Merge pull request #1184 from GeoscienceAustralia/feature/fixing-sand…

…box-links

Fixed two links to python Tools files

Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb

@@ -261,7 +261,7 @@
     "\n",
     "In addition to the `zonal_stats` parameter, we also need to set up a datacube query dictionary for the Open Data Cube query such as `measurements` (the bands to load from the satellite), the `resolution` (the cell size), and the `output_crs` (the output projection). These options will be added to a query dictionary that will be passed into `collect_training_data` using the parameter `collect_training_data(dc_query=query, ...)`. The query dictionary will be the only argument in the **feature layer function** which we will define and describe in a moment.\n",
     "\n",
-    "> Note: `collect_training_data` also has a number of additional parameters for handling ODC I/O read failures, where polygons that return an excessive number of null values can be resubmitted to the multiprocessing queue. Check out the [docs](../Tools/gen/dea_tools.classification.ipynb) to learn more.\n"
+    "> Note: `collect_training_data` also has a number of additional parameters for handling ODC I/O read failures, where polygons that return an excessive number of null values can be resubmitted to the multiprocessing queue. Check out the [docs](../../Tools/dea_tools/classification.py) to learn more.\n"
    ]
   },
   {

Real_world_examples/Scalable_machine_learning/4_Classify_satellite_data.ipynb

@@ -289,9 +289,9 @@
    "source": [
     "### Loop through test locations and predict\n",
     "\n",
-    "For every location we listed in the `test_locations` dictionary, we calculate the feature layers, and then use the DEA function [predict_xr](../Tools/gen/dea_tools.classification/#dea_tools.classification.predict_xr) to classify the data.\n",
+    "For every location we listed in the `test_locations` dictionary, we calculate the feature layers, and then use the DEA function [predict_xr](../../Tools/dea_tools/classification.py) to classify the data.\n",
     "\n",
-    "The `predict_xr` function is an xarray wrapper around the sklearn estimator `.predict()` and `.predict_proba()` methods, and relies on [dask-ml](https://ml.dask.org/) [ParallelPostfit](https://ml.dask.org/modules/generated/dask_ml.wrappers.ParallelPostFit.html) to run the predictions with dask. `Predict_xr` can compute predictions, prediction probabilites, and return the input feature layers. Read the [documentation](../Tools/gen/dea_tools.classification/#dea_tools.classification.predict_xr) for more insights into this function's capabilities."
+    "The `predict_xr` function is an xarray wrapper around the sklearn estimator `.predict()` and `.predict_proba()` methods, and relies on [dask-ml](https://ml.dask.org/) [ParallelPostfit](https://ml.dask.org/modules/generated/dask_ml.wrappers.ParallelPostFit.html) to run the predictions with dask. `Predict_xr` can compute predictions, prediction probabilites, and return the input feature layers. Read the [documentation](../../Tools/dea_tools/classification.py) for more insights into this function's capabilities."
    ]
   },
   {