Fixing links

Beginners_guide/01_Jupyter_notebooks.ipynb

@@ -208,7 +208,7 @@
     "\n",
     "To rename this notebook to something more useful, right-click on it in the file browser and select \"Rename\".\n",
     "\n",
-    "> **Note:** The `dea-notebooks` repository provides a **template notebook** containing a consistent structure and style that is recommended for all DEA Jupyter notebooks. To use this template rather than start a notebook from scratch, click [this link to open the \"DEA_notebooks_template.ipynb\"](../DEA_notebooks_template.ipynb) notebook, then click \"File\" and \"Save Notebook As...\" to create a copy of the template in your desired location."
+    "> **Note:** The `dea-notebooks` repository provides a **template notebook** containing a consistent structure and style that is recommended for all DEA Jupyter notebooks. To use this template rather than start a notebook from scratch, click [this link to open the \"DEA_notebooks_template.ipynb\"](../../DEA_notebooks_template.ipynb) notebook, then click \"File\" and \"Save Notebook As...\" to create a copy of the template in your desired location."
    ]
   },
   {

DEA_products/DEA_Landsat_Surface_Reflectance.ipynb

@@ -1611,7 +1611,7 @@
    "metadata": {},
    "source": [
     "We can now pass this CRS to `dc.load()`'s `output_crs` parameter to load Landsat data in its native spatial grid.\n",
-    "The native resolution of all DEA Landsat Surface Reflectance data (with the exception of Landsat 8's panchromatic band; see the [Pansharpening notebook](../How_to_guides/Pan_sharpening_Brovey.ipynb)) is 30 m, so we can supply this to `resolution` directly without having to obtain it from the data."
+    "The native resolution of all DEA Landsat Surface Reflectance data (with the exception of Landsat 8's panchromatic band; see the [Pansharpening notebook](../How_to_guides/Pansharpening.ipynb)) is 30 m, so we can supply this to `resolution` directly without having to obtain it from the data."
    ]
   },
   {

How_to_guides/Machine_learning_with_ODC.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "## Description\n",
     "\n",
-    "This notebook demonstrates a potential workflow using functions from the [dea_tools.classification](../Tools/gen/dea_tools.classification/) script to implement a supervised learning landcover classifier within the [ODC (Open Data Cube)](https://www.opendatacube.org.ipynb) framework. \n",
+    "This notebook demonstrates a potential workflow using functions from the [dea_tools.classification](../Tools/gen/dea_tools.classification.ipynb) script to implement a supervised learning landcover classifier within the [ODC (Open Data Cube)](https://www.opendatacube.org.ipynb) framework. \n",
     "\n",
     "For larger model training and prediction implementations this notebook can be adapted into a Python file and run in a distributed fashion.\n",
     "\n",

How_to_guides/Virtual_products.ipynb

@@ -1900,7 +1900,7 @@
     "\n",
     "To load virtual products using Dask, pass the `dask_chunks` specification to the `load` method.\n",
     "\n",
-    "> **Note:** For more information about using Dask, refer to the [Parallel processing with Dask](09_Parallel_processing_with_Dask.ipynb) notebook."
+    "> **Note:** For more information about using Dask, refer to the [Parallel processing with Dask](../Beginners_guide/09_Parallel_processing_with_Dask.ipynb) notebook."
    ]
   },
   {

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/#L231) 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/gen/dea_tools.classification.ipynb#L231) 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/#L239) 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/gen/dea_tools.classification.ipynb#L239) for more insights into this function's capabilities."
    ]
   },
   {
@@ -665,9 +665,9 @@
     "Congratulations, you have successfully created a cropland model for Western Australia! If you're perfectly happy with the results, then this pixel-based classification can be the final point in your workflow.   However, in reality, ML workflows like the one you've just been through are an **iterative process**. If we weren't happy with the classifications, then we have a few options to improve the model:\n",
     "\n",
     "1. Conduct feature selection to remove features that might be confounding our model.\n",
-    "2. Consider adding new features to the model. This would require editing and re-running the `collect_training_data` function in the [Extracting_training_data](1_Extracting_training_data.ipynb) notebook to add new features to our training dataset.\n",
+    "2. Consider adding new features to the model. This would require editing and re-running the `collect_training_data` function in the [Extracting_training_data](1_Extract_training_data.ipynb) notebook to add new features to our training dataset.\n",
     "3. Try using a different model (e.g. instead of using a Random Forest Classifier we could use a Support Vector Machine - this will require editing and re-running the [Evaluate_optimize_fit_classifier](3_Evaluate_optimize_fit_classifier.ipynb) notebook).\n",
-    "3. Collect more training data in the regions where our classifier is doing poorly. This can be done through the platforms suggested in [Extracting_training_data](1_Extracting_training_data.ipynb).\n",
+    "3. Collect more training data in the regions where our classifier is doing poorly. This can be done through the platforms suggested in [Extracting_training_data](1_Extract_training_data.ipynb).\n",
     "\n",
     "We can also potentially improve our classifications by moving to the next notebook in this series. The next notebook explores converting the pixel-based classification into an object-based classification using an image segmentation algorithm."
    ]

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/>`__
+`dea\_tools.classification </notebooks/Tools/gen/dea_tools.classification.ipynb>`__
 package. These functions are introduced and explained further in the
 relevant sections of the notebooks.
 

Real_world_examples/Shipping_lane_identification.ipynb

@@ -111,7 +111,7 @@
     "\n",
     "To set up distributed computing with Dask, you need to first set up a Dask client using the function below:\n",
     "\n",
-    "> **Note:** For more information about using Dask, refer to the [Parallel processing with Dask](09_Parallel_processing_with_Dask.ipynb) notebook."
+    "> **Note:** For more information about using Dask, refer to the [Parallel processing with Dask](../Beginners_guide/09_Parallel_processing_with_Dask.ipynb) notebook."
    ]
   },
   {