celeri is a python-based package designed to image earthquake cycle activity including the spatial and time varying fault coupling across geometrically complex fault systems at large scales. It features:
- Friendly Jupyter notebook examples
- Web-based model building with
celeri_ui - Graphical comparisions of model results with
result_manager - 3D visualzation of model geometry and results with
parsli - Fast and automated block closure on the sphere
- Small memory footprint (via distance weighted eigenmodes)
- Blazingly fast elastic calculations (via Ben Thompson's C-based triangular dislocation calculations)
- Easy IO with standard file types (.csv, .json, .hdf5)
- Rapid model diffs with
celeri_report.py
To set up a development conda environment, install pixi and run the following command in the celeri folder.
pixi shellAlternatively, run the following commands in the celeri folder.
conda config --prepend channels conda-forge
conda env create
conda activate celeri
pip install --no-use-pep517 -e .
From here you can launch model runs with celeri_solve.
To run notebooks from VSCode:
- cd to the
celerifolder. - Use
code .to start VSCode form the command line. - Navigate to the notebook you'd like to run.
- Click on the python environment selector near the upper right hand corner of the VSCode window.
- Select the "default" shell.
- Run notebook.
If you wish to submit a PR then please run pre-commit install to install the pre-commit hooks.
A large number of input files can be involved in a model run. We assume that a project is arranged using the following folder structure:
project_name/
├── README.md
├── notebooks/
│ ├── block_model.ipynb
│ ├── visualize_results.ipynb
│ └── resolution_tests.ipynb
├── data/
| ├── command/
│ | ├── command_001.json
│ | └── command_NNN.json
│ ├── segment/
│ │ ├── segment_001.csv
│ │ └── segment_NNN.csv
│ ├── block/
│ │ ├── block_001.csv
│ │ └── block_NNN.csv
│ ├── station/
│ │ ├── station_001.csv
│ │ └── station_NNN.csv
│ ├── mesh/
│ | ├── mesh_params_001.json
│ | ├── mesh_params_NNN.json
│ | ├── mesh_001.msh
│ | └── mesh_NNN.msh
| └── operators/
│ ├── elastic_001.hdf5
│ └── elastic_NNN.hdf5
└── runs/
├── 2022-02-20-17-01-39/
│ ├── 2022-02-20-17-01-39.log
│ ├── elastic_operators.hdf5
│ ├── model_segment.csv
│ ├── model_block.csv
│ └── model_station.csv
└── NNNN-NN-NN-NN-NN-NN/
├── NNNN-NN-NN-NN-NN-NN.log
├── elastic_operators.hdf5
├── model_segment.csv
├── model_block.csv
└── model_station.csv
The files listed above flow into celeri through command.json file. Files with dark orange background shading are required (automatically generated) and those with light blue background shading are optional (not automatically generated)
flowchart TD
classDef required fill:#f96;
subgraph input_files
command.json:::required --> segment.csv:::required
command.json:::required --> block.csv:::required
command.json:::required --> station.csv:::required
subgraph meshes
mesh_parameters.json
mesh_parameters.json --> mesh_001.msh
mesh_parameters.json --> mesh_NNN.msh
end
command.json --> meshes
command.json --> elastic_operators_precomputed.hdf5
command.json --> los.csv
end
subgraph celeri
notebook.ipynb:::required
end
subgraph output_files
model_segment.csv:::required
model_block.csv:::required
elastic_operators.hdf5
model_station.csv:::required
model_los.csv
end
input_files --> celeri
celeri --> output_files
We think celeri is pretty great but there are other alternatives worth considering: