tensor-network-visualization 1.5.2

Minimal Matplotlib visualizations for TensorKrowch, TensorNetwork, Quimb, TeNPy, and traced PyTorch/NumPy einsum tensor networks.

Tensor-Network-Visualization

Minimal Matplotlib visualizations for TensorKrowch, TensorNetwork, Quimb, TeNPy, and traced PyTorch/NumPy einsum tensor networks.

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

What This Library Does

Tensor-network libraries expose very different Python objects, but this package gives them two aligned plotting entry points:

fig, ax = show_tensor_network(...)
fig, ax = show_tensor_elements(...)

Internally, the library:

1. normalizes backend objects into one graph model,
2. computes a layout,
3. draws the graph in 2D or 3D with Matplotlib,
4. optionally adds figure controls for view/label/scheme toggles.

The goal is to keep the public API small while still being useful for notebooks, papers, debugging, and saved figures.

Install

PyPI package name: tensor-network-visualization
Import package: tensor_network_viz

Requires Python 3.11 or newer.

Base install

python -m pip install tensor-network-visualization

Base dependencies are only numpy, matplotlib, and networkx.

Optional extras

Need	Install
TensorKrowch support	python -m pip install "tensor-network-visualization[tensorkrowch]"
TensorNetwork support	python -m pip install "tensor-network-visualization[tensornetwork]"
Quimb support	python -m pip install "tensor-network-visualization[quimb]"
TeNPy support	python -m pip install "tensor-network-visualization[tenpy]"
Traced einsum(...) support	python -m pip install "tensor-network-visualization[einsum]"
Interactive Jupyter widgets	python -m pip install "tensor-network-visualization[jupyter]"

Windows and Linux quick start

Windows (PowerShell):

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -U pip
python -m pip install "tensor-network-visualization[quimb]"

Linux / macOS:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install "tensor-network-visualization[quimb]"

The API in One Minute

show_tensor_network

Use show_tensor_network for figure lifecycle:

show_tensor_network(
    network,
    *,
    engine=None,
    view=None,
    config=None,
    ax=None,
    show_controls=True,
    show=True,
)

• engine: optional backend override. If omitted, the library auto-detects it.
• view: "2d" or "3d". If omitted, it starts in "2d".
• config: all visual behavior lives here.
• ax: render into an existing Matplotlib axis.
• show_controls: if False, the figure is saved/rendered without the embedded control panel.
• show: if False, nothing is displayed automatically.

show_tensor_elements

Use show_tensor_elements to inspect tensor values:

show_tensor_elements(
    data,
    *,
    engine=None,
    config=None,
    ax=None,
    show_controls=True,
    show=True,
)

When several tensors are present, the figure keeps one tensor active at a time and uses a slider to switch between them. The interactive controls are grouped: basic (elements, magnitude, log_magnitude, distribution, data), complex (real, imag, phase), and diagnostic (sign, signed_value, sparsity, nan_inf).

• data: single tensor, iterable of tensors, supported backend-native tensor collections, or an EinsumTrace with live tensor values.
• engine: optional backend override. If omitted, the library auto-detects it.
• config: tensor-inspection behavior lives here.
• data mode now includes global stats, per-axis summaries, and top-k coordinates by magnitude.
• ax: render a single tensor into an existing Matplotlib axis.
• show_controls: if True, add compact Matplotlib group + mode controls and, when needed, a tensor slider.
• show: if False, nothing is displayed automatically.

PlotConfig

Use PlotConfig for visual behavior:

from tensor_network_viz import PlotConfig

config = PlotConfig(
    show_tensor_labels=True,
    show_index_labels=False,
    hover_labels=True,
    show_contraction_scheme=False,
    tensor_label_refinement="auto",
)

This is where you configure:

• labels,
• hover tooltips,
• contraction-scheme overlays,
• styling,
• layout iterations,
• custom positions,
• label-refinement policy,
• static/export-oriented rendering choices.

TensorElementsConfig

Use TensorElementsConfig for tensor inspection behavior:

from tensor_network_viz import TensorElementsConfig

config = TensorElementsConfig(
    mode="auto",
    max_matrix_shape=(256, 256),
    histogram_bins=40,
    topk_count=8,
    robust_percentiles=(1.0, 99.0),
    shared_color_scale=False,
    highlight_outliers=False,
)

This is where you configure:

• the active inspection mode,
• row/column axis grouping for rank > 2 tensors,
• heatmap downsampling limits,
• histogram sampling and bin count,
• data-summary depth (topk_count),
• optional robust/shared scaling and outlier overlays.

If you want to start in a specific grouped view, pass mode="real", mode="imag", mode="phase", mode="log_magnitude", mode="sparsity", mode="nan_inf", mode="sign", or mode="signed_value" directly in TensorElementsConfig(...).

Most Common Workflows

Interactive figure with controls

from tensor_network_viz import PlotConfig, show_tensor_network

fig, ax = show_tensor_network(
    network,
    config=PlotConfig(
        show_tensor_labels=False,
        show_index_labels=False,
        hover_labels=True,
    ),
)

Clean export with no embedded controls

from tensor_network_viz import PlotConfig, show_tensor_network

fig, ax = show_tensor_network(
    network,
    config=PlotConfig(
        show_tensor_labels=True,
        show_index_labels=False,
    ),
    show_controls=False,
    show=False,
)
fig.savefig("network.png", bbox_inches="tight")

Inspect tensor values

from tensor_network_viz import TensorElementsConfig, show_tensor_elements

fig, ax = show_tensor_elements(
    trace,
    config=TensorElementsConfig(mode="auto"),
    show=False,
)
fig.savefig("tensor-elements.png", bbox_inches="tight")

Faster render for large graphs

config = PlotConfig(
    tensor_label_refinement="never",
    layout_iterations=120,
)

Copy-Paste Examples by Backend

The library supports auto-detection, but using engine=... in examples is often clearer.

TensorKrowch

import tensorkrowch as tk
from tensor_network_viz import PlotConfig, show_tensor_network

network = tk.TensorNetwork(name="demo")
left = tk.Node(shape=(2, 2), axes_names=("a", "b"), name="L", network=network)
right = tk.Node(shape=(2, 2), axes_names=("b", "c"), name="R", network=network)
left["b"] ^ right["b"]

fig, ax = show_tensor_network(
    network,
    engine="tensorkrowch",
    config=PlotConfig(show_tensor_labels=True),
)

TensorNetwork

import numpy as np
import tensornetwork as tn
from tensor_network_viz import PlotConfig, show_tensor_network

left = tn.Node(np.ones((2, 2)), name="L", axis_names=("a", "b"))
right = tn.Node(np.ones((2, 2)), name="R", axis_names=("b", "c"))
left["b"] ^ right["b"]

fig, ax = show_tensor_network(
    [left, right],
    engine="tensornetwork",
    config=PlotConfig(show_tensor_labels=True),
)

Quimb

import numpy as np
import quimb.tensor as qtn
from tensor_network_viz import PlotConfig, show_tensor_network

tensors = [
    qtn.Tensor(np.ones((2, 3)), inds=("i0", "b0"), tags={"T0"}),
    qtn.Tensor(np.ones((3, 2)), inds=("b0", "i1"), tags={"T1"}),
]
network = qtn.TensorNetwork(tensors)

fig, ax = show_tensor_network(
    network,
    engine="quimb",
    config=PlotConfig(show_tensor_labels=True),
)

TeNPy

from tenpy.networks.mps import MPS
from tenpy.networks.site import SpinHalfSite
from tensor_network_viz import PlotConfig, show_tensor_network

sites = [SpinHalfSite() for _ in range(4)]
mps = MPS.from_product_state(sites, ["up", "up", "up", "up"], bc="finite")

fig, ax = show_tensor_network(
    mps,
    engine="tenpy",
    config=PlotConfig(show_tensor_labels=True),
    show_controls=False,
    show=False,
)

einsum

from tensor_network_viz import PlotConfig, pair_tensor, show_tensor_network

trace = [
    pair_tensor("A0", "x0", "r0", "pa,p->a"),
    pair_tensor("r0", "A1", "r1", "a,apb->pb"),
]

fig, ax = show_tensor_network(
    trace,
    engine="einsum",
    config=PlotConfig(show_contraction_scheme=True),
)

For fuller backend examples, see docs/backends.md.

PlotConfig Fields You Will Actually Use Often

Field	Why it matters
show_tensor_labels	Draw tensor names on nodes.
show_index_labels	Draw index labels on bonds and dangling legs.
hover_labels	Enable hover tooltips in interactive sessions.
show_contraction_scheme	Draw contraction-step regions.
contraction_playback	Start with playback controls enabled when controls are shown.
contraction_scheme_cost_hover	Show contraction details in the playback panel.
tensor_label_refinement	"auto", "always", or "never" for the expensive label-fit pass.
layout_iterations	Force-layout effort.
positions	Supply custom node coordinates.
figsize	Matplotlib figure size when the figure is created internally.

Example Launcher

The repository ships a typed demo launcher:

python examples/run_demo.py <engine> <example> [options]

Useful examples:

python examples/run_demo.py quimb hyper --view 2d
python examples/run_demo.py tenpy chain --view 2d --save tenpy_chain.png --no-show
python examples/run_demo.py einsum ellipsis --view 3d --scheme

More details, including an exhaustive copy-paste command list for every CLI example: examples/README.md

There is also a minimal inspection example that only uses base dependencies:

python examples/tensor_elements_demo.py

Troubleshooting

Symptom	What to try
Saved figure includes buttons/sliders	Use show_controls=False.
Hover tooltips do nothing	Use an interactive Matplotlib backend; hover is not useful for PNG-only runs.
Big graphs are slow	Set tensor_label_refinement="never", reduce layout_iterations, or pass positions.
Unsupported tensor network engine	Install the matching extra or pass the correct backend object.
show_tensor_elements(...) fails on TensorKrowch nodes	Materialize the node tensors first; shape-only nodes do not expose element values.
show_tensor_elements(...) fails on manual pair_tensor(...) lists	Use an EinsumTrace with live tensors instead; manual trace steps only describe contractions.
Blank / duplicate Jupyter figure	Assign fig, ax = show_tensor_network(...) instead of leaving the tuple as the last line.

Documentation Map

• docs/guide.md: workflow-oriented guide to the public API.
• docs/backends.md: copy-paste backend-specific examples.
• examples/README.md: demo launcher and batch-render usage.
• examples/tensor_elements_demo.py: minimal tensor inspection example.
• CHANGELOG.md: release notes.

Development

Create and use a local virtual environment:

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -r requirements.dev.txt

Run the project checks:

.\.venv\Scripts\python scripts\verify.py