fix ci on windows and mac

Author: scottyhq

.github/workflows/nocache.yaml

@@ -27,11 +27,19 @@ jobs:
           cache: true
           activate-environment: true
 
+      # https://github.com/xarray-contrib/xarray-tutorial/issues/311
+      - name: Configure graphviz
+        if: matrix.runs-on == 'macos-latest'
+        run: |
+          dot -c
+
       - name: Build JupyterBook
+        id: jb-build
+        continue-on-error: true
         run: |
           jupyter-book build ./ --warningiserror --keep-going
 
       - name: Dump Build Logs
-        if: always()
+        if: steps.jb-build.outcome == 'failure'
         run: |
-          if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi
+          cat _build/html/reports/**/*.log

.github/workflows/qaqc.yaml

@@ -1,6 +1,7 @@
 name: QualityContol
 
 on:
+  workflow_dispatch:
   pull_request:
     branches:
       - main

advanced/apply_ufunc/example-interp.ipynb

@@ -562,7 +562,7 @@
    "source": [
     "`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",
     "\n",
-    "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",
+    "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",
     "\n",
     "We need to specify some extra information:\n",
     "\n",
@@ -606,7 +606,7 @@
     "tags": []
    },
    "source": [
-    "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."
+    "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."
    ]
   },
   {

intermediate/remote_data/remote-data.ipynb

@@ -178,7 +178,7 @@
    "id": "11",
    "metadata": {},
    "source": [
-    "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",
+    "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",
     "\n"
    ]
   },