docs: fix doc code highlights

This commit fixes several incorrectly emphasized code lines in the documentation.
rickstaa · Feb 8, 2024 · 596fe84 · 596fe84
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diff --git a/docs/source/usage/hyperparameter_tuning.rst b/docs/source/usage/hyperparameter_tuning.rst
@@ -48,14 +48,14 @@ Consider the example in ``stable_learning_control/examples/pytorch/sac_ray_hyper
    :language: python
    :linenos:
    :lines: 32-
-   :emphasize-lines: 12, 15-30, 38-49, 56, 59-66, 70-95, 98
-
-In this example, a boolean on line ``12`` can enable Weights & Biases logging. On lines ``15-30``, we first create a small wrapper
-function that ensures that the Ray Tuner serves the hyperparameters in the SLC algorithm's format. Following lines ``38-49`` setup
-a Weights & Biases callback if the ``USE_WANDB`` constant is set to ``True``. On line ``56``, we then set the starting point for
-several hyperparameters used in the hyperparameter search. Next, we define the hyperparameter search space on lines ``59-66`` 
-while we initialise the Ray Tuner instance on lines ``70-95``. Lastly, we start the hyperparameter search by calling the
-Tuners ``fit`` method on line ``98``.
+   :emphasize-lines: 12, 15-29, 38-48, 55, 58-65, 69-94, 97
+
+In this example, a boolean on line ``12`` can enable Weights & Biases logging. On lines ``15-29``, we first create a small wrapper
+function that ensures that the Ray Tuner serves the hyperparameters in the SLC algorithm's format. Following lines ``38-48`` setup
+a Weights & Biases callback if the ``USE_WANDB`` constant is set to ``True``. On line ``55``, we then set the starting point for
+several hyperparameters used in the hyperparameter search. Next, we define the hyperparameter search space on lines ``58-65`` 
+while we initialise the Ray Tuner instance on lines ``69-94``. Lastly, we start the hyperparameter search by calling the
+Tuners ``fit`` method on line ``97``.
 
 When running the script, the Ray tuner will search for the best hyperparameter combination. While doing so will print
 the results to the ``stdout``, a TensorBoard logging file and the Weights & Biases portal. You can check the TensorBoard logs using the

diff --git a/docs/source/usage/running.rst b/docs/source/usage/running.rst
@@ -533,7 +533,7 @@ Consider the example in ``stable_learning_control/examples/pytorch/sac_exp_grid_
 .. literalinclude:: /../../examples/pytorch/sac_exp_grid_search.py
    :language: python
    :linenos:
-   :lines: 16-
+   :lines: 17-
    :emphasize-lines: 22-28, 31
 
 After making the ExperimentGrid object, parameters are added to it with

diff --git a/docs/source/usage/saving_and_loading.rst b/docs/source/usage/saving_and_loading.rst
@@ -245,7 +245,7 @@ In this example, observe that
 
 * On line 6, we import the algorithm we want to load.
 * On line 12-14, we use the :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.load_config` method to restore the hyperparameters that were used during the experiment. This saves us time in setting up the correct hyperparameters.
-* on line 15, we use the :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.load_config` method to restore the environment used during the experiment. This saves us time in setting up the environment.
+* on line 15, we use the :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.load_env` method to restore the environment used during the experiment. This saves us time in setting up the environment.
 * on line 17, we import the model weights.
 * on line 18-19, we load the saved weights onto the algorithm.
 
@@ -297,7 +297,7 @@ In this example, observe that
 * On line 12-14, we use the :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.load_config` method
   to restore the hyperparameters that were used during the experiment. This saves us time in setting up the correct
   hyperparameters.
-* on line 15, we use the :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.load_config` method to
+* on line 15, we use the :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.load_env` method to
   restore the environment used during the experiment. This saves us time in setting up the environment.
 * on line 17, we import the model weights.
 * on line 18-19, we load the saved weights onto the algorithm.

diff --git a/docs/source/utils/loggers.rst b/docs/source/utils/loggers.rst
@@ -232,7 +232,7 @@ In this example, observe that
 * On lines 107 :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.setup_pytorch_saver` is used to prepare the logger to save the key elements of the CNN model.
 * On line 141, diagnostics are saved to the logger's internal state via :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.store`.
 * On line 147, the CNN model saved once per epoch via :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.save_state`.
-* On lines 60-65, :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.log_tabular` and :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.dump_tabular` are
+* On lines 150-157, :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.log_tabular` and :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.dump_tabular` are
   used to write the epoch diagnostics to file. Note that the keys passed into :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.log_tabular` are the same as the
   keys passed into :meth:`~stable_learning_control.utils.log_utils.logx.EpochLogger.store`. We use the ``tb_write=True`` option to enable TensorBoard logging.