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Getting Started Guide (#96)
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CONTRIBUTING.md

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Welcome to `geocat-applications`! Thank you for your interest in contributing to this project! This guide
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describes how to contribute to `geocat-applications` and help us expand this reference for others.
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If you have any questions, you can also reach us by email at [[email protected]]([email protected])
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If you have any questions, please leave us a message on [GeoCAT Applications Issues](https://github.com/NCAR/geocat-applications/issues)
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or in [GeoCAT Applications Discussions](https://github.com/NCAR/geocat-applications/discussions). You
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can also reach us by email at [[email protected]]([email protected]).
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## How to Contribute
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GETTING_STARTED.md

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# Getting Started
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## What is GeoCAT Applications?
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```{include} README.md
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:language: md
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:start-after: <!-- readme_start -->
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:end-before: <!-- readme_end -->
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```
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## New to Python?
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If you are new to Python or want to learn more, [Project Pythia](https://projectpythia.org/) offers high-quality,
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geoscience-oriented Python tutorials
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[Pythia Foundations](https://foundations.projectpythia.org/landing-page.html) covers prerequisites that
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will be helpful to learn for GeoCAT Applications like:
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- Getting Started with Python
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- Getting Started with Jupyter
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- Working with NumPy and Pandas
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- Plotting with Matplotlib
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## New to Jupyter Notebooks?
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[Jupyter Notebooks](https://docs.jupyter.org/en/latest/what_is_jupyter.html#what-is-a-computational-notebook-anyway)
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provide an interactive interface to write, execute, and visualize code. Traditionally, code is written in a script and
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executed in order from beginning to end each time a script is run. Instead, Jupyter Notebooks allow you to divide code into cells that can be executed individually and in any order. Each cell in a Jupyter Notebook
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can be filled with executable code, markdown, or raw code. Jupyter Notebooks have grown in popularity because they offer a simple
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method to include plain English description alongside code to improve readability. In addition, because each cell
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can be run in any order, it is possible to organize a notebook to avoid having to constantly re-run parts of a script that might
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take longer to run than others, like like retrieving data. The outputs of each cell appear below the cell without the
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need for typing `print` or `plt.show()`.
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In addition to Pythia Foundations "[Getting Started with Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html)"
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, you can see the [Project Jupyter Documentation](https://docs.jupyter.org/en/latest/#what-is-a-notebook) for more information.
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or [try it out yourself](https://jupyter.org/try) online.
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Each tutorial and resource page of GeoCAT Applications is built from Jupyter notebooks, however, on the GeoCAT Applications
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webpage, each page is a static resource. For information about how to download GeoCAT application to run locally, please see
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the [Contributor Guide](https://ncar.github.io/geocat-applications/CONTRIBUTING.html).
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If you have any questions, please leave us a message on [GeoCAT Applications Issues](https://github.com/NCAR/geocat-applications/issues)
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or in [GeoCAT Applications Discussions](https://github.com/NCAR/geocat-applications/discussions). You can also reach us by email at
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README.md

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| **License** | [![License][license-badge]][repo-link] |
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| :----------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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## What is [GeoCAT Applications](https://ncar.github.io/geocat-applications/)?
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<!-- readme_start -->
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GeoCAT Applications is a community resource managed by the GeoCAT team. Inspired by
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[NCL Applications](https://www.ncl.ucar.edu/Applications/).
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in NCL in Python, including any algorithmic differences, guidance regarding replication under different
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conditions or circumstances, and any other relevant comparisons between the NCL and Python functionality
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This is part of the GeoCAT team's commitment to NCL and NSF NCAR's [Pivot to Python](https://www.ncl.ucar.edu/Document/Pivot_to_Python/)
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These pages assume that the user has a working knowledge of NCL and are looking for transitional
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resources for specific functions. They also are not intended to be a comprehensive explanation of
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the Python recommendations, but rather a guide for users who are already familiar with the NCL
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function and are looking for “equivalent” Python code
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<!-- readme_end -->
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## Contributing
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If you are interesting in contributing to `geocat-applications`, please check out our

index.rst

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`NCL Applications <https://www.ncl.ucar.edu/Applications/>`_ page, this is designed to be a quick reference
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demonstrating capabilities within the Scientific Python Ecosystem that may be relevant to your geoscience workflows.
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New to Python or GeoCAT Applications? Check out the `Getting Started <GETTING_STARTED.md>`_ guide!
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Python Examples
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---------------
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If you're looking for NCL to Python examples, please visit :ref:`ncl_applications`.
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.. toctree::
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:maxdepth: 2
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:hidden:
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:caption: Overview
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Getting Started <GETTING_STARTED.md>
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.. toctree::
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:maxdepth: 2
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:hidden:

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