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Commit 8bcdd9f

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Fix CI on windows and mac (#312)
* fix ci on windows and mac * always succeed with linkcheck * fix linkcheck
1 parent 3e5d4ab commit 8bcdd9f

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‎.github/workflows/nocache.yaml‎

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cache: true
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activate-environment: true
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# https://github.com/xarray-contrib/xarray-tutorial/issues/311
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- name: Configure graphviz
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if: matrix.runs-on == 'macos-latest'
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run: |
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dot -c
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- name: Build JupyterBook
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id: jb-build
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continue-on-error: true
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run: |
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jupyter-book build ./ --warningiserror --keep-going
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- name: Dump Build Logs
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if: always()
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if: steps.jb-build.outcome == 'failure'
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run: |
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if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi
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cat _build/html/reports/**/*.log

‎.github/workflows/qaqc.yaml‎

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name: QualityContol
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on:
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workflow_dispatch:
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pull_request:
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branches:
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- main
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with open('./_config.yml', 'w') as f:
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yaml.dump(data, f)
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# Checking links is flaky, so continue-on-error: true
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- name: Check External Links
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timeout-minutes: 5
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continue-on-error: true
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if: always()
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run: |
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jupyter-book build ./ --builder linkcheck

‎advanced/apply_ufunc/example-interp.ipynb‎

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"source": [
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"`np.vectorize` is a very convenient function but is unfortunately slow. It is only marginally faster than writing a for loop in Python and looping. A common way to get around this is to write a base interpolation function that can handle nD arrays in a compiled language like Fortran and then pass that to `apply_ufunc`.\n",
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"\n",
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"Another option is to use the numba package which provides a very [convenient `guvectorize` decorator](https://numba.pydata.org/numba-doc/latest/user/vectorize.html#the-guvectorize-decorator). Any decorated function gets compiled and will loop over any non-core dimension in parallel when necessary. \n",
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"Another option is to use the numba package which provides a very [convenient `guvectorize` decorator](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator). Any decorated function gets compiled and will loop over any non-core dimension in parallel when necessary. \n",
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"\n",
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"We need to specify some extra information:\n",
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"\n",
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"tags": []
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},
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"source": [
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"The warnings are about [object-mode compilation](https://numba.pydata.org/numba-doc/latest/user/performance-tips.html#no-python-mode-vs-object-mode) relating to the `print` statement. This means we don't get much speed up. We'll keep the `print` statement temporarily to make sure that `guvectorize` acts like we want it to."
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"The warnings are about [object-mode compilation](https://numba.readthedocs.io/en/stable/user/performance-tips.html) relating to the `print` statement. This means we don't get much speed up. We'll keep the `print` statement temporarily to make sure that `guvectorize` acts like we want it to."
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]
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},
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{

‎advanced/apply_ufunc/numba-vectorization.ipynb‎

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"\n",
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"Another option is to use the [numba package](https://numba.pydata.org/) which provides two very convenient decorators to build [numpy universal functions or ufuncs](https://numba.readthedocs.io/en/stable/user/vectorize.html):\n",
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"1. [`vectorize`](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-vectorize-decorator) for functions that act on scalars, and \n",
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"2. [`guvectorize`](https://numba.pydata.org/numba-doc/latest/user/vectorize.html#the-guvectorize-decorator) for functions that operates on subsets of the array along core-dimensions. Any decorated function gets compiled and will loop over the loop dimensions in parallel when necessary. \n",
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"2. [`guvectorize`](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator) for functions that operates on subsets of the array along core-dimensions. Any decorated function gets compiled and will loop over the loop dimensions in parallel when necessary. \n",
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"\n",
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"For `apply_ufunc` the key concept is that we must provide `vectorize=False` (the default) when using Numba vectorized functions. \n",
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"Numba handles the vectorization (or looping) and `apply_ufunc` handles converting Xarray objects to bare arrays and handling metadata."

‎intermediate/remote_data/remote-data.ipynb‎

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"The `open_dataset()` method is our entry point to n-dimensional data with xarray, the first argument we pass indicates what we want to open and is used by xarray to get the right backend and in turn is used by the backend to open the file locally or remote. The accepted types by xarray are:\n",
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"\n",
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"\n",
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"* **str**: \"my-file.nc\" or \"s3:://my-zarr-store/data.zarr\"\n",
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"* **str**: `my-file.nc` or `s3:://my-zarr-store/data.zarr`\n",
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"* **os.PathLike**: Posix compatible path, most of the times is a Pathlib cross-OS compatible path.\n",
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"* **BufferedIOBase**: some xarray backends can read data from a buffer, this is key for remote access.\n",
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"* **AbstractDataStore**: This one is the generic store and backends should subclass it, if we do we can pass a \"store\" to xarray like in the case of Opendap/Pydap\n",
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"id": "11",
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"metadata": {},
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"source": [
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"xarray iterated through the registered backends and netcdf4 returned a `\"yes, I can open that extension\"` see: [netCDF4_.py#L618](https://github.com/pydata/xarray/blob/6c2d8c3389afe049ccbfd1393e9a81dd5c759f78/xarray/backends/netCDF4_.py#L618). However, **the backend doesn't know how to \"talk\" to a remote store** and thus it fails to open our file.\n",
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"xarray iterated through the registered backends and netcdf4 returned a `\"yes, I can open that extension\"` see: [netCDF4_.py#L618](https://github.com/pydata/xarray/blob/6c2d8c3389afe049ccbfd1393e9a81dd5c759f78/xarray/backends/netCDF4_.py#L618). However, **the backend doesn't know how to \"talk\" to a remote store** and thus it fails to open our file.\n",
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"\n"
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]
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},

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