frameplot 0.5.6

Turn Python-defined pipeline graphs into presentation-ready SVG and PNG diagrams.

frameplot

Turn Python-defined pipeline graphs into presentation-ready SVG and PNG diagrams.

한국어 README

frameplot is a compact Python library for rendering left-to-right pipeline diagrams with clean defaults. Define nodes, edges, groups, and optional detail panels in plain Python, then export polished SVG for documentation or high-resolution PNG for slides and papers.

Theme Gallery

All built-in presets stay on a white canvas. The same hero pipeline is rendered below once per theme so you can compare them directly.

Soft Retro	Retro

Research	Dark

Cyberpunk	Monochrome

Why frameplot?

• Clean and Professional: Left-to-right architecture diagrams with modern defaults.
• Diagram as Code: Define your pipeline in Python, get deterministic SVG/PNG outputs.
• Detail Panels: Unique feature to expand a summary node into a lower inset mini-graph for deep dives.
• Deep Customization: Fine-tune typography, spacing, colors, and corner radii via Theme.
• White-Canvas Themes: Built-in presets stay presentation-friendly on white backgrounds.
• Presentation Ready: High-quality SVG for web/docs and PNG for slides or papers.

Install

python -m pip install frameplot

PNG export depends on CairoSVG and may require Cairo or libffi packages from the host OS.

Quickstart

from frameplot import Edge, Group, Node, Pipeline

pipeline = Pipeline(
    nodes=[
        Node("start", "Start", "Receive request"),
        Node("fetch", "Fetch Data", "Load source tables"),
        Node("retry", "Retry", "Loop on transient failure", fill="#FFF2CC"),
        Node("done", "Done", "Return result", fill="#D9EAD3"),
    ],
    edges=[
        Edge("e1", "start", "fetch"),
        Edge("e2", "fetch", "retry", dashed=True),
        Edge("e3", "retry", "fetch", color="#C0504D"),
        Edge("e4", "fetch", "done"),
    ],
    groups=[
        Group("g1", "Execution", ["start", "fetch", "retry"], edge_ids=["e2"]),
    ],
)

svg = pipeline.to_svg()
pipeline.save_svg("pipeline.svg")
pipeline.save_png("pipeline.png")

Edge-to-Edge Joins

from frameplot import Edge, Node, Pipeline

pipeline = Pipeline(
    nodes=[
        Node("source", "Source", "Primary request"),
        Node("worker", "Worker", "Prepare response"),
        Node("audit", "Audit", "Write side log", fill="#DBEAFE"),
        Node("done", "Done", "Return result", fill="#D9EAD3"),
    ],
    edges=[
        Edge("e1", "source", "worker"),
        Edge("e2", "worker", "done"),
        Edge("e3", "audit", "e2", merge_symbol="+", color="#2563EB"),
    ],
)

Public API

Top-level imports are the supported public API:

• Node(id, title, subtitle=None, fill=None, stroke=None, text_color=None, metadata=None, width=None, height=None)
• Edge(id, source, target, color=None, dashed=False, merge_symbol=None, metadata=None)
• Group(id, label, node_ids, edge_ids=(), stroke=None, fill=None, metadata=None)
• DetailPanel(id, focus_node_id, label, nodes, edges, groups=(), stroke=None, fill=None, metadata=None)
• Theme(...)
• Pipeline(nodes, edges, groups=(), detail_panel=None, theme=None)

Edge.target may reference either a node id or another edge id. When targeting another edge, merge_symbol="+" or "x" renders a join badge at the merge point.

Pipeline exposes:

• to_svg() -> str
• save_svg(path) -> None
• to_png_bytes(scale=4.0) -> bytes
• save_png(path, scale=4.0) -> None

Modeling Guidance

• Keep the main graph at one abstraction level. Frameplot lays the pipeline out as a dependency-driven left-to-right graph, not as a freeform block diagram.
• Use DetailPanel for repeated block internals or per-stage mechanics that would otherwise create long-range edges in the main graph.
• Use Group to highlight nearby related nodes, not to force distant nodes to stay together. Groups are visual overlays and routing obstacles, so very wide groups can increase route detours.

Advanced Example: Multi-cloud Data Pipeline

The hero image at the top and the theme gallery above are generated from examples/theme_heroes.py, using the shared pipeline definition in examples/hero_pipeline.py. Together they showcase:

• Complex Routing: Seamlessly connecting AWS (S3/Lambda) to GCP (Pub/Sub/Dataflow) services.
• Contextual Details: Using a DetailPanel to explain the internal Spark Job Pipeline of the "Dataflow" node.
• Soft Retro Styling: Applying the built-in Theme.soft_retro() preset on a white canvas.

Design Notes

• Layout is intentionally left-to-right in v0.x.
• Edge labels are not supported yet, but edge-to-edge joins can render optional + / x badges.
• Groups stay visual overlays, and routes leaving or re-entering grouped nodes bend outside grouped areas.
• Detail panels render as separate lower insets attached to a focus node in the main flow.
• If a sample looks stretched or routes far outside the intended block, the graph usually mixes stage-level flow with internal logic in one plane; move the internals into a DetailPanel.

Development

python -m venv .venv
source .venv/bin/activate
python -m pip install -e '.[dev]'
python -m pytest -q

Release publishing is automated through GitHub Actions and PyPI Trusted Publishing. Bump the version in pyproject.toml, create a tag like v0.4.0, and push the tag to trigger a release from .github/workflows/workflow.yml.