Add tests for open_dataset and open_mfdataset

Cover the new wrappers: a basic write/read round trip, opening a
NETCDF3_64BIT_DATA (CDF5) file with an explicit engine (exercising the
backend-sniffing crash the wrappers work around), resolving the engine from
mpas_tools.io.default_engine when engine is None (restoring the global
afterward), and a multi-file open_mfdataset smoke test.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Author: xylar

conda_package/tests/test_io.py

@@ -5,7 +5,8 @@
 import pytest
 import xarray as xr
 
-from mpas_tools.io import write_netcdf
+import mpas_tools.io
+from mpas_tools.io import open_dataset, open_mfdataset, write_netcdf
 from mpas_tools.logging import LoggingContext
 
 from .util import get_test_data_file
@@ -59,6 +60,64 @@ def test_write_netcdf_cdf5_format(tmp_path):
     assert not os.path.exists(tmp_file)
 
 
+def test_open_dataset_basic(tmp_path):
+    # Write a file then read it back via the wrapper
+    arr = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    ds = xr.Dataset({'foo': (('x',), arr)})
+    out_file = tmp_path / 'test_open_basic.nc'
+    write_netcdf(ds, str(out_file))
+    ds2 = open_dataset(str(out_file))
+    assert set(ds.dims) == set(ds2.dims)
+    assert 'foo' in ds2.data_vars
+    np.testing.assert_array_equal(ds2['foo'].values, arr)
+    ds2.close()
+
+
+def test_open_dataset_cdf5(tmp_path):
+    # Opening a CDF5 (NETCDF3_64BIT_DATA) file with an explicit engine should
+    # succeed; this exercises the bug the wrapper works around.
+    arr = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    ds = xr.Dataset({'foo': (('x',), arr)})
+    out_file = tmp_path / 'test_open_cdf5.nc'
+    write_netcdf(ds, str(out_file), format='NETCDF3_64BIT_DATA')
+    ds2 = open_dataset(str(out_file), engine='netcdf4')
+    np.testing.assert_array_equal(ds2['foo'].values, arr)
+    ds2.close()
+
+
+def test_open_dataset_default_engine(tmp_path):
+    # When engine is None, the wrapper should use mpas_tools.io.default_engine
+    arr = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+    ds = xr.Dataset({'foo': (('x',), arr)})
+    out_file = tmp_path / 'test_open_default_engine.nc'
+    write_netcdf(ds, str(out_file), format='NETCDF3_64BIT_DATA')
+    saved_engine = mpas_tools.io.default_engine
+    try:
+        mpas_tools.io.default_engine = 'netcdf4'
+        ds2 = open_dataset(str(out_file))
+        np.testing.assert_array_equal(ds2['foo'].values, arr)
+        ds2.close()
+    finally:
+        mpas_tools.io.default_engine = saved_engine
+
+
+def test_open_mfdataset(tmp_path):
+    # Smoke test: write two files along a dimension and open them combined
+    out_files = []
+    for index in range(2):
+        arr = np.array([index], dtype=np.float32)
+        ds = xr.Dataset({'foo': (('Time',), arr)})
+        out_file = tmp_path / f'test_open_mf_{index}.nc'
+        write_netcdf(ds, str(out_file))
+        out_files.append(str(out_file))
+    ds2 = open_mfdataset(
+        out_files, engine='netcdf4', combine='nested', concat_dim='Time'
+    )
+    assert ds2.sizes['Time'] == 2
+    np.testing.assert_array_equal(ds2['foo'].values, [0.0, 1.0])
+    ds2.close()
+
+
 def test_write_netcdf_int64_conversion_and_attr(tmp_path):
     # Create a dataset with int64 variable and an attribute
     arr = np.array([1, 2, 3], dtype=np.int64)