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Historical Weather Data APS

Disclaimer

The example Python source code is meant merely as a starting point to quickly interact and retrieve insights from ADMA. It is in no way intended for production workloads.

Historical Weather Data​

Once you have successfully created a Party and Farm, you can use the Historical Weather service to pull historical weather data. This is a four-step process due to the asynchronous nature of the service. These four steps have been captured and automated in tutorial/example/historical_weather.py included in the downloadable tutorial zipfile.

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  1. Submit a job request for a specific Party, Field, and inclusive Start and End years. The JSON for the request looks like the following. Note that in this case resourceId is the fieldId as indicated by the resourceType.
{
  "requestPath": "/v1/historical-weather-data/job-name-1",
  "partnerRequestBody": {
    "interval": "daily",
    "partyId": "party1",
    "jobs": [
      {
        "resourceType": "Field",
        "resourceId": "field1",
        "weatherStartDate": "2023-01-01",
        "weatherEndDate": "2023-02-01",
        "temperatureUnit": "C"
      }
    ]
  }
}
  1. Poll for the status of the job until the status of the job is SUCCEEDED.
  2. Use the insightId included in the successful status response to retrieve the insight attachment ID.
  3. Use the retrieved insight Attachment ID to download the attachment containing the predictions for all years.

Before we execute the tutorial command that wraps these steps, a little explanation is in order. A new directory will be created in the root of the adma directory named output. Within the output directory, the following directory structure will be created: party_id/resource_id/solution_id. This directory will hold log files of submitted requests and subsequent responses. In the following example the log file is named 2023-01-01-5617cf3e-a17f-417f-8ef5-aef4a81c9294.txt. This directory will also hold the generated historical weather insight JSON and CSV file.

Output Directory Structure​

output
└── 551c7fb3-8b9c-4b5c-8ecc-d6830325ce12
    └── b159f824-4d12-4b40-aa38-b98f0c876edf
        └── bayerAgPowered.historicalWeather
            ├── 2023-01-01-5617cf3e-a17f-417f-8ef5-aef4a81c9294.txt
            ├── 2023-01-01-5617cf3e-a17f-417f-8ef5-aef4a81c9294-b5038030-9074-4a77-a974-c6874bc0026c.json
            └── 2023-01-01-5617cf3e-a17f-417f-8ef5-aef4a81c9294-d7561838-7d30-4777-88da-d7c8600fb588.csv
Sample Output Download

A sample output from this example may be downloaded as hw-output.

E.g.

avgAtmosPressure,avgDewPointTemperature,avgSoilMoisture,avgSoilTemperature,avgSnowDepth,avgWindSpeed,maxAtmosPressure,maxDewPointTemperature,maxDownwardSolarRadiation,maxNetSolarRadiation,maxSoilTemperature,maxTemp,maxWindSpeed,minAtmosPressure,minDewPointTemperature,minSoilTemperature,minTemp,minWindSpeed,totalCloudCover,totalDownwardSolarRadiation,totalNetSolarRadiation,totalPrecipitation,weatherDate
988.70,-1.99,0.50,-0.25,0.02,13.57,996.65,3.10,0.27,0.19,-0.22,4.50,18.70,981.42,-5.00,-0.32,-3.30,10.40,0.23,0.93,0.64,0.02,2023-01-01
1002.73,-4.34,0.49,-0.34,0.02,9.35,1005.82,-2.50,0.28,0.19,-0.22,-1.70,16.90,997.51,-5.90,-0.44,-5.90,2.50,0.42,0.93,0.64,1.70,2023-01-02
998.31,-3.85,0.48,-0.32,0.02,10.14,1004.29,-2.60,0.22,0.15,-0.11,-1.50,13.70,993.72,-5.80,-0.46,-3.80,4.00,1.00,0.71,0.49,2.57,2023-01-03
1005.60,-7.23,0.48,-0.54,0.03,8.61,1006.86,-4.20,0.16,0.11,-0.11,-3.50,13.00,1004.18,-12.90,-1.68,-11.70,3.20,0.97,0.55,0.38,0.96,2023-01-04
1006.72,-12.97,0.48,-3.13,0.03,9.41,1013.06,-11.10,0.29,0.20,-1.49,-8.70,13.30,1003.35,-14.40,-4.32,-13.00,5.80,0.66,0.99,0.68,0.07,2023-01-05

Generate a Historical Weather insight​

Note that you will need to replace the party_id and farm_id "REPLACE_ME" values with your own party_id and farm_id in tutorial/example/historical_weather.py to generate and retrieve the insight and its attachment.

python -m tutorial.example.historical_weather