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@ -1,120 +1,129 @@
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from flask import Flask, session, g, render_template_string
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from flask import Flask, session, g, render_template_string, Response
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from loguru import logger
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import json
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import plotly.express as px
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import plotly.graph_objects as po
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from matplotlib.ticker import ScalarFormatter
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import pandas as pd
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import psycopg
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import sqlalchemy
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import time
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import io
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from app import app
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from app import oidc
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@app.route('/ntpserver')
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def ntpserver():
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try:
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dbh = psycopg.connect()
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engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
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query = """
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select time_bucket('5 minutes', time) as bucket,
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device,
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avg(cast(values->'rootdisp'->>'value' as float)) as rootdisp,
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max(cast(values->'stratum'->>'value' as int)) as stratum
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from measurements
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where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
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application = 'TSM' and attributes->>'Label' = 'david'
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group by bucket, device
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order by bucket, device
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"""
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@app.route('/ntp/stratum-rootdisp.png')
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def stratum_rootdisp_png():
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dbh = psycopg.connect()
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engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
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query = """
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select time_bucket('5 minutes', time) as bucket,
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attributes->>'Label' as device,
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avg(cast(values->'rootdisp'->>'value' as float)) as rootdisp,
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max(cast(values->'stratum'->>'value' as int)) as stratum
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from measurements
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where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
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application = 'SNMP' and attributes->>'Label' IN ('harrison', 'david')
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group by bucket, attributes->>'Label'
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order by bucket, attributes->>'Label'
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"""
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df = pd.read_sql(query, con=engine)
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df['rootdisp'] = df['rootdisp'] / 1e6
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df = pd.read_sql(query, con=engine)
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# Extract date for title
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plot_date = df['bucket'].dt.date.iloc[0] if not df.empty else "Unknown Date"
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# Create figure with two side-by-side subplots
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fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
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for i, device in enumerate(['harrison', 'david']):
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ax1 = axes[i]
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ax2 = ax1.twinx()
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device_df = df[df['device'] == device]
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ax1.plot(device_df['bucket'], device_df['rootdisp'], 'r-', label='Root Dispersion')
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ax1.set_xlabel('Time')
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ax1.set_ylabel('Root Dispersion (ms)', color='r')
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ax1.tick_params(axis='y', labelcolor='r')
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ax2.plot(device_df['bucket'], device_df['stratum'], 'b-', label='Stratum')
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ax2.set_ylabel('Stratum', color='b')
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ax2.tick_params(axis='y', labelcolor='b')
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ax2.set_yticks(range(int(device_df['stratum'].min()), int(device_df['stratum'].max()) + 1))
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ax1.set_title(f'{device.capitalize()}')
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ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
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fig.autofmt_xdate(rotation=45)
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fig.suptitle(f'Stratum and Root Dispersion - {plot_date}')
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fig.tight_layout()
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img_io = io.BytesIO()
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plt.savefig(img_io, format='png')
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img_io.seek(0)
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plt.close(fig)
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fig = po.Figure()
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fig.add_trace(po.Scatter(x=df['bucket'], y=df['rootdisp'], mode='lines', name='Root Dispersion', yaxis='y1', line=dict(color='red')))
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fig.add_trace(po.Scatter(x=df['bucket'], y=df['stratum'], mode='lines', name='Stratum', yaxis='y2', line=dict(color='blue')))
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fig.update_layout(
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title='NTP Server Numbers',
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# Linke Y-Achse
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yaxis=dict(
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title='Root Dispersion',
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ticksuffix=' ms'
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),
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# Rechte Y-Achse
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yaxis2=dict(
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title='Stratum',
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overlaying='y', # Legt die zweite Y-Achse über die erste
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side='right', # Setzt sie auf die rechte Seite
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tickmode='linear', # Stellt sicher, dass die Ticks in festen Intervallen sind
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dtick=1, # Zeigt nur ganzzahlige Ticks
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),
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legend=dict(x=0.05, y=1) # Position der Legende
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)
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graph_html_1 = fig.to_html(full_html=False, default_height='30%')
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query = """
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select time_bucket('5 minutes', time) as bucket,
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device,
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avg(cast(values->'time-req-pkts'->>'value' as float)) as packets
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from measurements
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where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
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application = 'SNMP' and attributes->>'Label' = 'david'
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group by bucket, device
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order by bucket, device
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"""
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df = pd.read_sql(query, con=engine)
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fig_2 = px.line(df, x='bucket', y='packets')
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fig_2.update_layout(
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xaxis_title="",
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yaxis_title="",
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yaxis_ticksuffix="p/s",
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title=f"Time Requests"
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)
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graph_html_2 = fig_2.to_html(full_html=False, default_height='30%')
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query = """
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select time_bucket('5 minutes', time) as bucket,
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device,
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avg(cast(values->'load1'->>'value' as float)) as loadaverage1min
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from measurements
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where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
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application = 'SNMP' and attributes->>'Label' = 'david'
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group by bucket, device
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order by bucket, device
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"""
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df = pd.read_sql(query, con=engine)
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fig_3 = px.line(df, x='bucket', y='loadaverage1min')
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fig_3.update_layout(
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xaxis_title="",
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yaxis_title="",
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title=f"CPU Load"
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)
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graph_html_3 = fig_3.to_html(full_html=False, default_height='30%')
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return render_template_string(f"""
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<html>
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<head>
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<title>NTP Server Numbers</title>
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</head>
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<body>
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{graph_html_1}
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{graph_html_2}
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{graph_html_3}
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</body>
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</html>
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""")
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except Exception as e:
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raise Exception(f"Error when querying NTP server values: {e}")
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finally:
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if dbh is not None:
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dbh.close()
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return Response(img_io, mimetype='image/png')
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@app.route('/ntp/packets-load.png')
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def packets_load_png():
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dbh = psycopg.connect()
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engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
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query = """
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select time_bucket('5 minutes', time) as bucket,
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attributes->>'Label' as device,
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avg(cast(values->'load1'->>'value' as float)) as load,
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avg(cast(values->'processed-pkts'->>'value' as int)) as packets
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from measurements
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where time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval and
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application = 'SNMP' and attributes->>'Label' IN ('harrison', 'david')
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group by bucket, attributes->>'Label'
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order by bucket, attributes->>'Label'
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"""
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df = pd.read_sql(query, con=engine)
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# Extract date for title
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plot_date = df['bucket'].dt.date.iloc[0] if not df.empty else "Unknown Date"
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# Create figure with two side-by-side subplots
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fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
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for i, device in enumerate(['harrison', 'david']):
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ax1 = axes[i]
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ax2 = ax1.twinx()
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device_df = df[df['device'] == device]
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ax1.plot(device_df['bucket'], device_df['load'], 'r-', label='CPU Load')
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ax1.set_xlabel('Time')
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ax1.set_ylabel('Load', color='r')
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ax1.tick_params(axis='y', labelcolor='r')
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ax2.plot(device_df['bucket'], device_df['packets'], 'b-', label='Processed Packets')
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ax2.set_ylabel('Packets', color='b')
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ax2.tick_params(axis='y', labelcolor='b')
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ax1.set_title(f'{device.capitalize()}')
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ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
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fig.autofmt_xdate(rotation=45)
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fig.suptitle(f'CPU Load and Processed Packets - {plot_date}')
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fig.tight_layout()
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img_io = io.BytesIO()
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plt.savefig(img_io, format='png')
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img_io.seek(0)
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plt.close(fig)
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return Response(img_io, mimetype='image/png')
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