semvec/pypi-packages huggingface dataset:
whois-charman 0.1.4
A cross-blockchain event prediction exchange arbitrage platform
Requires Python: >=3.8
Keywords: arbitrage, prediction markets, blockchain, trading, finance, kalshi, polymarket, cryptocurrency, betting, financial analysis



# whois-charman

## 项目描述

一个跨区块链事件预测交易所进行事件查询，并且计算多方赔率总和进行套利的平台。
分为 centry作为中心进行监控事件、提醒、交易等核心功能；agent下的代码针对不同预测事件交易所实现事件爬取，实时获取数据，并统合成统一标准返回

## 核心 

    核心原理是 通过centry 监控各个交易所，找到同一个事件，但正反赔率之和小于1的两个交易所，进行套利。

1. 第一种套利方式
    
    比如 A 交易所中 预测事件1的赔率为（Yes 0.6 ， No 0.4） B 交易所中 预测事件1的赔率为（Yes 0.5 ，No 0.5）
那么购买A交易所的 No 和 B交易所的 Yes，就可以实现套利。

2. 第二种套利方式
    
    在第一种的基础上，发现现在的赔率提高，如 A的No 变成0.5， B的yes变成0.6. 则直接将已购买的No和yes进行平仓进行获利。

3. 第三种套利方式

    对单事件进行扫尾盘监控，如果很快能到平盘，则进行套利。

## 结构

    centry 和 Agents 两个部分都可独立运行， 在centry中配置agent。
    1. agent 负责实现各个交易所的预测事件爬取，并返回统一标准的数据。
    2. centry 负责用户管理，监控各个agent返回的数据，进行事件匹配，并计算赔率，进行套利交易。

## 概念
    
    1. 交易所账户：
        各个预测事件区块链交易所的账户，一般来说，每个交易所账户会有一个API
    2. centry用户：
        指centry系统的使用用户，一个centry用户会有名下多个交易所的交易所账户。

### centry 中

    1. centry用户管理。
        每增加一个agent 都需要配置一个交易所账户和API
    2. 事件监控
    3. 事件提醒
    4. 事件套利交易： 这个交易会委托给agent最终实现。

### agent 中

    1. 交易所账户配置，针对各个交易所单独实现，每个交易所一个agent来
    2. 事件爬取
    3. 事件数据统一标准返回
    4. 最终交易： 交易
trysoma_api_client 0.0.6
soma
Requires Python: >=3.9
Keywords: OpenAPI, OpenAPI-Generator, soma

# trysoma_api_client

Python client for the [Soma](https://github.com/trysoma/soma) REST API.

## Overview

This package provides a typed API client for interacting with the Soma server. It is auto-generated from the OpenAPI specification and provides full type hints support.

**Note:** Most users should use `trysoma_sdk` instead, which provides a higher-level API for building agents. This package is useful for direct API access or building custom integrations.

## Installation

```bash
pip install trysoma_api_client
# or
uv add trysoma_api_client
```

## Quick Start

```python
import trysoma_api_client
from trysoma_api_client.rest import ApiException

# Create a configuration
configuration = trysoma_api_client.Configuration(
    host="http://localhost:8080"
)

# Create an API client
with trysoma_api_client.ApiClient(configuration) as api_client:
    # Create an API instance
    task_api = trysoma_api_client.TaskApi(api_client)

    # List tasks
    tasks = task_api.list_tasks()
    print(tasks)

    # Get a specific task
    task = task_api.get_task_by_id(task_id="task-123")
    print(task)
```

## Available APIs

- `TaskApi` - Manage tasks and send messages
- `BridgeApi` - Manage provider instances and invoke functions
- `EncryptionApi` - Manage encryption keys
- `SecretApi` - Manage secrets
- `EnvironmentVariableApi` - Manage environment variables
- `A2aApi` - Agent-to-agent protocol endpoints

## Requirements

Python 3.9+

## Documentation

For comprehensive documentation and guides, visit [https://docs.trysoma.ai/](https://docs.trysoma.ai/)

## Repository

[https://github.com/trysoma/soma](https://github.com/trysoma/soma)
docx2tiptap 0.2.1
Parse DOCX files and convert to TipTap JSON format
Requires Python: >=3.10
Keywords: document, docx, parser, prosemirror, tiptap, word

# docx2tiptap

Parse DOCX files and convert to TipTap/ProseMirror JSON format.

## Installation

```bash
pip install docx2tiptap
```

## Usage

```python
from docx2tiptap import parse_docx, to_tiptap, create_docx_from_tiptap

# Parse DOCX to TipTap JSON
with open("document.docx", "rb") as f:
    elements, comments = parse_docx(f.read())
    tiptap_doc = to_tiptap(elements, comments)

# Export TipTap JSON back to DOCX
docx_buffer = create_docx_from_tiptap(tiptap_doc)
with open("output.docx", "wb") as f:
    f.write(docx_buffer.read())
```

## Features

- Parse DOCX paragraphs, headings, and tables
- Handle merged cells (colspan/rowspan)
- Preserve track changes (insertions/deletions)
- Extract and export comments
- Convert to/from TipTap JSON format

## License

AGPL-3.0-or-later
comfy-uiapi 0.1.0
Python client for ComfyUI-uiapi - programmatic control of ComfyUI workflows
Requires Python: >=3.11
Keywords: api, client, comfyui, image-generation, stable-diffusion

# comfy-uiapi

Python client for [ComfyUI-uiapi](https://github.com/oxysoft/ComfyUI-uiapi) - programmatic control of ComfyUI workflows.

## Installation

```bash
pip install comfy-uiapi
```

Or for development:

```bash
pip install -e /path/to/comfy-uiapi-client
```

## Quick Start

```python
from comfy_uiapi import ComfyClient

# Connect to ComfyUI with uiapi extension
client = ComfyClient("127.0.0.1:8188")
client.ensure_connection()

# Set workflow fields
client.set("prompt.text", "a beautiful landscape, photorealistic")
client.set("ksampler.seed", 42)
client.set("ksampler.steps", 20)

# Execute and get result
result = client.execute()  # Returns numpy array (RGB)
```

## Two Execution Modes

### 1. WebUI Mode (default)

Manipulates the workflow graph in the browser via uiapi:

```python
client = ComfyClient("127.0.0.1:8188")
client.require_webui = True  # default

client.set("prompt.text", "hello world")
result = client.execute()
```

Requires: Browser with ComfyUI WebUI open and connected.

### 2. Workflow API Mode

Posts JSON workflow directly to `/prompt` endpoint - no browser needed:

```python
import json

# Load workflow JSON (exported from ComfyUI)
with open("workflow_api.json") as f:
    workflow = json.load(f)

client = ComfyClient("127.0.0.1:8188")
client.require_webui = False

# Execute with field overrides
result = client.execute_workflow(
    workflow,
    fields=[
        ("prompt.text", "a cat in space"),
        ("ksampler.seed", 123),
    ]
)
```

## Model Downloads

```python
from comfy_uiapi import ComfyClient, ModelDef

client = ComfyClient("127.0.0.1:8188")

models = {
    "sd_xl_base_1.0.safetensors": ModelDef(
        huggingface="stabilityai/stable-diffusion-xl-base-1.0"
    ),
    "my_lora.safetensors": ModelDef(
        civitai="https://civitai.com/api/download/models/12345"
    ),
}

client.download_models(models)
```

## Async API

All methods have async variants:

```python
import asyncio
from comfy_uiapi import ComfyClient

async def main():
    client = ComfyClient("127.0.0.1:8188")
    await client.ensure_connection_async()
    await client.set_async("prompt.text", "async generation")
    result = await client.execute_async()

asyncio.run(main())
```

## License

MIT