A motivating demo for MARA to work toward.

demo/mara_join.py

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+"""Demo of a spatial join using Qarray and dask_geopandas for MARA."""
+import dask.dataframe as dd
+import dask_geopandas as gdd
+import xarray as xr
+import qarray as qr
+
+
+mv_df = dd.read_csv(
+  'https://raw.githubusercontent.com/wildlife-dynamics/ecoscope/master/tests/'
+  'sample_data/vector/movbank_data.csv'
+)
+
+mv_df['timestamp'] = dd.to_datetime(mv_df['timestamp'], utc=True)
+mv_df['geometry'] = gdd.points_from_xy(
+  mv_df['location-long'], mv_df['location-lat']
+)
+# What is the CRS?
+mv_gdf = gdd.from_dask_dataframe(mv_df, 'geometry')
+
+era5_ds = xr.open_zarr(
+  'gs://gcp-public-data-arco-era5/ar/'
+  '1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2',
+  chunks={'time': 240, 'level': 1}
+)
+era5_wind_ds = era5_ds[['u_component_of_wind', 'v_component_of_wind']].sel(
+  time=slice(mv_gdf['timestamp'].min(), mv_gdf['timestamp'].max())
+)
+era5_wind_df = qr.to_dd(era5_wind_ds)
+# What is the CRS?
+era5_wind_df['geometry'] = gdd.points_from_xy(
+  era5_wind_df['longitude'], era5_wind_df['latitude'], era5_wind_df['level']
+)
+era5_wind_gdf = gdd.from_dask_dataframe(era5_wind_df, 'geometry')
+
+
+# Only an inner spatial join is supported right now (in dask_geopandas).
+intersection = mv_gdf.sjoin(era5_wind_gdf)
+print(intersection.head())
+