EarthPy: Software Submission for Review

Submitting Author: Leah Wasser (@lwasser)
All current maintainers: (@lwasser, @nkorinek, @mbjoseph, @joemcglinchy, @jlpalomino)
Package Name: earthpy One-Line Description of Package: A package built to support working with spatial data using open source python Repository Link: https://github.com/earthlab/earthpy Version submitted: 0.7 Editor: @luizirber Reviewer 1: @HaoZeke Reviewer 2: @sgillies Archive: JOSS DOI: Version accepted: v 0.7.5 Date accepted (month/day/year): 11/06/2019

Paste the full DESCRIPTION file inside a code block below:

Python is a generic programming language designed to support many different applications. Because of this, many commonly performed spatial tasks for science including plotting and working with spatial data take many steps of code. EarthPy takes advantage of functionality developed for raster data (rasterio) and vector data (geopandas) and simplifies the code needed to :

    Stack raster bands from data such as Landsat into an easy to use numpy array
    Work with masks to set bad pixels such a those covered by clouds and cloud-shadows to NA (mask_pixels())
    Plot rgb (color), color infrared and other 3 band combination images (plot_rgb())
    View histograms of sets of raster
    Create discrete (categorical) legends

EarthPy also has an io module that allows users to

    Quickly access pre-created datasubsets used in the earth-analytics courses hosted on www.earthdatascience.org
    Download other datasets that they may want to use in their workflows.

Scope

Please indicate which category or categories this package falls under:
- [ ] Data retrieval
- [ ] Data extraction
- [ ] Data munging
- [ ] Data deposition
- [ ] Reproducibility
- [x] Geospatial
- [ ] Education
- [ ] Data visualization*

* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see this section of our guidebook.

Explain how the and why the package falls under these categories (briefly, 1-2 sentences): This package wraps around rasterio and geopandas to make working with geospatial data easier.
Who is the target audience and what are scientific applications of this package?
The target audience is people working with different types of raster and vector data in python. There are many operations that are often repeated by users that require a lot of code. This package simplifies these operations so users can quickly explore their data.
Are there other Python packages that accomplish the same thing? If so, how does yours differ? Not that we know of! this is why we created this package.
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has an OSI approved license
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions.
[x] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

Publication options
[x] Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements](https://joss.readthedocs.io/en/latest/submitting.html#submission-requirements). Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements](https://joss.readthedocs.io/en/latest/submitting.html#submission-requirements): "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements](https://joss.readthedocs.io/en/latest/submitting.html#what-should-my-paper-contain) with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*

Code of conduct

[x] I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

P.S. Have feedback/comments about our review process? Leave a comment here

NOTE: I am actually not sure what research application means according to Joss!! may followup with Arfon on this.

pyOpenSci / software-submission