mayoxx / powerex-test

powerex-test

Connection

• Connection Id: tutorial_pg_conn
• Connection Type: postgres
• Host: postgres
• Schema: airflow
• Login: airflow
• Password: airflow
• Port: 5432

Run in docker

echo -e "AIRFLOW_UID=$(id -u)" > .env
AIRFLOW_UID=50000
docker compose up airflow-init
docker compose build
docker compose up

Example plot for temperature and rolling averages to verify the data

import matplotlib.pyplot as plt
#import matplotlib.dates as dt
import pandas as pd
import requests

# LOAD FROM meteo DB, here loaded by GET request
url = f"https://api.open-meteo.com/v1/ecmwf?latitude=48.1482&longitude=17.1067&hourly=temperature_2m,relativehumidity_2m,rain,windspeed_10m,winddirection_10m"
response = requests.request("GET", url)
json_data = response.json()
values_raw = list(json_data['hourly'].values())
data = list(zip(range(len(values_raw[0])), *values_raw))
labels = ["id", "time", "temperature_2m", "relativehumidity_2m", "rain", "windspeed_10m", "winddirection_10m"]
df = pd.DataFrame(data, columns=[labels])
df['mean_rolling_temperature_2m'] = round(df['temperature_2m'].rolling(4, min_periods=1).mean(), 2)
# LOAD FROM meteo DB

temperature = df['temperature_2m']
t_average = df['mean_rolling_temperature_2m']
# timedates = dt.date2num(df['time'])

plt.figure(figsize=(10, 5))
plt.plot(temperature, 'k-', label='Original')
plt.plot(t_average, 'r-', label='Running average')
# plt.plot_date(timedates, temperature)
plt.ylabel('Temperature (deg C)')
plt.xlabel('Date')
plt.grid(linestyle=':')
plt.fill_between(t_average.index, 0, t_average.squeeze(), color='r', alpha=0.1)
plt.legend(loc='upper left')
plt.show()