add precipitation to historic data

data_pipelines/historical_weather_data.py

@@ -1,7 +1,8 @@
 import openmeteo_requests
 
-import requests_cache
+import matplotlib.pyplot as plt
 import pandas as pd
+import requests_cache
 from retry_requests import retry
 
 from compute_et0_adjusted import compute_et0
@@ -77,7 +78,9 @@ def aggregate_hourly_weather_data(
 		"temperature_2m": ["min", "max"],
 		"relative_humidity_2m": ["min", "max"],
 		"wind_speed_10m": "mean",
+		"precipitation": "mean",
 		"shortwave_radiation": "mean",
+		"et0_fao_evapotranspiration": "mean",
 	})
 
 	monthly_data = pd.DataFrame.from_dict({
@@ -86,8 +89,10 @@ def aggregate_hourly_weather_data(
 		"air_temperature_max": resampled_data[("temperature_2m", "max")],
 		"relative_humidity_min": resampled_data[("relative_humidity_2m", "min")],
 		"relative_humidity_max": resampled_data[("relative_humidity_2m", "max")],
+		"precipitation": resampled_data[("precipitation", "mean")],
 		"wind_speed": resampled_data[("wind_speed_10m", "mean")],
 		"irradiance": resampled_data[("shortwave_radiation", "mean")],
+		"et0_fao_evapotranspiration": resampled_data[("et0_fao_evapotranspiration", "mean")],
 	})
 
 	return monthly_data
@@ -96,10 +101,14 @@ def aggregate_hourly_weather_data(
 if __name__ == '__main__':
 	latitude = 47
 	longitude = 3
-	start_year = 2020
-	end_year = 2021
+	start_year = 2000
+	end_year = 2024
 	df = download_historical_weather_data(latitude, longitude, start_year, end_year)
 	monthly_df = aggregate_hourly_weather_data(df)
 
 	et0 = compute_et0(monthly_df, latitude, longitude)
-	print(et0)
+	monthly_df["et0"] = et0
+
+	plt.plot(monthly_df["et0_fao_evapotranspiration"] * 100)
+	plt.plot(monthly_df["et0"] + 5)
+	plt.show()

requirements.txt

@@ -17,4 +17,5 @@ requests_cache
 retry_requests
 fuzzywuzzy
 plotly
-pvlib
+pvlib
+matplotlib