PyGMT: Software Submission for Review

Submitting Author: Name (@github_handle)
Package Name: PyGMT
One-Line Description of Package: A Python interface for the Generic Mapping Tools
Repository Link: https://github.com/GenericMappingTools/pygmt
Version submitted:
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD

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Description

• Include a brief paragraph describing what your package does:

PyGMT is a library for processing geospatial and geophysical data and making publication quality maps and figures. It provides a Pythonic interface for the Generic Mapping Tools (GMT), a command-line program widely used in the Earth Sciences.

Scope

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

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

• Explain how the and why the package falls under these categories (briefly, 1-2 sentences):

  • Data munging : Several data processing modules are available to work on tabular and grid data (https://www.pygmt.org/v0.4.0/api/index.html#data-processing) including functions like blockmedian, surface, grdtrack, etc
  • Data visualization : Plotting publication quality maps and figures is a strong point of PyGMT (https://www.pygmt.org/v0.4.0/api/index.html#plotting). There is a comprehensive set of tools for plotting 2D and 3D data, both geospatial and non-geospatial. It is also possible to add standard map elements like a colorbar or legend, and make multi-panel subplot figures.
  • Geospatial : PyGMT is inherently a geospatial package that can handle both vector and raster data. Several map projections are supported (https://www.pygmt.org/v0.4.0/projections/index.html), and there is support for plotting shapefiles and GeoTiff/NetCDF files.

• Who is the target audience and what are scientific applications of this package?

  • The primary target audience of PyGMT is the geoscience community, including anybody working on fields like Earth Observation, Geophysics, Oceanography, Seismology, Planetary Sciences, etc. The package offers a way for users to perform general geoprocessing tasks and create high quality illustrations of their results for posters or publications.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

  Yes, there are several geospatial Python packages such as:

  • cartopy for plotting vector data (matplotlib based)
  • geopandas for working with vector data
  • rasterio, rioxarray and xarray-spatial for working with raster data

  These tools are typically focused on one thing only (e.g. plotting maps, vector data, raster data). PyGMT allows users to mix vector and raster data easily, so that users can:

  • Produce a map with points/lines/polygons and raster grids in the same plot, e.g. plot weather station points on top of a raster Digital Elevation Model, while including map elements (e.g. scalebar, compass arrow, colorbar, legend, inset overview maps etc)
  • Extract a series of elevation values along a transect line (vector) from a NetCDF/GeoTiff grid (raster) using grdtrack
  • Convert a table of xyz points (vector) to a 2D grid (raster) using surface via a spline-based interpolation method.

  PyGMT integrates with the PyData ecosystem. It allows users to process and plot data stored in NumPy arrays, Pandas DataFrames, Xarray DataArray/Datasets, GeoPandas GeoDataFrames, as well as from standard file formats like CSV and GeoTiff/NetCDF files. There are also plans to integrate with other scientific libraries like ObsPy (https://github.com/GenericMappingTools/pygmt/issues/967) in the future.

  However, PyGMT can only produce static plots unlike packages like Geoviews which allow for interactive plotting (e.g. panning and zooming). It is also non-trivial to scale out data processing tasks and/or plotting of big datasets (>10GB) that don't fit in RAM to multiple processors/clusters as with libraries like dask-geopandas because of limitations in the GMT C package that PyGMT wraps around.

• 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:

  https://github.com/pyOpenSci/software-review/issues/43

Technical checks

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

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

Publication options

• 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. 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: "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 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

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

• Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Code of conduct

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

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