Ecosystem

PyGMT provides a Python interface to the Generic Mapping Tools (GMT), which is a command line program that provides a wide range of tools for manipulating geospatial data and making publication-quality maps and figures. 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, PyGMT also relies on several optional packages to provide additional functionality for users.

This page was adapted from GeoPandas’s Ecosystem page.

PyGMT dependencies

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

NumPy*

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

pandas*

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

Xarray*

Xarray is an open source project and 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

IPython provides a rich toolkit to help you make the most of using Python interactively. Its main components are a powerful interactive Python shell and a Jupyter kernel to work with Python code in Jupyter notebooks and other interactive frontends.

PyGMT relies on IPython to provide a rich interactive experience in Jupyter notebooks.

GeoPandas

GeoPandas is an open source project to make working with geospatial data in Python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types. Geometric operations are performed by Shapely. GeoPandas further depends on pyogrio for file access and Matplotlib for plotting.

PyGMT doesn’t directly rely on GeoPandas, but provides support of GeoPandas’s two main data structure, geopandas.GeoDataFrame and geopandas.GeoSeries, which can be directly used in data processing and plotting functions/methods of PyGMT.

contextily

contextily is a small Python package to retrieve tile maps from the internet. It can add those tiles as basemap to matplotlib figures or write tile maps to disk into geospatial raster files.

In PyGMT, pygmt.datasets.load_tile_map and pygmt.Figure.tilemap rely on it.

rioxarray

rioxarray is a geospatial Xarray extension powered by rasterio. Built on top of rasterio, it enables 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 rioxarray in several aspects:

1. To save multi-band rasters to temporary files in GeoTIFF format, to support processing and plotting 3-D xarray.DataArray images.

2. To write CRS information to the xarray.DataArray objects.

3. 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

Apache Arrow is a development platform for in-memory analytics. It contains a set of technologies that enable big data systems to process and move data fast. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. The Arrow Python bindings (also named “PyArrow”) have first-class integration with NumPy, pandas, and built-in Python objects. They are based on the C++ implementation of Arrow.

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.

PyGMT ecosystem

Various packages rely on PyGMT for geospatial data processing, analysis, and visualization. Below is an incomplete list (in no particular order) of tools which form the PyGMT-related ecosystem.

Note

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