semvec/pypi-packages huggingface dataset:
visidex 0.2.7
Keypoint detection and visual retrieval library for image identification.
Requires Python: <4.0.0,>=3.9.21

# VisiDex: Keypoint Detection and Visual Retrieval Library

## Pacote detection
Contém classes e funções para detecção de keypoints e pontos singulares em imagens.
### Classes
- Detecção de keypoints usando o algoritmo REKD.

Exemplo de uso:
````
from visidex.detection import REKD

rd = REKD()
image = ...
keypoints = rd.detect(image)
````
- SingularPoints - Detecção de pontos singulares em imagens.
````
from visidex.detection import SingularPoints

sp = SingularPoints()
image = ...
points = sp.detect(image)
````
### apoio
- Python 3.9+
- PyTorch 2.1+
- Kornia 0.8.0+
- e2cnn 0.2.3+
- pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
Bohrmodel 0.1.0
Cálculos del modelo de Bohr (Radios, Energía, Transiciones).
Requires Python: >=3.8

# BohrModel: Modelo del Átomo de Bohr

[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![GitHub Repo](https://img.shields.io/badge/GitHub-repo-blue.svg)](https://github.com/XxyamzxX/-tomo_bohr)

---

## Descripción

**BohrModel** es una librería en Python que implementa el modelo del átomo de Bohr para átomos hidrogenoides.  
Permite realizar cálculos de **niveles de energía**, **radios de órbitas**, **transiciones electrónicas** y generar **representaciones gráficas** de manera interactiva.

El paquete está diseñado siguiendo buenas prácticas de **desarrollo de software científico**, con modularidad, pruebas unitarias y documentación clara.

---

## Características

- Cálculo de niveles de energía de electrones en un átomo hidrogenoide.
- Cálculo de radios de órbitas electrónicas.
- Cálculo de transiciones electrónicas: energía, frecuencia y longitud de onda de los fotones.
- Representación gráfica de niveles de energía y órbitas electrónicas.
- Menú interactivo en consola para seleccionar propiedades y gráficos.
- Modular: cálculos de energía, radios, transiciones y gráficos en módulos separados.

---

## Instalación

Se recomienda usar un **entorno virtual**:

```bash
git clone https://github.com/XxyamzxX/-tomo_bohr.git
cd -tomo_bohr
pip install -e .


-tomo_bohr/
│── pyproject.toml
│── README.md
│   └── bohrmodel/
│       ├── energia.py
│       ├── radios.py
│       ├── transiciones.py
│       ├── graficos.py
│       └── atom.py
mad-metric 0.0.6
A metric for acoustic music evaluation based on MAUVE and MERT
Requires Python: >=3.8

# MAD
Implementation for MAUVE Audio Divergence (MAD) as described in [Aligning Text-to-Music Evaluation with Human Preferences](https://arxiv.org/abs/2503.16669). Sound examples for the meta-evaluation data can be found on our [demo page](https://mad-metric-83cde1d399d1.herokuapp.com/). The MusicPrefs dataset is available on [Huggingface](https://huggingface.co/datasets/i-need-sleep/musicprefs/tree/main).

## Installation
Install using pip:
```
pip install mad_metric
```
Or, install from source:
```
git clone https://github.com/i-need-sleep/mad.git
cd mad
pip install -e .
```

## Usage
Programmic usage:
```
from mad_metric import compute_mad
score = compute_mad(
    eval_dir='DIR_OF_AUDIO_FILES_TO_BE_EVALUATED',
    ref_dir='DIR_OF_REFERENCE_AUDIO_FILES',
    [optional arguments...]
)
```
Command line usage:
```
mad_metric [-h] [--eval_dir EVAL_DIR] [--ref_dir REF_DIR] [--eval_embs_dir EVAL_EMBS_DIR] [--ref_embs_dir REF_EMBS_DIR] [--log_csv LOG_CSV] [--batch_size BATCH_SIZE] [--model_name MODEL_NAME] [--layer LAYER]
                  [--aggregation AGGREGATION]
```
* `eval_dir`: The directory of audio files to be evaluated.
* `eval_embs_dir`: If both `eval_dir` and `eval_embs_dir` are specified, the computed embeddings of the files will be stored under `eval_embs_dir`. If only `eval_embs_dir` is provided, the MAUVE score will be computed directly using the provided embeddings. At least one of `eval_dir` and `eval_embs_dir` must be provided.
* `ref_dir`: Similar to `eval_dir`. We use [FMA-Pop](https://github.com/microsoft/fadtk/blob/main/datasets/fma_pop_tracks.csv) in our experiments.
* `ref_embs_dir`: Similar to `eval_embs_dir`.
* `log_csv`: The path to the csv file logging the MAD scores.
* `batch_size`: The batch size used when computing embeddings.
* `model_name`, `layer`, `aggregation`: Specifies which embeddings are used to compute the divergence. Defaults to the best setup with MERT according to our synthetic meta-evaluation with FMA-Pop as the reference set.
gitmate-ai 2.0.0
GitMate - AI Git Assistant for Terminal
Requires Python: >=3.8


# 🧠 GitMate — AI Git Terminal Assistant

GitMate is a terminal-based assistant powered by **LangChain + your choice of LLM (Gemini, OpenAI, Claude)**.  
It helps you execute Git and shell commands interactively, detects common errors like merge conflicts or fatal errors, and offers AI-powered help — right inside your terminal.

---

## ⚙️ Features

- 💬 Talk to the AI with `@bot your question`
- 🧠 Pick your preferred model: OpenAI GPT-4o, Gemini 2.0, or Claude 3
- 🤖 Auto-detect Git errors and offer intelligent help
- 💾 Logs the entire terminal session
- ⚡ Fully automated mode with CLI flags (`--model`, `--api-key`)

---

## 🚀 Quickstart

### 1. Install

```bash
pip install gitmate-ai
````

### 2. Run Interactively

```bash
gitmate
```

You'll be prompted to select an LLM model and enter your API key.

---

## 🔧 Command-Line Arguments

| Flag        | Description                              | Example            |
| ----------- | ---------------------------------------- | ------------------ |
| `--model`   | Select LLM: `openai`, `gemini`, `claude` | `--model gemini`   |
| `--api-key` | Provide your API key                     | `--api-key sk-...` |

Skip all prompts:

```bash
gitmate --model claude --api-key YOUR_KEY
```

---

## 🤖 Usage Examples

### Ask the bot directly:

```bash
@bot how do I revert the last commit?
```

### Handle errors interactively:

If GitMate detects an error (like a merge conflict), it will ask:

```
🚨 I noticed a merge conflict. Do you want help resolving it?
```

---

## 📓 Session Logs

All terminal activity is logged automatically as:

```
git_session_YYYYMMDD_HHMMSS.log
```

Useful for reviewing your Git workflow or debugging sessions.

---

## 🧩 Future Ideas

* `--command` flag: Run a single Git command with AI help and exit
* Task Performer Agent
* Markdown output formatting
* Live Git watch mode for auto-analysis
* API key manager / config file support
---

## 🛡️ Disclaimer

This tool runs Git and shell commands using Python’s `subprocess`.
Always review AI suggestions before executing potentially destructive commands.

---

## 👨‍💻 Author

Built by [Tejas Raundal](https://github.com/TejasRaundal)

---

## 📄 License

MIT License
micecraft 0.0.9
MiceCraft
Requires Python: >=3.8

# MiceCraft

MiceCraft is designed to create, monitor and control smart habitats for animals.

> [!TIP]
> Public release coming fall 2025.

> [!CAUTION]
> This MiceCraft repository is the public version and will get progressive updates from the private closed-beta repository

## Install package

```
pip install micecraft
```

## Website

[https://micecraft.org](https://micecraft.org)

## Wiki and docs:

[MiceCraft Wiki](https://github.com/fdechaumont/micecraft/wiki)

## Github

[MiceCraft GitHub](https://github.org/micecraft)

## Main versions

Version  | Change | Date
------------- | ------------- | -------------
0.0.1  | Setting up :tada: | September 2025
0.0.5 | Added: FED3 Device <br> Added: ComManager <br> Added: DeviceEvent <br> Added: Alarm <br> Added: Mail | September 2025
0.0.6 | Added: CameraRecorder & associated example + wiki | September 2025
0.0.7 | Added: Lever | September 2025
0.0.8 | Added: <br>A lot of documentation (in the wiki)<br>Gate <br>Events <br>Report <br>First examples <br>Network <br>RFID reader| September 2025
pyclspv 0.0.1
Python bindings for clspv, a OpenCL C to SPIR-V compiler
Requires Python: >=3.9

# pyclspv

Python bindings for clspv

## Installation

### Debian/Ubuntu/Mint
Right now pyclspv can only be installed from source.

First, install the required build dependencies:
```bash
sudo apt install build-essential python3-dev
```

Then, simply install it via pip:
```bash
git clone https://codeberg.org/softcookiepp/pyclspv.git --recurse-submodules
cd pyclspv
pip install ".[test]"
```

### Windows
coming at some point

### Other Linux distributions
Coming much sooner

## Testing
To install and run the tests, do the following:
```bash
git clone https://codeberg.org/softcookiepp/pyclspv.git --recurse-submodules
cd pyclspv
pytest
```
convert-csv 0.2.3
A simple and easy-to-use CSV format converter that helps you process amount and date formats in batch.
Requires Python: >=3.8

# CSV格式转换工具

一个简单易用的CSV文件格式转换工具，可以帮助您批量处理CSV文件中的金额和日期格式。

## 功能特点

- 🔄 **自动编码检测** - 自动识别CSV文件的字符编码
- 💰 **金额格式化** - 将金额统一格式化为两位小数
- 📅 **日期格式化** - 将日期转换为指定格式
- ⚙️ **配置文件驱动** - 通过简单配置指定需要处理的列
- 🎯 **智能处理** - 自动处理特殊日期值（如9999/12/31）

## 安装要求

- Python 3.8 或更高版本
- 需要安装的Python库：
  - pandas
  - chardet

## 快速开始

### 方法一：命令行使用（推荐）

1. **创建示例配置文件**
   ```bash
   convert-csv create_config
   ```

2. **编辑配置文件**
   打开生成的 `config.json` 文件，根据您的CSV文件结构进行修改。

3. **运行转换**
   ```bash
   convert-csv 输入文件.csv
   ```

### 方法二：交互式使用

直接运行程序，按提示输入信息：
```bash
convert-csv
```

## 配置文件说明

配置文件为JSON格式，包含两个主要部分：

### 金额列配置
```json
{
  "amount_columns": ["金额", "价格", "费用"]
}
```

### 日期列配置
```json
{
  "date_columns": {
    "交易时间": "YYYY-MM-DD HH:mm:ss",
    "数据日期": "YYYY-MM-DD",
    "创建时间": "YYYY/MM/DD HH:mm:ss"
  }
}
```

### 支持的日期格式
- `YYYY-MM-DD` - 年-月-日
- `YYYY-MM-DD HH:mm:ss` - 年-月-日 时:分:秒
- `YYYY/MM/DD` - 年/月/日
- `YYYY/MM/DD HH:mm:ss` - 年/月/日 时:分:秒

## 使用示例

### 基本用法
```bash
# 使用默认配置
convert-csv data.csv

# 指定输出文件
convert-csv data.csv -o output.csv

# 指定配置文件
convert-csv data.csv -c my_config.json
```

### 完整示例
```bash
# 1. 创建配置文件
convert-csv create_config

# 2. 编辑配置文件，设置需要处理的列
# 3. 运行转换
convert-csv sales_data.csv -c config.json -o sales_data_processed.csv
```

## 配置文件示例

```json
{
  "amount_columns": [
    "销售金额",
    "成本价格",
    "运费"
  ],
  "date_columns": {
    "订单日期": "YYYY-MM-DD",
    "发货时间": "YYYY-MM-DD HH:mm:ss",
    "创建日期": "YYYY/MM/DD"
  }
}
```

## 输出说明

- 程序会自动在输入文件同目录下生成输出文件
- 输出文件名格式：`原文件名-output.csv`
- 输出文件统一使用UTF-8编码

## 常见问题

### 1. 程序提示"编码检测置信度较低"
这是正常现象，程序会自动尝试使用常见的中文编码（GBK、GB2312等）继续处理。

### 2. 某些日期无法正确转换
请检查原始数据的日期格式是否规范，如有特殊格式可能需要手动处理。

### 3. 金额格式不正确
确保金额列中只包含数字、小数点和负号，其他字符会被自动过滤。

### 4. 找不到配置文件
如果没有指定配置文件，程序会在当前目录查找 `config.json`，如果不存在则使用默认配置。

## 技术支持

如果您遇到问题：
1. 检查CSV文件是否可以正常打开
2. 确认配置文件中的列名与CSV文件中的列名完全一致
3. 查看程序输出的警告信息，了解具体问题

## 版本信息

当前版本：v0.2.3

---

💡 **提示**：首次使用时建议先使用 `create_config` 参数创建示例配置文件，然后根据您的实际需求进行修改。