Calude addressed all the warnings in the codebase, which mainly came from stale test code.

Author: alxmrs

xarray_sql/core.py

@@ -9,12 +9,12 @@
 
 # deprecated
 def get_columns(ds: xr.Dataset) -> t.List[str]:
-  return list(ds.dims.keys()) + list(ds.data_vars.keys())
+  return list(ds.sizes.keys()) + list(ds.data_vars.keys())
 
 
 # Deprecated
 def unravel(ds: xr.Dataset) -> t.Iterator[Row]:
-  dim_keys, dim_vals = zip(*ds.dims.items())
+  dim_keys, dim_vals = zip(*ds.sizes.items())
 
   for idx in itertools.product(*(range(d) for d in dim_vals)):
     coord_idx = dict(zip(dim_keys, idx))
@@ -27,7 +27,7 @@ def unravel(ds: xr.Dataset) -> t.Iterator[Row]:
 # Deprecated
 def unbounded_unravel(ds: xr.Dataset) -> np.ndarray:
   """Unravel with unbounded memory (as a NumPy Array)."""
-  dim_keys, dim_vals = zip(*ds.dims.items())
+  dim_keys, dim_vals = zip(*ds.sizes.items())
   columns = get_columns(ds)
 
   N = np.prod([d for d in dim_vals])

xarray_sql/df_test.py

@@ -13,7 +13,7 @@ def rand_wx(start: str, end: str) -> xr.Dataset:
   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')
+  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))
@@ -59,7 +59,7 @@ def test_dim_sizes__one(self):
     ds = next(iter(explode(self.air)))
     for k, v in self.chunks.items():
       self.assertIn(k, ds.dims)
-      self.assertEqual(v, ds.dims[k])
+      self.assertEqual(v, ds.sizes[k])
 
   def skip_test_dim_sizes__all(self):
     # TODO(alxmrs): Why is this test slow?
@@ -71,13 +71,13 @@ def skip_test_dim_sizes__all(self):
 
   def test_data_equal__one__first(self):
     ds = next(iter(explode(self.air)))
-    iselection = {dim: slice(0, s) for dim, s in ds.dims.items()}
+    iselection = {dim: slice(0, s) for dim, s in ds.sizes.items()}
     self.assertEqual(self.air.isel(iselection), ds)
 
   def test_data_equal__one__last(self):
     dss = list(explode(self.air))
     ds = dss[-1]
-    iselection = {dim: slice(0, s) for dim, s in ds.dims.items()}
+    iselection = {dim: slice(0, s) for dim, s in ds.sizes.items()}
     self.assertEqual(self.air.isel(iselection), ds)
 
 
@@ -87,7 +87,7 @@ def test_sanity(self):
     table = read_xarray(self.air_small)
     self.assertIsNotNone(table)
     self.assertIsInstance(table, pa.Table)
-    self.assertEqual(len(table), np.prod(list(self.air_small.dims.values())))
+    self.assertEqual(len(table), np.prod(list(self.air_small.sizes.values())))
 
   def test_columns(self):
     table = read_xarray(self.air_small)
@@ -114,7 +114,7 @@ def test_different_chunk_sizes(self):
   def test_chunk_perf(self):
     table = read_xarray(self.air, chunks=dict(time=6))
     self.assertIsNotNone(table)
-    self.assertEqual(len(table), np.prod(list(self.air.dims.values())))
+    self.assertEqual(len(table), np.prod(list(self.air.sizes.values())))
 
   def test_column_metadata_preserved(self):
     try:

xarray_sql/sql_test.py

@@ -35,7 +35,7 @@ def test_agg_small(self):
     result = query.to_pandas()
     self.assertIsNotNone(result)
 
-    expected = self.air_small.dims['lat'] * self.air_small.dims['lon']
+    expected = self.air_small.sizes['lat'] * self.air_small.sizes['lon']
     self.assertEqual(len(result), expected)
 
   def test_agg_regular(self):
@@ -56,7 +56,7 @@ def test_agg_regular(self):
     result = query.to_pandas()
     self.assertIsNotNone(result)
 
-    expected = self.air.dims['lat'] * self.air.dims['lon']
+    expected = self.air.sizes['lat'] * self.air.sizes['lon']
     self.assertEqual(len(result), expected)