srtm 0.3.0

High-performance SRTM elevation data library

htg - High-performance SRTM Elevation Library

Ultra-fast SRTM elevation queries in Python. Built with Rust, delivering 3.5x faster performance than the most popular Python SRTM library.

Python bindings for the htg Rust library, providing blazingly fast elevation queries from SRTM .hgt files with sub-microsecond latency.

Installation

pip install srtm

Quick Start

import srtm

# Create service with up to 100 cached tiles
service = srtm.SrtmService("/path/to/srtm", cache_size=100)

# Query elevation (Mount Fuji)
elevation = service.get_elevation(35.3606, 138.7274)
print(f"Elevation: {elevation}m")  # 3776

# Interpolated query for smoother results
elevation = service.get_elevation_interpolated(35.3606, 138.7274)
print(f"Elevation: {elevation:.2f}m")  # 3776.42

# Check cache performance
stats = service.cache_stats()
print(f"Cache hit rate: {stats.hit_rate:.1%}")

Utility Functions

import srtm

# Convert coordinates to filename
filename = srtm.lat_lon_to_filename(35.5, 138.7)
print(filename)  # "N35E138.hgt"

# Parse filename to coordinates
coords = srtm.filename_to_lat_lon("N35E138.hgt")
print(coords)  # (35, 138)

# Void value constant
print(srtm.VOID_VALUE)  # -32768

SRTM Data

Download SRTM .hgt files from:

• https://dwtkns.com/srtm30m/
• https://earthexplorer.usgs.gov/

Performance

htg-python delivers exceptional performance through its Rust core and PyO3 bindings, significantly outperforming traditional Python SRTM libraries.

Benchmarks vs Popular Python Libraries

Comparison using local .hgt files only (fair, apples-to-apples test):

Library	Implementation	Per Query	Throughput	vs htg-python
htg-python	Rust + PyO3	0.41 μs	2,419,110 q/s	1.0x (baseline) ⚡
srtm.py	Pure Python (256 ⭐)	1.43 μs	697,654 q/s	3.5x slower
srtm4	Python + C++ subprocess	99,630 μs	10 q/s	241,017x slower

Key findings:

• 3.5x faster than srtm.py (most popular, fair comparison)
• 241,000x faster than srtm4 (subprocess overhead dominates)
• Sub-microsecond latency - queries complete in 0.41 microseconds
• 2.4 million queries/second on a single thread

Benchmark environment: Python 3.12, macOS (Apple Silicon). See BENCHMARKS.md for full methodology.

Why So Fast?

• Zero-copy memory access: Memory-mapped I/O eliminates data copying
• No subprocess overhead: Direct Rust function calls via PyO3 (unlike srtm4's subprocess approach)
• Optimized compilation: LLVM optimizations with inline expansion
• Efficient caching: In-memory LRU cache vs disk-based caching

Real-World Performance

import srtm
service = srtm.SrtmService("/path/to/data", cache_size=100)

# Single query: ~0.4 microseconds (sub-millisecond!)
elevation = service.get_elevation(35.3606, 138.7274)

# Batch queries: ~147k per second on a single thread
for lat, lon in coordinates:
    elevation = service.get_elevation(lat, lon)

Production-ready: Can handle millions of requests per second with multiple cores.

License

MIT