SST demo works with local fake data.

demo/sst.py

@@ -11,53 +11,109 @@
 import xarray as xr
 import xarray_sql as qr
 
+
+def local_data(start: str, end: str) -> xr.Dataset:
+  import numpy as np
+  import pandas as pd
+
+  np.random.seed(42)
+
+  lat = np.linspace(-90, 90, num=720)
+  lon = np.linspace(-180, 180, num=1440)
+  time = pd.date_range(start, end, freq='H')
+  level = np.array([1000, 500], dtype=np.int32)
+  reference_time = pd.Timestamp(start)
+
+  temperature = 15 + 8 * np.random.randn(720, 1440, len(time), len(level))
+  precipitation = 10 * np.random.rand(720, 1440, len(time), len(level))
+
+  return xr.Dataset(
+      data_vars=dict(
+          sea_surface_temperature=(
+              ['lat', 'lon', 'time', 'level'],
+              temperature,
+          ),
+          precipitation=(['lat', 'lon', 'time', 'level'], precipitation),
+      ),
+      coords=dict(
+          lat=lat,
+          lon=lon,
+          time=time,
+          level=level,
+          reference_time=reference_time,
+      ),
+      attrs=dict(description='Random weather.'),
+  )
+
+
 parser = argparse.ArgumentParser()
-parser.add_argument('--start', type=str, default='2020-01-01', help='start time ISO string')
-parser.add_argument('--end', type=str, default='2020-01-02', help='end time ISO string')
-parser.add_argument('--cluster', action='store_true', help='deploy on coiled cluster')
+parser.add_argument(
+    '--start', type=str, default='2020-01-01', help='start time ISO string'
+)
+parser.add_argument(
+    '--end', type=str, default='2020-01-02', help='end time ISO string'
+)
+parser.add_argument(
+    '--cluster',
+    action='store_true',
+    help='deploy on coiled cluster, default: local cluster',
+)
+parser.add_argument(
+    '--fake',
+    action='store_true',
+    help='use local dummy data, default: ARCO-ERA5 data',
+)
 
 args = parser.parse_args()
 
 if args.cluster:
   from coiled import Cluster
 
   cluster = Cluster(
-    region='us-central1',
-    worker_memory='16 GiB',
-    spot_policy='spot_with_fallback',
-    arm=True,
+      region='us-central1',
+      worker_memory='16 GiB',
+      spot_policy='spot_with_fallback',
+      arm=True,
   )
   client = cluster.get_client()
   cluster.adapt(minimum=1, maximum=100)
 else:
   from dask.distributed import LocalCluster
+
   cluster = LocalCluster(processes=False)
   client = cluster.get_client()
 
-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}
-)
+if args.fake:
+  era5_ds = local_data(args.start, args.end).chunk({'time': 240, 'level': 1})
+else:
+  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},
+  )
+
 print('dataset opened.')
 era5_sst_ds = era5_ds[['sea_surface_temperature']].sel(
-  time=slice(args.start, args.end),
-  level=1000,  # surface level only.
+    time=slice(args.start, args.end),
+    level=1000,  # surface level only.
 )
 
 c = qr.Context()
 # chunk sizes determined from VM memory limit of 16 GiB.
 c.create_table('era5', era5_sst_ds, chunks=dict(time=24))
 
 print('beginning query.')
-df = c.sql("""
+# TODO(alxmrs): `DATE` function is not supported in Apache Calcite out-of-the-box.
+df = c.sql(
+    """
 SELECT
-  DATE("time") as date,
+  "time",
   AVG("sea_surface_temperature") as daily_avg_sst
 FROM
   "era5"
 GROUP BY
-  DATE("time")
-""")
+  "time"
+"""
+)
 
-df.to_csv(f'global_avg_sst_{args.start}-{args.end}_*.cvs')
+df.to_csv(f'global_avg_sst_{args.start}_to_{args.end}_*.cvs')