Software and Data#

On this page you’ll find information about the computing environment and datasets that we’ll be using in this tutorial.

Computing environment#

Below, you’lll see a list of the python libraries we’ll be using in this example. This is the full list of libraries across all notebooks.

import os
import json
import urllib.request
import numpy as np
import xarray as xr
import rioxarray as rxr
import geopandas as gpd
import pandas as pd

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

from shapely.geometry import Polygon
from shapely.geometry import Point
import as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy
import cartopy.feature as cfeature

from geocube.api.core import make_geocube
import flox
import s3fs

This tutorial also uses several functions that are stored in the script It is located in the github repo for this tutorial. If you clone the repo, it should be available to import to the tutorial notebooks. Otherwise, if you would like to use, download the script and move it to your working directory.

Running tutorial on the cloud#

This link will launch a preconfigured jupyterlab environment on

Running tutorial material locally#

To run the notebooks contained in this tutorial on your local machine

create the itslivetools_env conda environment (conda env create -f environment-unpinned.yml) based on the environment.yml file here. This should work on any platform (linux, osx, windows) and will install the latest versions of all dependencies.

Alternatively, the code repository for this tutorial ( also contains “lock” files for Linux (conda-linux-64.lock.yml) and MacOS (conda-osx-64.lock.yml) that pin exact versions of all required python packages for a reproducible computing environment.


The velocity data that we’ll be using is from the ITS_LIVE dataset. This dataset contains global coverage of land ice velocity data at various temporal frequencies and in various formats. Follow the link to explore the data that’s available for a particular region you may be interested in. ITS_LIVE has multiple options for data access; this example will focus on using zarr datacubes that are stored in s3 buckets on AWS.

ITS_LIVE velocity data is accessed in a raster format and the data covers a large swath of terrain covering land that is glaciated and non-glaciated. We want to select just the pixels that cover glaciated surfaces; to do this, we use glacier outlines from the Randolph Glacier Inventory. The RGI region used in this tutorial is made available as a GeoParquet file in the tutorial repository.

Head to the next page to see how we start accessing and working with this data