semvec/pypi-packages huggingface dataset:
napari-roi-manager 0.0.6
A ROI Manager Widget with an UI similar to ImageJ
Requires Python: >=3.10

# napari-roi-manager

[![License BSD-3](https://img.shields.io/pypi/l/napari-roi-manager.svg?color=green)](https://github.com/hanjinliu/napari-roi-manager/raw/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/napari-roi-manager.svg?color=green)](https://pypi.org/project/napari-roi-manager)
[![Python Version](https://img.shields.io/pypi/pyversions/napari-roi-manager.svg?color=green)](https://python.org)
[![tests](https://github.com/hanjinliu/napari-roi-manager/workflows/tests/badge.svg)](https://github.com/hanjinliu/napari-roi-manager/actions)
[![codecov](https://codecov.io/gh/hanjinliu/napari-roi-manager/branch/main/graph/badge.svg)](https://codecov.io/gh/hanjinliu/napari-roi-manager)
[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-roi-manager)](https://napari-hub.org/plugins/napari-roi-manager)

A ROI Manager Widget with an UI similar to ImageJ.

![](https://github.com/hanjinliu/napari-roi-manager/blob/main/images/demo.gif)

&check; The layer is just a Shapes layer, so it is compatible with any other plugins.

&check; Supports importing and exporting ImageJ ROI files.

----------------------------------

This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.


## Installation

You can install `napari-roi-manager` via [pip]:

    pip install napari-roi-manager



To install latest development version :

    pip install git+https://github.com/hanjinliu/napari-roi-manager.git


## Contributing

Contributions are very welcome. Tests can be run with [tox], please ensure
the coverage at least stays the same before you submit a pull request.

## License

Distributed under the terms of the [BSD-3] license,
"napari-roi-manager" is free and open source software

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin

[file an issue]: https://github.com/hanjinliu/napari-roi-manager/issues

[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
ragscope 0.1.0
A comprehensive suite for evaluating RAG pipelines.
Requires Python: >=3.8

# 🏆 RAG Evaluation Suite

A comprehensive, asynchronous, and framework-agnostic library for evaluating Retrieval-Augmented Generation (RAG) pipelines.

[![PyPI version](https://badge.fury.io/py/rag-eval-suite.svg)](https://badge.fury.io/py/rag-eval-suite)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This tool provides a complete, end-to-end workflow for RAG evaluation—from automatically synthesizing a high-quality test set from your own documents to running a suite of sophisticated, AI-powered diagnostic metrics.

---

## ✨ Key Features

- **Comprehensive "RAG Triad" Metrics:** Evaluate **Context Relevance**, **Faithfulness**, and **Answer Relevance**.
- **Advanced Diagnostics:** Includes **Answer Completeness** to pinpoint why an answer is strong or weak.
- **Automated Test Case Generation:** Use the built-in **Data Synthesizer** to create test cases from any document.
- **High-Performance Async Pipeline:** Powered by `asyncio` for fast, parallel execution.
- **Framework-Agnostic:** Works with any RAG system—LangChain, LlamaIndex, or plain Python.
- **Flexible Judge Model:** Supports GPT-4o, Claude 3 Opus, or local models (e.g., via Ollama).

---

## 🚀 Quick Start

### 1. Installation

```bash
pip install rag-eval-suite
```


### 2. Run Your First Evaluation
Create a Python script using the RAGEvaluator:

```bash
import asyncio
from rag_eval_suite import RAGEvaluator
from rag_eval_suite.data_models import TestCase, RAGResult

# 1. Instantiate the evaluator (uses local Ollama model by default)
evaluator = RAGEvaluator(judge_model="ollama/llama3")

# 2. Define test case and RAG system output
test_case = TestCase(
    question="What are the notable features of the stadium's pitch and roof?",
    ground_truth_context=["The stadium features a fully retractable roof... and a hybrid pitch..."],
    ground_truth_answer="The stadium has a fully retractable roof and a hybrid pitch."
)

rag_result = RAGResult(
    retrieved_context=["The stadium features a fully retractable roof... and a hybrid pitch..."],
    final_answer="The stadium has a fully retractable roof."  # Intentionally incomplete
)

# 3. Run the evaluation
async def main():
    evaluation_result = await evaluator.aevaluate(test_case, rag_result)
    print(evaluation_result.scores)

if __name__ == "__main__":
    asyncio.run(main())
```

---

### 🔧 Configuration
To use other models (e.g., OpenAI’s GPT-4o), configure via environment variables:

```
import os
os.environ["OPENAI_API_KEY"] = "sk-..."

evaluator = RAGEvaluator(judge_model="gpt-4o")
```

---

### 🤝 Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request.

---

### 📄 License
This project is licensed under the MIT License.
entrns 0.1.1b0
Librería simple para crear bots básicos entrenados con tokenización y corrección ligera.
Requires Python: >=3.6

# Entrns

**Entrns** es una librería ligera para crear bots básicos entrenables con funcionalidades simples como:

- Corrección básica de texto.
- Entrenamiento simulado con temporizador.
- CLI interactiva para pruebas directas.
- Soporte para personalización del nombre del bot.
- Modo rápido de entrenamiento con argumento `--saltar-timer`.
- (Nuevo en 0.1.1b0) Prevención de congelamientos simples durante ejecución.

## Instalación

```bash
pip install entrns
emota-loader 2.0.0
Add your description here
Requires Python: >=3.10

# 📦 emota_loader — Python Dataloader for EmoTa Dataset

**EmoTa: A Tamil Emotional Speech Dataset** (Thevakumar et al., CHiPSAL 2025) is the first open-access emotional speech corpus in Tamil, designed to capture the dialectal diversity of Sri Lankan Tamil speakers[^1].

| Statistic                  | Value                                          |
|---------------------------|------------------------------------------------|
| Utterances                | 936 (22 speakers × 19 sentences × 5 emotions) |
| Speakers                  | 22 native Sri Lankan Tamil (11 male, 11 female) |
| Sentences                 | 19 semantically neutral sentences              |
| Emotions                  | angry, happy, sad, fear, neutral               |
| Inter-annotator Agreement | Fleiss’ Kappa = 0.74                           |
| Baseline F1 Scores        | XGBoost: 0.91, Random Forest: 0.90            |

---

## 🔧 Installation

You can install the package from PyPI using:

```bash
pip install emota_loader
````

> Make sure to download the [EmoTa dataset](https://rtuthaya.staff.uom.lk/contact-for-resources) separately and point the loader to its root directory.

---

## 🚀 Sample Usage

```python
from emota_loader import EmoTaDataset

# Point to extracted dataset root folder
dataset = EmoTaDataset(root_dir="path/to/EmoTa").samples

print(f"Loaded {len(dataset)} samples")

sample = dataset[0]
print(f"  Audio Path      : {sample.audio_path}")
print(f"  Speaker ID      : {sample.speaker_id}")
print(f"  Speaker Gender  : {sample.speaker_gender}")
print(f"  Speaker Age     : {sample.speaker_age}")
print(f"  Speaker Region  : {sample.speaker_region}")
print(f"  Sentence ID     : {sample.sentence_id}")
print(f"  Transcript      : {sample.transcript}")
print(f"  Emotion         : {sample.emotion}")
```

### Example Output

```
Loaded 936 samples

  Audio Path      : EmoTa/19_18_ang.wav
  Speaker ID      : 19
  Speaker Gender  : male
  Speaker Age     : 25
  Speaker Region  : northern
  Sentence ID     : 18
  Transcript      : நான் உன்னை சந்திக்க வேண்டும்.
  Emotion         : angry
```

---

## 📄 Citation

Please cite the dataset as:

```bibtex
@inproceedings{thevakumar-etal-2025-emota,
  title = "{E}mo{T}a: A {T}amil Emotional Speech Dataset",
  author = "Thevakumar, Jubeerathan and Thavarasa, Luxshan and Sivatheepan, Thanikan and Kugarajah, Sajeev and Thayasivam, Uthayasanker",
  booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
  year = "2025",
  pages = "193--201",
  address = "Abu Dhabi, UAE",
  publisher = "International Committee on Computational Linguistics"
}
```

---

## 📘 License

Academic use only — see the [EmoTa dataset license](https://github.com/aaivu/EmoTa/blob/main/LICENSE.md) for details.

---

[^1]: Thevakumar, J., Thavarasa, L., et al. (2025). [*EmoTa: A Tamil Emotional Speech Dataset*](https://aclanthology.org/2025.chipsal-1.19.pdf). Proceedings of CHiPSAL 2025.
dillnet 1.0.5
Dill custom model.
Requires Python: >=3.6

# dillnet

Dill custom model.

### Usage:
```python
import torch
from dillnet import DillNet

# Define model configuration
dim = 512         # Dimensionality
depth = 6         # Number of layers
heads = 8         # Number of heads

# Initialize the DillNet model
model = DillNet(dim=dim, depth=depth, heads=heads)

# Create a sample input (batch of token embeddings)
# For example: batch size = 2, sequence length = 128
x = torch.randn(2, 128, dim)

# Pass input through the model
output = model(x)

# Output shape should match input: (batch_size, seq_length, dim)
print("Output shape:", output.shape)
```
gc7-uuu2 1.0.0
Un module avec plein d'outils utiles pour devs en PyMoX (PY-thon - MO-jo - Flet-X)
Requires Python: >=3.10

# PyMoX - GC7

Trousse à outils utiles pour devs en PyMoX

---

## Rapide mémo

```bash
py -m venv .venv
.\.venv\Scripts\activate
pip install -r requirements.txt

py -m build
twine check dist/*  --verbose
twine upload dist/* --verbose

git log --oneline

semantic-release version
semantic-release version --print

semantic-release publish

semantic-release --noop version
Cela te dira si une version serait générée.

semantic-release version --commit --tag --no-push

Cela va :
Lire les commits
Calculer la prochaine version (ex: 0.2.0)
Modifier __version__ dans gc7/__init__.py
Créer un commit et un tag localement

Remove-Item -Recurse -Force dist, src/gc7/gc7.egg-info

git commit --allow-empty -m "feat: add dummy feature for version bump"
git commit --allow-empty -m "fix: déclenchement de la release 1.0.1" 
git push origin main

voir conventional commits: https://www.conventionalcommits.org/en/v1.0.0/

En cas de volonté de revenir sur une version précédente (Problème avec PyPI: Interdit de reculer):
git revert <hash du commit> (pour ramener la main du dépôt GH)
dans le __ini__.py, mettre : __version__='x.y.z' (> au dernier de PyPI)
refaire le build + renvoi:
py -m build    
twine upload dist/*

Désinstaller tous les packages de l'env global :
pip freeze | ForEach-Object { pip uninstall -y $_ }

# 1. Crée une branche temporaire pour garder les fichiers
git checkout --orphan temp_branch

# 2. Ajoute tous les fichiers à cette nouvelle branche
git add .

# 3. Fais un commit initial
git commit -m "Initial commit"

# 4. Supprime l'ancienne branche main
git branch -D main

# 5. Renomme la branche temporaire en main
git branch -m main

# 6. Force le push vers GitHub (⚠️ cela écrase l’historique distant)
git push -f origin main


```
gc7-uuu 1.0.0
Un module avec plein d'outils utiles pour devs en PyMoX (PY-thon - MO-jo - Flet-X)
Requires Python: >=3.10

# PyMoX - GC7

Trousse à outils utiles pour devs en PyMoX

---

## Rapide mémo

```bash
py -m venv .venv
.\.venv\Scripts\activate
pip install -r requirements.txt

py -m build
twine check dist/*  --verbose
twine upload dist/* --verbose

git log --oneline

semantic-release version
semantic-release version --print

semantic-release publish

semantic-release --noop version
Cela te dira si une version serait générée.

semantic-release version --commit --tag --no-push

Cela va :
Lire les commits
Calculer la prochaine version (ex: 0.2.0)
Modifier __version__ dans gc7/__init__.py
Créer un commit et un tag localement

Remove-Item -Recurse -Force dist, src/gc7/gc7.egg-info

git commit --allow-empty -m "feat: add dummy feature for version bump"
git commit --allow-empty -m "fix: déclenchement de la release 1.0.1" 
git push origin main

voir conventional commits: https://www.conventionalcommits.org/en/v1.0.0/

En cas de volonté de revenir sur une version précédente (Problème avec PyPI: Interdit de reculer):
git revert <hash du commit> (pour ramener la main du dépôt GH)
dans le __ini__.py, mettre : __version__='x.y.z' (> au dernier de PyPI)
refaire le build + renvoi:
py -m build    
twine upload dist/*

Désinstaller tous les packages de l'env global :
pip freeze | ForEach-Object { pip uninstall -y $_ }

# 1. Crée une branche temporaire pour garder les fichiers
git checkout --orphan temp_branch

# 2. Ajoute tous les fichiers à cette nouvelle branche
git add .

# 3. Fais un commit initial
git commit -m "Initial commit"

# 4. Supprime l'ancienne branche main
git branch -D main

# 5. Renomme la branche temporaire en main
git branch -m main

# 6. Force le push vers GitHub (⚠️ cela écrase l’historique distant)
git push -f origin main


```