Merge remote-tracking branch 'refs/remotes/origin/main'

compute_et0_adjusted.py

@@ -0,0 +1,70 @@
+import docs.agro_indicators as agro_indicators
+import datetime
+import pandas as pd
+import numpy as np
+
+
+def compute_et0(
+    df: pd.DataFrame,
+    latitude: float,
+    longitude: float
+):
+    """
+    Compute reference evapotranspiration.
+
+    Parameters
+    ----------
+    df : DataFrame
+        The input dataframe containing sensor data.
+
+    latitude : float
+        Latitude of the location.
+    longitude : float
+        Longitude of the location
+
+    Returns
+    -------
+    arraylike
+        Daily reference evapotranspiration.
+    """
+
+    irradiance = df.irradiance
+    Tmin = df.air_temperature_min
+    Tmax = df.air_temperature_max
+    T = (Tmin + Tmin) / 2
+    RHmin = df.relative_humidity_min
+    RHmax = df.relative_humidity_max
+    WS = df.wind_speed
+    JJulien = df.day_of_year
+
+    l = agro_indicators.et0(
+            irradiance,
+            T,
+            Tmax,
+            Tmin,
+            RHmin,
+            RHmax,
+            WS,
+            JJulien,
+            latitude,
+            longitude,
+        )
+
+    return l
+
+
+if __name__ == "__main__":
+    data_test = pd.DataFrame()
+    data_test["irradiance"] = [20, 30, 40]
+    data_test["air_temperature_min"] = [10, 15, 20]
+    data_test["air_temperature_max"] = [20, 25, 30]
+    data_test["relative_humidity_min"] = [50, 60, 70]
+    data_test["relative_humidity_max"] = [50, 60, 70]
+    data_test["wind_speed"] = [5, 10, 15]
+    data_test["day_of_year"] = [1, 32, 60]
+
+    latitude = 40.7128
+    longitude = 74.0060
+    
+    et0 = compute_et0(data_test, latitude, longitude)
+    print(et0)

data_pipelines/historical_weather_data.py

@@ -4,17 +4,19 @@ import requests_cache
 import pandas as pd
 from retry_requests import retry
 
+from compute_et0_adjusted import compute_et0
+
 
 def download_historical_weather_data(
-		latitude: float,
-		longitude: float,
-		start_year: int,
-		end_year: int,
+        latitude: float,
+        longitude: float,
+        start_year: int,
+        end_year: int,
 ) -> pd.DataFrame:
 	# Setup the Open-Meteo API client with cache and retry on error
-	cache_session = requests_cache.CachedSession('.cache', expire_after = -1)
-	retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
-	openmeteo = openmeteo_requests.Client(session = retry_session)
+	cache_session = requests_cache.CachedSession('.cache', expire_after=-1)
+	retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
+	openmeteo = openmeteo_requests.Client(session=retry_session)
 
 	# Make sure all required weather variables are listed here
 	# The order of variables in hourly or daily is important to assign them correctly below
@@ -22,12 +24,11 @@ def download_historical_weather_data(
 	params = {
 		"latitude": latitude,
 		"longitude": longitude,
-		"start_date": f"{start_year}-01-01",
-		"end_date": f"{end_year}-12-31",
-		"daily": ["temperature_2m_max", "temperature_2m_min", "temperature_2m_mean",
-				  "precipitation_sum", "precipitation_hours",
-				  "shortwave_radiation_sum",
-				  "et0_fao_evapotranspiration"],
+		"start_date": f"{start_year}-02-08",
+		"end_date": f"{end_year}-02-22",
+		"hourly": ["temperature_2m", "relative_humidity_2m",
+				   "precipitation", "et0_fao_evapotranspiration",
+				   "wind_speed_10m", "shortwave_radiation"],
 		"timezone": "GMT"
 	}
 	responses = openmeteo.weather_api(url, params=params)
@@ -39,32 +40,66 @@ def download_historical_weather_data(
 	print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
 	print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")
 
-	# Process daily data. The order of variables needs to be the same as requested.
-	daily = response.Daily()
-	daily_temperature_2m_max = daily.Variables(0).ValuesAsNumpy()
-	daily_temperature_2m_min = daily.Variables(1).ValuesAsNumpy()
-	daily_temperature_2m_mean = daily.Variables(2).ValuesAsNumpy()
-	daily_precipitation_sum = daily.Variables(3).ValuesAsNumpy()
-	daily_precipitation_hours = daily.Variables(4).ValuesAsNumpy()
-	daily_shortwave_radiation_sum = daily.Variables(5).ValuesAsNumpy()
-	daily_et0_fao_evapotranspiration = daily.Variables(6).ValuesAsNumpy()
-
-	daily_data = {
-		"date": pd.date_range(
-			start=pd.to_datetime(daily.Time(), unit="s", utc=True),
-			end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True),
-			freq=pd.Timedelta(seconds=daily.Interval()),
+	# Process hourly data. The order of variables needs to be the same as requested.
+	hourly = response.Hourly()
+	hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
+	hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy()
+	hourly_precipitation = hourly.Variables(2).ValuesAsNumpy()
+	hourly_et0_fao_evapotranspiration = hourly.Variables(3).ValuesAsNumpy()
+	hourly_wind_speed_10m = hourly.Variables(4).ValuesAsNumpy()
+	hourly_shortwave_radiation = hourly.Variables(5).ValuesAsNumpy()
+
+	hourly_data = {
+		"temperature_2m": hourly_temperature_2m,
+		"relative_humidity_2m": hourly_relative_humidity_2m,
+		"precipitation": hourly_precipitation,
+		"et0_fao_evapotranspiration": hourly_et0_fao_evapotranspiration,
+		"wind_speed_10m": hourly_wind_speed_10m,
+		"shortwave_radiation": hourly_shortwave_radiation,
+	}
+	hourly_dataframe = pd.DataFrame(
+		index=pd.date_range(
+			start=pd.to_datetime(hourly.Time(), unit="s", utc=True),
+			end=pd.to_datetime(hourly.TimeEnd(), unit="s", utc=True),
+			freq=pd.Timedelta(seconds=hourly.Interval()),
 			inclusive="left"
 		),
-		"temperature_2m_max": daily_temperature_2m_max,
-		"temperature_2m_min": daily_temperature_2m_min,
-		"temperature_2m_mean": daily_temperature_2m_mean,
-		"precipitation_sum": daily_precipitation_sum,
-		"precipitation_hours": daily_precipitation_hours,
-		"shortwave_radiation_sum": daily_shortwave_radiation_sum,
-		"et0_fao_evapotranspiration": daily_et0_fao_evapotranspiration
-	}
+		data=hourly_data,
+	)
+
+	return hourly_dataframe
+
+
+def aggregate_hourly_weather_data(
+		hourly_data: pd.DataFrame,
+) -> pd.DataFrame:
+	resampled_data = hourly_data.resample("1ME").agg({
+		"temperature_2m": ["min", "max"],
+		"relative_humidity_2m": ["min", "max"],
+		"wind_speed_10m": "mean",
+		"shortwave_radiation": "mean",
+	})
+
+	monthly_data = pd.DataFrame.from_dict({
+		"day_of_year": resampled_data.index.dayofyear,
+		"air_temperature_min": resampled_data[("temperature_2m", "min")],
+		"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")],
+		"wind_speed": resampled_data[("wind_speed_10m", "mean")],
+		"irradiance": resampled_data[("shortwave_radiation", "mean")],
+	})
+
+	return monthly_data
+
 
-	daily_dataframe = pd.DataFrame(data = daily_data)
+if __name__ == '__main__':
+	latitude = 47
+	longitude = 3
+	start_year = 2020
+	end_year = 2021
+	df = download_historical_weather_data(latitude, longitude, start_year, end_year)
+	monthly_df = aggregate_hourly_weather_data(df)
 
-	return daily_dataframe
+	et0 = compute_et0(monthly_df, latitude, longitude)
+	print(et0)

docs/agro_indicators.py

@@ -1,6 +1,6 @@
 import numpy as np
 from pvlib.location import lookup_altitude
-from agroclimatic_indicators.pyeto import fao
+from docs.pyeto import fao
 
 def et0(irradiance, T, Tmax, Tmin, RHmin, RHmax, WS, JJulien, latitude, longitude):
     """

requirements.txt

@@ -16,4 +16,5 @@ openmeteo_requests
 requests_cache
 retry_requests
 fuzzywuzzy
-plotly
+plotly
+pvlib