semvec/pypi-packages huggingface dataset:
qvecopt-provider 0.2.2
QVecOpt Provider 是一个用于集成 Qiskit 与 QVecOpt 后端的 Python 包
License: MIT
Requires Python: >=3.7

# QVecOpt Provider

QVecOpt Provider 是一个用于集成 Qiskit 与 QVecOpt 后端的 Python 包。它允许用户通过 Qiskit 风格的接口，将量子电路提交到 QVecOpt 后端进行编译与仿真。

## 主要特性
- 提供 Qiskit 兼容的 Provider/Backend 接口
- 支持量子电路的编译（transpile）与运行（run）
- 支持获取量子电路的计数结果和状态向量

## 目录结构
```
qvecopt_provider/
    __init__.py
    backend.py
    job.py
    provider.py
    result.py
LICENSE
README.md
requirements.txt
```

## 安装依赖
```bash
pip install -r requirements.txt
```

## 使用方法
以下为典型用法：

```python
from qiskit import QuantumCircuit
from qvecopt_provider import QVecOptProvider

num_qubits = 29
provider = QVecOptProvider(url="http://<your-qvecopt-server>:8080", max_qubits=27)
backend = provider.get_backend("QVecOpt")

qc = QuantumCircuit(num_qubits, num_qubits)
qc.h(0)
qc.cx(0, 1)
qc.cx(1, 2)

compiled_circuit = backend.transpile(qc)

# 运行电路，initial_state为初始态，wipedisk为是否在计算前清空存储量子态的所有存储器
job = backend.run(compiled_circuit, initial_state=8192, wipedisk=True)
result = job.result()

# 获取计数结果
print(result.get_counts())

# 获取状态向量
statevector = result.get_statevector()
```

## 主要模块说明
- `provider.py`：实现 Qiskit Provider 接口，负责后端管理
- `backend.py`：实现 Qiskit Backend 接口，负责电路编译与运行
- `job.py`：实现 Job 对象，负责任务提交与结果获取
- `result.py`：实现 Result 对象，负责封装计算结果

## 依赖
- qiskit
- requests

## 备注
- 请确保 QVecOpt 服务端已启动并可访问。
- `initial_state` 参数和 `wipedisk` 参数请根据实际需求调整。
vearne-akshare-mcp 0.2.3
An MCP server capable of retrieving A-share, Hong Kong stock, and U.S. stock data using AkShare.
License: MIT
Requires Python: >=3.8

# akshare-mcp
an MCP server capable of retrieving A-share, Hong Kong stock, and U.S. stock data using AkShare.

## Usage
### 1. Install and Run
```
pip install vearne-akshare-mcp
```
### Method 1: Run with transport 'stdio'
```
python3 -m vearne_akshare_mcp
```
### Method 2: Run with transport 'streamable-http'
```
python3 -m vearne_akshare_mcp --http --bind="127.0.0.1" --port=8090
```
Server URL: http://127.0.0.1:8090/mcp/

### 2. use with uvx
``` 
"mcpServers": {
    "vearne-akshare-mcp": {
        "command": "uvx",
        "args": ["vearne_akshare_mcp"]
    }
}
```
### 3 develop
```
cd src/vearne_akshare_mcp


```

## Thanks
[akfamily/akshare](https://github.com/akfamily/akshare)

## Inspired by akshare-one-mcp
[zwldarren/akshare-one-mcp](https://github.com/zwldarren/akshare-one-mcp)
thirukkural-vk 1.0.2
A complete Thirukkural dataset with metadata and search utilities.
License: MIT
Requires Python: >=3.8
Keywords: thirukkural, kural, tamil

# Thirukkural API (Python Package)

A complete Thirukkural dataset (1330 kurals) bundled with metadata and search utilities.

## Features

✔️ Get all Kurals  
✔️ Get a single Kural  
✔️ Metadata (paal, iyal, adhigaram)  
✔️ Search by adhigaram, paal, iyal
gratekeeper 0.7.0
GRatekeeper is a lightweight GitHub API helper with proactive rate-limit throttling
License: MIT
Requires Python: >=3.10

# GRatekeeper

GRatekeeper (“GitHub Ratekeeper”) is a lightweight companion for monitoring and controlling GitHub API usage. It gives you:

- A lean, configurable dashboard (`gk-dash` / `gratekeeper-dashboard`) that shows every quota bucket, Actions activity, and billing minutes, with quick toggles and themes.
- A drop-in `requests.Session` wrapper (`RateLimitedGitHubClient`) that enforces a soft floor before you hit zero, plus optional killswitches for runaway jobs.

Why it’s different from simple retries/backoff:

- **Adaptive throttling before zero:** While other libraries offer automatic retries and backoff, Gratekeeper slows down your requests _before_ you hit the limit, making it safe to aggressively script your application's API calls without worrying about crossing over into the dreaded _secondary rate limit_, possibly encurring penalties or prolonged cooldown periods.
- **Multi-process friendly:** The dashboard/socket lets multiple scripts share live bucket state so one noisy job doesn’t blind the others.
- **Last-resort brakes:** A killswitch can stop all new requests during incidents, avoiding secondary rate limits.

## Quick start

Install (Python 3.10+):

```bash
pip install gratekeeper
```

Use the client:

```python
from gratekeeper import RateLimitedGitHubClient

client = RateLimitedGitHubClient()  # picks up $GITHUB_TOKEN
me = client.get_json("/user")
print(me["login"])
```

Run the dashboard:

```bash
gk-dash --refresh 10 --fetch 30 --actions my-org/my-repo
# prefer a simpler table? add --ui table
```

Common tasks:

- Customize HTTP: pass `token`, `base_url`, `user_agent`, or `bucket="search"` to isolate quotas.
- Disable throttling temporarily: `enable_ratekeeping=False` on the client.
- Themes/presets/bucket toggles/Actions on/off are built into the Textual UI; see the dashboard guide below.

## Security and compatibility

- The socket bridge is local-only but unauthenticated; change the path or disable with `--socket none` / `GRATEKEEPER_SOCKET=none` on shared hosts. On Windows, the bridge is disabled automatically.
- Works with REST and GraphQL; buckets stay separate unless you intentionally merge them via `bucket=...`.

## Where to go next

- Rate-limit math, soft floor, killswitch details: [EXPLAINER.md](EXPLAINER.md)
- Advanced client features (polling, listeners, socket bridge, logging, dev/demos): [ADVANCED_USAGE.md](ADVANCED_USAGE.md)
- Dashboard controls (themes, Active-only/manual presets, digit toggles, Hide Actions): [docs/dashboard-bucket-visibility.md](docs/dashboard-bucket-visibility.md)
- Local dev, tests, demos: [CONTRIBUTING.md](CONTRIBUTING.md) and `scripts/run_demo_scenarios.py`