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remove cdms2 (#8441)
* remove `cdms2` from all environments * remove any code related to `cdms2` * remove the `mypy` override for `cdms2` * remove the special environment files for python 3.11 * whats-new * more info on the replacement * remove the `cdms2` methods from the api docs * remove all mentions of UV-CDAT / cdms2 from the faq * also change the binder environment
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.binder/environment.yml

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@@ -6,7 +6,6 @@ dependencies:
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- boto3
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- bottleneck
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- cartopy
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- cdms2
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- cfgrib
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- cftime
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- coveralls
@@ -38,5 +37,4 @@ dependencies:
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- toolz
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- xarray
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- zarr
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- pip:
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- numbagg
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- numbagg

.github/workflows/ci.yaml

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@@ -67,13 +67,7 @@ jobs:
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run: |
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echo "TODAY=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
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if [[ "${{matrix.python-version}}" == "3.11" ]]; then
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if [[ ${{matrix.os}} == windows* ]]; then
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echo "CONDA_ENV_FILE=ci/requirements/environment-windows-py311.yml" >> $GITHUB_ENV
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else
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echo "CONDA_ENV_FILE=ci/requirements/environment-py311.yml" >> $GITHUB_ENV
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fi
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elif [[ ${{ matrix.os }} == windows* ]] ;
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if [[ ${{ matrix.os }} == windows* ]] ;
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then
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echo "CONDA_ENV_FILE=ci/requirements/environment-windows.yml" >> $GITHUB_ENV
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elif [[ "${{ matrix.env }}" != "" ]] ;

ci/requirements/all-but-dask.yml

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@@ -3,13 +3,11 @@ channels:
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- conda-forge
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- nodefaults
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dependencies:
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- python=3.10
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- black
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- aiobotocore
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- boto3
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- bottleneck
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- cartopy
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- cdms2
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- cftime
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- coveralls
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- flox

ci/requirements/environment-py311.yml

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ci/requirements/environment-windows-py311.yml

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ci/requirements/environment-windows.yml

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- boto3
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- bottleneck
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- cartopy
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# - cdms2 # Not available on Windows
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- cftime
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- dask-core
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- distributed

ci/requirements/environment.yml

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- boto3
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- bottleneck
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- cartopy
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- cdms2
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- cftime
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- dask-core
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- distributed

ci/requirements/min-all-deps.yml

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- boto3=1.24
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- bottleneck=1.3
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- cartopy=0.20
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- cdms2=3.1
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- cftime=1.6
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- coveralls
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- dask-core=2022.7

doc/api.rst

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load_dataarray
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open_dataarray
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DataArray.as_numpy
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DataArray.from_cdms2
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DataArray.from_dict
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DataArray.from_iris
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DataArray.from_series
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DataArray.to_cdms2
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DataArray.to_dask_dataframe
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DataArray.to_dataframe
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DataArray.to_dataset

doc/getting-started-guide/faq.rst

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.. _Iris: https://scitools-iris.readthedocs.io/en/stable/
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.. _Cartopy: https://scitools.org.uk/cartopy/docs/latest/
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`UV-CDAT`__ is another Python library that implements in-memory netCDF-like
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variables and `tools for working with climate data`__.
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__ https://uvcdat.llnl.gov/
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__ https://drclimate.wordpress.com/2014/01/02/a-beginners-guide-to-scripting-with-uv-cdat/
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We think the design decisions we have made for xarray (namely, basing it on
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pandas) make it a faster and more flexible data analysis tool. That said, Iris
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and CDAT have some great domain specific functionality, and xarray includes
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methods for converting back and forth between xarray and these libraries. See
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:py:meth:`~xarray.DataArray.to_iris` and :py:meth:`~xarray.DataArray.to_cdms2`
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for more details.
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has some great domain specific functionality, and xarray includes
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methods for converting back and forth between xarray and Iris. See
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:py:meth:`~xarray.DataArray.to_iris` for more details.
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What other projects leverage xarray?
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