las-read-rs 0.1.7

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

las_read_rs 🦀

High-performance LAS (Log ASCII Standard) file reader written in Rust with Python bindings. A faster alternative to pure Python implementations, capable of parsing large geophysical log files efficiently.

⚡ Performance

Benchmarked with a 92 MB LAS file (400 curves, 21,842 points each):

Library	Read Time	Speedup
las_read_rs (Rust)	~1.0s	9x faster 🚀
lasio (Python)	~9.0s	baseline

🚀 Installation

Requirements:

• Python 3.8 or higher
• Supported OS: Windows, macOS, Linux

pip install las_read_rs

Optional dependencies for export features:

pip install pandas          # For DataFrame and CSV export
pip install openpyxl        # For Excel export
pip install polars          # For Polars DataFrame

📖 Quick Start

import lasio_rs

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

# Access metadata
print(las.version['VERS'].value)  # "2.0"
print(las.well['WELL'].value)     # Well name

# Access curve data directly
depth = las['DEPT']  # Returns list of floats
gr = las['GR']       # Gamma Ray values

# List all curves
for curve_name in las.keys():
    curve = las.curves[curve_name]
    print(f"{curve_name} ({curve.unit}): {len(curve.data)} points")

📊 DataFrame Conversion

# Convert to pandas DataFrame
df = las.to_df()
print(df.head())

# Convert to polars DataFrame
df_polars = las.to_polars()

💾 Export Formats

CSV Export

las.to_csv("output.csv")

Excel Export

las.to_excel("output.xlsx", sheet_name="Well Data")

LAS Export (2.0 and 3.0)

# Export as LAS 2.0
las.to_las("output_v2.las", version="2.0")

# Export as LAS 3.0
las.to_las("output_v3.las", version="3.0")

🔧 API Reference

lasio_rs.read(path)

Reads a LAS file and returns a LASFile object.

LASFile Properties

Property	Description
version	Version section (VERS, WRAP)
well	Well information (STRT, STOP, STEP, NULL, WELL, etc.)
curves	Curve metadata and data
params	Parameter section

LASFile Methods

Method	Description
las[mnemonic]	Get curve data as list
las.keys()	List curve mnemonics
las.to_df()	Convert to pandas DataFrame
las.to_polars()	Convert to polars DataFrame
las.to_csv(path)	Export to CSV
las.to_excel(path)	Export to Excel
las.to_las(path, version)	Export to LAS format

🏗️ Building from Source

Requirements:

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

git clone https://github.com/jemilianofl/lasio-rs.git
cd lasio-rs
pip install maturin
maturin develop --release

📋 Supported LAS Versions

• ✅ LAS 2.0
• ✅ LAS 3.0 (read support)
• ✅ Export to LAS 2.0/3.0

🤝 Compatibility with lasio

las_read_rs provides a similar API to the popular lasio library:

# lasio style
import lasio
las = lasio.read("file.las")
depth = las['DEPT']

# las_read_rs style (same!)
import lasio_rs
las = lasio_rs.read("file.las")
depth = las['DEPT']

📄 License

MIT

🙏 Acknowledgments

• Built with PyO3 for Python bindings
• Uses nom for parsing
• Inspired by lasio