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Diff for: CONTRIBUTING.md

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# Contributing to RasterFrames
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Thanks for your interest in this project.
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Community contributions are always welcome. To get started, please review this document,
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the [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and reach out to
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us on [gitter](https://gitter.im/locationtech/rasterframes) so the community can help you get started!
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## Project Description
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LocationTech RasterFrames brings the power of Spark DataFrames to geospatial
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raster data, empowered by the map algebra and tile layer operations of
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GeoTrellis. The underlying purpose of RasterFrames is to allow data scientists
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and software developers to process and analyze geospatial-temporal raster data
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with the same flexibility and ease as any other Spark Catalyst data type. At its
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core is a user-defined type (UDT) called TileUDT, which encodes a GeoTrellis
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Tile in a form the Spark Catalyst engine can process. Furthermore, we extend the
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definition of a DataFrame to encompass some additional invariants, allowing for
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geospatial operations within and between RasterFrames to occur, while still
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maintaining necessary geo-referencing constructs.
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The official home of RasterFrames under the Eclipse Foundation may be found here:
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* https://projects.eclipse.org/projects/locationtech.rasterframes
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* Build documentation: `sbt makeSite`
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* Spark shell with RasterFrames initialized: `sbt console`
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## Contribution Process
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RasterFrames uses GitHub pull requests (PRs) for accepting contributions.
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Please fork the repository, create a branch, and submit a PR based off the `master` branch.
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During the PR review process comments may be attached. Please look out for comments
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and respond as necessary.
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## Contact
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Help, questions and community dialog are supported via Gitter:

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[![Join the chat at https://gitter.im/locationtech/rasterframes](https://badges.gitter.im/locationtech/rasterframes.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
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_RasterFrames™_ brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of [GeoTrellis](https://geotrellis.io/).
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RasterFrames® brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. The recent explosion of EO data from public and private satellite operators presents both a huge opportunity as well as a challenge to the data analysis community. It is _Big Data_ in the truest sense, and its footprint is rapidly getting bigger.
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RasterFrames provides a DataFrame-centric view over arbitrary raster data, enabling spatiotemporal queries, map algebra raster operations, and compatibility with the ecosystem of Spark ML algorithms. By using DataFrames as the core cognitive and compute data model, it is able to deliver these features in a form that is both accessible to general analysts and scalable along with the rapidly growing data footprint.
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<img src="docs/src/main/paradox/RasterFramePipeline.png" width="600px"/>
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Please see the [Getting Started](http://rasterframes.io/getting-started.html) section of the Users' Manual to start using RasterFrames.
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## Documentation
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## User Resources
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* [RasterFrames Users' Manual](http://rasterframes.io/)
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* [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebooks/)
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* [Gitter Channel](https://gitter.im/locationtech/rasterframes)
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* [Submit an Issue](https://github.com/locationtech/rasterframes/issues)
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## Contributing
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* [Users' Manual](http://rasterframes.io/)
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* [API Documentation](http://rasterframes.io/latest/api/index.html)
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* [List of available UDFs](http://rasterframes.io/latest/api/index.html#org.locationtech.rasterframes.RasterFunctions)
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* [RasterFrames Jupyter Notebook Docker Image](https://hub.docker.com/r/s22s/rasterframes-notebooks/)
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Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and reach out to us on [gitter](https://gitter.im/locationtech/rasterframes) so the community can help you get started!
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## Build instruction
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RasterFrames is part of the LocationTech Stack.
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First, you will need [sbt](https://www.scala-sbt.org/).
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<img src ="pyrasterframes/src/main/python/docs/static/rasterframes-locationtech-stack.png" width="600px" />
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Download the source from GitHub.
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It is written in Scala, but with Python bindings. If you wish to contribute to the development of RasterFrames, or you
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wish to build it from scratch, you will need [sbt](https://www.scala-sbt.org/). Then clone the repository from GitHub.
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```bash
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git clone https://github.com/locationtech/rasterframes.git
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cd rasterframes
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```
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To publish to hyou Ivy local repository:
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To publish to your local repository:
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```bash
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sbt publishLocal
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sbt makeSite
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```
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The `pyrasterframes` build instructions are located at [pyrasterframes/src/main/python/README.md](pyrasterframes/src/main/python/README.md)
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Additional, Python sepcific build instruction may be found at [pyrasterframes/src/main/python/README.md](pyrasterframes/src/main/python/README.md)
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## Copyright and License
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## Scala
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The latest Scala API documentation is available here:
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* @ref:[Scala API Documentation](http://rasterframes.io/latest/api/index.html)
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### Step 1: Load the catalog
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```scala

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