Quick-start
pip or conda).
- If you’re not sure whether Python is installed, or you’d prefer an “all-in-one” setup that manages interpreters and packages, consider installing Anaconda Distribution.
- Alternatively, grab the latest official installer for Windows, macOS, or Linux from python.org and follow their platform-specific instructions before returning here.
If you are running Python 3.9-3.12 on x86-64 or Apple Silicon:
python -m pip install --upgrade pip # upgrade installer
python -m pip install svvThis installs the core pre-built synthetic vascular-generation wheel from PyPI.
System requirements
- Python: 3.9 ≤ 3.12 (CPython). Other interpreters are untested.
- Disk: ≈ 300 MB for wheels & caches, plus project data.
- Memory: 2 GB RAM minimum (8 GB recommended for large vascular trees).
- C++17 tool-chain: required only for source builds (see call-out below).
- Build-time Python deps:
cython ≥0.29 <3.0,wheel,numpy ≥1.22(pulled in automatically bypip).
Building from Source | Obtaining C++17 compiler tool-chains:
- Windows 10/11: Download Visual Studio 2022 Community and install the “Desktop development with C++” workload.
- macOS 11+:
xcode-select --installfor the Command-Line Tools, thenbrew install cmake ninja. - Ubuntu/Debian:
sudo apt update && sudo apt install build-essential cmake ninja-build
After the tool-chain is in place, pip install svv will fall back to a source
build automatically if no wheel matches your platform.
Choosing an installation method
| Method | Pros | Cons |
|---|---|---|
| Pip wheels (recommended) | Fast, no compiler | No GPU/MPI extras |
| Conda | Handles system libs | Feedstock under review |
| Build from source | Full control | Slowest; needs tool-chain |
Installing with pip
The PyPI wheels are pre-built for every major OS and Python 3.9 – 3.12, so installs are fast and compiler-free. This route is ideal for:
- quick experiments or tutorials,
- lightweight CI jobs,
- users who already manage Python via
pyenv, Homebrew, or the Windows Store.
Create a virtual environment first (recommended):
# Linux/macOS
python3 -m venv ~/.venvs/svv
source ~/.venvs/svv/bin/activate
pip install svv# Windows (PowerShell)
py -m venv %USERPROFILE%\venvs\svv
%USERPROFILE%\venvs\svv\Scripts\Activate.ps1
pip install svvvenv or virtualenv
environment for each project.
Isolating dependencies prevents version clashes between
svv and other python packages you might already use
or need later.
Installing with conda
conda can install not only Python packages but also the C/C++ and Fortran
libraries many scientific tools depend on.
It excels when you need:
- a single cross-platform workflow (Windows, macOS, Linux),
- MPI, OpenBLAS, or GPU builds of downstream solvers,
- inter-package version pinning handled for you.
Until the official conda-forge feedstock is merged, you can still use a
conda environment and install from PyPI inside it:
conda create -n svv python=3.11 pip
conda activate svv
pip install svvOnce the feedstock lands you'll be able to swap the final line for:
conda install -c conda-forge svvBuilding from source
For advanced users and developers who want to customize svv or contribute to svv.
git clone https://github.com/SimVascular/svVascularize.git
cd svVascularize
python -m pip install -e .[dev]Optional Hemodynamic CFD / ROM solvers
The svVascularize Python API is solver-agnostic. For flow analysis you can link to the SimVascular solver family shown in the table:
| Solver | Dimension | Best for… | Core deps |
|---|---|---|---|
svZeroDSolver |
0D lumped | fast circuit models, boundary conditions | NumPy & SciPy wheels |
svOneDSolver |
1D compliant tube | network wave propagation, pulse-wave | PETSc + MPI, petsc4py |
svMultiPhysics |
3D FEM / FSI | full spatial-resolved Navier–Stokes + wall mechanics | CMake ≥ 3.20, VTK ≥ 9, MPI, BLAS, LAPACK |
svZeroDSolver
Pre-built wheels do not yet exist for this solver; however, installation can be accomplished in nearly the same way via pip and git:
pip install git+https://github.com/simvascular/svZeroDSolver.gitThe solver exposes a run_lv_model() helper that you can
couple to an svv-generated network in < 1 s.
svOneDSolver
This solver relies on PETSc and MPI. The easiest route is to download the pre-built binaries from SimTK. These binaries are compatible with Ubuntu 18/20, macOS Catalina, and Windows 10.
mpi4py and SWIG are on your PATH first, otherwise the
build will fail with missing headers.
buildPy; however, python bindings for the latest macOS, Linux, or Windows
platforms have not been tested.
svMultiPhysics
Full 3D CFD/FSI binaries are published on SimTK. Download the latest installer for macOS/Linux platforms:
Verifying the installation
After installing svVascularize, verify the installation version by running the following scripts in the Python interpreter.
import svv
print(svv.__version__)To test that individual components of the package are correctly installed and operational run the test suite associated with the module from the terminal or shell.
python -m svv.testsTroubleshooting & FAQ
DLL load failed? Check bitness and Python version.