Fix typos found by codespell (#8375)

Co-authored-by: Maximilian Roos <5635139+max-sixty@users.noreply.github.com>

Author: DimitriPapadopoulos

doc/examples/apply_ufunc_vectorize_1d.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This example will illustrate how to conveniently apply an unvectorized function `func` to xarray objects using `apply_ufunc`. `func` expects 1D numpy arrays and returns a 1D numpy array. Our goal is to coveniently apply this function along a dimension of xarray objects that may or may not wrap dask arrays with a signature.\n",
+    "This example will illustrate how to conveniently apply an unvectorized function `func` to xarray objects using `apply_ufunc`. `func` expects 1D numpy arrays and returns a 1D numpy array. Our goal is to conveniently apply this function along a dimension of xarray objects that may or may not wrap dask arrays with a signature.\n",
     "\n",
     "We will illustrate this using `np.interp`: \n",
     "\n",

doc/user-guide/io.rst

@@ -819,7 +819,7 @@ with ``mode='a'`` on a Dataset containing the new variables, passing in an
 existing Zarr store or path to a Zarr store.
 
 To resize and then append values along an existing dimension in a store, set
-``append_dim``. This is a good option if data always arives in a particular
+``append_dim``. This is a good option if data always arrives in a particular
 order, e.g., for time-stepping a simulation:
 
 .. ipython:: python

xarray/backends/common.py

@@ -247,7 +247,7 @@ def sync(self, compute=True, chunkmanager_store_kwargs=None):
             chunkmanager = get_chunked_array_type(*self.sources)
 
             # TODO: consider wrapping targets with dask.delayed, if this makes
-            # for any discernible difference in perforance, e.g.,
+            # for any discernible difference in performance, e.g.,
             # targets = [dask.delayed(t) for t in self.targets]
 
             if chunkmanager_store_kwargs is None:

xarray/core/accessor_dt.py

@@ -601,7 +601,7 @@ class CombinedDatetimelikeAccessor(
     DatetimeAccessor[T_DataArray], TimedeltaAccessor[T_DataArray]
 ):
     def __new__(cls, obj: T_DataArray) -> CombinedDatetimelikeAccessor:
-        # CombinedDatetimelikeAccessor isn't really instatiated. Instead
+        # CombinedDatetimelikeAccessor isn't really instantiated. Instead
         # we need to choose which parent (datetime or timedelta) is
         # appropriate. Since we're checking the dtypes anyway, we'll just
         # do all the validation here.

xarray/core/computation.py

@@ -408,7 +408,7 @@ def _unpack_dict_tuples(
 
 def _check_core_dims(signature, variable_args, name):
     """
-    Chcek if an arg has all the core dims required by the signature.
+    Check if an arg has all the core dims required by the signature.
 
     Slightly awkward design, of returning the error message. But we want to
     give a detailed error message, which requires inspecting the variable in

xarray/core/groupby.py

@@ -989,7 +989,7 @@ def _flox_reduce(
             if kwargs["func"] not in ["sum", "prod"]:
                 raise TypeError("Received an unexpected keyword argument 'min_count'")
             elif kwargs["min_count"] is None:
-                # set explicitly to avoid unncessarily accumulating count
+                # set explicitly to avoid unnecessarily accumulating count
                 kwargs["min_count"] = 0
 
         # weird backcompat

xarray/core/missing.py

@@ -678,7 +678,7 @@ def interp_func(var, x, new_x, method: InterpOptions, kwargs):
 
     Notes
     -----
-    This requiers scipy installed.
+    This requires scipy installed.
 
     See Also
     --------
@@ -724,7 +724,7 @@ def interp_func(var, x, new_x, method: InterpOptions, kwargs):
             for i in range(new_x[0].ndim)
         }
 
-        # if useful, re-use localize for each chunk of new_x
+        # if useful, reuse localize for each chunk of new_x
         localize = (method in ["linear", "nearest"]) and new_x0_chunks_is_not_none
 
         # scipy.interpolate.interp1d always forces to float.

xarray/core/types.py

@@ -62,7 +62,7 @@
     _DTypeLikeNested = Any  # TODO: wait for support for recursive types
 
     # Xarray requires a Mapping[Hashable, dtype] in many places which
-    # conflics with numpys own DTypeLike (with dtypes for fields).
+    # conflicts with numpys own DTypeLike (with dtypes for fields).
     # https://numpy.org/devdocs/reference/typing.html#numpy.typing.DTypeLike
     # This is a copy of this DTypeLike that allows only non-Mapping dtypes.
     DTypeLikeSave = Union[

xarray/namedarray/core.py

@@ -255,7 +255,7 @@ def __init__(
     def __init_subclass__(cls, **kwargs: Any) -> None:
         if NamedArray in cls.__bases__ and (cls._new == NamedArray._new):
             # Type hinting does not work for subclasses unless _new is
-            # overriden with the correct class.
+            # overridden with the correct class.
             raise TypeError(
                 "Subclasses of `NamedArray` must override the `_new` method."
             )

xarray/tests/test_concat.py

@@ -622,7 +622,7 @@ def test_concat_errors(self):
             concat([data, data], "new_dim", data_vars=["not_found"])
 
         with pytest.raises(ValueError, match=r"global attributes not"):
-            # call deepcopy seperately to get unique attrs
+            # call deepcopy separately to get unique attrs
             data0 = deepcopy(split_data[0])
             data1 = deepcopy(split_data[1])
             data1.attrs["foo"] = "bar"