semvec/pypi-packages huggingface dataset:
mypylib 0.2.76
Your package description


# This is William Chen's private library

# Interfaces
# \_\_init\_\_
* DefaultOrderedDict() 好像沒用
* my_addressable_IP() 抓對外IP
* \_\_LINE\_\_() 顯示哪一行
* get_all_stock_code_name_dict() 用 Shioaji 測試帳號抓所有股票代碼和名稱
* get_day_trade_candidates() 抓可以當沖標的
* get_top_future_trade_volume_list() 抓股票期貨交易量排行
* get_stock_future_snapshot() 抓所有股票期貨的更新資料，開高低收
* get_stock_future_data() 從交易所網站抓所有股票期貨的保證金、級距等等資料
* get_punishment_list() 抓處置股名單
* get_TSE_short_selling_list() 抓上市公司是否可以放空名單
* get_OTC_short_selling_list() 抓上櫃公司是否可以放空名單
* get_short_selling_list() 抓所有公司是否可以放空名單
* mypylib_unit_test() Unit test for 以上函數
* parse_date_time() 把 str_data, str_time 轉成 datetime
* timeIt() 計時
* Ticks 處理 Decimal 版本 
* get_current_price_tick_dec() 目前價格的 tick
* get_ticks_between_dec() 計算兩個價格間的 ticks 數
* price_ticks_offset_dec() ticks offset
* get_limit_up_and_down_price_dec() 平盤價的漲停與跌停
* price_stop_profit_and_lose_dec() 抓漲停與跌停
* date_range() 日期區間 (Iterator)
* short_selling_to_csv() 抓2019年以來能不能放空名單
* get_future_ex() 從日期抓期貨附加代號，例如 M2
* is_end_of_contract_day() 是否是結算日
* load_all_shioaji_ticks() 回傳所有 shioaji ticks的檔案名稱，接下來給 MP 去做事
* build_dealer_downloader() 下載自營商資料（買進、賣出、避險等等)
* get_trade_days() 用 finMind API 傳回哪些是交易日的資料

# finmind
* get_finMind_date_range() 底層 function 
* get_finMind_TX_ticks() 抓 TX tick
* get_finMind_TAIEX_day_KLine() 抓大盤 K 線

# MVP
* Carey的 MVP

# tLineNotify
* tLineNotify() Thread enabled LineNotify

# tplaysound
* tplaysound() Thread enabled playsound

# tredis
* tredis() Thread enabled redis

# warrant
* read_warrant_bible() 讀權證達人寶典的library

# binance_copy_bot
* Binance_position_monitor() 抓幣安 leader boards的部位

# chdbif
* select_targets() 用來從 clickhouse 選要回測資料

# libexcel 早期寫的東西整理在這邊
* parse_date_time() 重複
* date_range() 
* rename_column() 把一些敏感的字元拿掉
* str_to_float()
* trim_price() 這個東西應該用 Decimal ，不應該用 float
* get_day_trade_candidates()
* load_opendays() 抓有交易的日期
* find_last_number_day() 這個應該不能用了
* calculate_call_percentage() 計算做多%數
* convert_to_str()
* load_cell() 處理 excel 相關
* load_value() 處理 excel 相關
* set_cell()
* column_idx()
* conv()
* calculate_percentage()
* get_history()
* build_date_policy()
* build_stock_price()
* build_stock_info()
* build_1mk_from_shioaji()
* build_1mk_profit()
* build_1mk_chart1()
* build_1mk_chart()
* build_1mk_data()
* build_1mk()
* build_simulation()
* build_simulation_total_result()
* build_simulation_result()
* build_days() 
* build_ohlc()
* build_column_ranking_each_day()
* build_dealer_downloader()
* build_ticks_related_info()
* build_dealer_info()
* build_win_rate()
* get_current_price_tick()
* get_limit_up_and_down_price()
* list_fullhistory()

# Interfaces

# sjtools:
## class Quote(dict): Shioaji quote 轉成 class
## class Market(dict): Shioaji tick 轉成 class
#
# class SJ_wrapper:
## Shioaji warrper ，讓 login 比較方便，還需要擴充
#
# class SJ_downloader(SJ_wrapper):
## 專門用來 下載 shioaji ticks 的
#
#
# unit_test_SJ_downloader()
## Unit test for SJ_downloader 
#
#
# converter_SJ_ticks_to_MC()
## 把 SJ ticks 轉成 MultiCharts 可以吃的格式
geb 1.0.0b11
GEB simulates the environment (e.g., hydrology, floods), the individual people, households and orginizations as well as their interactions at both small and large scale.
Requires Python: <3.14,>=3.13

GEB (Geographical Environmental and Behavioural model) simulates the environment (e.g., hydrology, floods), the individual people, households and orginizations as well as their interactions at both small and large scale. The model does so through a "deep" coupling of an agent-based model a hydrological model, a vegetation model and a hydrodynamic model. You can find full documentation [here](https://docs.geb.sh/).

The figure below shows a schematic overview of GEB.

![Schematic model overview of GEB.](https://raw.githubusercontent.com/GEB-model/GEB/refs/heads/main/docs/images/schematic_overview.svg "Schematic model overview")

## Cite as

### Model framework

> de Bruijn, J. A., Smilovic, M., Burek, P., Guillaumot, L., Wada, Y., and Aerts, J. C. J. H.: GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model, Geosci. Model Dev., 16, 2437–2454, [https://doi.org/10.5194/gmd-16-2437-2023](https://doi.org/10.5194/gmd-16-2437-2023), 2023.

### Applications

> Kalthof, M. W. M. L., de Bruijn, J., de Moel, H., Kreibich, H., and Aerts, J. C. J. H.: Adaptive behavior of farmers under consecutive droughts results in more vulnerable farmers: a large-scale agent-based modeling analysis in the Bhima basin, India, NHESS, [https://doi.org/10.5194/nhess-25-1013-2025](https://doi.org/10.5194/nhess-25-1013-2025), 2025.
oarepo-model-builder 4.1.2
A utility library that generates OARepo required data model files from a JSON specification file

# OARepo model builder

A library and command-line tool to generate invenio model project from a single model file.

Documentation for the model format and examples are at [https://narodni-repozitar.github.io/developer-docs/docs/technology/invenio/nrp-toolchain/edit-metadata](https://narodni-repozitar.github.io/developer-docs/docs/technology/invenio/nrp-toolchain/edit-metadata)

To compile your model into invenio sources, run

```bash
oarepo-compile-model <model.yaml> --output-directory <output-dir>
```
incompletions 0.1.0
Add your description here
Requires Python: >=3.11

# incompletions

Initial PyPI package scaffold for `incompletions`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
scruples 0.1.0
Add your description here
Requires Python: >=3.11

# scruples

Initial PyPI package scaffold for `scruples`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
rednote_mcp_plus 0.9.0
Plus version of MCP server for accessing RedNote
Requires Python: >=3.10

# RedNote-MCP-Plus

<p align="center"><img src= "docs/img/logo.png" width="600"/></p>

[![English](https://img.shields.io/badge/English-Click-yellow)](docs/README.en.md)
[![简体中文](https://img.shields.io/badge/简体中文-点击查看-orange)](README.md)
[![License](https://img.shields.io/badge/license-MIT-blue)](LICENSE)
[![PyPI version](https://badge.fury.io/py/rednote_mcp_plus.svg)](https://badge.fury.io/py/rednote_mcp_plus)

⚙️ 配备更全面工具集的小红书 MCP 服务器。

## 主要特性
- 简单易上手：只需要几行简单的命令即可体验工具能力，无需额外配置
- 自动化互动工具：包含发布、点赞、收藏、评论、关注等工具，助力自动化账号运营
- 自动化爬虫工具：包含搜索、笔记内容爬取、用户数据爬取等工具，助力内容搜索

## 工具清单
- ✅登录：使用其他工具前必须使用该工具登录小红书，保存登录态
- ❤️点赞：点赞任意笔记
- 📥收藏：收藏任意笔记
- 💬评论：评论任意笔记
- 👤关注：关注任意用户
- 🖼️发布笔记：发布任意图文笔记
- 🔍搜索笔记：关键词搜索笔记
- 📖笔记内容爬取：以MarkDown形式保存任意笔记
- 📊用户数据爬取：获取用户昵称、简介、标签、互动信息

## 快速开始

如果你想快速体验工具能力，按照以下步骤即可：

### 配置环境
1. 安装uv：
```
brew install uv
```

2. 安装playwright：
```
pip install playwright
playwright install
```

3. 安装node：
```
brew install node
```

4. 安装小红书MCP
```
uv tool install rednote_mcp_plus
```

### 快速试用

**一行命令启动MCP服务调试!**
```
npx @modelcontextprotocol/inspector uvx rednote_mcp_plus
```

如果一切顺利的话，现在你就可以看到如图所示列出的所有工具了，接下来你可以随意试用感兴趣的MCP工具了！

![MCP工具列表](docs/img/mcp_tools.png "MCP工具列表")

> ⚠️ **注意：** 请务必先使用manualLogin工具登录小红书后再体验其他工具！

### 效果展示

图中展示了获取笔记内容的结果，包含了标题、作者、图文/视频、正文、标签、互动数据、发布时间、发布地点等数据。

![小红书笔记](docs/img/note.png "小红书笔记")

## MCP服务器配置

以Cline配置为例：

```
{
  "mcpServers": {
    "RedNote_MCP_Plus": {
      "command": "uvx",
      "args": [
        "rednote_mcp_plus"
      ]
    }
  }
}
```