This was the second talk I gave at Scipy about GMT/Python, a wrapper that I'm building for the Generic Mapping Tools. It showed the progress that we made in the past year, what our struggles and successes were, and our plans for the future.
I gave a live demo using the notebook from try.gmtpython.xyz
Scipy records all of the presentations and makes them available on YouTube. Here is the video of mine:
I made the slides in Google Drive. You can see them below:
We are building a Python wrapper for the Generic Mapping Tools (GMT), a set of command-line programs used across the Earth, Atmospheric, and Ocean Sciences to process and visualize geographic data. At Scipy 2017, we presented the project goals and an initial prototype. The feedback received led to improvements in the design of the library, mainly the creation of an object-oriented API. We will present the newest developments including support for numpy arrays and pandas Dataframes, interactive visualization in the Jupyter notebook using NASA WorldWind, and more. Once again, we seek feedback from the community to guide us moving forward.
The GMT/Python library has been in development for
approximately 1 year. Much of the current design of the library was inspired by
the feedback that we received following our presentation at Scipy
2017. Since then, we
have been implementing this design, establishing a solid low-level API on which
to build the rest of the library, and exploring new ways to interface with the
Jupyter notebook. In this talk, we will present the current state of the
project, including: the design of the low-level wrapper for the GMT C API (the
gmt.clib.LibGMT class); the new object-oriented plotting API (the
gmt.Figure class); the support for numpy arrays and pandas Dataframes; using
GMT's built-in topography grids and sample datasets; interactive visualization
library; and more. An online demo of these
features is available through the Binder service at http://try.gmtpython.xyz.
We will also share the lessons learned from using ctypes to build the wrapper
and the changes that were required in the C API to make the wrapping process as
smooth as possible when porting to other languages. Finally, we will layout our
development plans and solicit feedback and contributions to help guide the
future of the project.
GMT has an extensive feature set that goes well beyond data visualization. It has sophisticated algorithms for processing and interpolating data in Cartesian and spherical coordinates that is still unmatched in the Scipy ecosystem. GMT is also the basis for specialized software like MB-System for processing and visualizing bathymetry and backscatter imagery data derived from multibeam, interferometry, and sidescan sonars and GMTSAR for processing Interferometric Synthetic-Aperture Radar (InSAR) data. A well designed wrapper for the GMT C API is the first step to bring these powerful tools to the Scipy community. The data visualization landscape in Python has grown immensely in the past few years with the advent of Boheh, Altair, Cartopy, Holoviews, etc. GMT/Python can help diversify this ecosystem and bring important lessons learned during the 28+ years of continuous development of GMT.