Point-in-Polygon Algorithm Performance Benchmark

System Status

Python Environment

Python Version: 3.14.2 (CPython)

NumPy Version: 2.4.4

Platform: Darwin arm64

Processor: arm

TimezoneFinder Configuration

C Implementation Available: False

Numba JIT Available: True

Performance Optimizations

✗ Using pure Python point-in-polygon implementation
✓ Numba JIT compilation enabled

Benchmark Configuration

Test Queries: 10,000

Algorithm Type: Point-in-Polygon

Test Data Type: Random timezone boundary polygons with random query points

Polygon Source: TimezoneFinder’s timezone boundary dataset

Test Methodology

Polygons Used: Random timezone boundary polygons from TimezoneFinder’s dataset

Query Points: Random geographic coordinates (longitude, latitude)

Test Process: Each test iteration selects a random polygon from the timezone boundary dataset and a random query point, then measures the time to determine if the point lies within the polygon

Polygon Characteristics: Real-world timezone boundaries with varying complexity, from simple rectangular shapes to highly detailed coastlines and political boundaries

Performance Results

Implementation	Average Time (s)	Throughput (queries/sec)
pt_in_poly_clang	6.3e-06	1.6e+05
pt_in_poly_python	2.2e-06	4.5e+05

Performance Summary

Python implementation WITH Numba is 1.8x faster than the C implementation

Note

Performance results may vary based on system configuration, compiler optimizations, runtime conditions, and the complexity of the randomly selected timezone boundary polygons used in each test run.