 if __name__ == '__main__':