pygrnwang
Documentation · Quickstart · 中文入门 · Examples
This Python package serves as the frontend for calculating and building a Green's function library for synthetic seismograms. The backend consists of Wang Rongjiang's program for calculating synthetic seismograms, including EDGRN/EDCMP, QSEIS_STRESS, SPGRN, and QSSP (Wang, 1999; Wang 2003; Wang and Wang 2007; Wang et al., 2017). The code includes two parallel modes: one using the multiprocessing library (single-node multi-process) and the other using MPI (multi-node).
The QSEIS06 and SPGRN2012 backends are deprecated in pygrnwang. For new calculations, use QSEIS2025 and SPGRN2020, respectively. Existing interfaces remain available; migration requires rebuilding libraries and validating the replacement backend's settings and output conventions.
Installation
Python 3.9 or later is supported. Building from source requires gfortran and setuptools >=77; installing a compatible wheel does not require gfortran.
Travel times use TauP 2.6.1 through a Java subprocess when a JDK (java and
javac on PATH) is available, and otherwise fall back to ObsPy. JPype is not
required. The Java bridge is compiled on the first query and cached for the
current Python process; importing the library does not start Java. Travel-time
tables send all distances to one Java process. Individual queries start one
Java process per call, so use create_tpts_table for large distance grids.
Wheels include TauP.jar inside the package and install a second copy in the
target environment's Scripts (Windows) or bin directory. Resource lookup
prefers the package copy and falls back to the environment copy.
- For user mode
pip install pygrnwang
On Linux and macOS, the current bulk runners require an editable source installation because of an existing executable-path limitation. Follow the installation guide for the complete platform instructions.
- For developer mode
Prepare and activate the environment and platform-specific compiler described in the installation guide, then:
git clone https://github.com/Zhou-Jiangcheng/pygrnwang.git
cd pygrnwang
pip install -e .
Usage
After cloning the repository and installing the current source, run the small QSEIS2025 example from the repository root:
python examples/qseis2025.py --output-dir examples/output/qseis2025
python examples/qseis2025.py --observables all --output-dir examples/output/qseis2025-all
The scripts prepare a model from bundled AK135 elastic parameters, compute three distances, read displacement (and optionally strain/stress), and save NPZ files, figures and a JSON run summary. Constant attenuation and small numerical grids are explicit tutorial choices, not convergence recommendations for research.
For a separate 300/600/900 km calculation with a longer output window:
python examples/qseis2025.py --regional --observables all
python examples/qseis06.py --regional
The backend comparison now includes a separate fresh 2 Hz / 1.25 s source calculation: five-backend regional displacement, QSEIS2025/QSSP2020 six-component stress, and Gaussian smoothing controls. The commands above retain the lighter 64 s tutorial protocol. Replot the documented new result from its committed arrays with:
python examples/plot_documented_comparison.py --output-dir examples/output/documented-comparison
Other complete workflows are listed in the example scripts. Read the scientific conventions before interpreting amplitudes, component order or time origins. The validation record records measured results and platform coverage.
Documentation development
Use an isolated Python 3.12 environment, then run:
python -m pip install -r docs/requirements.txt
python docs/api/check_coverage.py
python -m sphinx -b html -W --keep-going docs docs/_build/html
The build imports source directly and does not compile Fortran or run numerical tutorials. See development instructions for previewing the site, maintaining APIs and GitHub Pages deployment.
Reference
Wang, R. (1999). A simple orthonormalization method for stable and efficient computation of Green’s functions. Bulletin of the Seismological Society of America , 89 (3), 733–741. https://doi.org/10.1785/BSSA0890030733
Wang, R. (2003). Computation of deformation induced by earthquakes in a multi-layered elastic crust—FORTRAN programs EDGRN/EDCMP. Computers & Geosciences, 29(2), 195–207. https://doi.org/10.1016/S0098-3004(02)00111-5
Wang, R., & Wang, H. (2007). A fast converging and anti-aliasing algorithm for green’s functions in terms of spherical or cylindrical harmonics. Geophysical Journal International, 170(1), 239–248. https://doi.org/10.1111/j.1365-246X.2007.03385.x
Wang, R., Heimann, S., Zhang, Y., Wang, H., & Dahm, T. (2017). Complete synthetic seismograms based on a spherical self-gravitating earth model with an atmosphere–ocean–mantle–core structure. Geophysical Journal International, 210(3), 1739–1764. https://doi.org/10.1093/gji/ggx259
Zhou, J., Wang, R., & Zhang, Y. (2026). DynCFS: a program for modeling dynamic coulomb failure stress changes in layered elastic media. Geophysical Journal International, ggaf534. https://doi.org/10.1093/gji/ggaf534
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Release files for pygrnwang 3.0.1
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| Tags | CPython 3.9 Linux glibc 2.28+ x86-64 Linux glibc 2.5+ x86-64 |
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| Tags | CPython 3.9 macOS 26.0+ ARM64 |
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