merge main

CONTRIBUTING.md

@@ -33,9 +33,8 @@ Slack workspace, where conversation about when to hold the community calls in th
 place.
 
 **Slack Workspace**: Join
-our [DeepSensor Slack channel](https://docs.google.com/forms/d/e/1FAIpQLScsI8EiXDdSfn1huMp1vj5JAxi9NIeYLljbEUlMceZvwVpugw/viewform) for
-discussions, queries, and community interactions. Send us an email at [email protected] to
-request an invite.
+our DeepSensor Slack channel for
+discussions, queries, and community interactions. In order to join, [sign up for the Turing Environment & Sustainability stakeholder community](https://forms.office.com/pages/responsepage.aspx?id=p_SVQ1XklU-Knx-672OE-ZmEJNLHTHVFkqQ97AaCfn9UMTZKT1IwTVhJRE82UjUzMVE2MThSOU5RMC4u). The form includes a question on signing up for the Slack team, where you can find DeepSensor's channel.
 
 **Email**: If you prefer a more formal communication method or have specific concerns, please reach
 us at [email protected].

README.md

@@ -29,8 +29,7 @@ This allows DeepSensor users to focus on the science and rapidly iterate on idea
 
 DeepSensor is an experimental package, and we
 welcome [contributions from the community](https://github.com/alan-turing-institute/deepsensor/blob/main/CONTRIBUTING.md).
-We have an active Slack channel for code and research discussions; you can request to
-join [via this Google Form](https://docs.google.com/forms/d/e/1FAIpQLScsI8EiXDdSfn1huMp1vj5JAxi9NIeYLljbEUlMceZvwVpugw/viewform).
+We have an active Slack channel for code and research discussions; you can join by [signing up for the Turing Environment & Sustainability stakeholder community](https://forms.office.com/pages/responsepage.aspx?id=p_SVQ1XklU-Knx-672OE-ZmEJNLHTHVFkqQ97AaCfn9UMTZKT1IwTVhJRE82UjUzMVE2MThSOU5RMC4u). The form includes a question on signing up for the Slack team, where you can find DeepSensor's channel.
 
 ![DeepSensor example application figures](https://raw.githubusercontent.com/alan-turing-institute/deepsensor/main/figs/deepsensor_application_examples.png)
 

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/community/faq.md

@@ -46,7 +46,8 @@ Check our [](./contributing.md) guide.
 **Q: How can I get in touch with other contributors or maintainers?**
 
 **Answer:**
-[Request to join our Slack channel](https://docs.google.com/forms/d/e/1FAIpQLScsI8EiXDdSfn1huMp1vj5JAxi9NIeYLljbEUlMceZvwVpugw/viewform) to stay in touch with other contributors and maintainers.
+Request to join our Slack channel to stay in touch with other contributors and maintainers. You can join by [signing up for the Turing Environment & Sustainability stakeholder community](https://forms.office.com/pages/responsepage.aspx?id=p_SVQ1XklU-Knx-672OE-ZmEJNLHTHVFkqQ97AaCfn9UMTZKT1IwTVhJRE82UjUzMVE2MThSOU5RMC4u). The form includes a question on signing up for the Slack team, where you can find DeepSensor's channel.
+
 We also have a regular community Zoom call (join the Slack channel or get in touch to find out more).
 
 ---

docs/community/index.md

@@ -2,7 +2,6 @@
 
 The DeepSensor community is a group of users and contributors who are interested in the development of DeepSensor. The community is open to anyone who is interested in DeepSensor. The community is a place to ask questions, discuss ideas, and share your work.
 
-If you are interested in joining the community, please request to join our Slack channel via [this Google Form](https://docs.google.com/forms/d/e/1FAIpQLScsI8EiXDdSfn1huMp1vj5JAxi9NIeYLljbEUlMceZvwVpugw/viewform).
-Once your request has been accepted, you will have access to the Slack channel [here](https://ai4environment.slack.com/archives/C05NQ76L87R).
+If you are interested in joining the community, please request to join our Slack channel. You can join by [signing up for the Turing Environment & Sustainability stakeholder community](https://forms.office.com/pages/responsepage.aspx?id=p_SVQ1XklU-Knx-672OE-ZmEJNLHTHVFkqQ97AaCfn9UMTZKT1IwTVhJRE82UjUzMVE2MThSOU5RMC4u). The form includes a question on signing up for the Slack team, where you can find DeepSensor's channel.
 
 We welcome contributions from the community. If you are interested in contributing to DeepSensor, please read the [Contributing Guide](./contributing.md).

docs/index.md

@@ -7,12 +7,11 @@ neural processes.
 **Useful links**:
 [Code repository](https://github.com/alan-turing-institute/deepsensor) |
 [Issues](https://github.com/alan-turing-institute/deepsensor/issues) |
-[Slack join request form](https://docs.google.com/forms/d/e/1FAIpQLScsI8EiXDdSfn1huMp1vj5JAxi9NIeYLljbEUlMceZvwVpugw/viewform) |
+[Slack join request form](https://forms.office.com/pages/responsepage.aspx?id=p_SVQ1XklU-Knx-672OE-ZmEJNLHTHVFkqQ97AaCfn9UMTZKT1IwTVhJRE82UjUzMVE2MThSOU5RMC4u) |
 [Slack channel](https://ai4environment.slack.com/archives/C05NQ76L87R) |
 [DeepSensor Gallery](https://github.com/tom-andersson/deepsensor_gallery)
 
 
-
 ::::{grid} 1 1 2 2
 :gutter: 2
 

docs/research_ideas.md

@@ -7,10 +7,10 @@ Below are a non-exhaustive selection of research ideas that you could explore.
 It would be helpful to ensure you are familiar with the literature and
 resources in the [](resources.md) page before starting.
 
-Why not [join our Slack channel](https://docs.google.com/forms/d/e/1FAIpQLScsI8EiXDdSfn1huMp1vj5JAxi9NIeYLljbEUlMceZvwVpugw/viewform)
-and start a conversation around these ideas or your own? :-)
+Why not join our Slack channel and start a conversation around these ideas or your own? :-) You can join by [signing up for the Turing Environment & Sustainability stakeholder community](https://forms.office.com/pages/responsepage.aspx?id=p_SVQ1XklU-Knx-672OE-ZmEJNLHTHVFkqQ97AaCfn9UMTZKT1IwTVhJRE82UjUzMVE2MThSOU5RMC4u). The form includes a question on signing up for the Slack team, where you can find DeepSensor's channel.
 
 ## 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 +20,22 @@ 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 +44,7 @@ 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 +57,7 @@ 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)