Allow using a custom engine class directly in xr.open_dataset

xarray/backends/api.py

@@ -375,10 +375,11 @@ def open_dataset(
         scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like
         objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF).
     engine : {"netcdf4", "scipy", "pydap", "h5netcdf", "pynio", "cfgrib", \
-        "pseudonetcdf", "zarr"}, optional
+        "pseudonetcdf", "zarr"} or a custom backend class, optional
         Engine to use when reading files. If not provided, the default engine
         is chosen based on available dependencies, with a preference for
-        "netcdf4".
+        "netcdf4". A custom backend class can also be used, it requires some
+        specific methods to work properly.
     chunks : int or dict, optional
         If chunks is provided, it is used to load the new dataset into dask
         arrays. ``chunks=-1`` loads the dataset with dask using a single

xarray/backends/plugins.py

@@ -112,9 +112,14 @@ def guess_engine(store_spec):
 
 def get_backend(engine):
     """Select open_dataset method based on current engine"""
-    engines = list_engines()
-    if engine not in engines:
-        raise ValueError(
-            f"unrecognized engine {engine} must be one of: {list(engines)}"
-        )
-    return engines[engine]
+    if hasattr(engine, "open_dataset"):
+        backend = engine
+    else:
+        engines = list_engines()
+        if engine not in engines:
+            raise ValueError(
+                f"unrecognized engine {engine} must be one of: {list(engines)}"
+            )
+        backend = engines[engine]
+
+    return backend

xarray/tests/test_backends_api.py

@@ -1,6 +1,9 @@
+import numpy as np
+
+import xarray as xr
 from xarray.backends.api import _get_default_engine
 
-from . import requires_netCDF4, requires_scipy
+from . import assert_identical, requires_netCDF4, requires_scipy
 
 
 @requires_netCDF4
@@ -14,3 +17,25 @@ def test__get_default_engine():
 
     engine_default = _get_default_engine("/example")
     assert engine_default == "netcdf4"
+
+
+def test_custom_engine():
+    expected = xr.Dataset(
+        dict(a=2 * np.arange(5)), coords=dict(x=("x", np.arange(5), dict(units="s")))
+    )
+
+    class CustomBackend:
+        open_dataset_parameters = None
+
+        def open_dataset(
+            filename_or_obj,
+            drop_variables=None,
+            **kwargs,
+        ):
+            return expected.copy(deep=True)
+
+        def guess_can_open(filename_or_obj):
+            return False
+
+    actual = xr.open_dataset("fake_filename", engine=CustomBackend)
+    assert_identical(expected, actual)