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"""
Jahreswerte PV-Energie
{graph_html_1}
{graph_html_2}
{graph_html_3}
| Query 1: | {duration1:.3f} s | Graph 1: | {duration2:.3f} s |
| Query 2: | {duration3:.3f} s | Graph 2: | {duration4:.3f} s |
| Query 3: | {duration5:.3f} s | Graph 3: | {duration6:.3f} s |
| Total: | {duration7:.3f} s | | |
""")
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,
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(
title='NTP Server Numbers',
# 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
)
graph_html = fig.to_html(full_html=False, default_height='30%')
return render_template_string(f"""
NTP Server Numbers
{graph_html}
""")
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__':
app.run(port=8080)
else:
exposed_app = ProxyFix(app, x_for=1, x_host=1)