diff --git a/asv_bench/benchmarks/dataset_io.py b/asv_bench/benchmarks/dataset_io.py index 0af8084dd21..fac4986f9d0 100644 --- a/asv_bench/benchmarks/dataset_io.py +++ b/asv_bench/benchmarks/dataset_io.py @@ -527,7 +527,7 @@ def time_read_dataset(self, engine, chunks): class IOReadCustomEngine: def setup(self, *args, **kwargs): """ - The custom backend does the bare mininum to be considered a lazy backend. But + The custom backend does the bare minimum to be considered a lazy backend. But the data in it is still in memory so slow file reading shouldn't affect the results. """ diff --git a/asv_bench/benchmarks/merge.py b/asv_bench/benchmarks/merge.py index 043de35bdf7..6c8c1e9da90 100644 --- a/asv_bench/benchmarks/merge.py +++ b/asv_bench/benchmarks/merge.py @@ -41,7 +41,7 @@ def setup(self, strategy, count): data = np.array(["0", "b"], dtype=str) self.dataset_coords = dict(time=np.array([0, 1])) self.dataset_attrs = dict(description="Test data") - attrs = dict(units="Celcius") + attrs = dict(units="Celsius") if strategy == "dict_of_DataArrays": def create_data_vars(): diff --git a/doc/user-guide/io.rst b/doc/user-guide/io.rst index c0e88634705..eeb7813ae15 100644 --- a/doc/user-guide/io.rst +++ b/doc/user-guide/io.rst @@ -565,7 +565,7 @@ HDF5 ---- `HDF5`_ is both a file format and a data model for storing information. HDF5 stores data hierarchically, using groups to create a nested structure. HDF5 is a more -general verion of the netCDF4 data model, so the nested structure is one of many +general version of the netCDF4 data model, so the nested structure is one of many similarities between the two data formats. Reading HDF5 files in xarray requires the ``h5netcdf`` engine, which can be installed diff --git a/doc/whats-new.rst b/doc/whats-new.rst index 77d2249960f..a25f87468af 100644 --- a/doc/whats-new.rst +++ b/doc/whats-new.rst @@ -888,7 +888,7 @@ Bug fixes By `Michael Niklas `_. - Fix side effects on index coordinate metadata after aligning objects. (:issue:`6852`, :pull:`6857`) By `Benoît Bovy `_. -- Make FacetGrid.set_titles send kwargs correctly using `handle.udpate(kwargs)`. (:issue:`6839`, :pull:`6843`) +- Make FacetGrid.set_titles send kwargs correctly using `handle.update(kwargs)`. (:issue:`6839`, :pull:`6843`) By `Oliver Lopez `_. - Fix bug where index variables would be changed inplace. (:issue:`6931`, :pull:`6938`) By `Michael Niklas `_. @@ -4751,7 +4751,7 @@ Bug fixes - Corrected a bug with incorrect coordinates for non-georeferenced geotiff files (:issue:`1686`). Internally, we now use the rasterio coordinate transform tool instead of doing the computations ourselves. A - ``parse_coordinates`` kwarg has beed added to :py:func:`~open_rasterio` + ``parse_coordinates`` kwarg has been added to :py:func:`~open_rasterio` (set to ``True`` per default). By `Fabien Maussion `_. - The colors of discrete colormaps are now the same regardless if `seaborn` diff --git a/xarray/backends/netCDF4_.py b/xarray/backends/netCDF4_.py index b5c3413e7f8..e3f97d7bac2 100644 --- a/xarray/backends/netCDF4_.py +++ b/xarray/backends/netCDF4_.py @@ -538,7 +538,7 @@ class NetCDF4BackendEntrypoint(BackendEntrypoint): """ Backend for netCDF files based on the netCDF4 package. - It can open ".nc", ".nc4", ".cdf" files and will be choosen + It can open ".nc", ".nc4", ".cdf" files and will be chosen as default for these files. Additionally it can open valid HDF5 files, see diff --git a/xarray/core/dataarray.py b/xarray/core/dataarray.py index dc0b2032a37..57757179af0 100644 --- a/xarray/core/dataarray.py +++ b/xarray/core/dataarray.py @@ -4463,7 +4463,7 @@ def broadcast_equals(self: T_DataArray, other: T_DataArray) -> bool: [2, 2]]) Dimensions without coordinates: X, Y - .equals returns True if two DataArrays have the same values, dimensions, and coordinates. .broadcast_equals returns True if the results of broadcasting two DataArrays against eachother have the same values, dimensions, and coordinates. + .equals returns True if two DataArrays have the same values, dimensions, and coordinates. .broadcast_equals returns True if the results of broadcasting two DataArrays against each other have the same values, dimensions, and coordinates. >>> a.equals(b) False diff --git a/xarray/core/indexes.py b/xarray/core/indexes.py index dffc012c582..9972896d6df 100644 --- a/xarray/core/indexes.py +++ b/xarray/core/indexes.py @@ -307,7 +307,7 @@ def reindex_like(self: T_Index, other: T_Index) -> dict[Hashable, Any]: def equals(self: T_Index, other: T_Index) -> bool: """Compare this index with another index of the same type. - Implemenation is optional but required in order to support alignment. + Implementation is optional but required in order to support alignment. Parameters ---------- diff --git a/xarray/core/rolling.py b/xarray/core/rolling.py index dcd01a0e0f1..67389527a98 100644 --- a/xarray/core/rolling.py +++ b/xarray/core/rolling.py @@ -572,7 +572,7 @@ def _numpy_or_bottleneck_reduce( and not is_duck_dask_array(self.obj.data) and self.ndim == 1 ): - # TODO: renable bottleneck with dask after the issues + # TODO: re-enable bottleneck with dask after the issues # underlying https://github.com/pydata/xarray/issues/2940 are # fixed. return self._bottleneck_reduce( diff --git a/xarray/plot/dataarray_plot.py b/xarray/plot/dataarray_plot.py index 3f7b1568e64..4df1938f89f 100644 --- a/xarray/plot/dataarray_plot.py +++ b/xarray/plot/dataarray_plot.py @@ -186,7 +186,7 @@ def _prepare_plot1d_data( # dimensions so the plotter can plot anything: if darray.ndim > 1: # When stacking dims the lines will continue connecting. For floats - # this can be solved by adding a nan element inbetween the flattening + # this can be solved by adding a nan element in between the flattening # points: dims_T = [] if np.issubdtype(darray.dtype, np.floating): diff --git a/xarray/plot/dataset_plot.py b/xarray/plot/dataset_plot.py index b0774c31b17..4f703045f17 100644 --- a/xarray/plot/dataset_plot.py +++ b/xarray/plot/dataset_plot.py @@ -730,7 +730,7 @@ def _temp_dataarray(ds: Dataset, y: Hashable, locals_: dict[str, Any]) -> DataAr coords = dict(ds.coords) # Add extra coords to the DataArray from valid kwargs, if using all - # kwargs there is a risk that we add unneccessary dataarrays as + # kwargs there is a risk that we add unnecessary dataarrays as # coords straining RAM further for example: # ds.both and extend="both" would add ds.both to the coords: valid_coord_kwargs = {"x", "z", "markersize", "hue", "row", "col", "u", "v"} diff --git a/xarray/plot/facetgrid.py b/xarray/plot/facetgrid.py index 93a328836d0..2b348d8bedd 100644 --- a/xarray/plot/facetgrid.py +++ b/xarray/plot/facetgrid.py @@ -76,7 +76,7 @@ class FacetGrid(Generic[T_Xarray]): The general approach to plotting here is called "small multiples", where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship - conditioned on one ore more other variables is often called a "trellis + conditioned on one or more other variables is often called a "trellis plot". The basic workflow is to initialize the :class:`FacetGrid` object with diff --git a/xarray/plot/utils.py b/xarray/plot/utils.py index 70e8bd3fdb9..b8cc4ff7349 100644 --- a/xarray/plot/utils.py +++ b/xarray/plot/utils.py @@ -1461,7 +1461,7 @@ def _calc_widths(self, y: DataArray) -> DataArray: def _calc_widths(self, y: np.ndarray | DataArray) -> np.ndarray | DataArray: """ - Normalize the values so they're inbetween self._width. + Normalize the values so they're in between self._width. """ if self._width is None: return y @@ -1473,7 +1473,7 @@ def _calc_widths(self, y: np.ndarray | DataArray) -> np.ndarray | DataArray: # Use default with if y is constant: widths = xdefault + 0 * y else: - # Normalize inbetween xmin and xmax: + # Normalize in between xmin and xmax: k = (y - np.min(y)) / diff_maxy_miny widths = xmin + k * (xmax - xmin) return widths @@ -1821,8 +1821,8 @@ def _guess_coords_to_plot( ) # If dims_plot[k] isn't defined then fill with one of the available dims, unless - # one of related mpl kwargs has been used. This should have similiar behaviour as - # * plt.plot(x, y) -> Multple lines with different colors if y is 2d. + # one of related mpl kwargs has been used. This should have similar behaviour as + # * plt.plot(x, y) -> Multiple lines with different colors if y is 2d. # * plt.plot(x, y, color="red") -> Multiple red lines if y is 2d. for k, dim, ign_kws in zip(default_guess, available_coords, ignore_guess_kwargs): if coords_to_plot.get(k, None) is None and all(