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fix: typo spelling grammar (#5745)
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doc/ecosystem.rst

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@@ -16,7 +16,7 @@ Geosciences
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- `climpred <https://climpred.readthedocs.io>`_: Analysis of ensemble forecast models for climate prediction.
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- `geocube <https://corteva.github.io/geocube>`_: Tool to convert geopandas vector data into rasterized xarray data.
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- `GeoWombat <https://github.com/jgrss/geowombat>`_: Utilities for analysis of remotely sensed and gridded raster data at scale (easily tame Landsat, Sentinel, Quickbird, and PlanetScope).
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- `infinite-diff <https://github.com/spencerahill/infinite-diff>`_: xarray-based finite-differencing, focused on gridded climate/meterology data
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- `infinite-diff <https://github.com/spencerahill/infinite-diff>`_: xarray-based finite-differencing, focused on gridded climate/meteorology data
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- `marc_analysis <https://github.com/darothen/marc_analysis>`_: Analysis package for CESM/MARC experiments and output.
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- `MetPy <https://unidata.github.io/MetPy/dev/index.html>`_: A collection of tools in Python for reading, visualizing, and performing calculations with weather data.
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- `MPAS-Analysis <http://mpas-analysis.readthedocs.io>`_: Analysis for simulations produced with Model for Prediction Across Scales (MPAS) components and the Accelerated Climate Model for Energy (ACME).

doc/roadmap.rst

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@@ -37,7 +37,7 @@ Why has xarray been successful? In our opinion:
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labeled multidimensional arrays, rather than solving particular
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problems.
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- This facilitates collaboration between users with different needs,
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and helps us attract a broad community of contributers.
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and helps us attract a broad community of contributors.
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- Importantly, this retains flexibility, for use cases that don't
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fit particularly well into existing frameworks.
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@@ -82,7 +82,7 @@ We think the right approach to extending xarray's user community and the
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usefulness of the project is to focus on improving key interfaces that
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can be used externally to meet domain-specific needs.
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We can generalize the community's needs into three main catagories:
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We can generalize the community's needs into three main caeagories:
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- More flexible grids/indexing.
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- More flexible arrays/computing.

doc/user-guide/computation.rst

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@@ -214,7 +214,7 @@ We can also manually iterate through ``Rolling`` objects:
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While ``rolling`` provides a simple moving average, ``DataArray`` also supports
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an exponential moving average with :py:meth:`~xarray.DataArray.rolling_exp`.
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This is similiar to pandas' ``ewm`` method. numbagg_ is required.
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This is similar to pandas' ``ewm`` method. numbagg_ is required.
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.. _numbagg: https://github.com/shoyer/numbagg
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doc/user-guide/io.rst

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@@ -360,7 +360,7 @@ Scaling and type conversions
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These encoding options work on any version of the netCDF file format:
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- ``dtype``: Any valid NumPy dtype or string convertable to a dtype, e.g., ``'int16'``
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- ``dtype``: Any valid NumPy dtype or string convertible to a dtype, e.g., ``'int16'``
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or ``'float32'``. This controls the type of the data written on disk.
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- ``_FillValue``: Values of ``NaN`` in xarray variables are remapped to this value when
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saved on disk. This is important when converting floating point with missing values
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`any string encoding recognized by Python <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ if you feel the need to deviate from UTF-8,
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by setting the ``_Encoding`` field in ``encoding``. But
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`we don't recommend it <http://utf8everywhere.org/>`_.
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- The character dimension name can be specifed by the ``char_dim_name`` field of a variable's
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- The character dimension name can be specified by the ``char_dim_name`` field of a variable's
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``encoding``. If the name of the character dimension is not specified, the default is
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``f'string{data.shape[-1]}'``. When decoding character arrays from existing files, the
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``char_dim_name`` is added to the variables ``encoding`` to preserve if encoding happens, but
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The library ``h5netcdf`` allows writing some dtypes (booleans, complex, ...) that aren't
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allowed in netCDF4 (see
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`h5netcdf documentation <https://github.com/shoyer/h5netcdf#invalid-netcdf-files>`_).
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This feature is availabe through :py:meth:`DataArray.to_netcdf` and
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This feature is available through :py:meth:`DataArray.to_netcdf` and
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:py:meth:`Dataset.to_netcdf` when used with ``engine="h5netcdf"``
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and currently raises a warning unless ``invalid_netcdf=True`` is set:
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doc/whats-new.rst

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@@ -369,7 +369,7 @@ New Features
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- Added :py:meth:`DataArray.curvefit` and :py:meth:`Dataset.curvefit` for general curve fitting applications. (:issue:`4300`, :pull:`4849`)
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By `Sam Levang <https://github.com/slevang>`_.
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- Add options to control expand/collapse of sections in display of Dataset and
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DataArray. The function :py:func:`set_options` now takes keyword aguments
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DataArray. The function :py:func:`set_options` now takes keyword arguments
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``display_expand_attrs``, ``display_expand_coords``, ``display_expand_data``,
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``display_expand_data_vars``, all of which can be one of ``True`` to always
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expand, ``False`` to always collapse, or ``default`` to expand unless over a
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Announcements of note:
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- Xarray is now a NumFOCUS fiscally sponsored project! Read
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`the anouncement <https://numfocus.org/blog/xarray-joins-numfocus-sponsored-projects>`_
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`the announcement <https://numfocus.org/blog/xarray-joins-numfocus-sponsored-projects>`_
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for more details.
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- We have a new :doc:`roadmap` that outlines our future development plans.
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By `Willi Rath <https://github.com/willirath>`_.
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- You can now explicitly disable any default ``_FillValue`` (``NaN`` for
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floating point values) by passing the enconding ``{'_FillValue': None}``
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floating point values) by passing the encoding ``{'_FillValue': None}``
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(:issue:`1598`).
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By `Stephan Hoyer <https://github.com/shoyer>`_.
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These methods return a new Dataset (or DataArray) with updated data or
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coordinate variables.
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- ``xray.Dataset.sel`` now supports the ``method`` parameter, which works
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like the paramter of the same name on ``xray.Dataset.reindex``. It
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like the parameter of the same name on ``xray.Dataset.reindex``. It
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provides a simple interface for doing nearest-neighbor interpolation:
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.. use verbatim because I can't seem to install pandas 0.16.1 on RTD :(
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xray.DataArray([1, 2, np.nan, 3]).mean()
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You can turn this behavior off by supplying the keyword arugment
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You can turn this behavior off by supplying the keyword argument
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``skipna=False``.
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These operations are lightning fast thanks to integration with bottleneck_,

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