Fixing warnings from Sphinx/JupyterBook

deepsensor/active_learning/acquisition_fns.py

@@ -31,7 +31,7 @@ def __init__(
             context_set_idx (int):
                 Index of context set to add new observations to when computing
                 the acquisition function.
-          target_set_idx (int):
+            target_set_idx (int):
                 Index of target set to compute acquisition function for.
         """
         self.model = model

deepsensor/data/loader.py

@@ -25,7 +25,7 @@ class TaskLoader:
         - If all data passed as paths, load the data and overwrite the paths with the loaded data
         - Either all data is passed as paths, or all data is passed as loaded data (else ``ValueError``)
         - If all data passed as paths, the TaskLoader can be saved with the ``save`` method
-        (using config)
+          (using config)
 
     Args:
         task_loader_ID:

deepsensor/data/task.py

@@ -54,8 +54,7 @@ def summarise_repr(cls, k, v) -> str:
                 Value of the task dictionary.
 
         Returns:
-            str:
-                String representation of the task.
+            str: String representation of the task.
         """
         if v is None:
             return "None"
@@ -357,14 +356,10 @@ def append_obs_to_task(
         option plus ability to remove observations.
 
     Args:
-        task (:class:`deepsensor.data.task.Task`:):
-            The task to modify.
-        X_new (array-like):
-            New observation coordinates.
-        Y_new (array-like):
-            New observation values.
-        context_set_idx (int):
-            Index of the context set to append to.
+        task (:class:`deepsensor.data.task.Task`:): The task to modify.
+        X_new (array-like): New observation coordinates.
+        Y_new (array-like): New observation values.
+        context_set_idx (int): Index of the context set to append to.
 
     Returns:
         :class:`deepsensor.data.task.Task`:
@@ -459,15 +454,14 @@ def concat_tasks(tasks: List[Task], multiple: int = 1) -> Task:
             the computational graph in graph mode.
 
     Returns:
-        :class:`~.data.task.Task`
-            Task containing multiple batches.
+        :class:`~.data.task.Task`: Task containing multiple batches.
 
     Raises:
-        ValueError
+        ValueError:
             If the tasks have different numbers of target sets.
-        ValueError
+        ValueError:
             If the tasks have different numbers of targets.
-        ValueError
+        ValueError:
             If the tasks have different types of target sets (gridded/
             non-gridded).
     """

deepsensor/plot.py

@@ -99,7 +99,9 @@ def task(
 
     with plt.rc_context(params):
         fig, axes = plt.subplots(
-            nrows=nrows, ncols=ncols, figsize=(ncols * figsize, nrows * figsize)
+            nrows=nrows,
+            ncols=ncols,
+            figsize=(ncols * figsize, nrows * figsize),
         )
         if nrows == 1:
             axes = axes[np.newaxis]
@@ -303,7 +305,8 @@ def offgrid_context(
     colors: Optional[str] = None,
     **scatter_kwargs,
 ) -> None:
-    """Plot the off-grid context points on ``axes``.
+    """
+    Plot the off-grid context points on ``axes``.
 
     Uses a provided :class:`~.data.processor.DataProcessor` to unnormalise the
     context coordinates if provided.
@@ -334,7 +337,7 @@ def offgrid_context(
             Additional keyword arguments to pass to the scatter plot.
 
     Returns:
-        None.
+        None
     """
     if markers is None:
         # all matplotlib markers
@@ -1018,7 +1021,9 @@ def prediction(
 
                 # rotate date times
                 ax.set_xticklabels(
-                    ax.get_xticklabels(), rotation=45, horizontalalignment="right"
+                    ax.get_xticklabels(),
+                    rotation=45,
+                    horizontalalignment="right",
                 )
 
             ax.set_title(f"{var_ID} {param}")

docs/research_ideas.md

@@ -11,6 +11,7 @@ Why not [join our Slack channel](https://docs.google.com/forms/d/e/1FAIpQLScsI8E
 and start a conversation around these ideas or your own? :-)
 
 ## Transfer learning from regions of dense observations to regions of sparse observations
+
 Since the `ConvNP` is a data-hungry model, it does not perform well if only trained on a
 small number of observations, which presents a challenge for modelling variables that
 are poorly observed.
@@ -20,19 +21,22 @@ of sparse observations?
 Does the performance improve?
 
 ## Sensor placement for forecasting
+
 Previous active learning research with ConvNPs has only considered sensor placement for interpolation.
 Do the sensor placements change when the model is trained for forecasting?
 
 See, e.g., Section 4.2.1 of [Environmental sensor placement with convolutional Gaussian neural processes](https://doi.org/10.1017/eds.2023.22).
 
 ## U-Net architectural changes
+
 The `ConvNP` currently uses a vanilla U-Net architecture.
 Do any architectural changes improve performance, such as batch normalisation or dropout?
 
 This would require digging into the [`neuralprocesses.construct_convgnp` method](https://github.com/wesselb/neuralprocesses/blob/f20572ba480c1279ad5fb66dbb89cbc73a0171c7/neuralprocesses/architectures/convgnp.py#L97)
 and replacing the U-Net module with a custom one.
 
 ## Extension to continuous time observations
+
 The `ConvNP` currently assumes that the observations are on a regular time grid.
 How can we extend this to continuous time observations, where the observations are not necessarily
 on a regular time grid?
@@ -41,6 +45,7 @@ For example, can we pass a 'time of observation' auxiliary input to the model?
 What are the limitations of this approach?
 
 ## Training with ablations for interpretability
+
 Since the `ConvNP` operates on sets of observations, it is possible to ablate observations
 and see how the model's predictions change.
 Thus, the `ConvNP` admits unique interpretability opportunities.
@@ -53,6 +58,7 @@ Then, at test time, ablate context sets and measure the change in the model's pr
 or performance.
 
 ## Monte Carlo sensor placement using AR sampling
+
 The `GreedyAlgorithm` for sensor placement currently uses the model's mean prediction
 to infill missing observations at query sites.
 However, one could also draw multiple [AR samples](user-guide/prediction.ipynb)