Debugged demo join; now focused on tuning performance.

demo/mara_join.py

@@ -7,28 +7,34 @@
 
 mv_df = dd.read_csv(
   'https://raw.githubusercontent.com/wildlife-dynamics/ecoscope/master/tests/'
-  'sample_data/vector/movbank_data.csv'
+  'sample_data/vector/movbank_data.csv',
 )
 
-mv_df['timestamp'] = dd.to_datetime(mv_df['timestamp'], utc=True)
+mv_df['timestamp'] = dd.to_datetime(mv_df['timestamp'])
+mv_df.set_index('timestamp', drop=False, sort=True)
 mv_df['geometry'] = gdd.points_from_xy(
-  mv_df['location-long'], mv_df['location-lat']
+  mv_df, 'location-long', 'location-lat', crs=4326
+)
+timerange = slice(
+  mv_df.timestamp.min().compute(),
+  mv_df.timestamp.max().compute(),
 )
-# What is the CRS?
 mv_gdf = gdd.from_dask_dataframe(mv_df, 'geometry')
 
+# For MARA, we'd replace this with an Xee call.
 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}
+  chunks={'time': 48, '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())
+  time=timerange,
+  level=1000,  # surface level only.
 )
 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_df, 'longitude', 'latitude',
 )
 era5_wind_gdf = gdd.from_dask_dataframe(era5_wind_df, 'geometry')