Installation¶

From PyPI (Recommended)¶

# Install the base package
pip install voronoimap

# Install with fast KDTree-accelerated nearest-neighbor lookups
pip install voronoimap[fast]

From Source¶

# Clone the repository
git clone https://github.com/sauravbhattacharya001/VoronoiMap.git
cd VoronoiMap

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

Dependencies¶

Package	Required	Purpose
Python 3.6+	✅	Runtime
NumPy ≥ 1.20	Optional	Array operations for KDTree
SciPy ≥ 1.7	Optional	KDTree for O(log n) NN lookups
pytest ≥ 7.0	Dev only	Test runner
pytest-cov ≥ 4.0	Dev only	Coverage reporting

Optional Dependency Groups¶

BaseFastDev

pip install voronoimap

Includes only the core algorithm. Uses brute-force O(n) nearest-neighbor.

pip install voronoimap[fast]

Adds NumPy + SciPy for O(log n) KDTree nearest-neighbor lookups. Recommended for datasets with more than ~100 points.

pip install voronoimap[dev]

Includes everything in fast plus pytest and coverage tools for development.

Verify Installation¶

# Check that the CLI works
voronoimap --help

# Or from source
python vormap.py --help

Expected output:

usage: vormap.py [-h] [--runs RUNS] [--bounds S N W E] datafile n

Estimate Voronoi region count via random point sampling.

positional arguments:
  datafile          Point data filename inside the data/ directory
  n                 Expected number of Voronoi regions (sample size)

optional arguments:
  --runs RUNS       Number of independent estimation runs (default: 1)
  --bounds S N W E  Explicit search space boundaries