cf_xarray.datasets: hide auxiliary variables (#355)

Author: mathause

cf_xarray/datasets.py

@@ -12,8 +12,8 @@
 )
 
 ds_no_attrs = airds.copy(deep=True)
-for variable in ds_no_attrs.variables:
-    ds_no_attrs[variable].attrs = {}
+for _variable in ds_no_attrs.variables:
+    ds_no_attrs[_variable].attrs = {}
 
 
 # POM dataset
@@ -154,62 +154,72 @@
 )
 
 
-# Dataset with random data on a grid that is some sort of Mollweide projection
-XX, YY = np.mgrid[:11, :11] * 5 - 25
-XX_bnds, YY_bnds = np.mgrid[:12, :12] * 5 - 27.5
+def _create_mollw_ds():
+    # Dataset with random data on a grid that is some sort of Mollweide projection
+    XX, YY = np.mgrid[:11, :11] * 5 - 25
+    XX_bnds, YY_bnds = np.mgrid[:12, :12] * 5 - 27.5
 
-R = 50
-theta = np.arcsin(YY / (R * np.sqrt(2)))
-lat = np.rad2deg(np.arcsin((2 * theta + np.sin(2 * theta)) / np.pi))
-lon = np.rad2deg(XX * np.pi / (R * 2 * np.sqrt(2) * np.cos(theta)))
+    R = 50
+    theta = np.arcsin(YY / (R * np.sqrt(2)))
+    lat = np.rad2deg(np.arcsin((2 * theta + np.sin(2 * theta)) / np.pi))
+    lon = np.rad2deg(XX * np.pi / (R * 2 * np.sqrt(2) * np.cos(theta)))
 
-theta_bnds = np.arcsin(YY_bnds / (R * np.sqrt(2)))
-lat_vertices = np.rad2deg(np.arcsin((2 * theta_bnds + np.sin(2 * theta_bnds)) / np.pi))
-lon_vertices = np.rad2deg(XX_bnds * np.pi / (R * 2 * np.sqrt(2) * np.cos(theta_bnds)))
-
-lon_bounds = np.stack(
-    (
-        lon_vertices[:-1, :-1],
-        lon_vertices[:-1, 1:],
-        lon_vertices[1:, 1:],
-        lon_vertices[1:, :-1],
-    ),
-    axis=0,
-)
-lat_bounds = np.stack(
-    (
-        lat_vertices[:-1, :-1],
-        lat_vertices[:-1, 1:],
-        lat_vertices[1:, 1:],
-        lat_vertices[1:, :-1],
-    ),
-    axis=0,
-)
+    theta_bnds = np.arcsin(YY_bnds / (R * np.sqrt(2)))
+    lat_vertices = np.rad2deg(
+        np.arcsin((2 * theta_bnds + np.sin(2 * theta_bnds)) / np.pi)
+    )
+    lon_vertices = np.rad2deg(
+        XX_bnds * np.pi / (R * 2 * np.sqrt(2) * np.cos(theta_bnds))
+    )
 
-mollwds = xr.Dataset(
-    coords=dict(
-        lon=xr.DataArray(
-            lon,
-            dims=("x", "y"),
-            attrs={"units": "degrees_east", "bounds": "lon_bounds"},
+    lon_bounds = np.stack(
+        (
+            lon_vertices[:-1, :-1],
+            lon_vertices[:-1, 1:],
+            lon_vertices[1:, 1:],
+            lon_vertices[1:, :-1],
         ),
-        lat=xr.DataArray(
-            lat,
-            dims=("x", "y"),
-            attrs={"units": "degrees_north", "bounds": "lat_bounds"},
+        axis=0,
+    )
+    lat_bounds = np.stack(
+        (
+            lat_vertices[:-1, :-1],
+            lat_vertices[:-1, 1:],
+            lat_vertices[1:, 1:],
+            lat_vertices[1:, :-1],
         ),
-    ),
-    data_vars=dict(
-        lon_bounds=xr.DataArray(
-            lon_bounds, dims=("bounds", "x", "y"), attrs={"units": "degrees_east"}
+        axis=0,
+    )
+
+    mollwds = xr.Dataset(
+        coords=dict(
+            lon=xr.DataArray(
+                lon,
+                dims=("x", "y"),
+                attrs={"units": "degrees_east", "bounds": "lon_bounds"},
+            ),
+            lat=xr.DataArray(
+                lat,
+                dims=("x", "y"),
+                attrs={"units": "degrees_north", "bounds": "lat_bounds"},
+            ),
         ),
-        lat_bounds=xr.DataArray(
-            lat_bounds, dims=("bounds", "x", "y"), attrs={"units": "degrees_north"}
+        data_vars=dict(
+            lon_bounds=xr.DataArray(
+                lon_bounds, dims=("bounds", "x", "y"), attrs={"units": "degrees_east"}
+            ),
+            lat_bounds=xr.DataArray(
+                lat_bounds, dims=("bounds", "x", "y"), attrs={"units": "degrees_north"}
+            ),
+            lon_vertices=xr.DataArray(lon_vertices, dims=("x_vertices", "y_vertices")),
+            lat_vertices=xr.DataArray(lat_vertices, dims=("x_vertices", "y_vertices")),
         ),
-        lon_vertices=xr.DataArray(lon_vertices, dims=("x_vertices", "y_vertices")),
-        lat_vertices=xr.DataArray(lat_vertices, dims=("x_vertices", "y_vertices")),
-    ),
-)
+    )
+
+    return mollwds
+
+
+mollwds = _create_mollw_ds()
 
 forecast = xr.decode_cf(
     xr.Dataset.from_dict(