semvec/pypi-packages huggingface dataset:
playground-common-utils 1.4.2
汎用ユーティリティ（ファイル操作、ログ出力など）
Requires Python: >=3.10

# playground_common_utils

Pythonで汎用的な**ファイル操作・ディレクトリ操作・ログ出力**を支援するライブラリです。

---

## 🚀 インストール

インストールするには：

```bash
pip install playground-common-utils
```

## 📦 主な機能

### 確認系
- `is_exist(path)` : パスの存在確認
- `is_file(path)` : ファイルか確認
- `is_dir(path)` : ディレクトリか確認
- `is_match_first_keyword(target, first_keyword)` : 文字列の最初のキーワードが一致しているか
- `is_match_last_keyword(target, last_keyword)` : 文字列の最後のキーワードが一致しているか

### 作成系
- `create_file(path)` : 空ファイル作成
- `create_dir(path)` : ディレクトリ作成

### 取得系
- `read_current_dir()` : カレントディレクトリ取得
- `read_file(path)` : ファイル内容取得
- `read_list_dir(path)` : ディレクトリ内一覧取得

### 更新系
- `overwrite_file(path, content)` : ファイルに上書き
- `append_file(path, content)` : ファイルに追記

### 削除系
- `delete_file(path)` : ファイル削除
- `delete_dir(path)` : ディレクトリ削除

### 操作系
- `copy_file(src_path, dst_path)` : ファイルコピー
- `move_file(src_path, dst_path)` : ファイル移動

### ログ出力系
`logger.debug("Debug test")` : デバッグ ログ出力
`logger.info("Debug test")` : インフォ ログ出力
`logger.worning("Debug test")` : ワーニング ログ出力
`logger.error("Debug test")` : エラー ログ出力
`logger.critical("Debug test")` : クリティカル ログ出力

logger.set_level(WARNING)　: Warning以上のログ出力可能 / DEBUG,INFOログは出力しない 
---

## 🛠️ 使用例

``` python
ファイル作成・書き込み・削除
from playground_common_utils.files import *

# ファイル作成
create_file('sample.txt')

# ファイル上書き
overwrite_file('sample.txt', 'Hello World')

# ファイル削除
delete_file('sample.txt')
```

## ディレクトリ操作
``` python
from playground_common_utils.files import *

# ディレクトリ作成
create_dir('new_folder')

# ディレクトリ内一覧取得
items = read_list_dir('new_folder')

# ディレクトリ削除
delete_dir('new_folder')
```

## ログ出力
```python
from playground_common_utils.logger import logger
logger.debug("Debug test")
# 🐞 2025-05-20 21:45:19 [DEBUG] | <stdin> / <module>  [1] | Debug test

logger.info("Info test")
# ℹ️  2025-05-20 21:45:33 [INFO] | <stdin> / <module>  [2] | Info test

logger.warning("Warning test")
# ⚠️  2025-05-20 21:45:40 [WARNING] | <stdin> / <module>  [3] | Warning test

logger.error("Error test")
# 🚨 2025-05-20 21:45:51 [ERROR] | <stdin> / <module>  [4] | Error test

logger.critical("Critical test")
# 🔥 2025-05-20 21:45:58 [CRITICAL] | <stdin> / <module>  [5] | Critical test
```

## HTTP通信
```python
from playground_common_utils.logger import http, HTTP_Method

response = fetch(url="http://example.com", # Endpoint URL
      method=HTTP_Method.GET, # GET / POST / PUT / DELETE
      request_headers={"Content-Type": "application/json"}, # Default = {"Content-Type": "application/json"}
      request_dict={}) # python dict型 ⚠️ 内部的にjsonに変換しリクエスト送信します
```

## ⚙️ 対応バージョン

* Python 3.7以上

## 📄 ライセンス

MITライセンス

## 👤 作者情報

Author: Hiroki Umatani
Project URL: [Github](https://github.com/HirokiUmatani/playground_common_utils)

playground-common-utilsは、実業務に直結するファイル管理作業の効率化を目指して開発されました。
営業・開発・レポート作成などのプロジェクトを圧倒的スピードで推進するための基盤ライブラリです。
api2mdx 0.2.2
Generate MDX API documentation from Python source code
Requires Python: >=3.10
Keywords: documentation, api, mdx, python

# api2mdx

`api2mdx` is a Python tool that generates `mdx` documentation for Python APIs.
Under the hood, it uses Griffe.

`api2mdx` is designed to work in tandem with custom components like `<ParametersTable>`, `<ApiSignature>`, etc. Currently they live in `@mirascope/website` or `@mirascope/docs-viewer`, but later they will be extracted to `@mirascope/ui`.

## Usage

Generate API documentation from Python source code:

```bash
python -m api2mdx.main --source-path ./src --package mypackage --output ./docs/api
```

### Options

- `--source-path`: Path to the source code directory
- `--package`: Python package name to document  
- `--output`: Path where generated documentation should be written
- `--docs-path`: Path within the package where docs are located (default: docs/api)
- `--pattern`: Optional pattern to regenerate only matching files
- `--output-directives`: Optional path to output intermediate directive files for debugging

### Example

```bash
python -m api2mdx.main \
  --source-path ./snapshots \
  --package mirascope_v2_llm \
  --output ./snapshots/mdx \
  --output-directives ./snapshots/directives
```

## Snapshot Regeneration

The project includes test snapshots for validation. To regenerate all snapshots:

```bash
uv run regenerate-snapshots
```

This will:
1. Generate MDX documentation files in `snapshots/mdx/`
2. Generate intermediate directive files in `snapshots/directives/` (useful for debugging)
3. Process documentation links and generate metadata

### Snapshot Structure

```
snapshots/
├── mirascope_v2_llm/        # Source Python code
├── mdx/                     # Generated MDX documentation  
└── directives/              # Intermediate directive files (for debugging)
```

## How It Works

1. **API Discovery**: Scans Python modules and respects `__all__` exports as the source of truth
2. **Path Generation**: Uses export structure for file organization (e.g., `responses/__init__.py` exports `Response` → `responses/Response.mdx`)
3. **Directive Processing**: Generates Griffe directives and processes them into MDX
4. **Metadata Generation**: Creates TypeScript metadata files for navigation
ds-python 0.1.4
Add your description here
Requires Python: >=3.12

# 📊 `ds-python` - Estatísticas de containers em tempo real


### 📺 __Demo:__
![](assets/clip.gif)

### 📝 __Objetivo:__
Esse projeto é uma altenativa ao comando `docker stats` para o monitoramento de containers em execução, esse projeto foi inspirado na ferramenta [ds](https://github.com/rafaelrcamargo/ds)


### 💻 __Stack:__
- Python (3.12) - __runtime__
- dashing - __charts__
- rich - __loading__
- briefcase - __package builder__

### 🪛 __Instalação:__
Existem duas maneiras de instalação, nativa ou virtual

usando o __pipx__
```bash
pipx install ds-python
```

usando o __uv__
```bash
uv tool install ds-python
```

usando o __uvx__
```bash
uvx tool install ds-python
```


todos os exemplos acima envolvem a virtualização do projeto,
caso queira se aventurar em uma instalação nativa siga os passos abaixo:
#### 1. Clonando o projeto
```bash
git clone https://github.com/gpocas/ds-python
```

#### 2. Instalando as dependencias:
```bash
cd ds-python
uv sync
```

#### 3. Gerando o pacote:
```bash
uv run briefcase build
uv run briefcase package
```

#### 4. Instalando o pacote (Ubuntu):
```bash
sudo apt install pacote.deb
```
edpm 3.1.19
Easy Dependency Package Manager


# edpm

**edpm** (Easy Dependency Package Manager) is a lightweight tool for managing external dependencies in C++/CMake (and occasionally Python) projects. 
Unlike heavy package managers (e.g., Spack) or mixed approaches (like CMake’s FetchContent), 
edpm cleanly separates dependency fetching/building from your main build process—keeping things reproducible and straightforward.

## Key Features
- **Manifest & Lock File**: edpm uses a YAML-based plan (“manifest”) and a lock file to ensure predictable, repeatable installs.
- **Minimal Overhead**: Installs a known set of scientific/engineering packages without building an entire OS worth of sub-libraries.
- **Environment Scripts**: Automatically generates shell scripts (bash/csh) so your tools can locate installed packages via `PATH`, `LD_LIBRARY_PATH`, and `CMAKE_PREFIX_PATH`.
- **Extensible Recipes**: Supports Git + CMake, tarballs, local filesystems, and specialized recipes (e.g., Geant4, ROOT).

## Quick Start
1. **Install edpm**:
   ```bash
   pip install --upgrade edpm
   ```
2. **Initialize & Install**:
   ```bash
   # Create or edit your plan (plan.edpm.yaml) then install
   edpm add root geant4
   edpm install
   ```
3. **Use the Environment**:
   ```bash
   source <(edpm env)   # or edpm env csh > env.csh
   ```
   This makes your newly installed packages available to your shell or CMake builds.

## Why edpm?
- Avoid “version hell” and heavy-lift solutions like Spack when you only need a few well-defined libraries.
- Keep the build and install steps separate: simpler debugging, faster CI, and more control over dependencies.
- Clean integration with CMake through environment variables or optional `CMakePresets.json` / `EDPMConfig.cmake`.

## Contributing & Development
- To hack on edpm itself, clone this repository and install in editable mode:
  ```bash
  pip install -e .
  ```
- We welcome new recipes for scientific libraries, feature requests, and issue reports.

edpm is open-source under the **MIT License**. For complete documentation, recipes, and usage details, please visit the [GitHub project page](https://github.com/DraTeots/edpm).
```
streamsad 0.1.4
A light-weight, streaming speech activity detection (SAD) module, designed for real-time speech-only classification
Requires Python: >=3.10

# streamsad

`streamsad` is a streaming-oriented Speech Activity Detection (SAD) module that operates frame by frame, without requiring access to the full audio signal (unlike batch processing). Unlike simple energy-based Voice Activity Detection (VAD), it accurately detects human speech while ignoring music, background noise, and silence. Powered by an efficient ONNX model and a post-processing algorithm inspired by WebRTC (using ring buffer smoothing), it runs entirely on the CPU with minimal overhead, making it ideal for real-time voice interfaces, ASR frontends, and low-resource deployments.

# Dependencies

This module has been tested and works correctly with Python 3.10 through 3.13.

The following third-party dependencies are required to use `streamsad`:

- `numpy`
- `onnxruntime`

# How to Use

Here is an example of how to use the `streamsad` module:

```python
import numpy as np
from streamsad import SAD

# Initialize the SAD model
sad = SAD()

# Create an example audio stream (e.g., 2 seconds of random audio at 16kHz)
audio_np_array = np.random.randn(32000).astype(np.float32)

# Detect speech segments
segments = sad(audio_np_array)

# Print the detected segments
print(segments)
```

# Installation

You can install `streamsad` using pip:

```bash
pip install streamsad
```

# Testing

After installing the module, you can run unit tests using `pytest`:

```bash
pytest -s tests/test_sad.py
```