semvec/pypi-packages huggingface dataset:
pyfonts 1.2.0
A simple and reproducible way of using fonts in matplotlib
Requires Python: >=3.9
Keywords: matplotlib, font, reproducibility, google, bunny

<div align="center" style="font-size: 1.6em">

# pyfonts

[![PyPI Downloads](https://static.pepy.tech/badge/pyfonts)](https://pepy.tech/projects/pyfonts)
![Coverage](https://raw.githubusercontent.com/y-sunflower/pyfonts/refs/heads/main/coverage-badge.svg)
![Python Versions](https://img.shields.io/badge/Python-3.9–3.14-blue)

<img src="https://github.com/JosephBARBIERDARNAL/static/blob/main/python-libs/pyfonts/image.png?raw=true" alt="Pyfonts logo" align="right" width="150px"/>

</div>

A **simple** and **reproducible** way of using fonts in matplotlib. In short, `pyfonts`:

- allows you to use all fonts from [**Google Font**](https://fonts.google.com/)
- allows you to use all fonts from [**Bunny Font**](https://fonts.bunny.net/) (GDPR-compliant alternative to Google Fonts)
- allows you to use any font from an **arbitrary URL**
- is **efficient** (thanks to its cache system)

<br>

## Quick start

- Google Fonts

```python
import matplotlib.pyplot as plt
from pyfonts import load_google_font

font = load_google_font("Fascinate Inline")

fig, ax = plt.subplots()
ax.text(x=0.2, y=0.5, s="Hey there!", size=30, font=font)
```

![](https://raw.githubusercontent.com/y-sunflower/pyfonts/refs/heads/main/docs/img/quickstart.png)

- Bunny Fonts

```python
import matplotlib.pyplot as plt
from pyfonts import load_bunny_font

font = load_bunny_font("Barrio")

fig, ax = plt.subplots()
ax.text(x=0.2, y=0.5, s="Hey there!", size=30, font=font)
```

![](https://raw.githubusercontent.com/y-sunflower/pyfonts/refs/heads/main/docs/img/quickstart-2.png)

[**See more examples**](https://y-sunflower.github.io/pyfonts/#quick-start)

<br>

## Installation

```bash
pip install pyfonts
```
stt-utils 0.1.2
A utility library for working with speech-to-text transcriptions.
Requires Python: >=3.9
Keywords: stt, speech to text, whisper, alignment, utility, audio, timestamps, transcription, closed captions, subtitles

# stt-utils

[![PyPI version](https://badge.fury.io/py/stt-utils.svg)](https://pypi.org/project/stt-utils/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A utility library for working with word-level speech-to-text timestamps. The main goal is to simplify working with timestamps generated by openai's whisper model with "word" timestamp_granularities.

## Features
- Realign word timestamps with the full text
- Merge several transcriptions together
- (Optional dependency) Split long audio into smaller segments by the moments of silence.

## Installation

```bash
pip install stt-utils
```

## Usage

### Python API

Align word timestamps from openai whisper
```python
from stt_utils import UnprocessedTranscription, Transcription

transcription = openai_client.audio.transcriptions.create(
    model="whisper-1",
    file=audio_file,
    response_format="verbose_json",
    timestamp_granularities=["word"],
)

unprocessed_transcription = UnprocessedTranscription(**transcription.model_dump())
aligned_transcription = Transcription.from_unprocessed_transcription(unprocessed_transcription)

aligned_transcription.dump_prevew()
```

## Development
It is recomended to use [uv](https://docs.astral.sh/uv/) toolset for development.

## Testing
There are unittests available in the `tests/` directory.
```
uv run pytest
```


## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
labcodes 1.0.6
Simple routines for superconducting quantum circuits.
Requires Python: >=3.10

# LabCodes

Simple routines for superconducting quantum circuits.

# Contents

`fitter`: CurveFit and BatchFit handling fitting and fit datas.

`models`: Fitting models with pre-defined initial value guess functions.

`plotter`: Data plotting routines for data generated in 2d sweep or other experiments.

`fileio`: Reading and writing data files, supports Labber, LabRAD, LTspice;

`calc`: Numerical calculator for physical models.

`tomo`: Data processing routine for quantum state tomography and process tomograpy.

`misc`: Useful functions that not fitting anywhere else.

`frog`: Codes for FROG experiments only.

# Installation

```powershell
pip install labcodes
```

# Documentation

All functions, classes comes with necessary documentations in their docstrings. 
Check them out with command like `help(balabala)`.
To browser the contents within, try `dir(balabala)` or `help(module_name)`.

If you are using iPython or Jupyter, try `any_object?` or `any_object??`.

It is highly recommended to read the source codes. I tried to make it easy to read.

If there is any advice or suggestions, write an issue, or pull request. 😊
synthpose 0.1.0
A clean architecture implementation of SynthPose for pose estimation.
Requires Python: <4.0,>=3.10

# SynthPose: Clean Architecture Implementation

A robust, modular Python package for human pose estimation using RT-DETR and VitPose. This project has been refactored to follow Clean Architecture principles, making it suitable for research, extension, and integration into larger systems (e.g., Avalonia/C# apps).

## Features

- **Clean Architecture**: Separation of concerns into Domain, Application, Adapters, and Infrastructure layers.
- **State-of-the-Art Models**: 
  - Detection: RT-DETR (`PekingU/rtdetr_r50vd_coco_o365`)
  - Pose Estimation: VitPose (`stanfordmimi/synthpose-vitpose-huge-hf` or `base`)
- **Standardized Output**: OpenPose-compatible JSON format.
- **Configurable**: Support for YAML configuration and CLI arguments.
- **Type Safe**: Fully typed codebase using Python type hints and Pydantic.

## Architecture

The project is organized as follows:

- **`domain/`**: Core business logic and entities (`FrameData`, `Person`, `Keypoint`). No external dependencies.
- **`application/`**: Use cases and orchestration (`VideoProcessor`). Depends only on Domain.
- **`adapters/`**: Implementations of interfaces (`OpenCVVideoSource`, `RTDetrPersonDetector`, `VitPoseEstimator`, `OpenPoseResultWriter`). Depends on external libraries (`torch`, `cv2`).
- **`infrastructure/`**: Configuration and Entry points (`CLI`, `Settings`).

## Installation

This project uses [Poetry](https://python-poetry.org/) for dependency management.

```bash
# Install dependencies
poetry install
```

## Usage

### CLI

Run the processor via the `synthpose` command (defined in `pyproject.toml`):

```bash
# Basic usage
poetry run synthpose process path/to/video.mp4

# With options
poetry run synthpose process video.mp4 --mode base --device cpu

# Using a config file
poetry run synthpose process video.mp4 --config configs/default.yaml
```

### Configuration

You can use a YAML file to configure the pipeline:

```yaml
# configs/example.yaml
device: "cuda"
model_mode: "huge"
det_threshold: 0.3
vis_show_weight: true
```

## Output

- **Video**: `{input_name}_output.mp4` (Visualized result)
- **JSON**: `pose/{input_name}_json/{input_name}_{frame_id}.json` (Keypoints data)

## Testing

```bash
poetry run pytest
```

## License

MIT License

## References

- [VitPose Paper](https://arxiv.org/abs/2406.09788)
- [SynthPose Model](https://huggingface.co/stanfordmimi/synthpose-vitpose-huge-hf)
inverse-cai 0.4.6
Inverse Constitutional AI (ICAI) pipeline for interpreting pairwise preferences
Requires Python: !=3.14.0,>=3.10

This package contains the official implementation of the *Inverse Constitutional AI* (ICAI) algorithm [[paper]](https://arxiv.org/abs/2406.06560). ICAI compresses pairwise preference datasets into a readable list of principles (constitution) that the annotations appear to follow (e.g. "select the friendlier response"). ICAI principles provide an interpretable overview of a feedback dataset.

See the GitHub repo for docs and details: https://github.com/rdnfn/icai