las-read-rs 0.1.1

A high-performance LAS (Log ASCII Standard) file reader written in Rust

LAS_read_rs

LAS_read_rs is a high-performance Python library for reading LAS (Log ASCII Standard) files, written in Rust using pyo3 and nom. It is designed to be a faster alternative to pure Python implementations, capable of parsing large geophysical log files efficiently.

Features

• 🚀 High Performance: Built with Rust's nom parser combinators and parallelized with rayon for maximum speed.
• 🧵 Multi-threaded: parses the data section (~ASCII) in parallel chunks.
• 📦 Easy Installation: Distributed as a standard Python wheel via PyPI.
• 🐍 Pythonic Interface: Simple read() function returning a LASFile object.
• ✅ Standard Compliant: Supports LAS 2.0 (and 1.2 compatible structure).

Benchmarks

Parsed a LAS file with 100,000 lines of data:

• LAS_read_rs: ~0.8s (Pure parsing time) / ~3.8s (Including CLI overhead benchmarks)
• Pure Python: Significantly slower (typically 10x-50x slower depending on implementation).

Installation

pip install LAS_read_rs

Usage

import lasio_rs

# Read a LAS file
las = lasio_rs.read("path/to/well_log.las")

# Access Version Information
print(las.version)
# Output (JSON representation):
# {
#   "VERS": {"mnemonic": "VERS", "value": "2.0", "descr": "CWLS LOG ASCII STANDARD - VERSION 2.0"},
#   "WRAP": {"mnemonic": "WRAP", "value": "NO", "descr": "One line per depth step"}
# }

# Access Well Metadata (e.g., STRT, STOP, NULL)
# Note: Currently exposed mainly via debug getters or verifying structure
# Future versions will expose a full dictionary-like interface.

print(f"Well Name: {las.well_name}")

Structure

The library maps LAS sections to Rust structs:

• ~Version -> las.version
• ~Well -> las.well
• ~Curves -> las.curves (Metadata)
• ~ASCII -> Data columns (Stored internally as efficient vectors)

Building from Source

Requirements:

• Python 3.7+
• Rust (cargo, rustc)
• maturin

pip install maturin
maturin develop --release

License

MIT