Skip to main content

🍇 Supercharge your Python with rich logging, precise timing, and seamless serialization.

Project description

✨ Features

  • 🎨 Rich Logging: Beautiful, structured logging with loguru integration, multiple output formats (rich console, JSONL, file), and customizable profiles for different environments;
  • ⏱️ Precise Timing: Easy-to-use timing decorators and context managers with detailed statistics (mean, median, stdev) and automatic logging integration;
  • 📦 Multi-Format Serialization: Unified interface for JSON, TOML, and YAML serialization with Pydantic model support and customizable encoding/decoding hooks;
  • 👥 Human-Readable Formats: Intelligent conversion of numbers, durations, and throughput into human-readable strings with appropriate units and precision;
  • 🔄 Progress Tracking: Integrated progress bars with rich visualization, timing integration, and parallel processing support through joblib;
  • ⚙️ Smart Configuration: Environment-aware configuration system using Pydantic with automatic environment variable parsing and type-safe settings.

📦 Installation

To install liblaf-grapes, run the following command:

uv add liblaf-grapes

⌨️ Local Development

You can use Github Codespaces for online development:

Or clone it for local development:

gh repo clone liblaf/grapes
cd grapes
mise run install

🤝 Contributing

Contributions of all types are more than welcome, if you are interested in contributing code, feel free to check out our GitHub Issues to get stuck in to show us what you're made of.

PR Welcome

Contributors

🔗 More Projects

  • 🍇 Grapes - Supercharge your Python with rich logging, precise timing, and seamless serialization.
  • 🍉 Melon - A comprehensive Python library for 3D mesh processing with advanced I/O capabilities, proximity analysis, and integration with external mesh processing tools.
  • 🍊 Tangerine - Squeeze dynamic content into your files with Tangerine's template magic.
  • 🍋‍🟩 Lime - AI-powered Git commit assistant and repository documentation generator
  • 🍎 Apple - A JAX and Warp library for differentiable physics simulation, featuring elastic energy models and finite element methods.
  • 🍒 Cherries - Sweet experiment tracking with Comet, DVC, and Git integration.

📝 License

Copyright © 2025 liblaf.
This project is MIT licensed.

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

liblaf_grapes-6.1.0.tar.gz (31.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

liblaf_grapes-6.1.0-py3-none-any.whl (67.8 kB view details)

Uploaded Python 3

File details

Details for the file liblaf_grapes-6.1.0.tar.gz.

File metadata

  • Download URL: liblaf_grapes-6.1.0.tar.gz
  • Upload date:
  • Size: 31.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for liblaf_grapes-6.1.0.tar.gz
Algorithm Hash digest
SHA256 7995a83966ffe7034cc73504d09b9675df4c851320dc95d34fae2a8ff4dcf840
MD5 9024791cc1590a5914c499b24d79538a
BLAKE2b-256 2e19f26511e62bd5950a3d48df95fee2408c055e48824fdab2d2006f33ed4acd

See more details on using hashes here.

Provenance

The following attestation bundles were made for liblaf_grapes-6.1.0.tar.gz:

Publisher: release.yaml on liblaf/grapes

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file liblaf_grapes-6.1.0-py3-none-any.whl.

File metadata

  • Download URL: liblaf_grapes-6.1.0-py3-none-any.whl
  • Upload date:
  • Size: 67.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for liblaf_grapes-6.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 10da3b35f6c5ed4e5136328ad7e0bcfe4807a73fc19400bde3dd6fc1ef7feb7d
MD5 2f73f1db9d7b91eab65a779d2be92533
BLAKE2b-256 a8b5e31a21723671973981974ff3f2830dd23ebffd6d67100897ef5c79689935

See more details on using hashes here.

Provenance

The following attestation bundles were made for liblaf_grapes-6.1.0-py3-none-any.whl:

Publisher: release.yaml on liblaf/grapes

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page