213 lines
7.9 KiB
Python
213 lines
7.9 KiB
Python
from flask import Flask, session, g, render_template_string, Response
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from flask_session import Session
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from flask_oidc import OpenIDConnect
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from werkzeug.middleware.proxy_fix import ProxyFix
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from loguru import logger
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import redis
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import json
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import os
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import plotly.express as px
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import plotly.graph_objects as po
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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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try:
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redis_url = os.environ['REDIS_URL']
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oidc_client_secrets = os.environ['OIDC_CLIENT_SECRETS']
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secret_key = os.environ['SECRET_KEY']
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except KeyError as e:
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logger.error(f"Required environment variable not set ({e})")
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raise e
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app = Flask(__name__)
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app.config.update({
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'SECRET_KEY': secret_key,
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'SESSION_TYPE': 'redis',
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'SESSION_REDIS': redis.from_url(redis_url),
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'OIDC_CLIENT_SECRETS': json.loads(oidc_client_secrets),
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'OIDC_SCOPES': 'openid email',
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'OIDC_USER_INFO_ENABLED': True,
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'SESSION_USE_SIGNER': True,
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})
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Session(app)
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oidc = OpenIDConnect(app)
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@app.route('/token_debug', methods=['GET'])
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@oidc.require_login
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def token_debug():
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# Access Token vom Identity Provider abrufen
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access_token = oidc.get_access_token()
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return json.dumps({
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"access_token": access_token
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})
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@app.route('/')
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@oidc.require_login
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def index():
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try:
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stepX_time = time.time()
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dbh = psycopg.connect()
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engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
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step0_time = time.time()
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df = pd.read_sql("SELECT month, cast(year AS varchar), current_energy AS value FROM pv_energy_by_month", con=engine)
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step1_time = time.time()
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duration1 = step1_time - step0_time
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logger.info(f"{duration1=}")
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fig_1 = px.bar(df, x='month', y='value', color='year', barmode='group')
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step2_time = time.time()
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duration2 = step2_time - step1_time
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logger.info(f"{duration2=}")
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fig_1.update_layout(
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title=f"Jahreswerte Exportierte Energie {duration1:.3f}, {duration2:.3f}",
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xaxis_title="",
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yaxis_title="",
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legend_title="Jahr",
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xaxis=dict(
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tickmode="array",
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tickvals=list(range(1, 13)), # Monate 1–12
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ticktext=["Jan", "Feb", "Mär", "Apr", "Mai", "Jun", "Jul", "Aug", "Sep", "Okt", "Nov", "Dez"]
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),
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yaxis=dict(ticksuffix=" kWh")
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)
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graph_html_1 = fig_1.to_html(full_html=False, default_height='30%')
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step3_time = time.time()
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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)
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step4_time = time.time()
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duration3 = step4_time - step3_time
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logger.info(f"{duration3=}")
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fig_2 = px.line(df, x='bucket', y='avg_power')
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step5_time = time.time()
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duration4 = step5_time - step4_time
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logger.info(f"{duration4=}")
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fig_2.update_layout(
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xaxis_title="",
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yaxis_title="",
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title=f"Export gestern {duration3:.3f}, {duration4:.3f}",
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yaxis=dict(ticksuffix=" W")
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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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step6_time = time.time()
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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)
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step7_time = time.time()
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duration5 = step7_time - step6_time
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logger.info(f"{duration5=}")
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fig_3 = px.line(df, x='bucket', y='avg_power')
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step8_time = time.time()
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duration6 = step8_time - step7_time
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logger.info(f"{duration6=}")
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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"Export heute {duration5:.3f}, {duration6:.3f}",
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yaxis=dict(ticksuffix=" W")
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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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stepZ_time = time.time()
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duration7 = stepZ_time - stepX_time
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logger.info(f"{duration7=}")
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return render_template_string(f"""
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<html>
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<head>
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<title>Jahreswerte PV-Energie</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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<div style="height:9vh; background-color:lightgrey; font-family: Courier, Consolas, monospace;">
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<table style="border-collapse: collapse;">
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<style>
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td.smallsep {{ padding-right: 10px }}
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td.largesep {{ padding-right: 30px }}
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</style>
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<tr>
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<td class="smallsep">Query 1:</td><td class="largesep"> {duration1:.3f} s</td><td class="smallsep">Graph 1:</td><td> {duration2:.3f} s</td>
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</tr><tr>
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<td class="smallsep">Query 2:</td><td class="largesep"> {duration3:.3f} s</td><td class="smallsep">Graph 2:</td><td> {duration4:.3f} s</td>
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</tr><tr>
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<td class="smallsep">Query 3:</td><td class="largesep"> {duration5:.3f} s</td><td class="smallsep">Graph 3:</td><td> {duration6:.3f} s</td>
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</tr><tr>
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<td class="smallsep">Total:</td><td> {duration7:.3f} s</td><td></td><td></td>
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</tr>
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</table>
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</div>
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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 energy export 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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@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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avg(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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df = pd.read_sql(query, con=engine)
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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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# Linke Y-Achse
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yaxis=dict(
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title='Root Dispersion'
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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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img = fig.to_image(format='png')
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return Response(img, mimetype='image/png')
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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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if __name__ == '__main__':
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app.run(port=8080)
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else:
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exposed_app = ProxyFix(app, x_for=1, x_host=1)
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