mscompress 1.0.7.post2

Python bindings for mscompress, a compression library for mass spectrometry data

MScompress Python Library

Python bindings for MScompress, a high-performance compression library for mass spectrometry data.

Overview

The MScompress Python library provides a fast and efficient way to compress and decompress mass spectrometry data files in mzML format. It features:

• 🚀 High Performance: Multi-threaded compression/decompression with state-of-the-art speeds
• 📦 MSZ Format: Novel compressed format with random-access capabilities
• 🔄 Lossless & Lossy: Support for both lossless and lossy compression modes
• 🐍 Pythonic API: Clean, intuitive interface with NumPy integration
• 🎯 Direct Data Access: Extract spectra, m/z arrays, and intensity data without full decompression

Installation

From PyPI

pip install mscompress

From Source

Prerequisites:

• Python ≥ 3.9
• NumPy
• Cython
• C compiler (GCC, Clang, or MSVC)

Build and install:

git clone --recurse-submodules https://github.com/chrisagrams/mscompress.git
cd mscompress/python
pip install -e .

Quick Start

Basic Usage

import mscompress

# Read an mzML file
mzml = mscompress.read("data.mzML")

# Compress to MSZ format
mzml.compress("data.msz")

# Read compressed MSZ file
msz = mscompress.read("data.msz")

# Decompress back to mzML
msz.decompress("output.mzML")

Working with Spectra

import mscompress

# Open a file (mzML or MSZ)
file = mscompress.read("data.mzML")

# Access file metadata
print(f"File size: {file.filesize} bytes")
print(f"Total spectra: {len(file.spectra)}")

# Iterate through all spectra
for spectrum in file.spectra:
    print(f"Scan {spectrum.scan}: MS{spectrum.ms_level}")
    print(f"  Retention time: {spectrum.retention_time:.2f}s")
    print(f"  Data points: {spectrum.size}")

# Access specific spectrum by index
spectrum = file.spectra[0]

# Get m/z and intensity arrays as NumPy arrays
mz_array = spectrum.mz
intensity_array = spectrum.intensity

# Get combined peaks as 2D array [m/z, intensity]
peaks = spectrum.peaks

# Access spectrum metadata XML
xml_element = spectrum.xml

Compression Configuration

import mscompress

# Open mzML file
mzml = mscompress.read("data.mzML")

# Configure compression settings
mzml.arguments.threads = 8  # Use 8 threads
mzml.arguments.zstd_compression_level = 3  # Set ZSTD level (1-22)
mzml.arguments.blocksize = 100  # Spectra per block

# Compress with custom settings
mzml.compress("data.msz")

Random Access to Compressed Data

import mscompress

# Open compressed MSZ file
msz = mscompress.read("data.msz")

# Directly access specific spectrum without full decompression
spectrum_100 = msz.spectra[100]
print(f"Scan: {spectrum_100.scan}")
print(f"m/z range: {spectrum_100.mz[0]:.2f} - {spectrum_100.mz[-1]:.2f}")

# Extract binary data for a specific spectrum
mz_binary = msz.get_mz_binary(100)
intensity_binary = msz.get_inten_binary(100)
xml_metadata = msz.get_xml(100)

Context Manager Support

import mscompress

# Use with context manager for automatic resource cleanup
with mscompress.read("data.mzML") as file:
    for spectrum in file.spectra:
        # Process spectrum
        process_spectrum(spectrum)
# File is automatically closed

API Reference

Functions

read(path: str | bytes) -> MZMLFile | MSZFile

Opens and parses an mzML or MSZ file.

Parameters:

• path: Path to the file (string or bytes)

Returns:

• MZMLFile or MSZFile object depending on file type

Raises:

• FileNotFoundError: If file does not exist
• IsADirectoryError: If path points to a directory
• OSError: If file type cannot be determined

get_num_threads() -> int

Returns the number of available CPU threads.

get_filesize(path: str | bytes) -> int

Returns the size of a file in bytes.

Classes

MZMLFile

Handler for mzML format files.

Properties:

• path: File path (bytes)
• filesize: File size in bytes (int)
• format: Data format information (DataFormat)
• spectra: Collection of spectra (Spectra)
• positions: Division/position information (Division)
• arguments: Runtime configuration (RuntimeArguments)

Methods:

• compress(output: str | bytes): Compress to MSZ format
• get_mz_binary(index: int) -> np.ndarray: Extract m/z array for spectrum
• get_inten_binary(index: int) -> np.ndarray: Extract intensity array for spectrum
• get_xml(index: int) -> Element: Extract XML metadata for spectrum
• describe() -> dict: Get file description dictionary

MSZFile

Handler for MSZ (compressed) format files.

Properties:

• Same as MZMLFile

Methods:

• decompress(output: str | bytes): Decompress to mzML format
• get_mz_binary(index: int) -> np.ndarray: Extract m/z array for spectrum
• get_inten_binary(index: int) -> np.ndarray: Extract intensity array for spectrum
• get_xml(index: int) -> Element: Extract XML metadata for spectrum
• describe() -> dict: Get file description dictionary

Spectrum

Represents a single mass spectrum.

Properties:

• index: Spectrum index (int)
• scan: Scan number (int)
• ms_level: MS level (int)
• retention_time: Retention time in seconds (float)
• size: Number of data points (int)
• mz: m/z values (np.ndarray)
• intensity: Intensity values (np.ndarray)
• peaks: Combined m/z and intensity as 2D array (np.ndarray)
• xml: XML metadata element (Element)

Spectra

Collection of spectra with lazy loading and iteration support.

Methods:

• __len__(): Get total number of spectra
• __iter__(): Iterate over all spectra
• __getitem__(index: int): Access spectrum by index

RuntimeArguments

Runtime configuration for compression/decompression.

Properties:

• threads: Number of threads to use (int)
• blocksize: Number of spectra per block (int)
• mz_scale_factor: m/z scaling factor (int)
• int_scale_factor: Intensity scaling factor (int)
• target_xml_format: Target XML format (int)
• target_mz_format: Target m/z format (int)
• target_inten_format: Target intensity format (int)
• zstd_compression_level: ZSTD compression level 1-22 (int)

DataFormat

Data format information.

Properties:

• source_mz_fmt: Source m/z format (int)
• source_inten_fmt: Source intensity format (int)
• source_compression: Source compression type (int)
• source_total_spec: Total number of spectra (int)
• target_xml_format: Target XML format (int)
• target_mz_format: Target m/z format (int)
• target_inten_format: Target intensity format (int)

Methods:

• to_dict() -> dict: Convert to dictionary representation
• __str__() -> str: String representation

Division

Division structure containing data positions and scan information.

Properties:

• spectra: Spectrum data positions (DataPositions)
• xml: XML data positions (DataPositions)
• mz: m/z data positions (DataPositions)
• inten: Intensity data positions (DataPositions)
• size: Number of divisions (int)
• scans: Scan numbers (np.ndarray)
• ms_levels: MS levels (np.ndarray)
• ret_times: Retention times (np.ndarray or None)

DataPositions

Position information for data blocks.

Properties:

• start_positions: Start positions (np.ndarray[uint64])
• end_positions: End positions (np.ndarray[uint64])
• total_spec: Total number of spectra (int)

Examples

Extract All MS2 Spectra

import mscompress
import numpy as np

file = mscompress.read("data.mzML")

ms2_spectra = []
for spectrum in file.spectra:
    if spectrum.ms_level == 2:
        ms2_spectra.append({
            'scan': spectrum.scan,
            'rt': spectrum.retention_time,
            'mz': spectrum.mz,
            'intensity': spectrum.intensity
        })

print(f"Found {len(ms2_spectra)} MS2 spectra")

Compare Compression Ratios

import mscompress
import os

mzml = mscompress.read("data.mzML")
original_size = mzml.filesize

# Compress with different ZSTD levels
for level in [1, 3, 5, 10]:
    mzml.arguments.zstd_compression_level = level
    output = f"data_level_{level}.msz"
    mzml.compress(output)
    
    compressed_size = os.path.getsize(output)
    ratio = original_size / compressed_size
    print(f"Level {level}: {ratio:.2f}x compression")

Parallel Processing of Spectra

import mscompress
from multiprocessing import Pool

def process_spectrum(args):
    file_path, index = args
    file = mscompress.read(file_path)
    spectrum = file.spectra[index]
    # Your processing logic here
    return spectrum.scan, len(spectrum.mz)

file = mscompress.read("data.mzML")
indices = range(len(file.spectra))
args = [(file.path.decode(), i) for i in indices]

with Pool() as pool:
    results = pool.map(process_spectrum, args)

print(f"Processed {len(results)} spectra")

Filter and Extract Peaks

import mscompress
import numpy as np

file = mscompress.read("data.mzML")

# Extract peaks above intensity threshold
threshold = 1000.0

for spectrum in file.spectra:
    mask = spectrum.intensity > threshold
    filtered_mz = spectrum.mz[mask]
    filtered_intensity = spectrum.intensity[mask]
    
    print(f"Scan {spectrum.scan}: {len(filtered_mz)} peaks above threshold")

Performance Tips

1. Multi-threading: Set arguments.threads to match your CPU core count for optimal performance
2. Block size: Adjust arguments.blocksize based on your data - larger blocks may improve compression ratio but reduce random-access granularity
3. Compression level: ZSTD levels 1-3 offer good speed/ratio balance; higher levels improve compression at the cost of speed
4. Memory usage: When processing large files, iterate through spectra rather than loading all into memory

Type Hints

The library includes full type hints and stub files (.pyi) for improved IDE support and type checking:

import mscompress
from typing import Union

def process_file(path: Union[str, bytes]) -> None:
    file = mscompress.read(path)  # Type checker knows this is MZMLFile | MSZFile
    spectra = file.spectra  # Type: Spectra
    spectrum = spectra[0]  # Type: Spectrum
    mz = spectrum.mz  # Type: np.ndarray[np.float32 | np.float64]

Contributing

Contributions are welcome! Please see the main repository for guidelines: https://github.com/chrisagrams/mscompress

License

See the main repository for license information.

Support

For bug reports and feature requests, please open an issue: https://github.com/chrisagrams/mscompress/issues

Citation

If you use MScompress in your research, please cite our work (citation information coming soon).