dcel-map-generator 0.9.0

Generate a leaf-level DCEL map from a hierarchical zone tree

dcel-map-generator

Generate whimsical continent-style maps from a rooted hierarchy of zones. The pipeline produces a leaf-level DCEL planar subdivision, an optional rendered PNG, and a frontend-ready JSON bundle for interactive exploration.

Live Demo | GitHub

Installation

pip install dcel-map-generator

Requires Python 3.11+.

Quick Example

from dcel_builder import generate_dcel

dcel, report = generate_dcel(
    zone_edges_path="zone_edges.json",
    tree_stats_path="zone_tree_stats.json",
    zone_index_path="zone_index.json",
    seed=42,
)

interior_faces = [f for f in dcel.faces if not f.is_outer]
print(f"Generated {len(interior_faces)} leaf territories")

Python API

generate_dcel(...) -> (DCEL, report)

Build a leaf-level DCEL from a rooted zone tree.

generate_frontend_bundle(...) -> (bundle_dict, report)

Build a frontend-ready hierarchy bundle (JSON-serializable dict) that can be consumed by the companion React component @alonso-cancino/dcel-map-frontend.

generate_map_artifacts(...) -> (DCEL, report, tree, zone_index)

Low-level entry point that returns all intermediate artifacts.

All three functions accept the same parameters:

Parameter	Type	Default	Description
zone_edges_path	str | Path	required	Directed [parent, child] edge-list JSON
tree_stats_path	str | Path	required	Sidecar stats JSON (can be {})
zone_index_path	str | Path	required	{ "id": "name" } mapping JSON
seed	int | None	None	RNG seed for reproducible maps
resolution	int	512	Raster grid size H x H
land_fraction	float	0.40	Target fraction of land pixels
noise_exponent	float	2.3	Power-law exponent for spectral noise
warp_strength	float	0.10	Domain-warp amplitude (fraction of resolution)
split_mode	str	"contour_guided"	Split strategy: contour_guided, field_guided, or seeded
quiet	bool	False	Suppress progress output

CLI

The package installs a dcel-map command:

dcel-map \
  --zone-edges zone_edges.json \
  --tree-stats zone_tree_stats.json \
  --zone-index zone_index.json \
  --output dcel_map.json \
  --render --render-output map.png \
  --frontend-bundle map_bundle.json \
  --seed 42 --validate

Key flags:

Flag	Description
--output	Path for the output DCEL JSON
--render	Render the DCEL to a PNG image
--render-output	PNG output path (default: dcel_map.png)
--frontend-bundle	Generate a frontend-ready JSON bundle
--seed	RNG seed for reproducibility
--resolution	Raster grid size (default: 512)
--split-mode	contour_guided (default), field_guided, or seeded
--validate	Run structural invariant checks before writing
--quiet	Suppress progress output

Input Files

The generator expects three JSON files describing a rooted tree of zones:

zone_edges.json — directed parent-child pairs:

[[0, 1], [0, 2], [1, 3], [1, 4]]

zone_index.json — zone ID to display name:

{"0": "World", "1": "North", "2": "South", "3": "Tundra", "4": "Forest"}

zone_tree_stats.json — optional metadata (an empty {} is accepted).

Outputs

• DCEL JSON — serialized planar subdivision with vertices, half-edges, and faces. Each interior face is tagged with a zone_id from the input tree.
• PNG render — static map image colored by zone.
• Frontend bundle — JSON structure with SVG paths, bounding boxes, hierarchy, and zoom thresholds for interactive rendering.

How It Works

The pipeline is tree-first:

1. Load a rooted hierarchy from the edge-list
2. Generate a continent mask using spectral noise with domain warping
3. Recursively partition each parent region among its children using weighted splits
4. Extract leaf polygons from the raster partition
5. Build a DCEL planar subdivision

Split modes control how regions are divided:

• contour_guided — splits along raster contour lines (organic, natural-looking boundaries)
• field_guided — uses distance/flow fields for partitioning
• seeded — random Voronoi-like splits

License

MIT