Metadata-Version: 2.4
Name: sTiles
Version: 2026.8.14
Summary: Python bindings for the sTiles sparse Cholesky / selected-inverse framework
Author-email: Esmail Abdul Fattah <esmail.abdulfattah@kaust.edu.sa>
Project-URL: Homepage, https://esmail-abdulfattah.github.io/sTiles/
Project-URL: Repository, https://github.com/esmail-abdulfattah/sTiles
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.20
Requires-Dist: scipy>=1.5

# sTiles

High-performance **tile-based** framework for sparse **Cholesky factorization**,
**triangular solves**, and **selected inversion** of symmetric positive-definite
matrices, spanning the full spectrum from very sparse to fully dense.

This package is the **Python interface** to the `libstiles` engine. It is pure
`ctypes` (no compiler required): the correct native library for your platform is
downloaded automatically from the project's GitHub Release the first time you use it.

## Install

    pip install sTiles

## Example

    import scipy.sparse as sp
    from sTiles import sTiles

    s = sTiles(Q, cores=4, inverse=True)   # analyze + Cholesky factorize
    s.logdet            # log|Q|
    s.solve(b)          # solve Q x = b
    s.selinv_diag()     # diag(Q^-1), the marginal variances
    s.selinv_elm(i, j)  # any inverse element within the factor pattern

## Links

- Documentation: https://esmail-abdulfattah.github.io/sTiles/
- Source: https://github.com/esmail-abdulfattah/sTiles

Developed at KAUST. Free for research and academic use.
