semvec/pypi-packages huggingface dataset:
heliumcli 1.6.38
A CLI tool that provides a useful set of tools for maintaining, building, and deploying code in compatible projects
Requires Python: >=3.9

[![Version](https://img.shields.io/pypi/v/heliumcli)](https://pypi.org/project/heliumcli)
[![Python Versions](https://img.shields.io/pypi/pyversions/heliumcli.svg)](https://pypi.org/project/heliumcli)
[![Coverage](https://img.shields.io/codecov/c/github/HeliumEdu/heliumcli)](https://codecov.io/gh/HeliumEdu/heliumcli)
[![Build](https://img.shields.io/github/actions/workflow/status/HeliumEdu/heliumcli/build.yml)](https://github.com/HeliumEdu/heliumcli/actions/workflows/build.yml)
[![GitHub License](https://img.shields.io/github/license/HeliumEdu/heliumcli)](https://github.com/HeliumEdu/heliumcli/blob/main/LICENSE)

# Helium CLI

`helium-cli` provides a useful set of tools for maintaining, building, and deploying code in compatible projects.

## Getting Started

The Helium CLI is available on [PyPI](https://pypi.org/project/heliumcli/) and can be installed using `pip`:

```sh
pip install heliumcli
```

The utility is then available from the command line with `helium-cli`.

More details pertaining to project compatibility, installation, and configuration can be found in
the [Helium CLI Wiki](https://github.com/HeliumEdu/heliumcli/wiki).

## Contributing

Want to contribute financially? If you've found `helium-cli`
useful, [sponsorship](https://github.com/sponsors/alexdlaird) would
also be greatly appreciated!
cosma 0.5.0
Search engine for your files!
Requires Python: >=3.12

# Cosma

[![Version](https://img.shields.io/pypi/v/cosma)](https://pypi.org/project/cosma/)
[![Discord](https://img.shields.io/badge/Discord-Join%20Server-5865F2?style=flat&logo=discord&logoColor=white)](https://discord.gg/Zs6vUt4t5e)

<img align="right" src="./assets/logo.png" height="150px" alt="Cosma Logo">

Search engine for your files!

> [!CAUTION]
> This software is in early alpha! There will be lots of bugs.
> If you encounter any, please report them to the issue tracker.

### How It Works

Choose which directories to index, and Cosma will process all files
in those directories into a search-optimized index. It'll also
watch for changes to keep the index updated.

After files are indexed, you can search for them with natural language!
Cosma uses vector-powered search to find files quickly and easily.

Cosma can run 100% locally or in the cloud.

## Get Started

Cosma has been tested on MacOS ARM and Windows.
Linux support is coming soon!

### Installing

Cosma can be downloaded from PyPI.
We highly recommend you do this with [uv](https://docs.astral.sh/uv/getting-started/installation/).

```sh
uv tool install cosma
```

### Upgrading

To upgrade to the latest version:

```sh
uv tool upgrade cosma --no-cache
```

### Setup

Make sure you have [Ollama](https://ollama.com/) installed.

Cosma has a backend to serve search queries, so it must be started first.
This needs to always be running to watch for file changes and process files in the background.

```sh
cosma serve
```

### Running

To start the terminal UI and start searching, run search.

> [!IMPORTANT]  
> The backend must be running for this command to work (see above).

```sh
cosma search /path/to/directory/to/search
```
> [!WARNING]
> This will begin processing all files in the directory specified,
> which will take some time if running locally.

## CLI

Cosma includes a CLI with three output modes:

- **default** - Rich formatted output with colors and tables for interactive use
- `--plain` - Simple text output without colors, for piping and scripts
- `--json` - Structured JSON output for agents and programmatic access

```sh
cosma search "my query"               # Search indexed files
cosma index /path/to/dir              # Index a directory
cosma status                          # Check backend status
cosma watch add /path/to/dir          # Watch directory for changes
cosma files stats                     # Get file statistics
```

Run `cosma --help` for all commands.

## MacOS App

We're also working on a Mac app! If this seems like a useful project,
give us a star!

## Contributing

Cosma is open source, and we'd love to have you contribute!
Please feel free to open an issue or pull request with code changes,
or join our [Discord](https://discord.gg/Zs6vUt4t5e).
We'll have documentation for how best to contribute soon!
orchestra-foundation 1.0.0
永乐大典地基工具包 - Orchestra Foundation Kit for Python
Requires Python: >=3.9

# orchestra-foundation

永乐大典地基工具包 - Orchestra Foundation Kit for Python

## 安装

```bash
pip install orchestra-foundation
```

带 FastAPI 支持:
```bash
pip install orchestra-foundation[fastapi]
```

## 功能

- 标准化错误处理 (OrchestraError 体系)
- 结构化日志 (基于 loguru)
- 请求 ID 追踪 (Request ID Context)
- 配置管理 (支持 YAML + .env)

## 使用

### 错误处理

```python
from orchestra_foundation import (
    ParamError,
    NotFoundError,
    UnauthorizedError,
    ForbiddenError,
    InternalError,
    create_error,
)

# 使用预定义错误类
raise ParamError("邮箱格式不正确")
raise NotFoundError("用户", "123")
raise UnauthorizedError()

# 自定义错误码
raise create_error("900001", "自定义错误", detail="详细信息")
```

### 日志

```python
from orchestra_foundation import create_logger

logger = create_logger("UserService")

logger.debug("调试信息")
logger.info("用户登录成功", user_id="123")
logger.warning("登录尝试次数过多", user_id="123", attempts=5)
logger.error("登录失败", error=str(e))
```

### 请求 ID 追踪

```python
from orchestra_foundation import (
    get_request_id,
    set_request_id,
    generate_request_id,
    request_id_context,
)

# 手动设置
set_request_id("req-123")
print(get_request_id())  # req-123

# 上下文管理器
with request_id_context("req-456"):
    print(get_request_id())  # req-456
print(get_request_id())  # None (上下文结束)

# 自动生成
req_id = generate_request_id()  # req-abc123def456...
```

### 配置管理

```python
from orchestra_foundation import OrchestraConfig, create_config

# 初始化配置
config = create_config(config_dir="./config")

# 获取配置值
db_host = config.get("database.host", "localhost")
port = config.get("server.port", 3000)

# 获取环境变量
api_key = config.env("API_KEY")
secret = config.env_required("APP_SECRET")  # 不存在则抛出错误

# 类型转换
debug = config.env_bool("DEBUG", False)
port = config.env_int("PORT", 3000)
hosts = config.env_list("ALLOWED_HOSTS", ["localhost"])
```

### FastAPI 集成

```python
from fastapi import FastAPI
from orchestra_foundation.fastapi import (
    RequestIdMiddleware,
    orchestra_exception_handler,
)

app = FastAPI()

# 添加请求 ID 中间件
app.add_middleware(RequestIdMiddleware)

# 添加错误处理
app.add_exception_handler(Exception, orchestra_exception_handler)
```

## 错误码体系

### 模块码

| 代码 | 模块 |
|------|------|
| 00 | 系统级 |
| 10 | 用户/认证 |
| 20 | 业务逻辑 |
| 30 | 数据/存储 |
| 40 | 外部服务 |
| 50 | 文件/资源 |
| 60 | 网络 |
| 70 | 安全 |
| 90 | 项目自定义 |

### 预定义错误类

| 类 | 错误码 | HTTP 状态码 |
|---|--------|-------------|
| ParamError | 101001 | 400 |
| ParamMissingError | 101002 | 400 |
| NotFoundError | 204001 | 404 |
| UnauthorizedError | 103001 | 401 |
| ForbiddenError | 103002 | 403 |
| ConflictError | 205001 | 409 |
| RateLimitError | 706001 | 429 |
| InternalError | 008001 | 500 |

## 开发

```bash
# 安装开发依赖
pip install -e ".[dev]"

# 运行测试
pytest

# 运行测试并生成覆盖率报告
pytest --cov=orchestra_foundation --cov-report=html
```

## License

MIT
torchfea 1.0.4
Add your description here
Requires Python: >=3.12

FEA —— 基于 PyTorch 的有限元分析框架
======================================

一个支持非线性材料、接触分析与动力学（隐式/显式）的研究型 FEA 框架。采用模块化设计，装配（Assembly）统一管理几何、材料、载荷与约束，求解器层提供静力隐式、动力隐式（Newmark-β）与动力显式（中心差分）。

## 特色

- 装配-求解器分层，接口清晰，易扩展
- 稀疏矩阵装配与 Pardiso/CG 线性求解
- 动力学两种积分：Newmark-β（隐式）与中心差分（显式，集总质量）
- 接触（自接触/体-体接触）、压力、体力等载荷组件
- 基于 PyTorch，可使用 GPU 与自动微分做灵敏度/优化

## 快速开始

1) 安装依赖（略）。推荐 Python 3.10+、PyTorch（float64）。
2) 运行最小示例脚本（静力 + 表面压力）：

在仓库根目录下执行（Windows PowerShell）：

```powershell
python .\Docs\examples\run_static_pressure.py
```

脚本会读取 `tests/pressure_test/C3D4Less.inp`，在 `final_model` 上施加表面压力并固定底部节点，调用静力隐式求解，并将位移向量保存至 `out/static_pressure_GC.npy`。

3) 更多用法示例：

- 使用说明（从 INP 到求解、导出与可视化）见：`Docs/usage.md`
- 框架架构与数据流见：`Docs/structure.md`

## 目录导航

- `src/`torchfea：核心代码（装配、元素、载荷、约束、求解器等）
- `examples/`：各类算例与验证（元素、压力、接触、动力学、梯度/优化）
- `docs/`：架构与使用文档，`Docs/examples/` 提供可直接运行的示例脚本

## 许可

研究用途优先。若用于生产或商业，请先评估并完善必要的数值与工程健壮性保障。
iflow-mcp_globus_globus-mcp 0.1.1
Globus MCP server
Requires Python: >=3.13
Keywords: globus, llm, mcp

# Globus MCP Server

The Globus [MCP](https://modelcontextprotocol.io) Server enables LLM applications to interact
with [Globus](https://www.globus.org/) services.

## Supported Tools

### [Globus Transfer](https://docs.globus.org/api/transfer/)

- `globus_transfer_list_endpoints_and_collections` - List endpoints and collections the user has
access to
- `globus_transfer_search_endpoints_and_collections` - Use a filter string to search all endpoints
and collections that are visible to the user
- `globus_transfer_submit_task` - Submit a transfer task between two collections
- `globus_transfer_get_task_events` - Get a list of task events
- `globus_transfer_list_directory` - List contents of a directory on a collection

### [Globus Compute](https://docs.globus.org/compute/)

- `globus_compute_list_endpoints` - List endpoints that the user has access to
- `globus_compute_register_python_function` - Register a Python function
- `globus_compute_register_shell_command` - Register a shell command
- `globus_compute_submit_task` - Submit a task to an endpoint
- `globus_compute_get_task_status` - Retrieve the status and result of a task

## Configuration

The following configuration is compatible with most LLM applications that support MCP such as
[Claude Desktop](https://modelcontextprotocol.io/docs/develop/connect-local-servers):

```json
{
  "mcpServers": {
    "globus-mcp": {
      "command": "uvx",
      "args": ["globus-mcp"]
    }
  }
}
```

### Limiting Tool Registration

By default, the Globus MCP server registers tools for every service. To register tools for only
specific services, use the `--services` command-line flag:

```json
{
  "mcpServers": {
    "globus-mcp": {
      "command": "uvx",
      "args": [
        "globus-mcp",
        "--services",
        "compute"
      ]
    }
  }
}
```

### Specifying Client Credentials

If you've [registered a client application](https://docs.globus.org/api/auth/developer-guide/#register-app)
in the [Globus web UI](https://app.globus.org/settings/developers/), you can specify the client
credentials via the `GLOBUS_CLIENT_ID` and `GLOBUS_CLIENT_SECRET` environment variables:

```json
{
  "mcpServers": {
    "globus-mcp": {
      "command": "uvx",
      "args": ["globus-mcp"],
      "env": {
        "GLOBUS_CLIENT_ID": "...",
        "GLOBUS_CLIENT_SECRET": "..."
      }
    }
  }
}
```