Code cleanup

xarray/backends/api.py

@@ -102,12 +102,11 @@ def _get_default_engine_netcdf():
 
 def _get_default_engine(path: str, allow_remote: bool = False):
     if allow_remote and is_remote_uri(path):
-        engine = _get_default_engine_remote_uri()
+        return _get_default_engine_remote_uri()
     elif path.endswith(".gz"):
-        engine = _get_default_engine_gz()
+        return _get_default_engine_gz()
     else:
-        engine = _get_default_engine_netcdf()
-    return engine
+        return _get_default_engine_netcdf()
 
 
 def _validate_dataset_names(dataset):
@@ -282,7 +281,7 @@ def _chunk_ds(
 
     mtime = _get_mtime(filename_or_obj)
     token = tokenize(filename_or_obj, mtime, engine, chunks, **extra_tokens)
-    name_prefix = "open_dataset-%s" % token
+    name_prefix = f"open_dataset-{token}"
 
     variables = {}
     for name, var in backend_ds.variables.items():
@@ -295,8 +294,7 @@ def _chunk_ds(
             name_prefix=name_prefix,
             token=token,
         )
-    ds = backend_ds._replace(variables)
-    return ds
+    return backend_ds._replace(variables)
 
 
 def _dataset_from_backend_dataset(
@@ -308,12 +306,10 @@ def _dataset_from_backend_dataset(
     overwrite_encoded_chunks,
     **extra_tokens,
 ):
-    if not (isinstance(chunks, (int, dict)) or chunks is None):
-        if chunks != "auto":
-            raise ValueError(
-                "chunks must be an int, dict, 'auto', or None. "
-                "Instead found %s. " % chunks
-            )
+    if not isinstance(chunks, (int, dict)) and chunks not in {None, "auto"}:
+        raise ValueError(
+            f"chunks must be an int, dict, 'auto', or None. Instead found {chunks}."
+        )
 
     _protect_dataset_variables_inplace(backend_ds, cache)
     if chunks is None:
@@ -331,9 +327,8 @@ def _dataset_from_backend_dataset(
     ds.set_close(backend_ds._close)
 
     # Ensure source filename always stored in dataset object (GH issue #2550)
-    if "source" not in ds.encoding:
-        if isinstance(filename_or_obj, str):
-            ds.encoding["source"] = filename_or_obj
+    if "source" not in ds.encoding and isinstance(filename_or_obj, str):
+        ds.encoding["source"] = filename_or_obj
 
     return ds
 
@@ -515,7 +510,6 @@ def open_dataset(
         **decoders,
         **kwargs,
     )
-
     return ds
 
 
@@ -1015,8 +1009,8 @@ def to_netcdf(
         elif engine != "scipy":
             raise ValueError(
                 "invalid engine for creating bytes with "
-                "to_netcdf: %r. Only the default engine "
-                "or engine='scipy' is supported" % engine
+                f"to_netcdf: {engine!r}. Only the default engine "
+                "or engine='scipy' is supported"
             )
         if not compute:
             raise NotImplementedError(
@@ -1037,7 +1031,7 @@ def to_netcdf(
     try:
         store_open = WRITEABLE_STORES[engine]
     except KeyError:
-        raise ValueError("unrecognized engine for to_netcdf: %r" % engine)
+        raise ValueError(f"unrecognized engine for to_netcdf: {engine!r}")
 
     if format is not None:
         format = format.upper()
@@ -1049,9 +1043,8 @@ def to_netcdf(
     autoclose = have_chunks and scheduler in ["distributed", "multiprocessing"]
     if autoclose and engine == "scipy":
         raise NotImplementedError(
-            "Writing netCDF files with the %s backend "
-            "is not currently supported with dask's %s "
-            "scheduler" % (engine, scheduler)
+            f"Writing netCDF files with the {engine} backend "
+            f"is not currently supported with dask's {scheduler} scheduler"
         )
 
     target = path_or_file if path_or_file is not None else BytesIO()
@@ -1061,7 +1054,7 @@ def to_netcdf(
             kwargs["invalid_netcdf"] = invalid_netcdf
         else:
             raise ValueError(
-                "unrecognized option 'invalid_netcdf' for engine %s" % engine
+                f"unrecognized option 'invalid_netcdf' for engine {engine}"
             )
     store = store_open(target, mode, format, group, **kwargs)
 
@@ -1203,7 +1196,7 @@ def save_mfdataset(
     Data variables:
         a        (time) float64 0.0 0.02128 0.04255 0.06383 ... 0.9574 0.9787 1.0
     >>> years, datasets = zip(*ds.groupby("time.year"))
-    >>> paths = ["%s.nc" % y for y in years]
+    >>> paths = [f"{y}.nc" for y in years]
     >>> xr.save_mfdataset(datasets, paths)
     """
     if mode == "w" and len(set(paths)) < len(paths):
@@ -1215,7 +1208,7 @@ def save_mfdataset(
         if not isinstance(obj, Dataset):
             raise TypeError(
                 "save_mfdataset only supports writing Dataset "
-                "objects, received type %s" % type(obj)
+                f"objects, received type {type(obj)}"
             )
 
     if groups is None:

xarray/backends/cfgrib_.py

@@ -90,8 +90,7 @@ def get_dimensions(self):
 
     def get_encoding(self):
         dims = self.get_dimensions()
-        encoding = {"unlimited_dims": {k for k, v in dims.items() if v is None}}
-        return encoding
+        return {"unlimited_dims": {k for k, v in dims.items() if v is None}}
 
 
 class CfgribfBackendEntrypoint(BackendEntrypoint):

xarray/backends/common.py

@@ -69,9 +69,8 @@ def robust_getitem(array, key, catch=Exception, max_retries=6, initial_delay=500
             base_delay = initial_delay * 2 ** n
             next_delay = base_delay + np.random.randint(base_delay)
             msg = (
-                "getitem failed, waiting %s ms before trying again "
-                "(%s tries remaining). Full traceback: %s"
-                % (next_delay, max_retries - n, traceback.format_exc())
+                f"getitem failed, waiting {next_delay} ms before trying again "
+                f"({max_retries - n} tries remaining). Full traceback: {traceback.format_exc()}"
             )
             logger.debug(msg)
             time.sleep(1e-3 * next_delay)
@@ -336,7 +335,7 @@ def set_dimensions(self, variables, unlimited_dims=None):
             if dim in existing_dims and length != existing_dims[dim]:
                 raise ValueError(
                     "Unable to update size for existing dimension"
-                    "%r (%d != %d)" % (dim, length, existing_dims[dim])
+                    f"{dim!r} ({length} != {existing_dims[dim]})"
                 )
             elif dim not in existing_dims:
                 is_unlimited = dim in unlimited_dims

xarray/backends/h5netcdf_.py

@@ -37,8 +37,7 @@
 class H5NetCDFArrayWrapper(BaseNetCDF4Array):
     def get_array(self, needs_lock=True):
         ds = self.datastore._acquire(needs_lock)
-        variable = ds.variables[self.variable_name]
-        return variable
+        return ds.variables[self.variable_name]
 
     def __getitem__(self, key):
         return indexing.explicit_indexing_adapter(
@@ -102,7 +101,7 @@ def __init__(self, manager, group=None, mode=None, lock=HDF5_LOCK, autoclose=Fal
             if group is None:
                 root, group = find_root_and_group(manager)
             else:
-                if not type(manager) is h5netcdf.File:
+                if type(manager) is not h5netcdf.File:
                     raise ValueError(
                         "must supply a h5netcdf.File if the group "
                         "argument is provided"
@@ -233,11 +232,9 @@ def get_dimensions(self):
         return self.ds.dimensions
 
     def get_encoding(self):
-        encoding = {}
-        encoding["unlimited_dims"] = {
-            k for k, v in self.ds.dimensions.items() if v is None
+        return {
+            "unlimited_dims": {k for k, v in self.ds.dimensions.items() if v is None}
         }
-        return encoding
 
     def set_dimension(self, name, length, is_unlimited=False):
         if is_unlimited:
@@ -266,9 +263,9 @@ def prepare_variable(
                 "h5netcdf does not yet support setting a fill value for "
                 "variable-length strings "
                 "(https://github.com/shoyer/h5netcdf/issues/37). "
-                "Either remove '_FillValue' from encoding on variable %r "
+                f"Either remove '_FillValue' from encoding on variable {name!r} "
                 "or set {'dtype': 'S1'} in encoding to use the fixed width "
-                "NC_CHAR type." % name
+                "NC_CHAR type."
             )
 
         if dtype is str:
@@ -380,7 +377,7 @@ def open_dataset(
 
         store_entrypoint = StoreBackendEntrypoint()
 
-        ds = store_entrypoint.open_dataset(
+        return store_entrypoint.open_dataset(
             store,
             mask_and_scale=mask_and_scale,
             decode_times=decode_times,
@@ -390,7 +387,6 @@ def open_dataset(
             use_cftime=use_cftime,
             decode_timedelta=decode_timedelta,
         )
-        return ds
 
 
 if has_h5netcdf:

xarray/backends/locks.py

@@ -167,7 +167,7 @@ def locked(self):
         return any(lock.locked for lock in self.locks)
 
     def __repr__(self):
-        return "CombinedLock(%r)" % list(self.locks)
+        return f"CombinedLock({list(self.locks)!r})"
 
 
 class DummyLock:

xarray/backends/netCDF4_.py

@@ -122,25 +122,23 @@ def _encode_nc4_variable(var):
 def _check_encoding_dtype_is_vlen_string(dtype):
     if dtype is not str:
         raise AssertionError(  # pragma: no cover
-            "unexpected dtype encoding %r. This shouldn't happen: please "
-            "file a bug report at github.com/pydata/xarray" % dtype
+            f"unexpected dtype encoding {dtype!r}. This shouldn't happen: please "
+            "file a bug report at github.com/pydata/xarray"
         )
 
 
 def _get_datatype(var, nc_format="NETCDF4", raise_on_invalid_encoding=False):
     if nc_format == "NETCDF4":
-        datatype = _nc4_dtype(var)
-    else:
-        if "dtype" in var.encoding:
-            encoded_dtype = var.encoding["dtype"]
-            _check_encoding_dtype_is_vlen_string(encoded_dtype)
-            if raise_on_invalid_encoding:
-                raise ValueError(
-                    "encoding dtype=str for vlen strings is only supported "
-                    "with format='NETCDF4'."
-                )
-        datatype = var.dtype
-    return datatype
+        return _nc4_dtype(var)
+    if "dtype" in var.encoding:
+        encoded_dtype = var.encoding["dtype"]
+        _check_encoding_dtype_is_vlen_string(encoded_dtype)
+        if raise_on_invalid_encoding:
+            raise ValueError(
+                "encoding dtype=str for vlen strings is only supported "
+                "with format='NETCDF4'."
+            )
+    return var.dtype
 
 
 def _nc4_dtype(var):
@@ -178,7 +176,7 @@ def _nc4_require_group(ds, group, mode, create_group=_netcdf4_create_group):
                     ds = create_group(ds, key)
                 else:
                     # wrap error to provide slightly more helpful message
-                    raise OSError("group not found: %s" % key, e)
+                    raise OSError(f"group not found: {key}", e)
         return ds
 
 
@@ -203,7 +201,7 @@ def _force_native_endianness(var):
         # if endian exists, remove it from the encoding.
         var.encoding.pop("endian", None)
     # check to see if encoding has a value for endian its 'native'
-    if not var.encoding.get("endian", "native") == "native":
+    if var.encoding.get("endian", "native") != "native":
         raise NotImplementedError(
             "Attempt to write non-native endian type, "
             "this is not supported by the netCDF4 "
@@ -270,8 +268,8 @@ def _extract_nc4_variable_encoding(
         invalid = [k for k in encoding if k not in valid_encodings]
         if invalid:
             raise ValueError(
-                "unexpected encoding parameters for %r backend: %r. Valid "
-                "encodings are: %r" % (backend, invalid, valid_encodings)
+                f"unexpected encoding parameters for {backend!r} backend: {invalid!r}. Valid "
+                f"encodings are: {valid_encodings!r}"
             )
     else:
         for k in list(encoding):
@@ -282,10 +280,8 @@ def _extract_nc4_variable_encoding(
 
 
 def _is_list_of_strings(value):
-    if np.asarray(value).dtype.kind in ["U", "S"] and np.asarray(value).size > 1:
-        return True
-    else:
-        return False
+    arr = np.asarray(value)
+    return arr.dtype.kind in ["U", "S"] and arr.size > 1
 
 
 class NetCDF4DataStore(WritableCFDataStore):
@@ -313,7 +309,7 @@ def __init__(
             if group is None:
                 root, group = find_root_and_group(manager)
             else:
-                if not type(manager) is netCDF4.Dataset:
+                if type(manager) is not netCDF4.Dataset:
                     raise ValueError(
                         "must supply a root netCDF4.Dataset if the group "
                         "argument is provided"
@@ -359,10 +355,7 @@ def open(
 
         if lock is None:
             if mode == "r":
-                if is_remote_uri(filename):
-                    lock = NETCDFC_LOCK
-                else:
-                    lock = NETCDF4_PYTHON_LOCK
+                lock = NETCDFC_LOCK if is_remote_uri(filename) else NETCDF4_PYTHON_LOCK
             else:
                 if format is None or format.startswith("NETCDF4"):
                     base_lock = NETCDF4_PYTHON_LOCK
@@ -417,25 +410,22 @@ def open_store_variable(self, name, var):
         return Variable(dimensions, data, attributes, encoding)
 
     def get_variables(self):
-        dsvars = FrozenDict(
+        return FrozenDict(
             (k, self.open_store_variable(k, v)) for k, v in self.ds.variables.items()
         )
-        return dsvars
 
     def get_attrs(self):
-        attrs = FrozenDict((k, self.ds.getncattr(k)) for k in self.ds.ncattrs())
-        return attrs
+        return FrozenDict((k, self.ds.getncattr(k)) for k in self.ds.ncattrs())
 
     def get_dimensions(self):
-        dims = FrozenDict((k, len(v)) for k, v in self.ds.dimensions.items())
-        return dims
+        return FrozenDict((k, len(v)) for k, v in self.ds.dimensions.items())
 
     def get_encoding(self):
-        encoding = {}
-        encoding["unlimited_dims"] = {
-            k for k, v in self.ds.dimensions.items() if v.isunlimited()
+        return {
+            "unlimited_dims": {
+                k for k, v in self.ds.dimensions.items() if v.isunlimited()
+            }
         }
-        return encoding
 
     def set_dimension(self, name, length, is_unlimited=False):
         dim_length = length if not is_unlimited else None
@@ -473,9 +463,9 @@ def prepare_variable(
                 "netCDF4 does not yet support setting a fill value for "
                 "variable-length strings "
                 "(https://github.com/Unidata/netcdf4-python/issues/730). "
-                "Either remove '_FillValue' from encoding on variable %r "
+                f"Either remove '_FillValue' from encoding on variable {name!r} "
                 "or set {'dtype': 'S1'} in encoding to use the fixed width "
-                "NC_CHAR type." % name
+                "NC_CHAR type."
             )
 
         encoding = _extract_nc4_variable_encoding(

xarray/backends/netcdf3.py

@@ -125,8 +125,6 @@ def is_valid_nc3_name(s):
     """
     if not isinstance(s, str):
         return False
-    if not isinstance(s, str):
-        s = s.decode("utf-8")
     num_bytes = len(s.encode("utf-8"))
     return (
         (unicodedata.normalize("NFC", s) == s)