spheropack
Random sphere packings in Python, generated with the event-driven Lubachevsky-Stillinger algorithm: spheres move ballistically, collide and grow until a target density is reached or the packing jams or crystallises.
import spheropack as sp
p = sp.pack(n=1000, density=0.6, seed=1) # target volume fraction
q = sp.pack(n=1000, density="max", seed=1) # until jammed (isostatic within a few %)
q.positions, q.radii, q.density, q.status
q.to_csv("packing.csv")
- Periodic boxes, flat walls (slabs, closed boxes), cylinders (packed tubes), spherical containers and disks; 2D and 3D; mono- and polydisperse.
- A fast, exact C++20 event-driven core (header-only, no dependencies) with a nanobind Python interface. The GIL is released and Ctrl-C works.
- A well-defined, dimensionless growth rate and composable stopping criteria, including a quasi-static jamming protocol.
- Structure analysis: pair distribution function, contact numbers, rattlers (from the collision forces), isostaticity, Steinhardt bond order, crystallinity, density profiles next to walls, radial porosity profiles in tubes.
- A command-line tool, including drop-in replacements of the legacy
generate_periodic_packingandgenerate_packed_tube.
The next version will also make available the rejection-free, event-driven Monte Carlo method of E.A.J.F. Peters and G. de With, Rejection-free Monte Carlo sampling for general potentials, Phys. Rev. E 85, 026703 (2012).
Status: first release (0.1). The rejection-free Monte Carlo module follows in 0.2; see the roadmap.
Installation
pip install spheropack
Wheels are available for Linux, macOS and Windows (Python 3.10 and newer). Building
from source needs a C++20 compiler and CMake 3.18 or newer: pip install .
Optional extras: pip install "spheropack[plot]" for the matplotlib helpers.
Documentation
https://computational-chemical-engineering.github.io/spheropack: user guide, example
notebooks, Python and C++ reference. To build it locally from docs/:
pip install sphinx pydata-sphinx-theme myst-nb breathe sphinx-copybutton sphinxcontrib-bibtex matplotlib
cd docs && sphinx-build -b html . _build/html
Citing
If you use spheropack, please cite it (see CITATION.cff); for the rejection-free Monte Carlo method, cite Peters and de With (2012).
Contributing
See CONTRIBUTING.md. Bug reports and feature requests are welcome as issues.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for spheropack 0.1.0
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| spheropack-0.1.0.tar.gz | 86.5 kB | Details |
Built distributions (wheels)
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Release files / spheropack-0.1.0.tar.gz
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