Metadata-Version: 2.4
Name: svv
Version: 0.0.39
Summary: svVascularize (svv): A synthetic vascular generation, modeling, and simulation package
Author: Zachary Sexton
Author-email: zsexton@stanford.edu
License: MIT
Keywords: modeling,simulation,tissue-engineering,3d-printing,fluid-dynamics
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.26
Requires-Dist: scipy>=1.10.1
Requires-Dist: matplotlib>=3.7.5
Requires-Dist: usearch
Requires-Dist: scikit-image
Requires-Dist: tetgen
Requires-Dist: trimesh[all]
Requires-Dist: pyvista~=0.44.2
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: pymeshfix==0.17.0
Requires-Dist: numexpr
Requires-Dist: pyvistaqt
Requires-Dist: pyside6
Provides-Extra: accel
Requires-Dist: svv-accelerated==0.0.39; extra == "accel"
Provides-Extra: accelerated
Requires-Dist: svv-accelerated==0.0.39; extra == "accelerated"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: keywords
Dynamic: license
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# svVascularize


[![Version](https://img.shields.io/pypi/v/svv.svg?logo=pypi&label=PyPI%20version)](https:://pypi.org/project/svv/)
![Platform](https://img.shields.io/badge/platform-macOS%20|%20linux%20|%20windows-blue)
![Latest Release](https://img.shields.io/github/v/release/SimVascular/svVascularize?label=latest)
[![codecov](https://codecov.io/github/SimVascular/svVascularize/graph/badge.svg)](https://codecov.io/github/SimVascular/svVascularize)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15151168.svg)]()
[![Docs](https://img.shields.io/badge/docs-gh--pages-brightgreen)](https://simvascular.github.io/svVascularize/)

<p align="left">
The svVascularize (svv) is an open-source API for automated vascular generation and multi-fidelity hemodynamic simulation
written in Python. Often small-caliber vessels are difficult or infeasible to obtain from experimental data sources 
despite playing important roles in blood flow regulation and cell microenvironments. svVascularize aims to provide tissue 
engineers and computational hemodynamic scientists with de novo vasculature that can easily be applied in 
biomanufacturing applications or computational fluid dynamic (CFD) analysis.
</p>

* **Website:** https://simvascular.github.io/svVascularize/
* **PyPi:** https://pypi.org/project/svv/
* **Source code:** https://github.com/SimVascular/svVascularize

## Installation

The package is published on PyPI as `svv`:

```bash
pip install svv
```

On clusters / HPC systems (for example Stanford Sherlock), use a recent Python (3.9–3.12) and `pip`, and install into a clean virtual environment or user site-packages. The runtime dependencies now require `numpy>=1.26`, which has pre-built wheels for Python 3.12 on standard x86_64 Linux, so `pip install svv` should no longer try to build NumPy (or SciPy) from source on these systems.
