tensor-network-visualization 1.0.0

Minimal Matplotlib visualizations for TensorKrowch tensor networks.

Tensor-Network-Visualization

Minimal Matplotlib visualizations for TensorKrowch tensor networks.

Repository: https://github.com/DOKOS-TAYOS/Tensor-Network-Visualization

Features

• 2D and 3D plotting for TensorKrowch tensor networks
• Tensors rendered as nodes
• Contractions rendered as edges between tensors
• Dangling indices rendered as labeled stubs
• Self-contractions rendered as loops
• No UI; uses Matplotlib for rendering and NetworkX for graph layouts

Installation

As a dependency

Add to your project's pyproject.toml:

[project]
dependencies = ["tensor-network-visualization>=0.1.0"]

Or install with pip:

pip install tensor-network-visualization

Local development

Inside the project virtual environment:

.\.venv\Scripts\python -m pip install -e ".[dev]"

For runtime-only (editable install without dev tools):

.\.venv\Scripts\python -m pip install -e .

Usage

Networks must expose nodes or leaf_nodes (iterable or dict). Each node must have edges, axes_names, and name. Each edge must have node1, node2, and name.

from tensor_network_viz import PlotConfig, show_tensor_network

config = PlotConfig(figsize=(8, 6))
fig, ax = show_tensor_network(
    network,
    engine="tensorkrowch",
    view="2d",
    config=config,
)

You can also use the TensorKrowch-specific helpers directly:

from tensor_network_viz.tensorkrowch import plot_tensorkrowch_network_2d, plot_tensorkrowch_network_3d

Project layout

• examples/ — Demo scripts. Run python examples/tensor_network_demo.py mps 2d from the project root.
• scripts/ — Utility scripts (e.g. clean.py to remove caches and build artifacts).

Development

.\.venv\Scripts\python -m ruff check .
.\.venv\Scripts\python -m pyright
.\.venv\Scripts\python -m pytest