semvec/pypi-packages huggingface dataset:
chinese-stock-mcp 0.1.1
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Requires Python: >=3.10

# 股票数据 MCP Server

基于 [pysnowball](https://github.com/uname-yang/pysnowball) 库的 MCP Server，用于获取中国股票市场数据。
该 MCP 服务器能力参考 [pysnowball](https://github.com/uname-yang/pysnowball) 。

## 🚀 快速开始

### 手动安装
使用 uv:

```bash
uv tool install chinese-stock-mcp
```

### 🔌 MCP 集成

将此配置添加到您的 MCP 客户端配置文件中：

```json
{
    "mcpServers": {
        "chinese-stock-mcp": {
            "command": "uv",
            "args": [
                "tool",
                "run",
                "chinese-stock-mcp",
                "--token",
                "xueqiu-token"
            ]
        }
    }
}
```

注：
  - 如果不设置 `--token` 参数，很多功能将无法使用。 
  - [如何获取雪球的 Token](https://blog.crackcreed.com/diy-xue-qiu-app-shu-ju-api/)
pysru-accounting 0.0.2
SRU file encoder

Content
=======
This package contains writers for SRU files for the Swedish tax agency
(Skatteverket)
See: SKV 269 Teknisk beskrivning för elektronisk redovisning av Inkomstdeklaration 1 samt bilagor till Inkomstdeklaration 1, 2, 3 och 4

1. infosru.py:
   Writer for info.sru
mine-task 1.2.0
A client library for interacting with the task server.
Requires Python: >=3.8

# mine-task 使用说明

本库为 API Server 的 Python 客户端，支持用户注册、登录、定时任务管理、用户注销等功能。
本库目前支持用户定时推送自定义消息。
本库主要为了方便用户使用api服务，从简构建，未来将支持更多类型的任务，包括但不限于“自动推送订阅文章”、“自动推送频道内容”等等。

## 安装依赖

请先安装依赖：

```bash
pip install mine-task
```

## 快速开始

### 1. 导入库
```python
from client_lib import connect_session, create_normal_task  # 注意是client_lib，不是mine-task
```

### 2. 注册新用户，获取 API 密钥，<b>若已注册过的用户，此步骤可以跳过</b>
```python
session = connect_session()
api_key = session.create_user("terminal")  # 直接在控制台输出密钥
# 或保存到本地 json 文件
# json_path = session.create_user("json")
# print(json_path)  控制台会输出包含密钥的json文件的路径
print(api_key)
```
**输出示例：**
```
your key: 12345678-xxxx-xxxx-xxxx-xxxxxxxxxxxx
```

### 3. 登录服务
```python
server = session.login_server(api_key)
if server.status == 'ok':
    print("服务连接成功")
else:
    print("服务连接失败")
```
**输出示例：**
```
服务连接成功
```

### 4. 创建定时任务
这里使用create_normal_task方法，参数分别是“任务标题”，“任务消息的内容”，“是否每天执行”
```python
task = create_normal_task("Morning Notice", "Good morning! Have a nice day.", everyday=True)
status = server.sub(task, email="your@email.com")  # 提交任务需要“任务”、“用户的邮箱”两个参数
print(status)
```
**输出示例：**
```
Business creation completed
```

### 5. 查看任务状态
```python
# 查看简略的任务信息
server.task.status(detail=False)
# 查看详细的任务信息
server.task.status(detail=True)
```
**简略输出示例：**
```
[0] Morning Notice | Running: True
```
**详细输出示例：**
```
========================================
Index:        0
Title:        Morning Notice
Content:      Good morning! Have a nice day.
Email:        your@email.com
Cron:         0 8 * * *
Everyday:     True
Running:      True
Next Run:     2025-07-16 08:00:00
Job ID:       1234abcd-xxxx-xxxx-xxxx-xxxxxxxxxxxx
========================================
```

### 6. 修改任务
支持修改任务的任意参数：任务标题、任务内容、任务执行时间、是否每天执行
```python
server.task.modify("Morning Notice", new_content="新的内容", everyday=False)
server.task.modify(0, new_title="New Title", new_time="30 8 * * *")
```
**输出示例：**
```
Task updated
```

### 7. 删除任务
```python
server.task.rm("Morning Notice")  # 支持删除单任务
server.task.rm("Morning Notice", is_all=True)  # 支持删除同名的任务
server.task.rm(0)  # 支持根据索引删除任务
server.task.rm(["Morning Notice", "Another Task"])  # 支持删除多任务，需要输入列表类型
```
**输出示例：**
（无输出，删除成功）

### 8. 注销用户
```python
result = session.destroy_user(api_key)
print(result)
```
**输出示例：**
```
User unregistered and all tasks deleted.
```

## 其他说明
- 每个 API key 独立管理自己的任务，互不干扰。
- 任务类型目前仅支持 normal（普通定时任务）。
- 任务通知方式目前仅支持邮箱推送。
- 所有数据均在服务端加密存储。

如需更多帮助或定制功能，请联系开发者。
ibis-ml 0.1.4
IbisML is a library for building scalable ML pipelines using Ibis.
Requires Python: >=3.10

# IbisML

[![Build status](https://github.com/ibis-project/ibis-ml/actions/workflows/ci.yml/badge.svg)](https://github.com/ibis-project/ibis-ml/actions/workflows/ci.yml)
[![Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://ibis-project.github.io/ibis-ml/)
[![License](https://img.shields.io/github/license/ibis-project/ibis-ml.svg)](https://github.com/ibis-project/ibis-ml/blob/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/ibis-ml.svg)](https://pypi.org/project/ibis-ml/)

## What is IbisML?

IbisML is a library for building scalable ML pipelines using Ibis:

- Preprocess your data at scale on any [Ibis](https://ibis-project.org/)-supported
  backend.
- Compose [`Recipe`](/reference/core.html#ibis_ml.Recipe)s with other scikit-learn
  estimators using
  [`Pipeline`](https://scikit-learn.org/stable/modules/compose.html#pipeline-chaining-estimators)s.
- Seamlessly integrate with [scikit-learn](https://scikit-learn.org/stable/),
  [XGBoost](https://xgboost.readthedocs.io/en/stable/python/sklearn_estimator.html), and
  [PyTorch](https://skorch.readthedocs.io/en/stable/) models.

## How do I install IbisML?

```bash
pip install ibis-ml
```

## How do I use IbisML?

With recipes, you can define sequences of feature engineering steps to get your data
ready for modeling. For example, create a recipe to replace missing values using the
mean of each numeric column and then normalize numeric data to have a standard deviation
of one and a mean of zero.

```python
import ibis_ml as ml

imputer = ml.ImputeMean(ml.numeric())
scaler = ml.ScaleStandard(ml.numeric())
rec = ml.Recipe(imputer, scaler)
```

A recipe can be chained in a
[`Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)
like any other
[transformer](https://scikit-learn.org/stable/glossary.html#term-transformer).

```python
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

pipe = Pipeline([("rec", rec), ("svc", SVC())])
```

The pipeline can be used as any other estimator and avoids leaking the test set into the
train set.

```python
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
pipe.fit(X_train, y_train).score(X_test, y_test)
```
cistercian 1.0.1
A library for generating Cistercian Numeral images.
Requires Python: >=3.6
Keywords: numerals, cistercian, images

[![Cistercian](https://i.imgur.com/KCge7Lv.png)](https://github.com/teeale/Cistercian)
<h1 align="center">
  <b>Cistercian</b> - Image Generator for Cistercian Numerals
  <br>
</h1>

The medieval Cistercian numerals were developed by the Cistercian monastic order in the early thirteenth century. These numeral representations are compounded on a single stave to indicate more complex numbers. The Cistercians eventually abandoned the system in favor of the Arabic numerals, but marginal use outside the order continued until the early twentieth century.

This Python library will create an image of the relevant numeral based on the integer provided.

## Installation

Install from PyPi.

```
pip install cistercian
```

## Usage

The `create` function takes 4 arguments:
- The integer to convert.
- `background_colour`: Background colour of the image
- `line_colour`: Colour of the lines used to compose the numeral
- `height`: Image height. This uses a scale factor to ensure an appropriate image width

```py
import cistercian

>>> cistercian.create(3875, background_colour="teal", line_colour="black", height=350)
```