The Generic Mapping Tools

A Curated List of Python Resources for Earth Sciences

Please do not feed the models

Probabilistic Numerics in Python.

Deep Probabilistic Koopman: long-term time-series forecasting under quasi-periodic uncertainty

Examples of PyMC models, including a library of Jupyter notebooks.

Repository with the code and data for all figures from Chapter 9 of the IPCC AR6

Figure and data generation for Chapter 7 of the IPCC's Sixth Assessment Report, Working Group 1 (plus assorted other contributions)

Course notes for CS228: Probabilistic Graphical Models.

Tutorials in Jupyter Notebook for MintPy

Material for Philipp Hennig's course: Probabilistic Machine Learning, at Tubingen

LocalizeSL: Sea-level localization code for Kopp et al. (2014) projections framework

An Open Source Machine Learning Framework for Everyone

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Matlab Implementation of SM2RAIN code

Deep Learning for humans

InSAR Scientific Computing Environment version 2

Tigramite is a time series analysis python module for causal discovery. The Tigramite documentation is at

GMTSAR

Maximum Covariance Analysis in Python

Code for selected models from 'Statistical modeling of rates and trends in Holocene relative sea level' in Quaternary Science Reviews.

Notebooks for learning deep learning

Emulator of multi-model land ice projections: Edwards et al. (2021), Nature, Projected land ice contributions to twenty-first-century sea level rise.

Python script to run the copan:DISCOUNT model

Unified Complex Network and Recurrence Analysis Toolbox