PyGMT Ecosystem

The PyGMT ecosystem consists of the packages that PyGMT depends on and the packages that depend on PyGMT. Besides GMT itself, PyGMT integrates well with the scientific Python ecosystem, with NumPy for its fundamental array data structure, pandas for tabular data I/O and xarray for raster grids/images/cubes I/O. In addition to these core dependencies, it also relies on several optional packages to provide additional functionality for users. In turn, a growing number of packages build on PyGMT for geospatial data processing, analysis, and visualization.

PyGMT dependencies

The PyGMT ecosystem. This figure was originally published in the PyGMT paper in G-Cubed. The full publication is released under CC BY-NC 4.0. No modifications were made.

An asterisk (*) after the package name indicates the package is a required dependency of PyGMT.

NumPy*

The fundamental package for scientific computing in Python, providing a multidimensional array object and an assortment of routines for fast operations on arrays.

pandas*

A Python package providing fast, flexible, and expressive data structures designed to make working with tabular data easy and intuitive.

xarray*

A Python package that introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which allows for more intuitive, more concise, and less error-prone user experience.

IPython

A rich toolkit for using Python interactively, including a powerful interactive Python shell and a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends. PyGMT relies on it to provide a rich interactive experience in Jupyter notebooks.

GeoPandas

A Python package that extends the datatypes used by pandas to allow spatial operations on geometric types. PyGMT doesn’t directly rely on it, but supports its two main data structures, geopandas.GeoDataFrame and geopandas.GeoSeries, in data processing and plotting functions/methods.

contextily

A small Python package to retrieve tile maps from the internet. These tiles can be added as background of a map or saved to disk into geospatial raster files. In PyGMT, pygmt.datasets.load_tile_map and pygmt.Figure.tilemap rely on it.

rioxarray

A geospatial xarray extension powered by rasterio, enabling seamless reading, writing, and manipulation of multi-dimensional arrays with geospatial attributes such as coordinate reference systems (CRS) and spatial extent (bounds). PyGMT relies on it in in several aspects:

To save multi-band rasters to temporary files in GeoTIFF format, to support processing and plotting 3-D xarray.DataArray images.
To write CRS information to the xarray.DataArray objects.
To reproject raster tiles to the target CRS in pygmt.datasets.load_tile_map.

Note

We’re working towards avoiding temporary files when processing/plotting multi-band rasters in PR #3468.

PyArrow

The Python bindings for Apache Arrow, a development platform for in-memory analytics that specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware.

Note

If you have PyArrow installed, PyGMT does have some initial support for pandas.Series and pandas.DataFrame objects with Apache Arrow-backed arrays. Specifically, only uint/int/float, date32/date64 and string types are supported for now. Support for Duration types and GeoArrow geometry types is still a work in progress. For more details, see issue #2800.

Packages depending on PyGMT

Various packages rely on PyGMT for geospatial data processing, analysis, and visualization. Below is an incomplete list (in no particular order) of these tools.

Note

If your package relies on PyGMT, please let us know or add it by yourself.

This page was adapted from GeoPandas’s Ecosystem page.