HMC Lab is a numerical laboratory for research in Bayesian seismology, written in Python and Julia. Jump to Docker one-command setup.
- Website: https://hmclab.science
- Python documentation: https://python.hmclab.science
- Source code: https://github.com/larsgeb/hmclab
- Docker image: https://hub.docker.com/repository/docker/larsgebraad/hmclab
- Bug reports: https://github.com/larsgeb/hmclab/issues
It provides all the ingredients to set up probabilistic (and deterministic) inverse problems, appraise them, and analyse them. This includes a plethora of prior distributions, different physical modelling modules and various MCMC (and other) algorithms.
In particular it provides prior distributions, physics and appraisal algorithms.
Prior distributions:
- Normal
- Laplace
- Uniform
- Arbitrary composites of other priors
- Bayes rule
- User supplied distributions
Physics:
- Linear equations
- Straight ray tomography
- 3d source location
- 2d elastic full-waveform inversion
- User supplied physics
Algorithms:
- Hamiltonian Monte Carlo (and variations)
- Random Walk Metropolis Hastings
- Stein Variational Gradient Descent
- Gradient descent
- Interfaces to non-linear optimization methods from SciPy
- Animated versions of various algorithms
To get staerting with the tutorial and example notebooks, one can use a single command in Docker. This will pull a Docker image based on the Jupyter Datascience stack. The final container is approximately 5GB.
docker run -p 9123:9123 larsgebraad/hmclab \
start-notebook.sh --NotebookApp.token='hmclab' \
--NotebookApp.port='9123' --LabApp.default_url='/lab/tree/Home.ipynb'Then either copy-past the link from your terminal, or navigate manually to http://127.0.0.1:9123/lab/tree/Home.ipynb?token=hmclab.
All tutorial notebooks can also be accessed online in a non-interactive fashion. Simply use https://python.hmclab.science or use the following links:
Tutorials:
- Getting started.ipynb
- Tuning Hamiltonian Monte Carlo.ipynb
- Separate priors per dimension.ipynb
- Creating your own inverse problem.ipynb
- Running parallel Markov chains.ipynb
Demos:
- Sampling linear equations
- Sampling sparse linear equations
- Locating quakes on Grimsvötn, Iceland
- Elastic 2d FWI
For full installation instructions, including creating a proper Python environment, see the installation instructions.
Start with making sure that you have HDF5 or h5py installed properly.
Directly to your environment:
pip install -e git+git@github.com:larsgeb/hmclab.git@master#egg=hmclab
From the project root directory:
pip install -e .
If you want to develop within this repo, we recommend a few extra packages. They can also be installed using pip.
In Bash:
pip install -e git+git@github.com:larsgeb/hmclab.git@master#egg=hmclab[dev] # from github repo
pip install -e .[dev] # from local clone
... or Zsh (which requires escapes for brackets):
pip install -e git+git@github.com:larsgeb/hmclab.git@master#egg=hmclab\[dev\] # from github repo
pip install -e .\[dev\] # from local clone