Training Resources

The following external resources provide assistance in using the Analytics Engine platform.

climakitae

climakitae is a data processing Python library developed by the Analytics Engine team. It provides an analytical toolkit for working with downscaled CMIP6 data, including selecting models for a specific metric, deriving threshold-based assessments, and common data operations such as temporal and spatial aggregation and downloading timeseries for a weather station.

The documentation for the Analytic Engine’s open-source Python package, climakitae, may be viewed at climakitae.readthedocs.io. The public repository for the code can be found on GitHub: climakitae

climakitaegui

climakitaegui is a companion package to climakitae that adds GUI functionality for use directly within the Analytics Engine JupyterHub, making it easier to interact with data without writing code.

The documentation for the add-on Python package, climakitaegui, may be viewed at climakitaegui.readthedocs.io. The public repository for the code can be found on GitHub: climakitaegui

Pangeo

The Analytics Engine JupyterHub is built using the Pangeo ecosystem along with the two Python packages developed by the Analytics Engine team: climakitae and climakitaegui. Pangeo provides Python tools (including xarray, Dask, and JupyterLab) and cloud infrastructure that enables near-instantaneous access and fast processing of large climate and geoscience datasets.

Notebooks

The code for the Analytics Engine notebooks in development are publicly available and can be found in the Cal-Adapt: Analytics Engine - Notebooks Github repository.

Code Contributions

We welcome contributions of analyses using Analytics Engine data and climakitae Python library tools to demonstrate specific applications for climate data in California. Please see our contribution guidelines for guidance on contributing example analyses to the Analytics Engine.

Python

For help with getting started with the Python programming language, we recommend the Hitchhiker’s Guide to Python as well as the Pangeo Technical Architecture and Pangeo Guide for Scientists.

JupyterLab

For help with getting started with Jupyter Notebooks, we recommend the official JupyterLab documentation.