litl 0.1.2

Lightweight InTerface for Lossy compression

litl 🗜️

Lightweight InTerface for Lossy compression

A flexible Python framework for implementing, testing, and deploying lossy compression algorithms.

Features

• 🔧 Simple Compressors Interface: Simple and universal interface for implementing and sharing compressors
• 📊 Built-in Evaluation: Comprehensive quality metrics with batteries included (MSE, PSNR, SSIM, L1)
• 💾 Standardized Storage Format: Custom .litl file format for any compressor with user-defined metadata
• 🎯 Multiple Data Formats: Support for NumPy, PyTorch, and NetCDF
• 📈 Rich CLI: Beautiful command-line interface, in addition to rich Python API

Installation

pip install litl

Optional Dependencies

For NetCDF support:

pip install litl[netcdf]

CLI

1. Compress Data

litl compress <path_to_compressor.py:CompressorClassName> <data_to_compress.npy> <config.json> <output.litl>

2. Decompress Data

litl decompress <output.litl> <decompressed.npy>

3. Evaluate Compression (compress and decompress, recording quality metrics)

litl evaluate <path_to_compressor.py:CompressorClassName> <data_to_compress.npy> <config.json>

4. Inspect Compressed Files

litl info <output.litl>

Python API

Compress Data

import litl

compressed_blob, meta, metrics = litl.compress(
    compressor_name="path/to/compressor.py:ClassName",
    original_data_path="data/hurricane.npy",
    litl_file_path="path/to/compressed.litl"
)

print(f"Compressed data saved to {compressed_path}")
print(f"Metadata: {meta}")
print(f"Metrics: {metrics}")

litl.DotLitl.save(
    litl_file_path="path/to/compressed.litl",
    blob=compressed_blob,
    compressor_name="path/to/compressor.py:ClassName",
    meta=meta
)

Decompress Data

import litl

decompressed_data = litl.decompress(
    litl_file_path="path/to/compressed.litl",
    decompressed_path="data/decompressed.npy"
)

print(f"Decompressed data saved to {decompressed_path}")

data_numpy = decompressed_data.numpy()

Creating a Custom Compressor

Implement the Compressor interface:

from litl.compressors import Compressor, CompressorAbout
from litl.datawrapper import DataWrapper
from litl.blobs import ArrayBlob, Blob
import pydantic

class MyCompressor(Compressor):
    
    @classmethod
    def about(cls) -> CompressorAbout:
        return CompressorAbout(
            name="MyCompressor",
            description="A simple lossy compressor",
            version="1.0.0",
            author="Your Name"
        )
    
    @classmethod
    def blob_class(cls):
        # Specify the blob class used for compressed data. This type will be passed into your decompressed function
        return ArrayBlob
    
    @classmethod
    def compress(cls, data: DataWrapper, config: dict) -> tuple[Blob, dict]:
        # Your compression logic here

        # Return a blob containing compressed data.
        # Built-ins are:
        # - ArrayBlob for NumPy and PyTorch arrays
        # - ModelBlob for PyTorch models
        # - ByteBlob for byte arrays
        # If none of these work - extend the Blob class and implement a custom blob

        meta = {}  # Metadata required to reconstruct the data, can be any serializable dictionary
        return ArrayBlob(compressed_array), meta
    
    @classmethod
    def decompress(cls, blob: ArrayBlob, meta: dict) -> DataWrapper:
        # Your decompression logic here

        # Return Reconstructed data wrapped in DataWrapper
        return DataWrapper(decompressed)

DataWrapper

The DataWrapper class supports multiple formats for loading and saving data:

• NumPy: .npy, .np files
• PyTorch: .pt files
• NetCDF: .nc files (requires netCDF4)

from litl import DataWrapper

# Load data
data = DataWrapper.from_file("data.npy")

# Save data
data.save_to_file("output.nc")

# Access underlying arrays
numpy_array = data.numpy()
torch_tensor = data.tensor()

Built-in Blob Types

Choose the appropriate blob type for your compressed data:

• ArrayBlob: For NumPy arrays
• BytesBlob: For raw bytes
• ModelBlob: For PyTorch models

Evaluation Metrics

The Evaluator provides standard compression metrics:

Quality Metrics:

• Mean Squared Error (MSE)
• Peak Signal-to-Noise Ratio (PSNR)
• Structural Similarity Index (SSIM)
• L1 Loss

Size Metrics:

• Original size
• Compressed size
• Compression ratio

File Format

The .litl format (DotLitl) stores:

• Compressed data with Zstandard compression
• Compressor metadata and version
• Integrity checksums
• Configuration parameters

License

MIT License - see LICENSE for details.