Jupyter Notebooks

Jupyter notebooks provide executable examples of how to use nsidc-iceflow.

Prerequisites

The nsidc-iceflow notebooks are best approached with some familiarity with Python and its geoscience stack. If you feel like learning more about geoscience and Python, you can find great tutorials by CU Boulder’s Earth Lab here: Data Exploration and Analysis Lessons or by the Data Carpentry project: Introduction to Geospatial Concepts

Some Python packages/libraries that users may consider investigating include:

• icepyx: Library for ICESat-2 data users

• geopandas: Library to simplify working with geospatial data in Python (using pandas)

• h5py: Pythonic wrapper around the *HDF5 library

• matplotlib: Comprehensive library for creating static, animated, and interactive visualizations in Python

• vaex: High performance Python library for lazy Out-of-Core dataframes (similar to pandas), to visualize and explore big tabular data sets

nsidc-iceflow notebooks

• NSIDC Iceflow example provides an example of how to search for, download, and interact with ILATM1B v1 data for a small area of interest. This notebook also illustrates how to perform ITRF transformations to facilitate comparisons across datasets. To learn more about ITRF transformations, see the Applying Coordinate Transformations to Facilitate Data Comparison notebook.

• Using nsidc-iceflow with icepyx to Generate an Elevation Timeseries shows how to search for, download, and interact with a large amount of data across many datasets supported by nsidc-iceflow. It also illustrates how to utilize icepyx to find and access ICESat-2 data. Finally, the notebook provides a simple time-series analysis for elevation change over an area of interest across nsidc-iceflow supported datasets and ICESat-2.

Downloading nsidc-iceflow notebooks

Users may wish to try executing the nsidc-iceflow notebooks themselves. Iceflow notebooks can be downloaded from GitHub.