semvec/pypi-packages huggingface dataset:
isekai-django 0.1.0a7
A Django package
Requires Python: >=3.10

# ISEKAI

**ISEKAI** is a general-purpose ETL framework for Django. It helps you migrate any data (including files) from any source into your Django models using a clear, pluggable pipeline. ISEKAI makes it simple to bring data from anywhere into Django models.

## Why ISEKAI?

Django projects often need to bring data from a wide variety of sources into structured models, but writing ad-hoc scripts for this quickly becomes messy and repetitive. ISEKAI offers a clear, reusable framework for defining ETL pipelines so your migrations stay predictable, testable, and easy to maintain.

## Concepts

At the core of ISEKAI is the **Resource** model. A Resource represents a single distinct piece of data you want to migrate. As it moves through the pipeline, the Resource accumulates data and undergoes transformations, and by the final stage it resolves to a single Django model instance. In other words, every Resource eventually becomes one record in your database, and the pipeline describes how it gets there.

The pipeline has five stages:

1. **Seed** - Create the initial set of Resources, often by reading from a sitemap, CSV file, database table, or another source through custom seeders.
2. **Extract** - Using those keys, fetch the raw bytes from the source, whether that's through the web with the HTTPExtractor or any other system via a custom extractor.
3. **Mine** - Inspect extracted data to discover or 'mine" related Resources. For example, the HTMLImageMiner will create new Resources for each image it finds linked in an HTML page.
4. **Transform** - Using the data on the Resource, produce a target Spec (your project's model-shaped representation) that the Load stage will create in Django.
5. **Load** - Create the Django models from the Resource's Spec. This stage calculates the optimal build order for the objects, and can reconcile circular dependencies through a two-phase create mechanism.

Each stage is handled by **processors**. ISEKAI includes built-in processors for common use cases, but you can also write your own. This means you can extract resources from any source, apply custom transformations, and load into any Django model structure.

## License

MIT
weather-x 0.1.0
A weather information tool based on FastMCP framework with support for Chinese city weather alerts
Requires Python: >=3.12

# Weather-X 天气查询工具

Weather-X 是一个基于 FastMCP 框架开发的天气查询工具，可以获取中国各城市的天气信息和警报。

## 功能特性

- 获取指定城市的天气信息
- 查询天气警报
- 基于高德地图天气API实现
- 支持异步请求处理

## 安装

```bash
pip install weather-x
```

或从源码安装：

```bash
git clone <repository-url>
cd weather-x
pip install -e .
```

## 使用方法

```python
from weather import mcp

# 运行MCP服务器
mcp.run(transport='stdio')
```

或直接运行：

```bash
python main.py
```

## API接口

### get_alerts(city: str) -> str

获取指定城市的天气警报信息。

参数:
- city: 城市名称（例如：北京、上海）

返回:
- 包含天气信息的字符串

## 依赖

- Python >= 3.12
- fastmcp >= 2.12.4
- httpx >= 0.28.1
- mcp[cli] >= 1.18.0

## 配置

项目使用高德地图天气API，需要在代码中配置API密钥。

## 开发

安装开发依赖：

```bash
pip install -e ".[dev]"
```

## 许可证

MIT
robit 0.4.9
Service Worker Framework
Requires Python: >=3.7
Keywords: automation, bot, cron, cronjob, chronological, worker, job

# Robit

![Build](https://img.shields.io/github/actions/workflow/status/stratusadv/robit/run_tests.yml)
![Python Versions](https://img.shields.io/pypi/pyversions/robit)
![PyPI Version](https://img.shields.io/pypi/v/robit)
![Downloads](https://img.shields.io/pypi/dm/robit)

Chronological Automation Service Framework

A robot for your bits!

(Pronounced "Row-Bit")

## Features

- Lightweight (only one installed dependencies pytz which is also lightweight)
- Can run with monitoring interface or headless
- Very simple and easy to set up and configure 

## Documentation

Our documentation is available on our [website](https://robit.stratusadv.com), including guides, example, api reference and more.

<p align="center">
    <a href="https://robit.stratusadv.com">
        <img alt="Robit Logo" src="https://robit.stratusadv.com/static/img/robit_logo_256.png"/>
    </a>
</p>
python-metadata-parser 0.1.3
Parse Core Metadata, pyproject.toml, inline script metadata
Requires Python: >=3.9
Keywords: core-metadata, metadata, pep723, pyproject.toml

# python-metadata-parser

[![PyPI - Version](https://img.shields.io/pypi/v/python-metadata-parser.svg)](https://pypi.org/project/python-metadata-parser)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/python-metadata-parser.svg)](https://pypi.org/project/python-metadata-parser)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/FlavioAmurrioCS/python-metadata-parser/main.svg)](https://results.pre-commit.ci/latest/github/FlavioAmurrioCS/python-metadata-parser/main)

-----

## Table of Contents

- [python-metadata-parser](#python-metadata-parser)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [License](#license)

## Installation

```console
pip install python-metadata-parser
```

## License

`python-metadata-parser` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
ye-tracker 0.0.3
A Python interface for the Ye Tracker.
Requires Python: >=3.12

# Ye Tracker

This is an unofficial Python API for interacting with the [Ye Tracker](https://docs.google.com/spreadsheets/d/1vW-nFbnR02F9BEnNPe5NBejHRGPt0QEGOYXLSePsC1k), a project that catalogues information about the unreleased music of rapper and producer Ye.

This project is in progress, and is seeking help for further development!
syna 0.3.0
Syna is a lightweight machine learning framework inspired by DeZero.
Requires Python: >=3.11

# Syna

![PyPI - Version](https://img.shields.io/pypi/v/syna)
![GitHub License](https://img.shields.io/github/license/sql-hkr/syna)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/syna)
![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/sql-hkr/syna/ci.yml?label=CI)


Syna is a lightweight machine learning framework inspired by [DeZero](https://github.com/oreilly-japan/deep-learning-from-scratch-3). Built from scratch using only NumPy, it follows a define-by-run (dynamic computation graph) approach and includes a basic reinforcement learning framework.

Unlike most frameworks that implement reinforcement learning as a separate library, Syna provides everything in a single library.

Designed for beginners and researchers, Syna helps you learn the fundamentals of machine learning and the inner workings of frameworks like [PyTorch](https://github.com/pytorch/pytorch). Performance is not the focus, and GPU support is intentionally omitted to keep the code simple and easy to understand.


## Installation

Get the Syna Source

```bash
git clone https://github.com/sql-hkr/syna.git
cd syna
uv venv
source .venv/bin/activate
uv sync
```

Or, from [PyPI](https://pypi.org/project/syna/):

```bash
uv add syna
```

> [!IMPORTANT]
> To visualize the computation graph, you need to install [Graphviz](https://graphviz.org).
> ```bash
> brew install graphviz # macOS
> sudo apt install graphviz # Linux
> ```

## Getting started
### Computation Graph

Visualize the computation graph for the fifth derivative of tanh(x) with respect to x.

```python
import syna
import syna.functions as F
from syna import utils

x = syna.tensor(1.0)
y = F.tanh(x)
x.name = "x"
y.name = "y"
y.backward(create_graph=True)

iters = 4

for i in range(iters):
    gx = x.grad
    x.cleargrad()
    gx.backward(create_graph=True)

gx = x.grad
gx.name = "gx" + str(iters + 1)
utils.viz.plot_dot_graph(gx, verbose=False, to_file="tanh.svg")
```

The output graph is shown below.

![](/docs/_static/graph/tanh.svg)

### RL (Deep Q-Network; DQN)

Solve CartPole-v1 using the DQN algorithm.

```python
from syna.algo.dqn import DQNAgent
from syna.utils.rl import Trainer

trainer = Trainer(
    env_name="CartPole-v1",
    num_episodes=300,
    agent=DQNAgent(
        lr=3e-4,
    ),
)
trainer.train()
```

## API Reference
- [syna package](https://sql-hkr.github.io/syna/api/syna.html)

## License

Syna is licensed under the MIT License. See [LICENSE](LICENSE) for details.