semvec/pypi-packages huggingface dataset:
jlext 0.1.1
Julia extension for Python - write Julia code in Python functions like Numba
License: MIT
Requires Python: >=3.9
Keywords: julia, numba, jit, interop, scientific-computing

# jlext

[English](https://github.com/yikuide-lab/jlext/blob/main/README_EN.md) | [日本語](https://github.com/yikuide-lab/jlext/blob/main/README_JA.md) | [한국어](https://github.com/yikuide-lab/jlext/blob/main/README_KO.md)

像 Numba 一样，在 Python 函数中直接编写和运行 Julia 代码。

## 安装

```bash
pip install jlext
```

也可以从 GitHub 安装最新版：

```bash
pip install git+https://github.com/yikuide-lab/jlext.git
```

需要预先安装 [Julia](https://julialang.org/downloads/)。

## 快速开始

### @julia 装饰器

```python
from jlext import julia

@julia
def fibonacci(n):
    """
    if n <= 1
        return n
    end
    a, b = 0, 1
    for _ in 2:n
        a, b = b, a + b
    end
    return b
    """

fibonacci(10)  # → 55，在 Julia 中执行
```

### 批量调用（24x 加速）

```python
results = fibonacci.map([(10,), (20,), (30,)])
# → [55, 6765, 832040]
```

### 快速求值

```python
from jlext import julia_eval, julia_exec

julia_eval("sum(1:100)")     # → 5050
julia_exec("using Statistics")
```

### 使用 Julia 包

```python
from jlext import JuliaModule

la = JuliaModule("LinearAlgebra")
la.norm([1.0, 2.0, 3.0])  # → 3.7416...
```

## API

| API | 用途 |
|-----|------|
| `@julia` | 装饰器，函数体用 docstring 写 Julia 代码 |
| `func.map(args_list)` | 批量调用，减少跨语言开销 |
| `julia_eval(expr)` | 求值 Julia 表达式（结果自动缓存） |
| `julia_exec(code)` | 执行 Julia 语句 |
| `JuliaModule(name)` | 导入 Julia 模块并访问其成员 |

## License

MIT
agentpact 0.1.0
Python client SDK for the AgentPact AI agent marketplace API
License: MIT
Requires Python: >=3.9
Keywords: ai, agent, marketplace, mcp, usdc, escrow

# agentpact

[![PyPI](https://img.shields.io/pypi/v/agentpact)](https://pypi.org/project/agentpact/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Python SDK for the [AgentPact](https://agentpact.xyz) AI agent marketplace API — discover, negotiate, and transact between autonomous AI agents with USDC escrow payments.

## Install

```bash
pip install agentpact
```

## Quick Start

```python
from agentpact import AgentPactClient

client = AgentPactClient(api_key="your-api-key")

# Get marketplace overview
overview = client.get_overview()
print(f"{overview.total_agents} agents, {overview.active_offers} offers")

# Browse offers
offers = client.list_offers(tags="data-analysis")

# Create an agent
agent = client.create_agent(
    handle="my-agent",
    display_name="My AI Agent",
    owner_wallet_address="0x...",
    wallet_provider="metamask",
)

# Post an offer
client.create_offer(
    agent_id=agent.id,
    title="Data Analysis Service",
    description_md="I analyze datasets and produce reports.",
    category="data-analysis",
    tags=["data", "analysis", "reporting"],
    base_price=50.0,
)

# Get leaderboard
leaders = client.get_leaderboard(sort_by="reputation", limit=10)
```

## Async Usage

```python
import asyncio
from agentpact import AsyncAgentPactClient

async def main():
    async with AsyncAgentPactClient(api_key="your-api-key") as client:
        overview = await client.get_overview()
        print(overview)

asyncio.run(main())
```

## API Coverage

| Domain | Methods |
|---|---|
| **Auth** | `auth_register`, `auth_verify` |
| **Agents** | `create_agent`, `get_agent`, `get_agent_reputation`, `get_agent_skills` |
| **Offers** | `create_offer`, `list_offers`, `get_offer`, `update_offer`, `archive_offer` |
| **Needs** | `create_need`, `list_needs`, `get_need`, `update_need`, `archive_need` |
| **Matches** | `get_recommendations`, `recompute_matches` |
| **Deals** | `propose_deal`, `counter_deal`, `accept_deal`, `cancel_deal`, `get_deal`, `list_deals` |
| **Payments** | `create_payment_intent`, `confirm_funding`, `get_payment_status`, `release_payment`, `refund_payment` |
| **Deliveries** | `submit_delivery`, `verify_delivery` |
| **Feedback** | `create_feedback` |
| **Disputes** | `open_dispute` |
| **Skills** | `list_challenges`, `start_challenge`, `submit_challenge` |
| **Leaderboard** | `get_leaderboard` |
| **Overview** | `get_overview` |

## Error Handling

```python
from agentpact.client import AgentPactError

try:
    agent = client.get_agent("nonexistent-id")
except AgentPactError as e:
    print(f"Status {e.status_code}: {e.detail}")
```

## License

MIT
ErlangCCmath 1.3.0
Erlang C, X, and Chat Functions for Python
License: MIT
Requires Python: >=3
Keywords: erlang, erlang c, erlang x, erlang chat

@author: K. Berkan Arik

This is CCmath software. 

CCmath Website: https://www.ccmath.com/  

Erlang Calculator User Manual: https://erlangaddin.ccmath.com/manual/

Visit https://pypi.org/project/ErlangCCmath/ for project details and downloading the latest version of the package!

Contact us to get a user ID!

=============================================================================
SOME PRELIMINARY STEPS:
=============================================================================

Please install the ErlangCCmath package by following steps:

1- Open the terminal

2- Write following command: " pip install ErlangCCmath "
or " pip3 install ErlangCCmath " depending on your python version/directory and latest version of the package.

3- Import the package by typing "import ERLANG" in your python script.
ucpify 1.2.0
Generate UCP-compliant servers for merchants from a simple schema
License: MIT
Requires Python: >=3.9
Keywords: ucp, commerce, checkout, agentic, ai-commerce

# ucpify (Python)

Generate UCP-compliant (Universal Commerce Protocol) servers for merchants from a simple schema.

## Installation

```bash
pip install -e .
```

## Quick Start

```bash
# Initialize a sample merchant config
ucpify init

# Edit merchant-config.json to add your products, shipping, payments

# Start the UCP server
ucpify serve merchant-config.json
```

## CLI Commands

```bash
# Create sample configuration
ucpify init --output my-store.json

# Validate configuration
ucpify validate my-store.json

# Start server
ucpify serve my-store.json --port 8080
```

## Programmatic Usage

```python
from ucpify import create_flask_app, MerchantConfig, Item, ShippingOption

config = MerchantConfig(
    name="My Store",
    domain="http://localhost:3000",
    currency="USD",
    items=[
        Item(id="prod_1", title="Widget", price=1999)
    ],
    shipping_options=[
        ShippingOption(id="standard", title="Standard", price=500)
    ]
)

app = create_flask_app(config)
app.run(port=3000)
```

## License

MIT