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Making nice maps for posters with Python 🗺️+🐍

Making nice maps for posters with Python 🗺️+🐍

To communicate your results effectively to people 🧑‍🤝‍🧑, you may come to a point where making maps are needed.

These maps could be created for a conference poster, a presentation, or even for a social media 🐦 post!

In this tutorial 🧑‍🏫, we’ll focus on making basic 2D maps, and by the end of this lesson, you should be able to:

  • Set up basic map elements - basemap, overview map, title and axis annotations 🌐

  • Plot raster data (images/grids) and choose a Scientific Colour Map 🌈

  • Plot vector data (points/lines/polygons) with different styles 🗠

  • Save and export your map into a suitable format for your audience 😎

🎉 Getting started

Once you have an idea for what to map, you will need a way to draw it 🖌️.

There are plenty of ways to make maps 🗾, from pen and paper to Photoshop.

We’ll start by loading some of these tools, that help us to process and visualize our data 📊.

import icepyx as ipx  # for downloading and loading ICESat-2 data
import pygmt  # for making geographical maps and figures
import rioxarray  # for performing geospatial operations like reprojection
import xarray as xr  # for working with n-dimensional data

Just to make sure we’re on the same page, let’s check that we’ve got the same versions.

print(f"icepyx version: {ipx.__version__}")
pygmt.show_versions()

icepyx version: 0.6.2
PyGMT information:
  version: v0.6.0
System information:
  python: 3.9.10 | packaged by conda-forge | (main, Feb  1 2022, 21:24:11)  [GCC 9.4.0]
  executable: /usr/share/miniconda3/envs/hackweek/bin/python
  machine: Linux-5.13.0-1021-azure-x86_64-with-glibc2.31
Dependency information:
  numpy: 1.22.3
  pandas: 1.4.1
  xarray: 0.21.1
  netCDF4: 1.5.8
  packaging: 21.3
  ghostscript: 9.54.0
  gmt: 6.3.0
GMT library information:
  binary dir: /usr/share/miniconda3/envs/hackweek/bin
  cores: 2
  grid layout: rows
  library path: /usr/share/miniconda3/envs/hackweek/lib/libgmt.so
  padding: 2
  plugin dir: /usr/share/miniconda3/envs/hackweek/lib/gmt/plugins
  share dir: /usr/share/miniconda3/envs/hackweek/share/gmt
  version: 6.3.0

A note about layers 🍰

What do you do when you want to plot several datasets overlapping the same geographical area? 🤔

A general rule of thumb is to have the raster images on the ‘bottom’ 👎🏽, and the vector data plotted on ‘top’ 👍🏽.

Think of it like making a fancy birthday cake 🎂, starting with the dense cake flour (raster), and decorating the colourful icing on top!

0️⃣ The data

Download ATL14 Gridded Land Ice Height 🏔️

This is a 125m ATLAS/ICESat-2 L3B raster grid product over the cryosphere (ice) regions.

Specifically, this includes places like:

  • Antarctica (AA) 🇦🇶

  • Alaska (AK) 🏴󠁵󠁳󠁡󠁫󠁿

  • Arctic Canada North (CN) 🇨🇦

  • Arctic Canada South (CS) 🇨🇦

  • Greenland and peripheral ice caps (GL) 🇬🇱

  • Iceland (IS) 🇮🇸

  • Svalbard (SV) 🇸🇯

  • Russian Arctic (RA) 🇷🇺

🔖 References:

# Set up an instance of an icepyx Query object
# for a Region of Interest located over Iceland
region_iceland = ipx.Query(
    product="ATL14",  # ICESat-2 Gridded Annual Ice Product
    spatial_extent=[-28.0, 62.0, -10.0, 68.0],  # minlon, minlat, maxlon, maxlat
)

Inside of the region_iceland class instance are attributes that can be accessed using dot ‘.’ something.

⏩ Type out region_iceland. and press Tab to see some of them!

# Check that we've selected the right region
region_iceland.visualize_spatial_extent()