import kaleido, 2

.woodpecker.yml

@@ -13,12 +13,6 @@ steps:
       dockerfile: Dockerfile
     when:
       - event: [push, tag]
-  scan_image:
-    image: aquasec/trivy
-    commands:
-      - TRIVY_DISABLE_VEX_NOTICE=1 trivy image $FORGE_NAME/$CI_REPO:$CI_COMMIT_SHA --quiet --exit-code 1
-    when:
-      - event: [push, tag]
   deploy:
     image: quay.io/wollud1969/k8s-admin-helper:0.2.1
     environment:

src/requirements.txt

@@ -16,6 +16,7 @@ idna==3.10
 importlib_metadata==8.6.1
 itsdangerous==2.2.0
 Jinja2==3.1.5
+kaleido==1.0.0rc0
 loguru==0.7.3
 MarkupSafe==3.0.2
 msgspec==0.19.0

src/run.py

@@ -1,12 +1,16 @@
-from flask import Flask, session, g, render_template_string
+from flask import Flask, session, g, render_template_string, Response
 from flask_session import Session
 from flask_oidc import OpenIDConnect
+import kaleido
 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 plotly.io as pio
+import io
 import pandas as pd
 import psycopg
 import sqlalchemy
@@ -48,16 +52,19 @@ def token_debug():
 @oidc.require_login
 def index():
     try:
+        stepX_time = time.time()
         dbh = psycopg.connect()
         engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
 
-        start_time = time.time()
+        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 - start_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 - start_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="",
@@ -70,37 +77,44 @@ def index():
             ),
             yaxis=dict(ticksuffix=" kWh")
         )
-        graph_html_1 = fig_1.to_html(full_html=False, default_height='33%')
+        graph_html_1 = fig_1.to_html(full_html=False, default_height='30%')
 
-        start_time = time.time()
+        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)
-        step1_time = time.time()
+        step4_time = time.time()
-        duration1 = step1_time - start_time
+        duration3 = step4_time - step3_time
+        logger.info(f"{duration3=}")
         fig_2 = px.line(df, x='bucket', y='avg_power')
-        step2_time = time.time()
+        step5_time = time.time()
-        duration2 = step2_time - start_time
+        duration4 = step5_time - step4_time
+        logger.info(f"{duration4=}")
         fig_2.update_layout(
             xaxis_title="",
             yaxis_title="",
-            title=f"Export gestern {duration1:.3f}, {duration2:.3f}",
+            title=f"Export gestern {duration3:.3f}, {duration4:.3f}",
             yaxis=dict(ticksuffix=" W")
         )
-        graph_html_2 = fig_2.to_html(full_html=False, default_height='33%')
+        graph_html_2 = fig_2.to_html(full_html=False, default_height='30%')
 
-        start_time = time.time()
+        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)
-        step1_time = time.time()
+        step7_time = time.time()
-        duration1 = step1_time - start_time
+        duration5 = step7_time - step6_time
+        logger.info(f"{duration5=}")
         fig_3 = px.line(df, x='bucket', y='avg_power')
-        step2_time = time.time()
+        step8_time = time.time()
-        duration2 = step2_time - start_time
+        duration6 = step8_time - step7_time
+        logger.info(f"{duration6=}")
         fig_3.update_layout(
             xaxis_title="",
             yaxis_title="",
-            title=f"Export heute {duration1:.3f}, {duration2:.3f}",
+            title=f"Export heute {duration5:.3f}, {duration6:.3f}",
             yaxis=dict(ticksuffix=" W")
         )
-        graph_html_3 = fig_3.to_html(full_html=False, default_height='33%')
+        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>
@@ -111,6 +125,23 @@ def index():
                     {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>
         """)
@@ -121,6 +152,60 @@ def index():
             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,
+            avg(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(
+            # Linke Y-Achse
+            yaxis=dict(
+                title='Root Dispersion'
+            ),
+            
+            # 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
+        )
+
+        img = fig.to_image(format='png')
+        return Response(img, mimetype='image/png')
+
+    except Exception as e:
+        raise Exception(f"Error when querying NTP server values: {e}")
+    finally:
+        if dbh is not None:
+            dbh.close()
+
+
+
+
 
 
 if __name__ == '__main__':