Tensor0
Symmetry-aware tensors for Python, powered by Rust and JAX.
Work with conserved charges and symmetry-allowed tensor blocks, then compose, contract or differentiate them using JAX. Rust handles structural metadata; JAX handles numerical storage and automatic differentiation.
Pre-1.0 software. CUDA support is experimental.
Documentation · Installation · Usage · API · Support policy
Installation
Install from PyPI in a virtual environment:
# CPU
python -m pip install tensor0
# CUDA 12 (experimental)
python -m pip install "tensor0[cuda12]"
See CUDA support for GPU requirements and supported operations. Prebuilt wheels require no Rust or C++ compiler. See the installation guide for available platforms, supported Python versions, GPU setup and source builds.
A small symmetric tensor
import jax.numpy as jnp
from tensor0 import TensorMap, U1Irrep, hom, space, storage_dim
v = space(U1Irrep, {0: 2, 1: 3})
h = hom((v,), (v,))
tensor = TensorMap(h, jnp.arange(storage_dim(h), dtype=jnp.float32))
for sector, block in tensor.blocks():
print(sector, block.shape)
Main capabilities
- Symmetry-aware tensors — graded spaces, built-in sector families, and ordinary and diagonal tensor maps with symmetry-allowed blocks.
- Tensor algebra — transforms, contractions, blockwise linear algebra, decompositions and truncation.
- JAX integration —
jitand automatic differentiation within documented operation-specific limits.
Start with the usage guide, contraction guide and executable examples. Before 1.0, documented public API patch releases remain backward-compatible; breaking changes require a minor release and migration notes. See the support policy.
Contributing and benchmarks
See CONTRIBUTING.md for development and verification links, CHANGELOG.md for release changes, and the release checklist for installed-artifact acceptance. See the reproducible benchmark suites for measurements and tested configurations.
Acknowledgments
Tensor0's main design and architecture are based on TensorKit.jl, bringing these ideas to Python with JAX-backed storage and automatic differentiation.
For citation metadata, see CITATION.cff.
1 maintainer
Metadata
1 maintainer
Release files for tensor0 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tensor0-0.2.0.tar.gz | 277.2 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| tensor0-0.2.0-cp314-cp314-manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.28+ x86-64 | Details |
| tensor0-0.2.0-cp314-cp314-macosx_15_0_arm64.whl | CPython 3.14 | CPython 3.14 | macOS 15.0+ ARM64 | Details |
| tensor0-0.2.0-cp313-cp313-manylinux_2_28_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.28+ x86-64 | Details |
| tensor0-0.2.0-cp313-cp313-macosx_15_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 15.0+ ARM64 | Details |
| tensor0-0.2.0-cp312-cp312-manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.28+ x86-64 | Details |
| tensor0-0.2.0-cp312-cp312-macosx_15_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 15.0+ ARM64 | Details |
| tensor0-0.2.0-cp311-cp311-manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.28+ x86-64 | Details |
| tensor0-0.2.0-cp311-cp311-macosx_15_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 15.0+ ARM64 | Details |
Total release size: 101.9 MB
Release files / tensor0-0.2.0.tar.gz
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