semvec/pypi-packages huggingface dataset:
symgrad4t 0.2.1
A tiny symbolic autodiff demo built on Sympy.
Requires Python: >=3.9
Keywords: autodiff, symbolic, sympy, tensor

SymGrad4T
=========

一个用 Sympy 做符号表达的极简自动求导/张量广播示例，当前版本 **v0.2.1**。已支持经典三层 MLP 的符号反向传播（示例见 `examples/mlp.py`），打印出的 LaTeX 可直接粘贴到 https://www.latexlive.com/ 查看。

安装
----
- PyPI：`pip install symgrad4t`
- 更新：`pip install --upgrade symgrad4t`
- 本地开发：`pip install -e .`

运行测试
--------
- 项目根目录：`pytest -q`
- 仅库内测试：`cd python && pytest -q`

运行示例
--------
- `python examples/rms_norm/rms_norm.py`
- `python examples/matrix_arith.py`
- `python examples/matrix_sum.py`
- `python examples/mlp.py`（三层 MLP 符号反向）

MLP 梯度示例
------------
下图为 `examples/mlp.py` 生成的三层 MLP 梯度表达式（截自 LaTeX 渲染）：

![MLP Grad](media/mlp_grad.png)

Qwen2 MLP 梯度示例
------------------
下图为 `examples/qwen2_mlp.py` 生成的 Qwen2 风格 MLP 梯度表达式（截自 LaTeX 渲染）：

![Qwen2 MLP Grad](media/Qwen2mlp_grad.png)

已支持的 OP / 功能
------------------
- 元素级：`Add` / `Sub` / `Mul` / `Div`
- 幂与根：`Power`、标量幂、`Sqrt`
- 归约：`Sum(dim, keepdim=False)`（支持负维度）
- 广播：`Broadcast` / `Tensor.broadcast_to`（自动应用于元素级算子）
- 矩阵：`MatMul` / `Transpose`
- 非线性：`Max` / `ReLU` / `Softmax`
- 损失：`CrossEntropy`（内置 Softmax+CE 简化）
- 工具：`Tensor.latex_expr()` / `Tensor.latex_grad()`（乘号用 `\cdot` 提高可读性）
Office-Tweaks 0.1.0
утилита для работы с файлами
Requires Python: >=3.6

# Calculator Package

Утилита для конвертации pdf и docx файлов, множественного удаления и сжатия изображений.

## Установка

```bash
pip install Office_Tweaks
pip install -r requirements.txt
```

## Использование (для интерактивного режима)

```bash
Office_Tweaks.exe -i
```

## Пример использования (не для интерактивного режима)

```bash
Office_Tweaks.exe delete --delete-mode "mode" --delete-pattern "pattern" --delete-dir "dir"
```

## Лицензия
dgm-fit 0.3.0
The package implements the fitter interface for the dag-modelling framework
Requires Python: >=3.11

# dgm-fit

[![python](https://img.shields.io/badge/python-3.11-purple.svg)](https://www.python.org/)
[![pipeline](https://git.jinr.ru/dagflow-team/dgm-fit/badges/master/pipeline.svg)](https://git.jinr.ru/dagflow-team/dgm-fit/commits/master)
[![coverage report](https://git.jinr.ru/dagflow-team/dgm-fit/badges/master/coverage.svg)](https://git.jinr.ru/dagflow-team/dgm-fit/-/commits/master)

The fitter interface for the dag-modelling framework.

## Repositories

- Main repo:
  - Development/CI: https://git.jinr.ru/dagflow-team/dgm-fit
  - Contact/pypi/mirror: https://github.com/dagflow-team/dgm-fit
  - PYPI: https://pypi.org/project/dgm-fit
ozobot-webrtc 0.1.2
Ozobot WebRTC library
Requires Python: >=3.13

# ozobot-webrtc

A Python library for establishing WebRTC connections with Ozobot devices, enabling real-time communication through data channels.
This is a backend library implementing bare WebRTC functionality. Consult your robot documentation to learn which user library can control your robot.

## Features

- WebRTC peer-to-peer connections with Ozobot devices
- AMQP-based signaling through WebSocket or TCP transports to estabilish the connection
- Built on [aiortc](github.com/aiortc/aiortc) for WebRTC functionality

## Basic Usage

```python
import asyncio

from ozobot.webrtc.signaling import SignalingCaller
from ozobot.webrtc.messaging import create_channel_factory, MessagingChannelConfig

async def main() -> None:
  # Configure messaging
  config = MessagingChannelConfig(
    device_id="your-connection-key",
    username="your-username", 
    password="your-password"
  )

  # Establish connection
  async with create_channel_factory(config) as factory:
    caller = SignalingCaller(factory, "your-queue-name")
    connection, channels = await caller.signal(channels=("control",))

    # Use the data channels
    data_channel = channels[0]
    await data_channel.send(b"Hello Ozobot!")

    async for data in channel.receive_str():
      print(data)


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

## jwt authentication
If you don't have an username and password, you need to use jwt for authentication. In that case, use `get_jwt_token` from `ozobot.webrtc.signaling.token`
and use the resulting value as password. Keep username empty:
```python
from ozobot.webrtc.signaling.token import get_jwt_token, TOKEN_ENDPOINT_URL
await token = await get_jwt_token(TOKEN_ENDPOINT_URL, "your-connection-key", "server")
config = MessagingChannelConfig(
    device_id="your-connection-key",
    username="", 
    password=token,
)
```