| Name | girg-sampling JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | https://github.com/gavento/girg-sampling |
| Summary | Efficient sampling of Geometric Inhomogeneous Random Graphs (GIRG). Wrapper for C++ libraries libgirg and libhypergirg. |
| upload_time | 2023-10-17 23:48:51 |
| maintainer | |
| docs_url | None |
| author | Tomas Gavenciak |
| requires_python | >=3.8,<4 |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# GIRG sampling
A Python wrapper for the [GIRGs sampling library](https://github.com/chistopher/girgs) (C++).
Contains a direct wraper of the C++ library and NetworkX Graph generators (optional).
Efficiently generates Geometric Inhomogeneous Random Graphs (GIRGs) and Hyperbolic Random Graphs (HRGs).
See the paper [Efficiently Generating Geometric Inhomogeneous and Hyperbolic Random Graphs](https://arxiv.org/abs/1905.06706)
for details of the algorithm.
## Install
Install from PyPI as `girg-sampling` via `pip`, `poetry` etc.
To build the package locally, install Poetry package manager and run `poetry build`, optionally with `poetry install`.
To use `generateNetworkX` functions, you need to have the `networkx` package (not a default dependency of `girg-sampling`)
## Usage
```python
import girg_sampling
g = girg_sampling.girgs.generateNetworkX(n=135, ple=1.5, dim=4, deg=4.2, alpha=100, seed=41)
h = girg_sampling.hypergirgs.generateNetworkX(n=1001, alpha=0.75, T=0.7, deg=2.2, seed=None)
```
See [tests](https://github.com/gavento/girg-sampling/blob/master/tests/test_basic.py) for sample usage of the raw C++ wrappers.
## Changelog
* 0.1.0: A direct wrapper of the C++ graph generator functions.
* 0.2.0: Minor fixes, unify `seed` param, add e2e tests, build wheels for python up to 3.10
* 0.2.1: Update urllib3 dev-dependency for twine under Python 3.10
* 0.3.0: Add NetworkX wrapper, update python version, add docs.
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