!lsb_release -a
Get started with GRASS GIS in Google Colab
What is Colab?
Perhaps you have heard of Google Colaboratory or simply Colab. This is a hosted Jupyter Notebook service that requires no setup or configuration to use and provides free access to computing resources, including GPUs and TPUs. Colab is especially well suited to machine learning, data science, and education. Furthermore, it allows easy sharing of workflows which facilitates reproducibility.
Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them.
See Colab’s FAQ for more details: https://research.google.com/colaboratory/faq.html and follow the Google Colab blog in Medium at https://medium.com/google-colab.
Why GRASS GIS in Colab?
Since Colab offers Jupyter notebooks in a Linux environment it is really easy to install or even compile GRASS GIS there. Also, because of the integration with Google Drive, it is a great resource to run our workflows in the cloud and export the results or keep our GRASS projects and code there. This clearly facilitates teaching workshops or courses since attendants do not need to install or download anything on their own machines.
There are a couple of things to consider when working with GRASS GIS within Colab though. Users will need to install GRASS GIS every time they start a new working session or notebook. Furthermore, whatever files users download within Colab will last only during the current session. If the runtime gets disconnected because of inactivity, downloaded data and outputs created within Colab, will be lost too. If users instead, mount their own Google drive, download data and create their GRASS projects there, those will be preserved even if the runtime is disconnected or the session closed.
Install GRASS GIS in Colab
Start at https://colab.research.google.com/ and create a new notebook. Let’s first print system description to know where are we:
At the time of writing this tutorial, Colab has Linux Ubuntu 22.04.3 LTS. So we add the ppa:ubuntugis repository, update and install GRASS GIS. It might take a couple of minutes according to the resources available.
!add-apt-repository -y ppa:ubuntugis/ubuntugis-unstable
!apt update
!apt-get install -y grass-core grass-dev
print("INSTALLATION COMPLETE")
Check that GRASS GIS is installed by asking which version is there.
!grass --config version
Let’s add the GRASS python packages to PATH to be able to import the grass.script
and grass.jupyter
libraries.
# Import standard Python packages we need
import sys
import subprocess
# Ask GRASS GIS where its Python packages are to be able to run it from the notebook
sys.path.append("grass", "--config", "python_path"], text=True).strip()
subprocess.check_output([ )
# Import the GRASS GIS packages
import grass.script as gs
import grass.jupyter as gj
Download sample data
By default we have access to the /content
folder within Colab, and any data we download will be placed there. We can change that of course, it is just a Linux file system. In any case, we should bare in mind that whatever data we download within Colab, will disappear if the runtime gets disconected because of inactivity or once we close the Colab session.
Let’s get the North Carolina sample dataset into Colab to show a data download workflow.
!wget -c https://grass.osgeo.org/sampledata/north_carolina/nc_spm_08_grass7.zip -O nc.zip
We unzip the downloaded file in /content
!unzip nc.zip
and finally check it is indeed there:
import os
# List files and directories
os.listdir()
You should see nc_spm_08_grass7 sample dataset, which is a GRASS project.
Start GRASS in Colab
We have GRASS GIS installed and a sample project to play around, so we are ready to start GRASS GIS within the North Carolina project.
# Start GRASS in default project mapset
= gj.init("nc_spm_08_grass7") session
Let’s show the current GRASS GIS settings and check if the session actually works:
# GRASS was started in default project mapset called PERMANENT
gs.gisenv()
Just as an example, we will list the raster maps and display one of them using the InteractiveMap class.
type="raster") gs.list_grouped(
= gj.InteractiveMap()
m "landclass96")
m.add_raster( m.show()
Connect Colab with Google Drive
If we do not want to loose our GRASS projects when closing the Colab notebook, we can connect Colab with our Google Drive and upload, download or create our projects there. To be able to do any of that, we need to mount our drive first (i.e., similar to what we do with external drives). We first import the drive
library.
from google.colab import drive
Then, we define the mounting point. Running the cell below triggers a dialog to grant Colab access to our drive. It is possible to change accounts, too. Once that is complete, we will have access to everything we have in our GDrive folders and we can browse the content either with commands or from the left panel in the Colab notebook.
"/content/drive") drive.mount(
We can also mount our drive directly from the Colab interface as shown below:
Once the GDrive is mounted, we can either create a new project and start GRASS GIS there as shown above or start GRASS within an existing GRASS project in GDrive. Importantly, we can then process and analyse our data so that our data will remain in GDrive for the next time.
Create a new GRASS project in Google Drive
To create a new project we can use the create_project
function from the grass.script library as shown in the GRASS and Python tutorial. Let’s, for example, create a project with the EPSG code option:
"/content/drive/MyDrive/grassdata/latlong_wgs84", epsg="4326") gs.create_project(
Start GRASS GIS in an existing project
Either if you just created the project and want to start GRASS GIS there or if you already have your GRASS projects in GDrive, you just need to start GRASS wherever the project is, as shown above, given that GRASS GIS has been installed in the Colab session and libraries imported.
Cool, ah?! Enjoy!
The development of this tutorial was funded by the US National Science Foundation (NSF), award 2303651.