semvec/pypi-packages huggingface dataset:
neutrophils-classifier 1.0.1
A GUI application for neutrophil classification using deep learning models
License: MIT
Requires Python: >=3.9

# Neutrophils Maturation Classifier App

![alt text](./doc/screenshot.png "Screenshot")

A minimal 3D neutrophil maturation classification app with convolution neural network.

## Overview

This application allows for the automated classification of neutrophils at different maturation stages using a 3D convolutional neural network. It's designed to assist researchers and clinicians in analyzing 3D neutrophil morphology with geometric and CNN embedded features.

**Note**: GPU runtime only available on Linux.

## Features

- 3D visualization of neutrophil isosurfaces
- Real-time classification of neutrophil maturation stages
- Support for TIFF image input
- Batch processing capabilities

## Requirements

- Python 3.9 or higher
- CUDA-compatible GPU (recommended for GPU acceleration)

## Installation

### Simple Installation (Recommended)
```bash
pip install neutrophils-classifier
```

### Run the App
```bash
neutrophil-classifier
```

## For Developers

### Development Setup
1. Set up the conda environment:
```bash
git clone git@github.com:bpi-oxford/Neutrophils-Classifier-App.git
cd Neutrophils-Classifier-App
git submodule update --init --recursive
mamba env create -f env-minimal.yaml
mamba activate neutrophils-minimal
```

2. Install in editable mode:
```bash
pip install -e .
```

3. Run the application:
```bash
neutrophil-classifier
```

### Building and Publishing

See [`BUILD_GUIDE.md`](BUILD_GUIDE.md) for detailed build and publish instructions.
calibre-recipe-runner 0.1.0
A lightweight Python sandbox for running Calibre news recipes.
License: MIT

# calibre-recipe-runner

A lightweight Python sandbox for running Calibre news recipes.

The Calibre community has curated a treasure trove of over a thousand well-maintained [parsing recipes](https://github.com/kovidgoyal/calibre/tree/master/recipes) for news outlets around the world. These recipes are typically designed to run within Calibre itself — but what if you want to use them elsewhere?

`calibre-recipe-runner` is a lightweight sandbox that lets you execute Calibre news recipes outside of the Calibre environment. No installation of Calibre is required.

This tool:

- Dynamically loads `.recipe` files
- Emulates essential Calibre modules
- Extracts article feeds

## Example

Run the *Le Monde* recipe from the Calibre collection:
```
calibre-recipe-runner le_monde
```
Output:
```
https://www.lemonde.fr/article1.html
https://www.lemonde.fr/article2.html
```
Run any other recipe:
```
calibre-recipe-runner path/to/news.recipe
```

## Install

As a system-wide binary

```
uv tool install calibre-recipe-runner`
```

or

```
pipx install calibre-recipe-runner
```

As Python package

```
pip install calibre-recipe-runner
```

## Before first run & for occasional updates

Requires `curl` and `jq`.

```
calibre-recipe-runner --update
```
The recipe collection is stored in `~/.local/share/calibre-recipe-runner/recipes`.

## Limitations

- Most Calibre modules are stubbed or partially emulated.
- Recipes requiring authentication or complex browser automation may not work out of the box. PRs welcome.

## License

This project is licensed under the MIT License.
plot-reward-vs-steps 0.1.1
Simple RL reward plotting utility
License: MIT
Requires Python: >=3.7
Keywords: reinforcement-learning, plot, rewards, matplotlib, bandit

# plotrs

A **lightweight plotting utility** for visualizing rewards vs steps in Reinforcement Learning (RL), Multi-Armed Bandits (MAB), and other iterative training algorithms.

`plotrs` provides a single, easy-to-use function `plot_reward_vs_steps()` that plots rewards over time and can optionally smooth the curve using a moving average.

---

## ✨ Features
- 📈 **Simple, one-line plotting**
- 🎯 Perfect for **RL** or **MAB** experiments
- 🔍 Optional **moving average smoothing**
- 🖼 Clean **Matplotlib** visualization
- ⚡ Zero boilerplate — just call and plot

---

## 📦 Installation
```bash
pip install plotrs
Guarana-aag 0.1.1
Framework para uso no Python
License: MIT
Requires Python: >=3.13.0

# Guaraná
Baseado no Front_Alego, gem de Ruby que desenvolvi para a Assembleia Legislativa do Estado de Goiás, esse módulo Python instala um framework css funcional para módulos Flask ou mesmo para HTML estático, que dá a base para trabalhar, sem limitar com um estilo fixo caso decida alterar, mas também permite fazer um site rápido com mudanças mínimas.

Além dos arquivos de CSS, inclui alguns JS básicos para algumas funcionalidades pontuais e vários snippets de VSCode para facilitar o uso.

[Changelog](changelog.md)

## Instalando
Para o funcionamento é necessário Python 3.12 ou superior. No seu ambiente virtual execute:

```bash
pip install guarana_aag
```

Na pasta do seu sistema execute o comando
```bash
python -m guarana instalar
```

Como parâmetro opcional, utilize `--flask` caso seu sistema esteja zerado, isso vai instalar também os arquivos básicos para um sistema em flask, incluindo views como a 404.html

Para atualizar os arquivos do guarana, utilize o comando:
```bash
python -m guarana atualizar
```
Esse comando vai substituir o arquivo base com a nova versão do Guaraná.

Para uma atualização mais profunda, utilize o comando:
```bash
python -m guarana resetar
```
Esse comando vai gerar novamente os arquivos do Guaraná, incluindo os de usuário, como o `estilo.css`. Tenha um backup antes de utilizar esse comando ou você perderá muito de seu trabalho


## CSS a ser usado
Depois de instalado, serão criados alguns arquivos de `.css` na raíz.

- `_guarana_base.css` - esse arquivo contém a base do Guaraná e não deve ser alterado, já que é gerado dinamicamente
- `estilo.css` - o arquivo a ser trabalhado. Ele pode ser importado diretamente no HTML. Note, porém, que há muitos `@import` o que gera várias requisições HTTP
- `estilo-min.css` - todo o Guaraná compactado em um único CSS, importe no HTML para ser mais eficiente posteriormente. Esse arquivo é gerado dinamicamente através do comando:

```bash
python -m guarana min
```

## Estrutura Flask instalada
Os seguinte arquivos são instalados quando executa da função `--flask`:

- `rodar.py` - ativa o servidor para rodar o sistema
- `requirements.txt` - os módulos básicos necessários para rodar o Guaraná
- `app/config.py` - utilize esse arquivo para configurar o sistema com constantes utilizadas nos demais arquivos
- `app/routes.py` - as rotas para o índice e a tela de login
- `templates/...` - os 4 modelos básicos, incluindo o `modelo.html` que é a base para o Guaraná e todas as views. `login.html` e `internas.html` separa os tipos de páginas.

### Configurações
O arquivo de configurações (`config.py`) inclue as seguintes constantes:
- `ESTILO` - escolha qual o CSS a ser usado (como indicado na seção de CSS):
    - `padrao` - o estilo tradicional, não compactado
    - `min` - o estilo minificado. Esse é o valor padrão
- `TITULO` - inclua aqui o título do sistema, que aparece na tag `<title>`
steamboat-binf 1.1.1
A collection of tools/scripts for microbial bioinformatics
License: MIT
Requires Python: <4.0.0,>=3.10.0
Keywords: bioinformatics

[![Gitpod ready-to-code](https://img.shields.io/badge/Gitpod-ready--to--code-908a85?logo=gitpod)](https://gitpod.io/#https://github.com/rpetit3/steamboat)

# steamboat
A collection of tools/scripts for microbial bioinformatics
superset-loggers 0.1.3
Custom logger package
License: MIT
Requires Python: >=3.7

# Custom Logger Package

A simple customizable logger with both file and console output support.

## Installation

```bash
pip install superset-loggers
```

## Usage

```
from superset_loggers import Logger

logger = Logger().get_logger(log_file="app.log", verbose=True)
logger.info("This is an info message")
logger.error("This is an error message")
```
esphome-mqtt-timescaledb-ingestor 0.3.0
A robust Python service to ingest ESPHome MQTT data into a TimescaleDB multi-table schema.
License: MIT
Requires Python: >=3.7
Keywords: esphome, mqtt, timescaledb, iot, database, ingestor

# ESPHome MQTT to TimescaleDB Ingestor

A robust Python service that listens for ESPHome data on an MQTT broker and persists it into a structured, multi-table TimescaleDB database.

This project is designed to work with the standard ESPHome and Home Assistant discovery protocols, handling various message types including device discovery, entity configurations, real-time state updates, and commands.

## Features

* Connects to an MQTT broker and subscribes to all relevant ESPHome and Home Assistant topics.
* Intelligently processes and stores five distinct types of messages.
* Automatically creates the necessary database tables on the first run.
* Uses a multi-threaded, queue-based architecture for high performance and resilience.
* Supports intelligent batch processing for efficient database writes.

## Installation

Install the package directly from PyPI:

```bash
pip install esphome-mqtt-timescaledb-ingestor
```

## Usage

This package includes an interactive command-line interface (CLI) to help you get started.

### 1. Initial Configuration

After installing the package, run the `configure` command. This will launch an interactive setup wizard that will ask for your MQTT and database credentials and save them to a configuration file.

```bash
mqtt-ingestor configure
```

You will be prompted for the following:
-   MQTT Broker Host
-   MQTT Broker Port
-   MQTT Username & Password
-   Database Host
-   Database Port
-   Database Name
-   Database User & Password

### 2. Start the Ingestor Service

Once you have configured your credentials, you can start the ingestor service:

```bash
mqtt-ingestor start
```

The service will load your saved configuration and begin listening for messages.

## Testing

To run the tests, clone the repository, install the development dependencies, and run pytest:

```bash
git clone [https://github.com/paandayankur/mqtt-to-timescaledb-ingester.git](https://github.com/paandayankur/mqtt-to-timescaledb-ingester.git)
cd mqtt-to-timescaledb-ingester
poetry install --with dev
poetry run pytest
```

## Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request for any improvements or bug fixes.

## License

This project is licensed under the MIT License. See the `LICENSE` file for more details.