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Merged
merged 13 commits into from
Jul 6, 2023
14 changes: 9 additions & 5 deletions CONTRIBUTING.md
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## Contributing
# Contributing Guide

This repository is a great opportunity to start contributing to Xarray.
This tutorial repository is a great opportunity to start contributing to Xarray.

- Report bugs, request features or submit feedback as a [GitHub Issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues). First check existing issues: https://github.com/xarray-contrib/xarray-tutorial/issues !
- Report bugs, request features or submit feedback as a [GitHub Issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues). First check existing [issues](https://github.com/xarray-contrib/xarray-tutorial/issues) !

- Make fixes, add content or improvements using [GitHub Pull Requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests), the sections below go over this process in more detail:

```{seealso}
The Project Pythia Foundations material on [Github](https://foundations.projectpythia.org/foundations/getting-started-github.html) and Github workflows is a great place to start if you are new to this.
```

## Content Guidelines

Please note that examples submitted to this repository should follow these
guidelines:

1. Run top-to-bottom without intervention from the user
1. Not require external data sources that may disappear over time (external data sources that are highly unlikely to disappear are fine)
1. Not require external data sources that may disappear over time (external data sources that are highly unlikely to disappear are fine). Small datasets for tutorial purposes can be added [here](https://github.com/pydata/xarray-data/) if necessary.
1. Not be resource intensive, and should run within 2GB of memory
1. Be clear and contain enough prose to explain the topic at hand
1. Be concise and limited to one or two topics, such that a reader can get through the example within a few minutes of reading
Expand All @@ -31,7 +35,7 @@ cd xarray-tutorial

You'll need `conda` or `mamba`, which can be installed from https://github.com/conda-forge/miniforge

We also use [conda-lock](https://conda-incubator.github.io/conda-lock/) to ensure we have reproducible environments across different operating systems
We also use [conda-lock](https://conda.github.io/conda-lock/) to ensure we have reproducible environments across different operating systems

We also use [pre-commit hooks](https://pre-commit.com) to run styling and other checks before committing code.

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## Contributing

Contributions are welcome and greatly appreciated! See our [CONTRIBUTING.md](./CONTRIBUTING.md) document.

## Acknowledgements

This website is the result of many contributions from the Xarray community! We're very grateful for everyone's volunteered effort as well as [sponsored development](https://xarray.dev/#sponsors). Funding for SciPy 2022, SciPy 2023 tutorial materialdevelopment specifically was supported by NASA's Open Source Tools, Frameworks, and Libraries Program (award 80NSSC22K0345).
5 changes: 4 additions & 1 deletion _config.yml
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# https://jupyterbook.org/content/content-blocks.html?highlight=myst%20substitution#define-substitutions-for-your-whole-book
# https://jupyterbook.org/content/content-blocks.html#using-substitutions-in-links
myst_substitutions:
xarray_docs: https://xarray.dev
xarray_homepage: https://xarray.dev
xarray_docs: https://docs.xarray.dev
xarray_repo: https://github.com/pydata/xarray
xarray_forum: https://github.com/pydata/xarray/discussions
myst_enable_extensions:
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- sphinxext.rediraffe
- sphinx_exercise
config:
language: en # accessibility
# application/vnd.holoviews_load.v0+json, application/vnd.holoviews_exec.v0+json
suppress_warnings: ['mystnb.unknown_mime_type', 'misc.highlighting_failure']
notfound_context:
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rediraffe_redirects:
scipy-tutorial/00_overview.ipynb: overview/get-started.md
workshops/scipy2022/README.md: overview/fundamental-path/README.md

bibtex_reference_style: author_year # or label, super, \supercite
18 changes: 7 additions & 11 deletions _toc.yml
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chapters:
- file: overview/get-started.md
- file: overview/xarray-in-45-min
- file: overview/fundamental-path/README.md
- file: overview/learning-paths.md
sections:
- file: overview/fundamental-path/README.md

- caption: Fundamentals
chapters:
Expand Down Expand Up @@ -39,7 +41,9 @@ parts:
- file: intermediate/xarray_ecosystem
- file: intermediate/hvplot
- file: intermediate/cmip6-cloud
- file: data_cleaning/ice_velocity.ipynb
- file: data_cleaning/data_cleaning.md
sections:
- file: data_cleaning/ice_velocity

- caption: Advanced
chapters:
Expand Down Expand Up @@ -81,16 +85,8 @@ parts:
- file: workshops/online-tutorial-series/02_indexing
- file: workshops/online-tutorial-series/03_computation

- caption: Domain Specific
chapters:
- url: https://anopheles-genomic-surveillance.github.io/workshop-5/module-1-xarray.html
title: Genomics
- url: https://predictablynoisy.com/posts/2019/2019-10-22-xarray-neuro/
title: Electrophysiology

- caption: Reference
chapters:
- file: CONTRIBUTING
- file: reference/resources
- file: reference/glossary
- file: reference/bibliography
- file: reference/acknowledgements
5 changes: 5 additions & 0 deletions data_cleaning/data_cleaning.md
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# Data Tidying

```{tableofcontents}

```
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````

## Citation

✏️ To cite this material, you can use the recommended [Xarray citation](https://docs.xarray.dev/en/stable/getting-started-guide/faq.html#how-should-i-cite-xarray): {cite:p}`hoyerhamman2017`, {cite:p}`xarray_v202230`.

## Acknowledgements

This website is the result of many contributions from the Xarray community! We're very grateful for everyone's volunteered effort as well as [sponsored development](https://xarray.dev/#sponsors). Funding for SciPy 2022, SciPy 2023 tutorial material development specifically was supported by NASA's Open Source Tools, Frameworks, and Libraries Program (award 80NSSC22K0345).

## Bibliography

```{bibliography}
:style: plain
```
2 changes: 1 addition & 1 deletion overview/fundamental-path/README.md
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# Fundamental Learning Path
# Fundamentals Learning Path

This syllabus comes from Scipy 2022 tutorial workshop.
This path covers fundamental Xarray concepts with
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# Learning Paths

Learning Paths are guided tours through the material on the site.
These paths were originally built for in-person tutorials at the SciPy conference.
They are useful if you would like to work through the material in an organized manner.

Follow the links below to try out each path.

```{tableofcontents}

```
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24 changes: 19 additions & 5 deletions reference/resources.md
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Expand Up @@ -5,7 +5,14 @@ demonstrate real-world applications of Xarray in a variety of use cases. These
need not be explored in any particular sequence, instead they are meant to
provide a sampling of what Xarray can be used for.

## Weather/Climate Model Data
```{seealso}
1. The [Xarray blog](https://xarray.dev/blog) has a number of user stories showcasing Xarray in
many scientific domains.
2. [Project Pythia Foundations](https://foundations.projectpythia.org/) is a great collection of
material on Xarray and related packages.
```

## Weather and Climate

1. [Global Mean Surface Temperature from CMIP6](https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/):
Start with `global_mean_surface_temp.ipynb` then feel free to explore the
Expand All @@ -14,14 +21,21 @@ provide a sampling of what Xarray can be used for.
1. [National Water Model Streamflow Analysis](https://gallery.pangeo.io/repos/rsignell-usgs/esip-gallery/):
Start with `02_National_Water_Model.ipynb` then feel free to explore the rest
of the notebooks.

## Satellite Data

1. [Planetary Computer Examples](https://github.com/microsoft/PlanetaryComputerExamples)
1. The [Project Pythia Cookbooks](https://cookbooks.projectpythia.org/) are a great collection of
resources showcasing use of Xarray with real world datasets.
1. [Examples using the Microsoft Planetary Computer](https://github.com/microsoft/PlanetaryComputerExamples)

## Bayesian Statistical Modeling

1. [Xarray and PyMC3](https://mybinder.org/v2/gh/pymc-devs/pymc3/main?filepath=%2Fdocs%2Fsource%2Fnotebooks):
Start with `multilevel_modeling.ipynb` then feel free to explore the rest of
the notebooks. Also checkout [Arviz](https://arviz-devs.github.io/arviz/)
which uses Xarray as its data model.

## Genomics

1. [From "data analysis for genomic surveillance of African malaria vectors" ](https://anopheles-genomic-surveillance.github.io/workshop-5/module-1-xarray.html)

## Electrophysiology

1. ["Analyzing intracranial electrophysiology data with xarray"](https://chrisholdgraf.com/blog/2019/2019-10-22-xarray-neuro/)