semvec/pypi-packages huggingface dataset:
pymultirole-plugins 0.6.19
Sherpa multirole plugins
Requires Python: >=3.12

## Requirements

- Python 3.12+
- uv for package management and building
- Pydantic for the data parts.

## Installation
```
pip install uv
uv pip install pymultirole-plugins
```

## Development Setup
```
uv sync
```

## Running Tests
```
uv run pytest
```

## Linting and Formatting
```
uv run ruff check .
uv run ruff format --check .
```

## Generate Documentation
```
uv run --extra docs sphinx-build docs docs/_build
```

## Publish the Python Package to PyPI
- Increment the version of your package in the `__init__.py` file:
```
"""Sherpa multirole plugins"""

__version__ = 'x.y.z'
```
- Build and publish
```
uv build
uv publish
```
chromedriver-sync 1.0.1
自动检测环境并从 dreamshao/chromedriver 下载对应驱动的工具
Requires Python: >=3.6

# 🚀 ChromeDriver Sync Manager

**ChromeDriver Sync** 是一个轻量级的 Python 工具，旨在解决自动化测试中最头疼的问题：**Chrome 浏览器与驱动版本不匹配**。

它会自动检测你电脑上安装的 Chrome 版本和系统架构（Windows/Mac/Linux），并从 [dreamshao/chromedriver](https://github.com/dreamshao/chromedriver) 镜像仓库中精准下载对应的驱动文件。

---

## ✨ 核心特性

* **智能检测**：自动识别本地 Chrome 版本（支持 v115+ 后的新架构）。
* **全平台支持**：支持 Windows (32/64)、macOS (Intel/M1/M2/M3)、Linux (64位)。
* **交互式补救**：如果找不到完全匹配的版本，会询问是否尝试下载最新的稳定版。
* **速度优势**：专为国内开发者优化，避开 Google 官方源连接不稳定的问题。

---

## 📦 安装方式

通过 `pip` 即可一键安装：

```bash
pip install chromedriver-sync

```

---

## 🛠 使用方法

安装完成后，直接在你的终端（Terminal/Command Prompt）输入以下命令：

```bash
chromedriver-sync

```

### 运行效果展示

1. **自动匹配**：程序会输出 `检测到系统平台: mac-arm64` 和 `检测到 Chrome 版本: 121`。
2. **自动下载**：匹配成功后自动下载压缩包。
3. **解压就绪**：驱动会解压到当前执行命令的目录下，你可以直接在代码中引用。

---

## 🐍 在 Python 代码中调用

如果你想在代码运行初期自动更新驱动，可以这样集成：

```python
from chromedriver_sync.main import sync

# 运行同步逻辑，确保驱动是最新的
sync()

# 接下来启动你的 Selenium
from selenium import webdriver
driver = webdriver.Chrome()

```

---

## ❓ 常见问题 (FAQ)

**Q: 为什么我运行后提示“未找到匹配版本”？**
A: 这通常是因为你的 Chrome 刚刚更新，而官方驱动尚未发布。此时输入 `y` 下载仓库中最新的版本，通常是可以向下兼容的。

**Q: 如何将驱动安装到系统路径？**
A: 建议将下载后的 `chromedriver` 移动到 `/usr/local/bin` (Mac/Linux) 或将其所在文件夹添加到 Windows 的 `PATH` 环境变量中。

---

## 🌟 贡献与支持

如果你觉得这个工具有帮到你，请给 [dreamshao/chromedriver](https://github.com/dreamshao/chromedriver) 点一个 **Star** ⭐！这也是我持续更新驱动版本的动力。
social-auth-core 4.8.5
Python social authentication made simple.
Requires Python: >=3.10
Keywords: oauth, openid, saml, social auth

# Python Social Auth - Core

Python Social Auth is an easy to setup social authentication/registration
mechanism with support for several frameworks and auth providers.

## Description

This is the core component of the python-social-auth ecosystem, it
implements the common interface to define new authentication backends
to third parties services, implement integrations with web frameworks
and storage solutions.

## Documentation

Project documentation is available at https://python-social-auth.readthedocs.io/.

## Setup

```shell
$ pip install social-auth-core
```

## Contributing

Contributions are welcome!

Only the core and Django modules are currently in development. All others are in maintenance only mode, and maintainers are especially welcome there.

See the [CONTRIBUTING.md](https://github.com/python-social-auth/.github/blob/main/CONTRIBUTING.md) document for details.

## Versioning

This project follows [Semantic Versioning 2.0.0](https://semver.org/spec/v2.0.0.html).

## License

This project follows the BSD license. See the [LICENSE](LICENSE) for details.

## Donations

This project welcomes donations to make the development sustainable, you can fund Python Social Auth on following platforms:

- [GitHub Sponsors](https://github.com/sponsors/python-social-auth/)
- [Open Collective](https://opencollective.com/python-social-auth)
general-predictor 0.1.0
General Time Series Forecasting Framework based on Darts
Requires Python: <3.12,>=3.11

# General Predictor

通用时序预测框架，基于Darts库，支持任意时间序列预测任务。

## 特性

- **通用预测能力**：支持销售预测、需求预测、交通流量、电力市场预测等任意时序任务
- **多种模型支持**：LightGBM、XGBoost、CatBoost、LinearRegression
- **特征工程**：时间特征、滞后特征、滚动统计（内置数据泄露防范）
- **预测模式**：支持滚动预测、日前预测、单步预测
- **多时间粒度**：15分钟(96点)、30分钟(48点)、1小时(24点)、日度等
- **MCP协议支持**：可作为MCP Server与Claude等AI助手集成

## 安装

```bash
pip install general-predictor
```

## 快速开始

### 命令行使用

```bash
# 启动MCP Server
general-predictor
```

### Python API使用

```python
from general_predictor import TimeSeriesForecaster, ForecastConfig

# 创建配置
config = ForecastConfig(
    data_path='data/power_load.csv',
    target_col='load',
    datetime_col='datetime',
    freq='15min',
    model_type='lightgbm'
)

# 初始化预测器
forecaster = TimeSeriesForecaster(config)

# 加载数据
forecaster.load_data()
df = forecaster.preprocess_data()

# 准备特征
forecaster.prepare_features(df)

# 创建模型
forecaster.create_model()

# 滚动预测（D日预测D+1日）
predictions = forecaster.rolling_forecast(
    test_start_date='2025-12-01',
    test_end_date='2025-12-31'
)

# 评估
metrics = forecaster.evaluate()

# 保存结果
forecaster.save_results('forecast_results.csv')
```

## MCP工具

### forecast_rolling - 滚动预测

执行滚动时序预测（日前预测模式：D日预测D+1日）

```json
{
  "tool": "forecast_rolling",
  "arguments": {
    "data_path": "data/power_load.csv",
    "target_col": "load",
    "datetime_col": "datetime",
    "freq": "15min",
    "test_start_date": "2025-12-01",
    "test_end_date": "2025-12-31",
    "model_type": "lightgbm",
    "prediction_mode": "direct",
    "lag_preset": "preset1"
  }
}
```

### forecast_single - 单次预测

训练并预测未来N个时间点

```json
{
  "tool": "forecast_single",
  "arguments": {
    "data_path": "data/sales.csv",
    "target_col": "sales",
    "forecast_horizon": 30,
    "train_ratio": 0.8,
    "model_type": "lightgbm"
  }
}
```

### get_lag_presets_info - 获取滞后预设

获取滞后特征预设配置信息

```json
{
  "tool": "get_lag_presets_info",
  "arguments": {
    "freq": "15min"
  }
}
```

### evaluate_forecast - 评估预测

评估预测结果

```json
{
  "tool": "evaluate_forecast",
  "arguments": {
    "predictions_path": "forecast_results.csv"
  }
}
```

### create_feature_config - 特征配置建议

创建特征工程配置建议

```json
{
  "tool": "create_feature_config",
  "arguments": {
    "scenario": "power_load",
    "freq": "15min"
  }
}
```

## 应用场景

### 电力市场预测

- **光伏功率预测**：考虑太阳辐射、云层覆盖
- **风电功率预测**：考虑风速、风向
- **系统负荷预测**：考虑温度、节假日
- **日前/实时电价预测**：考虑供需关系

### 商业预测

- **销售预测**：考虑促销、季节性
- **需求预测**：考虑趋势、周期
- **库存预测**：考虑补货周期

### 交通预测

- **道路流量预测**：考虑早晚高峰
- **网络流量预测**：考虑用户行为

## 数据泄露防范

本框架内置数据泄露防范机制：

1. **滞后特征**：基于实际值的特征至少滞后2天
2. **滚动统计**：先shift至少2天，再计算滚动统计
3. **训练集划分**：使用时序分割，不使用未来数据训练

## 滞后特征预设

### preset1 - 推荐配置（平衡型）

- lags: [-7d, -3d, -2d, -1d, -4h]
- 适用于：电力负荷、电价、销售预测

### preset2 - 短期依赖

- lags: [-1d, -4h, -1]
- 适用于：光伏、风电、交通流量

### preset3 - 长期依赖

- lags: [-7d, -14d, -30d]
- 适用于：长期趋势预测

## 项目结构

```
general_predictor/
├── __init__.py
├── _version.py
├── server.py          # MCP Server
└── forecaster.py      # 核心预测功能
```

## 依赖

- Python >= 3.11, < 3.12
- u8darts[notorch] >= 0.30.0
- lightgbm
- xgboost
- catboost
- scikit-learn
- pandas
- numpy

## 许可证

MIT
llm-json-fix 0.0.1
使用 LLM 修正 json 文件
Requires Python: >=3.9

# llm_json_fix

使用 llm 对 json 进行修正的工具

### 运行一个文件修复的例子
```bash
python ./src/llm_fix_json.py  -m huawei_deepseek_v3_64k -s d:/data_store/001.ba_diag_op_discussion//S01_result/dis_2752192_2814790.txt
```
uncoverit 0.1.2
Async API client for uncoverit.org
Requires Python: >=3.9
Keywords: analysis, api, async, file, uncoverit, websocket

## PIP

### Installation
```shell 
pip install uncoverit
```

### Usage

```py
from uncoverit import UncoveritClient
from uncoverit.exceptions import *
```

Fetching balance:
```py
from uncoverit import UncoveritClient
async with await UncoveritClient.create(api_key) as client:
    print(f"Requests left: {client.requests_left}") # set and tracked internally
    print(f"Requests left: {await client.check_balance()}") # makes a http request to fetch the balance
    # prints "Requests left: 1000"
```
Checking if file exists (Upload function already does this:
```py
from uncoverit import UncoveritClient
async with await UncoveritClient.create(api_key) as client:
    fetched_sample = await client.fetch_static_report("a9e6ea632d75dfd3fa741bhd3659ac5e1423533dfa71c64720d48837cf61fd73")
    # fetched_sample here is None
```

Uploading a file:
```py
from uncoverit import UncoveritClient
async with await UncoveritClient.create(api_key) as client:
    # upload a file and get its analysis information
    uploaded_sample = await client.upload_sample("malware.exe")
    print(uploaded_sample.configs)
    print(uploaded_sample.tags)
    print(uploaded_sample.sha1)
    print(uploaded_sample.ssdeep)
    print(uploaded_sample.md5)
```

Fetching a static analysis report:
```py
from uncoverit import UncoveritClient
async with await UncoveritClient.create(api_key) as client:
    fetched_sample = await client.fetch_static_report("e9e6ea632d75dfd3fa741b4d3659ac5e1423533dfa71c64720d48837cf61fd73")
    print(fetched_sample.json_obj.get("configs")) # print the configs from the json object
    print(fetched_sample.configs) # or get it from the sample
    print(fetched_sample.duration) # static analysis duration

```