# rapidgeo
[](https://pypi.org/project/rapidgeo/)
[](https://rapidgeo.readthedocs.io/)
[](LICENSE)
Geographic calculations for Python: distance calculations, polyline encoding/decoding, line simplification, and curve similarity measures.
## Installation
```bash
pip install rapidgeo # Base package
pip install rapidgeo[numpy] # With NumPy support
```
## Quick Start
```python
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
```
## What it does
**Distance Calculations:**
- Haversine: Spherical Earth approximation, good for most uses
- Vincenty: Ellipsoidal Earth model for higher precision
- Euclidean: Flat plane calculations
- Batch operations for multiple points
**Polyline Encoding/Decoding:**
- Google Polyline Algorithm implementation
- Compress coordinate sequences to text strings
- Configurable precision levels
**Line Simplification:**
- Douglas-Peucker algorithm
- Reduce point count while preserving shape
- Configurable distance tolerance
**Curve Similarity:**
- Fréchet distance: Compare trajectories (point order matters)
- Hausdorff distance: Compare shapes (point order doesn't matter)
- Useful for GPS track analysis and route comparison
## Coordinate System
All coordinates use **longitude, latitude** ordering (lng, lat):
```python
# 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:**
```python
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:**
```python
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:**
```python
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](LICENSE-APACHE) or [MIT License](LICENSE-MIT) at your option.
Raw data
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"description": "# rapidgeo\n\n[](https://pypi.org/project/rapidgeo/)\n[](https://rapidgeo.readthedocs.io/)\n[](LICENSE)\n\nGeographic calculations for Python: distance calculations, polyline encoding/decoding, line simplification, and curve similarity measures.\n\n## Installation\n\n```bash\npip install rapidgeo # Base package\npip install rapidgeo[numpy] # With NumPy support\n```\n\n## Quick Start\n\n```python\nfrom rapidgeo.distance import LngLat\nfrom rapidgeo.distance.geo import haversine, vincenty_distance\n\n# Create coordinates (longitude, latitude)\nsf = LngLat.new_deg(-122.4194, 37.7749) # San Francisco\nnyc = LngLat.new_deg(-74.0060, 40.7128) # New York City\n\n# Calculate distance using Haversine formula\ndistance = haversine(sf, nyc)\nprint(f\"Distance: {distance / 1000:.1f} km\") # ~4,130 km\n```\n\n## What it does\n\n**Distance Calculations:**\n- Haversine: Spherical Earth approximation, good for most uses\n- Vincenty: Ellipsoidal Earth model for higher precision \n- Euclidean: Flat plane calculations\n- Batch operations for multiple points\n\n**Polyline Encoding/Decoding:**\n- Google Polyline Algorithm implementation\n- Compress coordinate sequences to text strings\n- Configurable precision levels\n\n**Line Simplification:**\n- Douglas-Peucker algorithm\n- Reduce point count while preserving shape\n- Configurable distance tolerance\n\n**Curve Similarity:**\n- Fr\u00e9chet distance: Compare trajectories (point order matters)\n- Hausdorff distance: Compare shapes (point order doesn't matter) \n- Useful for GPS track analysis and route comparison\n\n## Coordinate System\n\nAll coordinates use **longitude, latitude** ordering (lng, lat):\n\n```python\n# Correct\npoint = LngLat.new_deg(-122.4194, 37.7749) # lng first, lat second\n\n# Common mistake\n# point = LngLat.new_deg(37.7749, -122.4194) # lat, lng - WRONG\n```\n\n## Usage Examples\n\n**Compare GPS Tracks:**\n```python\nfrom rapidgeo.similarity.frechet import discrete_frechet\n\n# Two similar walking routes\nroute_a = [LngLat.new_deg(-122.419, 37.775), LngLat.new_deg(-122.418, 37.776)]\nroute_b = [LngLat.new_deg(-122.419, 37.775), LngLat.new_deg(-122.417, 37.777)]\n\nsimilarity = discrete_frechet(route_a, route_b)\nprint(f\"Routes differ by {similarity:.1f} meters\")\n```\n\n**Simplify GPS Tracks:**\n```python\nfrom rapidgeo.simplify import douglas_peucker\n\n# Reduce GPS track complexity\ndetailed_track = [LngLat.new_deg(-122.4, 37.7), LngLat.new_deg(-122.39, 37.71), ...]\nsimplified = douglas_peucker(detailed_track, tolerance=10.0) # 10m tolerance\nprint(f\"Reduced from {len(detailed_track)} to {len(simplified)} points\")\n```\n\n**Encode/Decode Polylines:**\n```python \nfrom rapidgeo.polyline import encode, decode\n\n# Compress GPS data for storage/transmission\npoints = [LngLat.new_deg(-122.4, 37.7), LngLat.new_deg(-122.3, 37.8)]\nencoded = encode(points, precision=5)\ndecoded = decode(encoded, precision=5)\n```\n\n## License\n\nLicensed under either of [Apache License, Version 2.0](LICENSE-APACHE) or [MIT License](LICENSE-MIT) at your option.\n",
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