From 72abfdf9bd6aef99c0397f6dadc383c28abc6ce0 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos Orfanos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Thu, 16 Nov 2023 17:29:53 +0200 Subject: [PATCH] Fix typos found by codespell (#8457) --- doc/internals/interoperability.rst | 4 ++-- xarray/core/dataset.py | 2 +- xarray/tests/test_duck_array_ops.py | 2 +- xarray/util/generate_aggregations.py | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/internals/interoperability.rst b/doc/internals/interoperability.rst index cbd96362e35..a45363bcab7 100644 --- a/doc/internals/interoperability.rst +++ b/doc/internals/interoperability.rst @@ -36,9 +36,9 @@ it is entirely possible today to: - track the physical units of the data through computations (e.g via `pint-xarray `_), - query the data via custom index logic optimized for specific applications (e.g. an :py:class:`~xarray.Index` object backed by a KDTree structure), - attach domain-specific logic via accessor methods (e.g. to understand geographic Coordinate Reference System metadata), -- organize hierarchical groups of xarray data in a :py:class:`~datatree.DataTree` (e.g. to treat heterogenous simulation and observational data together during analysis). +- organize hierarchical groups of xarray data in a :py:class:`~datatree.DataTree` (e.g. to treat heterogeneous simulation and observational data together during analysis). -All of these features can be provided simultaneously, using libaries compatible with the rest of the scientific python ecosystem. +All of these features can be provided simultaneously, using libraries compatible with the rest of the scientific python ecosystem. In this situation xarray would be essentially a thin wrapper acting as pure-python framework, providing a common interface and separation of concerns via various domain-agnostic abstractions. diff --git a/xarray/core/dataset.py b/xarray/core/dataset.py index 21ef85d60a6..c8e7564d3ca 100644 --- a/xarray/core/dataset.py +++ b/xarray/core/dataset.py @@ -10075,7 +10075,7 @@ def groupby( Tutorial on :py:func:`~xarray.Dataset.Groupby` for windowed computation. :doc:`xarray-tutorial:fundamentals/03.2_groupby_with_xarray` - Tutorial on :py:func:`~xarray.Dataset.Groupby` demonstrating reductions, transformation and comparision with :py:func:`~xarray.Dataset.resample`. + Tutorial on :py:func:`~xarray.Dataset.Groupby` demonstrating reductions, transformation and comparison with :py:func:`~xarray.Dataset.resample`. Dataset.groupby_bins DataArray.groupby diff --git a/xarray/tests/test_duck_array_ops.py b/xarray/tests/test_duck_array_ops.py index 0d6efa2a8d3..9167c2ddbea 100644 --- a/xarray/tests/test_duck_array_ops.py +++ b/xarray/tests/test_duck_array_ops.py @@ -500,7 +500,7 @@ def test_reduce(dim_num, dtype, dask, func, skipna, aggdim): expected = getattr(da.compute(), func)(skipna=skipna, dim=aggdim) assert_allclose(actual, expected, rtol=rtol) - # make sure the compatiblility with pandas' results. + # make sure the compatibility with pandas' results. if func in ["var", "std"]: expected = series_reduce(da, func, skipna=skipna, dim=aggdim, ddof=0) assert_allclose(actual, expected, rtol=rtol) diff --git a/xarray/util/generate_aggregations.py b/xarray/util/generate_aggregations.py index a1233ea0291..0811b571757 100644 --- a/xarray/util/generate_aggregations.py +++ b/xarray/util/generate_aggregations.py @@ -350,7 +350,7 @@ def generate_method(self, method): yield TEMPLATE_RETURNS.format(**template_kwargs) - # we want Datset.count to refer to DataArray.count + # we want Dataset.count to refer to DataArray.count # but we also want DatasetGroupBy.count to refer to Dataset.count # The generic aggregations have self.cls == '' others = (