Reverted some remaining links to be .py links

GeoscienceAustralia · Dec 18, 2023 · 87630ae · 87630ae
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diff --git a/DEA_products/DEA_Wetlands_Insight_Tool.ipynb b/DEA_products/DEA_Wetlands_Insight_Tool.ipynb
@@ -69,9 +69,9 @@
     "Licencing information is provided at the bottom of this notebook, and requires attribution.** \n",
     "\n",
     "## Related products\n",
-    "* [DEA Fractional Cover (Landsat)](https://docs.dea.ga.gov.au/notebooks/DEA_products/DEA_Fractional_Cover.ipynb)\n",
+    "* [DEA Fractional Cover (Landsat)](../DEA_products/DEA_Fractional_Cover.ipynb)\n",
     "* [DEA Tasseled Cap Indices Percentiles Calendar Year (Landsat)](https://docs.dea.ga.gov.au/data/product/dea-wetness-percentiles-landsat)\n",
-    "* [DEA Water Observations (Landsat)](https://docs.dea.ga.gov.au/notebooks/DEA_products/DEA_Water_Observations/)"
+    "* [DEA Water Observations (Landsat)](../DEA_products/DEA_Water_Observations.ipynb)"
    ]
   },
   {

diff --git a/How_to_guides/Generating_composites.ipynb b/How_to_guides/Generating_composites.ipynb
@@ -1131,7 +1131,7 @@
    "source": [
     "To get an image at a certain time, often there is missing data, due to clouds and other masking.  We can fill in these gaps by using data from surrounding times.\n",
     "\n",
-    "To generate these images, we can use the custom functions `first`, `last` and `nearest` from the [dea_datahandling](https://docs.dea.ga.gov.au/notebooks/Tools/gen/dea_tools.datahandling/) script.\n",
+    "To generate these images, we can use the custom functions `first`, `last` and `nearest` from the [datahandling](../Tools/dea_tools/datahandling.py) script.\n",
     "\n",
     "You can also use the in-built `.first()` and `.last()` methods when doing `groupby` and `resample` as described above. They are described in the [xarray documentation](http://xarray.pydata.org/en/stable/groupby.html#first-and-last) on grouped data."
    ]

diff --git a/Real_world_examples/Scalable_machine_learning/README.rst b/Real_world_examples/Scalable_machine_learning/README.rst
@@ -38,7 +38,7 @@ extensively documented. And secondly, a number of custom python
 functions have been written to ease the complexity of running ML on the
 ODC. These include (among others) ``collect_training_data``,
 and ``predict_xr``, both of which are contained in the
-`dea\_tools.classification </notebooks/Tools/gen/dea_tools.classification.ipynb>`__
+`dea\_tools.classification <../Tools/dea_tools/classification.py>`__
 package. These functions are introduced and explained further in the
 relevant sections of the notebooks.