AI-generated Key Takeaways
-
Learn how to access the forecast grid API using Python and the
requestsandxarraylibraries. -
Discover how to visualize forecast grid data using the provided Public Contrails API Tester notebook.
-
Understand how to access the forecast region API with Python and the
requestsandgeopandaslibraries.
Access the forecast grid API with Python:
import io
import requests
import xarray as xr
API_KEY = "AIza...(replace with your API key)"
params = {"time": "2025-05-15T20:00:00Z"}
response = requests.get(
url="https://contrails.googleapis.com/v2/grids",
params=params,
headers={"x-goog-api-key": API_KEY},
)
response.raise_for_status()
dataset = xr.load_dataset(io.BytesIO(response.content))
To visualize the data, use the Public_Contrails_API_Tester notebook.
To access the forecast region API with Python:
import requests
import geopandas as gpd
API_KEY = "AIza...(replace with your API key)"
params = {"time": "2025-05-15T20:00:00Z"}
response = requests.get(
url="https://contrails.googleapis.com/v2/regions",
params=params,
headers={"x-goog-api-key": API_KEY},
)
response.raise_for_status()
geodataframe = gpd.GeoDataFrame.from_features(response.json())