geors 0.1.1

Rust-powered drop-in replacement for geopy — geodesic, great-circle, polygon area, and more

geors

Rust-powered drop-in replacement for geopy.

Replaces every compute-bound function in geopy with a Rust implementation using Karney's geodesic algorithm via geographiclib-rs. I/O-bound operations (geocoding, rate limiting) forward transparently to geopy.

Performance

Operation	geopy	geors (Rust)	Speedup
Geodesic distance (scalar)	62.5 us	0.5 us	125x
Great-circle distance	3.1 us	0.1 us	31x
Full inverse (azimuths, scales)	42.1 us	0.8 us	52x
Direct problem	10.8 us	0.6 us	18x
Batch geodesic (1K points)	59.2 ms	0.06 ms	1,063x
Batch geodesic (10K points)	589 ms	0.29 ms	2,078x
Batch geodesic (1M points)	~59 sec	23.8 ms	~2,500x

Batch throughput: 42 million point-pairs/sec (rayon-parallel).

Verification

Verified to sub-nanometer precision against geographiclib and geopy:

• 10,000 Karney GeodTest cases -- max distance error: 7.5e-9 m (~7 nanometers), 0 cases >1mm
• 15 real-world city pairs -- max error: 9.3e-10 m (<1 nanometer)
• 13 edge cases -- identical points, poles, antipodal, antimeridian, near-antipodal (Vincenty failure cases) -- all exact match
• 1,000 random direct/inverse roundtrips -- max error: 5.6e-9 m
• 1,000 random cross-checks vs geopy -- max error: 5.6e-9 m
• Batch vs scalar consistency -- exact match (0.0 m difference)

pytest tests/test_python_compat.py -v     # 57 tests
python tests/verify_accuracy.py           # 37/37 comprehensive checks
python tests/bench_distance.py            # full benchmark suite

Installation

pip install geors

Usage

Drop-in replacement

# Before
from geopy.distance import geodesic, great_circle

# After (same API, 125x faster)
from geors.distance import geodesic, great_circle

Distance calculations

from geors.distance import geodesic, great_circle

# Geodesic (ellipsoidal, Karney's algorithm)
d = geodesic((40.7128, -74.0060), (51.5074, -0.1278))
print(d.km)        # 5570.248...
print(d.miles)     # 3461.358...
print(d.meters)    # 5570248.4...
print(d.feet)      # 18274764.0...
print(d.nautical)  # 3007.6...

# Great circle (spherical, Haversine)
d = great_circle((40.7128, -74.0060), (51.5074, -0.1278))
print(d.km)        # 5564.847...

Low-level functions

from geors._geors import distance, geodesic

# Scalar
meters = distance.geodesic_distance(40.7128, -74.0060, 51.5074, -0.1278)

# Full inverse (returns dict with s12, azi1, azi2, a12, ...)
result = distance.geodesic_inverse(40.7128, -74.0060, 51.5074, -0.1278)

# Direct problem (start + bearing + distance -> endpoint)
result = geodesic.direct(40.7128, -74.0060, 51.37, 5_554_000.0)

# Destination
lat2, lon2 = distance.geodesic_destination(40.7128, -74.0060, 51.37, 5_554_000.0)

Batch operations (numpy arrays, rayon-parallel)

import numpy as np
from geors._geors import distance

lat1 = np.random.uniform(-90, 90, 1_000_000)
lon1 = np.random.uniform(-180, 180, 1_000_000)
lat2 = np.random.uniform(-90, 90, 1_000_000)
lon2 = np.random.uniform(-180, 180, 1_000_000)

# Returns numpy array, computed in parallel across all cores
distances = distance.geodesic_distance_batch(lat1, lon1, lat2, lon2)
distances_gc = distance.great_circle_distance_batch(lat1, lon1, lat2, lon2)

Geocoding (forwards to geopy)

# Geocoding is I/O-bound, so we forward to geopy transparently
from geors.geocoders import Nominatim

geolocator = Nominatim(user_agent="my-app")
location = geolocator.geocode("New York City")
print(location.latitude, location.longitude)

What's Rust, what's forwarded

Component	Implementation	Why
geors.distance.geodesic	Rust (geographiclib-rs)	CPU-bound: Karney's iterative algorithm
geors.distance.great_circle	Rust	CPU-bound: Haversine trig
geodesic_distance_batch	Rust + rayon	Parallel array processing
geors.geocoders.*	Forwards to geopy	I/O-bound: HTTP API calls
geors.extra.rate_limiter	Forwards to geopy	I/O-bound: sleep/retry logic
Unit conversions	Rust	Simple arithmetic, zero overhead

Architecture

geors/
  crates/
    geo-math/     # High-precision math (trig in degrees, error-free sums, Accumulator)
    geors/        # Core library (geodesic, great-circle, polygon area, constants)
  src/            # PyO3 bindings (py_distance, py_geodesic, py_units)
  python/geors/   # Drop-in Python wrapper (Distance class, geocoder forwarding)

Built with PyO3 + maturin. Wheels for Linux, macOS, and Windows.

License

MIT