rapidgeo 0.1.2

Fast geographic and planar distance calculations with Python bindings

rapidgeo

Fast geographic algorithms for Python, including distance calculations, polyline encoding/decoding, line simplification, and curve similarity measures.

Installation

pip install rapidgeo          # Base package
pip install rapidgeo[numpy]   # With NumPy support

Quick Start

from rapidgeo.distance import LngLat
from rapidgeo.distance.geo import haversine, vincenty_distance

# Create coordinates (longitude, latitude)
sf = LngLat.new_deg(-122.4194, 37.7749)   # San Francisco
nyc = LngLat.new_deg(-74.0060, 40.7128)   # New York City

# Calculate distance using Haversine formula
distance = haversine(sf, nyc)
print(f"Distance: {distance / 1000:.1f} km")  # ~4,130 km

Features

Distance Calculations:

• Haversine: Fast, 0.5% accuracy for distances <1000km
• Vincenty: High precision, 1mm accuracy globally
• Euclidean: Planar distance for projected coordinates
• Batch operations for processing large datasets

Polyline Encoding/Decoding:

• Google Polyline Algorithm implementation
• Compress GPS tracks for efficient storage/transmission
• Full precision support

Line Simplification:

• Douglas-Peucker algorithm
• Reduce GPS track complexity while preserving shape
• Configurable tolerance thresholds

Curve Similarity:

• Fréchet distance: Compare trajectories (considers point order)
• Hausdorff distance: Compare shapes (order-independent)
• Early termination for threshold-based queries
• Ideal for GPS track analysis and map matching

Coordinate System

All coordinates use longitude, latitude ordering (lng, lat):

# Correct
point = LngLat.new_deg(-122.4194, 37.7749)  # lng first, lat second

# Common mistake
# point = LngLat.new_deg(37.7749, -122.4194)  # lat, lng - WRONG

Usage Examples

Compare GPS Tracks:

from rapidgeo.similarity.frechet import discrete_frechet

# Two similar walking routes
route_a = [LngLat.new_deg(-122.419, 37.775), LngLat.new_deg(-122.418, 37.776)]
route_b = [LngLat.new_deg(-122.419, 37.775), LngLat.new_deg(-122.417, 37.777)]

similarity = discrete_frechet(route_a, route_b)
print(f"Routes differ by {similarity:.1f} meters")

Simplify GPS Tracks:

from rapidgeo.simplify import douglas_peucker

# Reduce GPS track complexity
detailed_track = [LngLat.new_deg(-122.4, 37.7), LngLat.new_deg(-122.39, 37.71), ...]
simplified = douglas_peucker(detailed_track, tolerance=10.0)  # 10m tolerance
print(f"Reduced from {len(detailed_track)} to {len(simplified)} points")

Encode/Decode Polylines:

from rapidgeo.polyline import encode, decode

# Compress GPS data for storage/transmission
points = [LngLat.new_deg(-122.4, 37.7), LngLat.new_deg(-122.3, 37.8)]
encoded = encode(points, precision=5)
decoded = decode(encoded, precision=5)

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.