semvec/pypi-packages huggingface dataset:
freetube-import 0.38.0
Converts text and youtube .csv files into FreeTube playlists
Requires Python: >=3.8
Keywords: freetube, youtube, tube, playlist, import, cli, console, video, videos

![PyPI](https://img.shields.io/pypi/v/freetube-import?label=pypi%20package)![PyPI - Downloads](https://img.shields.io/pypi/dm/freetube-import)



# Freetube-import
Creates [FreeTube](https://freetubeapp.io/) .db playlist files from a list of youtube video urls separated by a newline (.txt) or from .csv files exported from 'Google takeout'.

Run the scrip with a path to a valid .txt file of youtube urls, or youtube's .csv playlist file. Then import the resulting .db file into FreeTube.



Install via pip:

      pip install freetube-import

https://pypi.org/project/freetube-import/

Basic usage:

      freetube-import <file>... <file2> <file3>

Help message:

      usage: freetube_import.py [-h] [-a] [-b] [-e] [-s] [filepath ...]

      Import youtube playlists

      positional arguments:
        filepath              path to a valid .txt or .csv playlist file or files

      optional arguments:
         -h, --help        show this help message and exit
         -a, --list-all    Takes all .txt and csv files as input from the current working directory.
         -e, --log-errors  Also lists the videos that failed the metadata fetch
         -s, --stdin       Takes stdin as input and outputs dirextly to stdout
         -n, --name NAME   sets a name for playlist, otherwise uses input filename
         -d, --debug       Debug mode with more info

While buggy and experimental `stdin` mode can used in scripts and automation. Not for average users.

       cat test.txt | freetube-import -s > std_test.db

It might be usefull to set a name that shows up in FreeTube. Otherwise in stdin mode a placeholder name is generated.

       cat test.txt | freetube-import -s -n playlist-name > std_test.db

pro tip: Try appending the ouput to FreeTube's own playlist.db file. So playlists get automatically added. (at your own risk, backup your files beforehand, close freetube to avoid file corruption)

      cat test.txt | freetube-import -s -n playlist-name >> your/path/FreeTube/playlists.db


Works without YouTube api through a custom version of [YouTube-search library](https://github.com/joetats/youtube_search/). Also works atleast on piped links, probably also on lists of Invidious links and other links that follow the standard youtube url format. VPN/proxy isn't strictly nessesary by my experience. I have run 1,5k videos videos through this in one sitting and gotten no ip blocks.

###  Dependencies 

       pip install requests
https://pypi.org/project/requests/

      pip install tqdm
https://pypi.org/project/tqdm/
piaso-tools 1.0.3
Precise Integrative Analysis of Single-cell Omics.
Requires Python: >=3.9

[![Stars](https://img.shields.io/github/stars/genecell/PIASO?logo=GitHub&color=yellow)](https://github.com/genecell/PIASO/stargazers)
[![PyPI](https://img.shields.io/pypi/v/piaso-tools?logo=PyPI)](https://pypi.org/project/piaso-tools)
[![Total downloads](https://static.pepy.tech/personalized-badge/piaso-tools?period=total&units=international_system&left_color=black&right_color=orange&left_text=downloads)](https://pepy.tech/project/piaso-tools)
[![Monthly downloads](https://static.pepy.tech/personalized-badge/piaso-tools?period=month&units=international_system&left_color=black&right_color=orange&left_text=downloads/month)](https://pepy.tech/project/piaso-tools)

# PIASO

#### Precise Integrative Analysis of Single-cell Omics

### Documentation

[piaso.org](https:///piaso.org) 

### Installation

Install from PyPI (stable release):
```bash
pip install piaso-tools
```
Install from GitHub (latest development version):
```bash
pip install git+https://github.com/genecell/PIASO.git
```

### Citation

If PIASO is useful for your research, please consider citing Wu, S.J., Dai, M. *et al*. Pyramidal neurons proportionately alter cortical interneuron subtypes. *Nature* (2026). https://doi.org/10.1038/s41586-025-09996-8

### Contact
Min Dai
dai@broadinstitute.org
iflow-mcp_godwin3737_mcp-m365-filesearch 0.1.0
Add your description here
Requires Python: >=3.11

# MCP Server - Microsoft 365 File Search  (SharePoint & OneDrive)

## Overview

A Model Context Protocol (MCP) server implementation that provides advanced file search capabilities within Microsoft 365. This server enables efficient file discovery, metadata analysis, and integration with business workflows by making available the content from SharePoint/OneDrive.



https://github.com/user-attachments/assets/bbe63c02-f6d9-4c9b-8f98-36fc22a081cc



## Components

### Tools

The server offers 2 core tools:

- **search_m365_files**  
  Perform a file search within the M365 environment.  
  **Input**:
  - `query` (string): The search term or criteria.  
  **Returns**: Array of file metadata objects. Metadata includes the file content summary, *drive ID*, and *file ID*, among other details.

- **get_file_content**  
  Retrieve content from a specific file.  
  **Input**:
  - `driveid` (string): The unique identifier of the parent drive.  
  - `fileid` (string): The unique identifier of the file.  
  **Returns**: File content as a binary stream.  
  **Note**: Uses a local cache to speed up repeat access.

### Caching

To improve performance and reduce redundant API calls, the server caches downloaded files locally. This is particularly useful when working with large documents or frequently accessed files.

- Cached files are stored in the `./src/mcp_m365_filesearch/.local/downloads` directory (relative to the project root).
- When a file is requested via `get_file_content`, the server first checks the cache.
- If the file is already cached, it is returned directly from disk without a new API call.

This feature ensures faster response times and efficient use of API rate limits.

## Usage with Claude Desktop

To integrate the server with Claude Desktop, update your `claude_desktop_config.json`:

```json
"mcpServers": {
  "M365 File Search (SharePoint/OneDrive)": {
    "command": "uv",
    "args": [
      "--directory",
      "full_path_to_parent_directory",
      "run",
      ".\\src\\mcp_m365_filesearch\\server.py"
    ],
    "env": {
      "CLIENT_ID": "MSGraph Client ID",
      "CLIENT_SECRET": "MS Graph Client Secret",
      "TENANT_ID": "TENANT ID",
      "REGION": "SEARCH REGION"
    }
  }
}
```

## Microsoft Graph App Registration

To use this server, you'll need to register an application in the [Azure Portal](https://portal.azure.com):

1. Register a new application.
2. Note down the **Client ID** and **Tenant ID**.
3. Create a **Client Secret** under **Certificates & Secrets**.
4. Under **API permissions**, add the following **delegated or application** permissions:
   - `Sites.Read.All`
   - `Files.Read.All`
5. Click **Grant admin consent** for these permissions.

Ensure these values are correctly set in your `env` configuration for the MCP server.

## License

This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).  
You are free to use, modify, and distribute it with proper attribution.
faster-cron 0.1.1
一个轻量、直观、支持异步与同步双模式的定时任务调度器
Requires Python: >=3.7

# FasterCron

[English](./README_EN.md) | 中文版

**FasterCron** 是一个轻量级、直观且功能强大的 Python 定时任务调度工具库。它完美支持 **Asyncio (异步)** 和 **Threading (多线程)** 双模式，专为需要高可靠性、简单配置和任务并发控制的场景设计。

---

## 🌟 核心特性

* **双模式支持**：一套逻辑同时提供 `AsyncFasterCron`（异步）和 `FasterCron`（同步多线程）两种实现。
* **任务级并发控制**：通过 `allow_overlap` 参数精准控制同一个任务是否允许重叠执行（单例模式 vs 并发模式）。
* **智能参数注入**：自动检测任务函数签名，按需注入包含调度时间、任务名称的 `context` 上下文。
* **标准 Cron 支持**：兼容 5 位（分时日月周）和 6 位（秒分时日月周）Cron 表达式。
* **健壮性**：内置异常捕获机制，单个任务崩溃不影响调度器运行。
* **无外部依赖**：仅使用 Python 标准库实现，轻量无负担。

---

## 📦 安装

您可以直接通过 pip 安装：

```bash
pip install faster-cron

```

或者直接将源码放入您的项目中。

---

## 🚀 快速上手

### 1. 异步模式 (Async Mode)

适用于使用了 `aiohttp`, `httpx` 或 `tortoise-orm` 等异步库的项目。

```python
import asyncio
from faster_cron import AsyncFasterCron

cron = AsyncFasterCron()


# 示例：每 5 秒执行一次，禁止重叠（若上一个任务没跑完，则跳过本次）
@cron.schedule("*/5 * * * * *", allow_overlap=False)
async def my_async_job(context):
    print(f"正在执行任务: {context['task_name']}, 计划时间: {context['scheduled_at']}")
    await asyncio.sleep(6)  # 模拟长耗时任务


async def main():
    await cron.start()


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

```

### 2. 同步模式 (Sync Mode)

适用于传统的阻塞式脚本或爬虫。

```python
from faster_cron import FasterCron
import time

cron = FasterCron()


# 示例：每秒执行一次，允许并发执行
@cron.schedule("* * * * * *", allow_overlap=True)
def my_sync_job():
    print("滴答，同步任务正在运行...")
    time.sleep(2)


if __name__ == "__main__":
    cron.run()

```

---

## 🛠 核心 API 说明

### 调度装饰器 `schedule`

| 参数 | 类型 | 默认值 | 说明 |
| --- | --- | --- | --- |
| `expression` | `str` | - | Cron 表达式。支持 `*`, `,`, `-`, `/`。 |
| `allow_overlap` | `bool` | `True` | **关键参数**。`True`: 时间点到达即执行；`False`: 若该任务的上一个实例未结束，则跳过本次执行循环。 |

### 上下文参数 `context`

如果您的任务函数接收名为 `context` 的参数，FasterCron 会自动注入以下字典：

* `task_name`: 函数名称。
* `scheduled_at`: 任务触发的精确 `datetime` 对象。
* `expression`: 该任务使用的 Cron 表达式。

---

## 📅 Cron 表达式参考

FasterCron 支持灵活的表达式定义：

* `* * * * * *` : 每秒执行。
* `*/5 * * * * *` : 每 5 秒执行。
* `0 0 * * * *` : 每整小时执行。
* `0 30 9-17 * * *` : 每天 9:00 到 17:00 之间的每半小时执行。
* `0 0 0 * * 0` : 每周日凌晨执行。

---

## 🧪 运行测试

本项目包含完善的单元测试。您可以使用 `pytest` 来验证功能：

```bash
# 安装测试依赖
pip install pytest pytest-asyncio

# 运行所有测试
pytest

```

---

## 📄 开源协议

MIT License.

---

**如果您觉得好用，欢迎点一个 Star！🌟**
syft-flwr 0.5.0
syft_flwr is an open source framework that facilitate federated learning projects using Flower over the SyftBox protocol
Requires Python: <3.14,>=3.12

# syft-flwr

**Easy, file-based, offline capable federated learning**

`syft-flwr` is an open-source framework that combines [Flower's](https://github.com/adap/flower) federated learning capabilities with file-based communication. Train machine learning models collaboratively across distributed datasets without centralizing data—with easy setup, offline capability, and no servers required.

![FL Training Process](https://github.com/OpenMined/syft-flwr/raw/main/notebooks/fl-diabetes-prediction/images/fltraining.gif)

## Key Features

- **File-Based Communication**: Train models without direct network connections—communication happens via file sync (Google Drive or [SyftBox](https://syftbox.net/))
- **Zero Infrastructure**: No servers to maintain, no complex networking setup—just notebooks and file sync
- **Offline Capable**: Asynchronous message passing enables training even with intermittent connectivity
- **Privacy by Design**: Data never leaves its source—only model updates are shared
- **Flower Integration**: Built on Flower's robust FL framework—supports FedAvg, custom strategies, and all standard Flower features

## Quick Start

The easiest way to get started is with our **Google Colab tutorial**—no local setup required:

📓 [Zero-Setup FL with Google Colab](notebooks/fl-diabetes-prediction/distributed-gdrive/README.md)

## Example Notebooks

| Example | Description | Communication |
|---------|-------------|---------------|
| [FL Diabetes (Google Drive)](notebooks/fl-diabetes-prediction/distributed-gdrive/README.md) | Train a diabetes prediction model across distributed Colab notebooks | Google Drive |
| [FL Diabetes (SyftBox)](notebooks/fl-diabetes-prediction/distributed/README.md) | Train a diabetes prediction model across distributed machines | SyftBox |
| [FL Diabetes (Local)](notebooks/fl-diabetes-prediction/local/README.md) | Local simulation for development and testing | Local |
| [Federated Analytics](notebooks/federated-analytics-diabetes/README.md) | Query statistics from private datasets and aggregate them | SyftBox |
| [FedRAG](notebooks/fedrag/README.md) | Privacy-preserving question answering with RAG | SyftBox |

## Installation

```bash
pip install syft-flwr
```

Or install from source:

```bash
pip install "git+https://github.com/OpenMined/syft-flwr.git@main"
```

## Documentation

📚 [Full Documentation](https://syft-flwr-documentation.openmined.org/)

## Development

See [DEVELOPMENT.md](DEVELOPMENT.md) for development setup and guidelines.

### Releasing

See [RELEASE.md](RELEASE.md) for the complete release process.

## Community

- 💬 [Slack](https://slack.openmined.org/) - Join #support-syftbox for questions
- 🐛 [Issues](https://github.com/OpenMined/syft-flwr/issues) - Report bugs or request features
- 🌟 Star this repo to support the project!

## License

[Apache 2.0](LICENSE)
cibw-install-pythons 0.2.0
Unofficial installer for cibuildwheel's Python versions.
Requires Python: >=3.8

# cibw-install-pythons

This is an *unofficial* script for installing the Python versions used by
[`cibuildwheel`](https://cibuildwheel.pypa.io/). Currently, only macOS is
supported.

**This script is intended for use in CI environments only.** It installs CPython
versions system-wide, and there is no easy way to change that behavior because
the CPython `pkg` files used by `cibuildwheel` cannot be relocated upon
installation. If you run this script on your development machine, **you will
likely break something.**

## Installation

```zsh
python -m pip install cibw-install-pythons
```

## Usage

```zsh
CI=1 python -m cibw-install-pythons macos
```

If the `CI=1` variable is not provided, `cibuildwheel` aborts by default to
avoid damaging systems not in CI environments.

CPython is always installed system-wide. PyPy and GraalPy are installed by
default in a cache under your `$HOME` directory. To change where these Python
versions are installed, use the `CIBW_CACHE_PATH` environment variable.

For example, to install in a system-wide cache,

```bash
CI=1 CIBW_CACHE_PATH=/Library/Caches/cibuildwheel python \
                                                  -m cibw-install-pythons macos
```
django-randomslugfield 0.3.0.post1
A Django field that automatically generates random slugs.

> **DO NOT USE!**
>
> This project is no longer maintained.
>
> It was originally made to replicate the style of URL you find on many websites to obfuscate integer based primary keys.
> However the implementation relies on expensive database lookups to prevent collisions.
>
> A better/modern alternative to this project would be something using [sqids](https://sqids.org/).


# django-randomslugfield

Django field that automatically generates random slugs.

e.g. *example.com/kEwD58P*

Tested with Python 2.7, 3.3+ and Django 1.4+.


## Getting it

To install django-randomslugfield:

```bash
    $ pip install django-randomslugfield
```


## Install

To enable `randomslugfield` in your project you need to add it to
`INSTALLED_APPS` in your projects `settings.py` file:

```python
    INSTALLED_APPS = (
        ...
        'randomslugfield',
        ...
    )
```


## Usage

Import `RandomSlugField` and use it in your model:

```python
    from django.db import models
    from randomslugfield import RandomSlugField

    class MyModel(models.Model):
        slug = RandomSlugField(length=7)
```

The `length` argument is required.


## Advanced Usage

By default randomslugfield generates its slug using these characters:

`abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789`

You can optionally exclude lowercase/uppercase/digits/vowels using
`exclude_lower=True`, `exclude_upper=True`, `exclude_digits=True`,
`exclude_vowels=True`.

You cannot exclude all characters.

Example:

```python
    class MyModel(models.Model):
        slug = RandomSlugField(length=7, exclude_lower=True)
```

The total number of unique slugs is determined by `characters^length`.

```
    62^9 = 13,537,086,546,263,552 possible slugs
    62^8 = 218,340,105,584,896 possible slugs
    62^7 = 3,521,614,606,208 possible slugs
    62^6 = 56,800,235,584 possible slugs
    62^5 = 916,132,832 possible slugs
```