deploy: fe39684

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 35dbdb2b27cad3442b8a3d7e176c0c97
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/community/code-of-conduct.rst.txt

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+=========================
+Developer Code of Conduct
+=========================
+
+..
+    TODO: write a code of conduct for DeepSensor/mirror the CODE-OF-CONDUCT.md document?

_sources/community/contributing.rst.txt

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+==========================
+Contributing to DeepSensor
+==========================
+
+..
+    TODO: write a intro to contributing to DeepSensor/mirror the CONTRIBUTE.md document?

_sources/community/faq.rst.txt

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+=============
+Community FAQ
+=============
+
+This FAQ aims to answer common questions about the DeepSensor library. It is our way to streamline the onboarding process and clarify expectations. 
+
+.. note::
+
+    If you have a question that is not answered here, please open an issue or submit a pull request.
+
+Questions
+---------
+
+**Q: What is the purpose of this project?**
+
+**Answer:** [Briefly describe the project, its objectives, and its intended audience.]
+
+---
+
+**Q: How can I contribute?**
+
+**Answer:** There are many ways to contribute, from writing code and fixing bugs to improving documentation or translating content. Check our :doc:`contribution guide <contributing>` for detailed steps.
+
+---
+
+**Q: Where can I find the contribution guidelines?**
+
+**Answer:** You can find our contribution guidelines in the CONTRIBUTING.md file in the root directory of the repository, or in the :doc:`contribution guide <contributing>`.
+
+---
+
+**Q: Do I need to sign a Contributor License Agreement (CLA)?**
+
+**Answer:** At the current time, we do not require a CLA from our contributors.
+
+---
+
+**Q: How do I report a bug?**
+
+**Answer:** Please submit an issue in our GitHub repository. Make sure to provide detailed information, including steps to reproduce the bug and the expected outcome.
+
+---
+
+**Q: How do I request a new feature?**
+
+**Answer:** Open a new issue on our GitHub repository and label it as a feature request. Describe the feature in detail and its potential benefits.
+
+---
+
+**Q: How do I set up the development environment?**
+
+**Answer:** Follow the instructions in our developer documentation. If you run into issues, ask in our community chat (on Slack) or :doc:`contact the core group of maintainers directly </contact>`.
+
+---
+
+**Q: Do you have a code of conduct?**
+
+**Answer:** Yes, we value a respectful and inclusive community. Please read our :doc:`Code of Conduct <code-of-conduct>` before contributing.
+
+---
+
+**Q: How can I get in touch with other contributors or maintainers?**
+
+**Answer:** Join our Slack team to stay in touch with other contributors and maintainers. We also have a standing community meeting, which is by invite, so :doc:`get in touch with the core group of maintainers </contact>` to receive an invite to them.
+
+---
+
+**Q: Can I contribute even if I'm not a coder?**
+
+**Answer:** Absolutely! Contributions can be made in the form of documentation, design, testing, and more. Everyone's skills are valuable. Join our Slack discussion to learn more.
+
+---
+
+**Q: How do I claim an issue to work on?**
+
+**Answer:** Comment on the issue expressing your interest to help out. If the issue is unassigned, a maintainer will likely assign it to you.
+
+---
+
+**Q: What's the process for proposing a significant change?**
+
+**Answer:** For significant changes, it's a good practice to first open a discussion or issue to gather feedback. Once there's a consensus, you can proceed with a pull request.
+
+---
+
+**Q: How can I get my pull request (PR) merged?**
+
+**Answer:** Ensure your PR follows the contribution guidelines, passes all tests, and has been reviewed by at least one maintainer. Address any feedback provided.
+
+---
+
+**Q: What's the project's release cycle?**
+
+**Answer:** We release updates in a rolling manner, following our roadmap planning. Critical bug fixes might be released as needed.
+
+---
+
+**Q: How is credit given to contributors?**
+
+**Answer:** Contributors are acknowledged in our release notes, and their contributions are forever recorded in the project's history.
+
+
+..
+    TODO: write more questions and answers here.

_sources/community/index.rst.txt

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+===========================================
+DeepSensor's user and contributor community
+===========================================
+
+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 join our Slack channel at https://deepsensor.slack.com. You can request an invitation to the Slack channel at `https://ai4environment.slack.com/signup`.
+
+We welcome contributions from the community. If you are interested in contributing to DeepSensor, please read the :doc:`contributing` guide.
+
+.. toctree::
+    :maxdepth: 1
+    :caption: Table of contents:
+
+    faq
+    contributing
+    code-of-conduct
+    roadmap

_sources/community/roadmap.rst.txt

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+==================
+DeepSensor Roadmap
+==================
+
+..
+    TODO: write an intro to the roadmap for DeepSensor + link to the GitHub project management board

_sources/contact.rst.txt

@@ -0,0 +1,7 @@
+Contact the developers
+======================
+
+[email protected]
+
+..
+    TODO: Add contact information here...

_sources/getting-started/extending.rst.txt

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+====================================
+Extending DeepSensor with new models
+====================================
+
+To extend DeepSensor with a new model, simply create a new class that inherits from ``deepsensor.model.DeepSensorModel`` and implement the low-level prediction methods defined in ``deepsensor.model.ProbabilisticModel``, such as ``.mean`` and ``.stddev``.
+
+In this example, we'll create a new model called ``ExampleModel``:
+
+.. code-block:: python
+
+    class ExampleModel(DeepSensorModel):
+        """
+        A very naive model that predicts the mean of the first context set
+        with a fixed stddev.
+        """
+
+        def __init__(self, data_processor: DataProcessor, task_loader: TaskLoader):
+            # Initiate the parent class (DeepSensorModel) with the
+            # provided data processor and task loader:
+            super().__init__(data_processor, task_loader)
+
+        def mean(self, task: Task):
+            """Compute mean at target locations"""
+            return np.mean(task["Y_c"][0])
+
+        def stddev(self, task: Task):
+            """Compute stddev at target locations"""
+            return 0.1
+
+        ...
+
+After creating ``ExampleModel`` in this way, it can be used in the same way as the built-in :class:`~deepsensor.model.convnp.ConvNP` model.
+
+See `this Jupyter notebook <https://github.com/tom-andersson/deepsensor_gallery/blob/main/demonstrators/extending_models.ipynb>`_ for more details.

_sources/getting-started/index.rst.txt

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+===============
+Getting started
+===============
+
+In this part of the documentation, we will show you how to get started with ``DeepSensor``. We will show you how to install ``DeepSensor`` and how to extend it. We will also provide you with some tutorials to get you started with ``DeepSensor``.
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Table of contents:
+
+   installation
+   extending
+   tutorials/index

_sources/getting-started/installation.rst.txt

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+Installation instructions
+=========================
+
+DeepSensor is a Python package that can be installed in a number of ways. In this section we will describe the two main ways to install the package.
+
+Install from `PyPI <https://pypi.org/project/deepsensor/>`_
+-----------------------------------------------------------
+
+If you want to use the latest stable release of DeepSensor and do not want/need access to the worked examples or the package's source code, we recommend installing from PyPI.
+
+This is the easiest way to install DeepSensor.
+
+- Install ``deepsensor``:
+
+  .. code-block:: bash
+
+    pip install deepsensor
+
+- Install the backend of your choice:
+
+  - Install ``tensorflow``:
+
+    .. code-block:: bash
+
+      pip install tensorflow
+
+  - Install ``pytorch``:
+
+    .. code-block:: bash
+
+      pip install torch
+
+Install from `source <https://github.com/tom-andersson/deepsensor>`_
+---------------------------------------------------------------------
+
+.. note::
+
+    You will want to use this method if you intend on contributing to the source code of DeepSensor.
+
+If you want to keep up with the latest changes to DeepSensor, or want/need easy access to the worked examples or the package's source code, we recommend installing from source.
+
+This method will create a ``DeepSensor`` directory on your machine which will contain all the source code, docs and worked examples.
+
+- Clone the repository:
+
+  .. code-block:: bash
+
+    git clone
+
+- Install ``deepsensor``:
+
+  .. code-block:: bash
+
+    pip install -e -v .
+
+- Install the backend of your choice:
+
+  - Install ``tensorflow``:
+
+    .. code-block:: bash
+
+        pip install tensorflow
+
+  - Install ``pytorch``:
+
+    .. code-block:: bash
+
+        pip install torch

_sources/getting-started/tutorials/index.rst.txt

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+=========
+Tutorials
+=========
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials:
+
+   quickstart

_sources/getting-started/tutorials/quickstart.rst.txt

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+====================
+Tutorial: Quickstart
+====================
+
+Here we will demonstrate a simple example of training a convolutional conditional neural process (ConvCNP) to spatially interpolate ERA5 data.
+
+We can go from imports to predictions with a trained model in less than 30 lines of code!
+
+.. code-block:: python
+
+    import deepsensor.torch
+    from deepsensor.data.loader import TaskLoader
+    from deepsensor.data.processor import DataProcessor
+    from deepsensor.model.convnp import ConvNP
+    from deepsensor.train.train import train_epoch
+
+    import xarray as xr
+    import pandas as pd
+    import numpy as np
+
+    # Load raw data
+    ds_raw = xr.tutorial.open_dataset("air_temperature")
+
+    # Normalise data
+    data_processor = DataProcessor(x1_name="lat", x1_map=(15, 75), x2_name="lon", x2_map=(200, 330))
+    ds = data_processor(ds_raw)
+
+    # Set up task loader
+    task_loader = TaskLoader(context=ds, target=ds)
+
+    # Set up model
+    model = ConvNP(data_processor, task_loader)
+
+    # Generate training tasks with up to 10% of grid cells passed as context and all grid cells
+    # passed as targets
+    train_tasks = []
+    for date in pd.date_range("2013-01-01", "2014-11-30")[::7]:
+        task = task_loader(date, context_sampling=np.random.uniform(0.0, 0.1), target_sampling="all")
+        train_tasks.append(task)
+
+    # Train model
+    for epoch in range(10):
+        train_epoch(model, train_tasks, progress_bar=True)
+
+    # Predict on new task with 10% of context data and a dense grid of target points
+    test_task = task_loader("2014-12-31", 0.1)
+    mean_ds, std_ds = model.predict(test_task, X_t=ds_raw)
+
+After training, the model can predict directly to `xarray` in your data's original units and coordinate system:
+
+.. code-block:: python
+
+    >>> mean_ds
+    <xarray.Dataset>
+    Dimensions:  (time: 1, lat: 25, lon: 53)
+    Coordinates:
+    * time     (time) datetime64[ns] 2014-12-31
+    * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
+    * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
+    Data variables:
+        air      (time, lat, lon) float32 246.7 244.4 245.5 ... 290.2 289.8 289.4
+
+We can also predict directly to `pandas` containing a timeseries of predictions at off-grid locations
+by passing a `numpy` array of target locations to the `X_t` argument of `.predict`:
+
+.. code-block:: python
+
+    # Predict at two off-grid locations for three days in December 2014
+    test_tasks = task_loader(pd.date_range("2014-12-01", "2014-12-31"), 0.1)
+    mean_df, std_df = model.predict(test_tasks, X_t=np.array([[50, 280], [40, 250]]).T)
+
+.. code-block:: python
+
+    >>> mean_df
+                                air
+    time       lat  lon              
+    2014-12-01 50.0 280.0  260.183056
+            40.0 250.0  277.947373
+    2014-12-02 50.0 280.0   261.08943
+            40.0 250.0  278.219599
+    2014-12-03 50.0 280.0  257.128185
+            40.0 250.0  278.444229
+
+This quickstart example is also `available as a Jupyter notebook <https://github.com/tom-andersson/deepsensor_demos/blob/main/demonstrators/quickstart.ipynb>`_ with added visualisations.