semvec/pypi-packages huggingface dataset:
abbrcompress 0.1.4
Token-saving abbreviation compressor for OpenAI LLM prompts
Requires Python: >=3.8

# abbrcompress

Token-saving abbreviation compression for OpenAI-compatible LLM prompts. 

`abbrcompress` identifies frequently repeated phrases in a prompt, replaces them with short abbreviations, and decodes the model response back to the original text — helping reduce token usage for long or repetitive prompts. Testing on real datasets shows reductions of 1-2% of tokens sending to OpenAI.

## What this library does

✅ Detects repeated n-grams

✅ Computes net token savings using tiktoken

✅ Applies abbreviations only when savings are positive

✅ Decodes model responses safely

❌ Does not modify OpenAI tokenization

❌ Does not guarantee savings for short prompts

---

## Install

```bash
pip install abbrcompress
# or
uv pip install abbrcompress
```

## Usage

```python
from abbrcompress.core import build_abbreviations, compress_prompt, decode_abbreviations, token_len

prompt = """your prompt text here"""

# Build abbreviation dictionary
abbr_dict, _ = build_abbreviations(prompt)

# Compress prompt
compressed_prompt = compress_prompt(prompt, abbr_dict)

# TODO: send compressed_prompt to OpenAI and get return_message as response
return_message = _  # Placeholder for OpenAI response

# Decode return from OpenAI
decoded_text = decode_abbreviations(return_message, abbr_dict)
```
bw-converter-python 0.0.1
A simple package to convert color images to black and white
Requires Python: >=3.7

# Black and White Image Converter

A simple Python package to convert color images to black and white.

## Installation

```bash
pip install bw-converter-python
```

## Usage

### Command Line

```bash
bw-converter input_image.jpg output_image.jpg
```

### Python

```python
from bw_converter.converter import process_image

process_image("input.jpg", "output.jpg")
```
rainwave-tools 2026.0
Tools for maintaining a local library of music for Rainwave
Requires Python: >=3.10

Tools for maintaining a local library of music for [Rainwave][]

## Install

    pip install rainwave-tools

## Tools

### Dealing with mp3 files and id3 tags

*   `apply_genres`: apply genres that are assigned using `assign_genres`
*   `assign_genres`: interactively assign genres to directories of mp3 files
*   `cdg`: manage cooldown groups (genre tags) in mp3 files
*   `normalize_filenames`: automatically rename all files in a directory based on information in id3 tags
*   `rename_artist`: change an artist name in mp3 files, keeping multiple artists intact
*   `retag`: interactively edit title, album, and artist tags on all mp3 files in a directory
*   `rgwipe`: remove all replaygain tags from mp3 files
*   `rwtag`: view and manipulate id3 tags on mp3 files
*   `tit3_tit2`: Copy the contents of TIT3 to TIT2
*   `titles`: interactively update titles on all mp3 files in a directory

### Dealing with album art files

*   `delete_art`: delete generated album art files for a specific album

### Dealing with the database

*   `album_search`: search the database for an album by name substring
*   `missing_art`: find albums that do not have album art
*   `move_ratings`: move song ratings from one song to another (e.g. when removing duplicate songs)
*   `song_search`: search the database for a song by title substring
*   `url_check`: check the validity of urls in the database

### Dealing with OCR

*   `ocremix.py`: Python module for parsing a remix info page on ocremix.org for metadata
*   `getocr`: download remixes from ocremix.org
*   `cleanocr`: update local files with metadata from ocremix.org
*   `ocra2ocr`: update local metadata when a song on an OCR album gets an official OCR release

[rainwave]: https://rainwave.cc/
nccl4py 0.1.1
NCCL4Py: Python bindings for NCCL
Requires Python: >=3.10

*****************************************************
nccl4py: Pythonic NCCL Communication for GPU Clusters
*****************************************************

.. image:: https://img.shields.io/badge/NVIDIA-black?logo=nvidia
   :target: https://www.nvidia.com/
   :alt: NVIDIA

`nccl4py <https://github.com/NVIDIA/nccl>`_ bridges Python's simplicity with the performance of NVIDIA Collective Communications Library (NCCL), and provides a Pythonic interface to NCCL library's functionality. It enables Python applications to leverage NCCL's GPU-accelerated multi-GPU and multi-node communication capabilities for distributed computing workloads.

nccl4py follows the NCCL SLA. The details of the NCCL SLA is available `here <https://docs.nvidia.com/deeplearning/nccl/sla/index.html>`_.

* `Homepage <https://developer.nvidia.com/nccl>`_
* `Repository <https://github.com/NVIDIA/nccl>`_
* `Documentation <https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html>`_
* `Issue tracker <https://github.com/NVIDIA/nccl/issues>`_

``nccl4py`` is under active development. Feedback and suggestions are welcome!


Installation
============

For CUDA 12.x:

.. code-block:: bash

   pip install nccl4py[cu12]

For CUDA 13.x:

.. code-block:: bash

   pip install nccl4py[cu13]
BHResist 0.3
Functionality to compute the thermal resistance of borehole heat exchangers
Requires Python: >=3.10

# BHResist

A pure python library for computing thermal resistance within single-u, double-u, and coaxial grouted borehole heat exchangers. For single and double u-tube configurations, the methods use the 1st-order closed-form multipole approximations, which typically produces results with less than 1% error when compared to the 10th-order multipole method. Coaxial borehole methods apply a simple 1D resistance network method.

This is intended to be a lightweight library that can be easily imported into any other Python tool, with no bulky dependencies.

## Documentation

Documentation for BHResist can be found at https://bhresist.readthedocs.io.

## Citation

Mitchell, Matt, Adams, Sonja, Lee, Edwin, and Swindler, Alexander. BHResist [SWR-25-57]. Computer Software. https://github.com/NREL/BHResist. USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office. 04 Apr. 2025. Web. doi:10.11578/dc.20250421.3.

## References

Hellström, G. 1991. "Ground Heat Storage: Thermal Analyses of Duct Storage Systems." PhD dissertation. Department of Mathematical Physics, University of Lund, Sweden.

Grundmann, R.M. 2016 "Improved design methods for ground heat exchangers." Master’s thesis, Oklahoma State University.

Javed, S. and J.D. Spitler. 2016. "Calculation of borehole thermal resistance." In _Advances in Ground-Source Heat Pump Systems_. Ed. S.J. Rees. Woodhead Publishing. https://doi.org/10.1016/B978-0-08-100311-4.00003-0

Javed, S., and J.D. Spitler. 2017. "Accuracy of borehole thermal resistance calculation methods for grouted single u-tube ground heat exchangers." _Applied Energy,_ 187:790-806. https://doi.org/10.1016/j.apenergy.2016.11.079

Claesson, J., and S. Javed. 2019. "Explicit multipole formulas and thermal network models for calculating thermal resistances of double U-pipe borehole heat exchangers." _Science and Technology for the Built Environment,_ 25(8) pp. 980–992. https://doi.org/10.1080/23744731.2019.1620565