semvec/pypi-packages huggingface dataset:
optical-table 1.0.2
A simple ray tracing and visualization tool for freespace optics.
Requires Python: >=3.8

<div align="center">

<img src="https://raw.githubusercontent.com/tim4431/optable/main/docs/assets/logo.png" alt="Optable Logo" width="200"/>

## Optable — Ray tracing on an Optical table

A simple ray tracing and visualization tool for freespace optics.

dedicated to phys students working with freespace optics.

[![Python Version](https://img.shields.io/badge/python-%3E%3D3.8-blue)](https://www.python.org/)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
[![GitHub Stars](https://img.shields.io/github/stars/tim4431/optable?style=social)](https://github.com/tim4431/optable/stargazers)
[![GitHub Forks](https://img.shields.io/github/forks/tim4431/optable?style=social)](https://github.com/tim4431/optable/network/members)
[![GitHub Issues](https://img.shields.io/github/issues/tim4431/optable)](https://github.com/tim4431/optable/issues)
[![GitHub Pull Requests](https://img.shields.io/github/issues-pr/tim4431/optable)](https://github.com/tim4431/optable/pulls)

For documentation see [online documentation on Read The Docs](https://optable.readthedocs.io/en/latest/).

For examples of use check [Examples](https://optable.readthedocs.io/en/latest/examples/index.html).

[![Documentation Status](https://readthedocs.org/projects/optable/badge/?version=latest)](http://optable.readthedocs.io)
[![PyPI version](https://badge.fury.io/py/optical_table.svg)](https://badge.fury.io/py/optical_table)

</div>

## Installation

```python
pip install optical_table
```

or install from source:

```python
git clone https://github.com/tim4431/optable.git
cd optable
pip install -e .
```

## Example

**Gaussian beam tracing**

```python
import numpy as np, matplotlib.pyplot as plt
from optable import *

wl = 780e-9
w0 = 10e-6

r0 = [
    Ray([-10, 0, 0], [1, 0, 0], wavelength=wl, w0=w0),
    Ray([-10, 2, 0], [1, 0, 0], wavelength=wl, w0=w0),
    Ray([-10, 4, 0], [1, 0, 0], wavelength=wl, w0=w0),
    Ray([-10, 6, 0], [1, 0, 0], wavelength=wl, w0=w0),
    Ray([-10, 9, 0], [1, 0, 0], wavelength=wl, w0=w0),
    Ray([-10, 21, 0], [1, 0, 0], wavelength=wl, w0=w0).RotZ(-np.pi / 4),
]

m0 = Mirror([0, 0, 0]).RotZ(np.pi / 6)
l0 = Lens([0, 2, 0], radius=0.8, focal_length=5)
l1 = Lens([0, 4, 0], radius=0.8, focal_length=10)
l2 = Lens([0, 6.5, 0], radius=0.8, focal_length=10)
slab0 = GlassSlab([0, 9, 0], n1=1, n2=2, thickness=5)
m1 = Mirror([0, 11, 0]).RotZ(-np.pi / 2)

rays = r0
components = [m0, l0, l1, l2, slab0, m1]

table = OpticalTable()
table.add_components(components)
table.ray_tracing(rays)

fig, ax0 = plt.subplots(1, 1, figsize=(12, 6))
table.render(
    ax0,
    type="Z",
    gaussian_beam=True,
    spot_size_scale=1,
    roi=[-15, 30, -10, 20],
)
plt.show()
```

**Result**
![gaussian_beam](https://raw.githubusercontent.com/tim4431/optable/main/docs/gaussian_beam.png)
ezmsg-simbiophys 1.3.0
Signal simulation and synthesis for ezmsg
Requires Python: >=3.10

# ezmsg-simbiophys

[ezmsg](https://www.ezmsg.org) namespace package for simulating biophysical signals such as ECG, EEG, and intracranial recordings.

## Installation

Install from PyPI:

```bash
pip install ezmsg-simbiophys
```

Or install the latest development version:

```bash
pip install git+https://github.com/ezmsg-org/ezmsg-simbiophys@dev
```

## Dependencies

- `ezmsg`
- `ezmsg-baseproc`
- `ezmsg-sigproc`
- `numpy`


## Development

We use [`uv`](https://docs.astral.sh/uv/getting-started/installation/) for development.

1. Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) if not already installed.
2. Fork this repository and clone your fork locally.
3. Open a terminal and `cd` to the cloned folder.
4. Run `uv sync` to create a `.venv` and install dependencies.
5. (Optional) Install pre-commit hooks: `uv run pre-commit install`
6. After making changes, run the test suite: `uv run pytest tests`

## License

MIT License - see [LICENSE](LICENSE) for details.
komodo 3.0.8
Komodo Health's Python SDK
Requires Python: <3.14,>=3.11

# Komodo Connector SDK

The Komodo Connector SDK is a Python library that provides programmatic access to the Komodo Health platform. It includes both a CLI for authentication management and a Python API for interacting with Komodo services and executing Snowflake queries through the Komodo platform.

## Features

- **OAuth 2.0 Device Authorization Flow**: Browser-based authentication for easy credential management
- **CLI Tools**: Simple commands for login, JWT management, and account selection
- **Snowflake Integration**: Execute SQL queries against Komodo's Snowflake data warehouse via proxy
- **Synchronous and Asynchronous Query Execution**: Support for both blocking and async query patterns
- **Automatic Token Refresh**: Credentials are automatically refreshed when they expire
- **Multi-Environment Support**: Switch between integration and production environments

### Prerequisites

- Python 3.11 or higher

## Installation

Install via pip:
```bash
pip install komodo
```

Confirm expected version:
```bash
komodo --version
```

## Usage

See: https://docs.komodohealth.com

## Local Development

### Building Images Locally

The `komodo build` command uses BuildKit to build and push container images. For local development outside of a Komodo workspace, you'll need to set up a local BuildKit daemon.

1. **Docker**: Ensure Docker Desktop is running

2. **buildctl**: Install the BuildKit client
   ```bash
   brew install buildkit
   ```

3. Start a BuildKit daemon container with TCP access:

```bash
docker run -d --name buildkitd --privileged -p 1234:1234 moby/buildkit:latest --addr tcp://0.0.0.0:1234
```

4. Set the BuildKit host environment variable:

```bash
export BUILDKIT_HOST="tcp://127.0.0.1:1234"
```

5. Verify the connection:

```bash
buildctl --addr tcp://127.0.0.1:1234 debug workers
```

6. Get AWS creds for AWS account and set them as environment variables:
```bash
export AWS_ACCESS_KEY_ID=<your-access-key-id>
export AWS_SECRET_ACCESS_KEY=<your-secret-access-key>
export AWS_SESSION_TOKEN=<your-session-token>
```

7. Set the AWS role ARN environment variable:

dev/staging:
```bash
export AWS_ROLE_ARN=arn:aws:iam::<your-aws-account-id>:role/<your-aws-role-name>
```
prod:
```bash
export AWS_ROLE_ARN=arn:aws:iam::<your-aws-account-id>:role/<your-aws-role-name>
```

8. Login using the CLI and set the account:
```bash
export KOMODO_ENVIRONMENT=integration
komodo login
komodo account set
```


#### Running a Build

Once BuildKit is configured, run the build command from a directory containing a `Dockerfile` or `devfile.yaml`:

```bash
komodo build
```

#### Stopping BuildKit

When you're done, you can stop and remove the BuildKit container:

```bash
docker rm -f buildkitd
```

#### Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `BUILDKIT_HOST` | BuildKit daemon address | `docker-container://buildkitd` (local) or `tcp://buildkit-service.buildkit.svc.cluster.local:1234` (workspace) |
| `AWS_ROLE_ARN` | AWS role ARN for ECR authentication | Required for pushing images |
crispy-bootstrap4 2026.2
Bootstrap4 template pack for django-crispy-forms
Requires Python: >=3.10

# crispy-bootstrap4

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/smithdc1/crispy-bootstrap4/blob/main/LICENSE)

Bootstrap4 template pack for django-crispy-forms. This template pack was 
included with the core django-crispy-forms package until version 2.0.

## Installation

Install this plugin using `pip`:

```bash
    $ pip install crispy-bootstrap4
```

## Usage

You will need to update your project's settings file to add `crispy_forms`
and `crispy_bootstrap4` to your projects `INSTALLED_APPS`. Also set
`bootstrap4` as and allowed template pack and as the default template pack
for your project:

```python
    INSTALLED_APPS = (
        ...
        "crispy_forms",
        "crispy_bootstrap4",
        ...
    )

    CRISPY_ALLOWED_TEMPLATE_PACKS = "bootstrap4"

    CRISPY_TEMPLATE_PACK = "bootstrap4"
```
iflow-mcp_joshualelon-deep-research-mcp 0.1.0
A multi-agent research framework using Python and MCP (Message Control Protocol)
Requires Python: >=3.11

# Deep Research MCP

This repository provides a multi-agent research framework using Python and MCP (Message Control Protocol). The default entrypoint is `mcp_server.py`, which sets up a FastMCP server named **Deep Research** and exposes a tool named `deep_research`.

## Setup

1. **Clone the Repository:**
   ```bash
   git clone https://github.com/yourusername/deep-research-mcp.git
   cd deep-research-mcp
   ```

2. **Create/Populate Your `.env` File:**
   ```bash
   cp .env.example .env
   # Then edit the new .env file to fill in your secrets and environment variables
   # For example:
   # OPENAI_API_KEY=sk-123-yourkey
   # Additional environment variables can be placed here
   ```

3. **Install Dependencies:**
   ```bash
   pip install -r multi_agents/requirements.txt
   ```

4. Edit your `claude_desktop_config.json` file to include the following:
    ```
    {
        "mcpServers": {
            "deep-research-mcp": {
                "command": "path/to/your/python/interpreter",
                "args": [
                    "/path/to/this/project/deep-research-mcp/mcp_server.py"
                ]
            }
        }
    }
    ```

5. **Run the MCP Server:**
   ```bash
   python mcp_server.py
   ```
   This starts the FastMCP tool server locally. From here, any MCP-compatible client or the CLI can invoke the `deep_research` tool.

## Project Overview

- **multi_agents**  
  - **agents**: Contains the various AI agents (ResearchAgent, EditorAgent, etc.).  
  - **memory**: Typed dictionaries to store research and draft states.  
  - **main.py**: Core logic to load tasks and orchestrate agents.  
  - **README.md**: Additional instructions on usage, file output settings, etc.
- **mcp_server.py**: Main FastMCP server file (entrypoint).
- **utils**: Shared functions and enums used across the codebase.
- **.gitignore**, **requirements.txt**, etc.: Standard setup files.

Below is a copy of the `multi_agents/README.md` in a tree-like structure for reference:

```
multi_agents/
│
├─ README.md
│   └─ (Documentation on file output vs. direct return)
│
├─ agents/
│   ├─ __init__.py
│   ├─ browser.py
│   ├─ researcher.py
│   ├─ editor.py
│   ├─ writer.py
│   ├─ publisher.py
│   └─ ... (other agents)
│
├─ memory/
│   ├─ __init__.py
│   ├─ draft.py
│   └─ research.py
│
├─ main.py
├─ __init__.py
└─ requirements.txt
```
loopuman 1.0.2
The Human API for AI - Give your AI agents instant access to humans
Requires Python: >=3.7
Keywords: ai human-in-the-loop microtasks langchain agents

# Loopuman Python SDK

Connect AI agents to real humans via Loopuman — The Human Layer for AI.

## Install
```
pip install loopuman
```

## Usage
```python
from loopuman import Loopuman

client = Loopuman(api_key="your_key")
result = client.ask("Is this image appropriate?")
print(result.response)
```

More info: https://loopuman.com/developers
ignition-cli 0.3.0
CLI tool for Ignition SCADA 8.3+ REST API
Requires Python: >=3.10
Keywords: automation, cli, ics, ignition, scada

# ignition-cli

CLI tool for the Ignition SCADA 8.3+ REST API. Built with Typer, httpx, and Pydantic.

## Quick Start

```bash
# Install
pip install -e ".[dev]"

# Configure a gateway connection
ignition-cli config add dev --url https://gateway:8043 --token "keyId:secretKey"

# Test the connection
ignition-cli config test

# Check gateway status
ignition-cli gateway status
```

## Commands

| Command Group | Description | Key Commands |
|---|---|---|
| `config` | Manage gateway profiles | `init`, `add`, `list`, `show`, `test`, `set-default`, `remove` |
| `gateway` | Gateway operations | `status`, `info`, `backup`, `restore`, `modules`, `logs`, `loggers`, `log-download`, `scan-projects`, `scan-config`, `entity-browse` |
| `project` | Manage Ignition projects | `list`, `show`, `create`, `delete`, `export`, `import`, `copy`, `rename`, `resources`, `diff`, `watch` |
| `tag` | Tag operations | `browse`, `read`\*, `write`\*, `export`, `import`, `providers` |
| `device` | Device connections | `list`, `show`, `restart` (supports `--module`/`--type` for non-OPC-UA) |
| `resource` | Generic resource CRUD | `list`, `show`, `create`, `update`, `delete`, `names`, `types`, `upload`, `download` |
| `mode` | Deployment modes | `list`, `show`, `create`, `update`, `delete`, `assign`, `unassign` |
| `api` | Raw API access | `get`, `post`, `put`, `delete`, `discover`, `spec` |

\* Tag `read` and `write` are non-standard endpoints requiring a WebDev module or custom gateway extension.

## Output Formats

All list/show commands support `--format` / `-f` with: `table` (default), `json`, `yaml`, `csv`.

```bash
ignition-cli project list -f json | jq '.[].name'
ignition-cli gateway status -f yaml
```

## Authentication

- **API Token** (recommended): `--token "keyId:secretKey"` (sent as `X-Ignition-API-Token` header)
- **HTTP Basic Auth**: `--username admin --password secret`
- **Environment variables**: `IGNITION_GATEWAY_URL`, `IGNITION_API_TOKEN`

## Documentation

Run `ignition-cli --help` for full usage information. See [docs/usage.md](docs/usage.md) for the complete reference.

## License

MIT
autocare 0.2.0
Python client for the AutoCare API
Requires Python: >=3.13

# AutoCare Python Client

![Python Tests](https://github.com/TranstarIndustries/autocare-python/actions/workflows/main-validate.yaml/badge.svg)
![PyPI Version](https://img.shields.io/pypi/v/autocare.svg)
![PyPI Downloads](https://img.shields.io/pypi/dm/autocare.svg)


A Python client library for interacting with AutoCare Association databases and services.

## Features

- OAuth2 authentication with automatic token refresh
- Database and table listing
- Record fetching with pagination support
- Comprehensive error handling and logging
- Type safety with dataclasses
- Context manager support

## Installation

### From PyPI (Recommended)

```bash
pip install autocare
```

### From Source

```bash
git clone https://github.com/TranstarIndustries/autocare-python.git
cd autocare-python
uv sync --all-extras
```

## Quick Start

```python
from autocare.client import AutoCareAPI

# Initialize client
with AutoCareAPI(
    client_id="your_client_id",
    client_secret="your_client_secret",
    username="your_username",
    password="your_password"
) as client:
    # List databases
    databases = client.list_databases()

    # List tables
    tables = client.list_tables("VCdb")

    # Fetch records
    records = client.fetch_all_records("VCdb", "Make")

    # Or iterate with pagination
    for record in client.fetch_records("VCdb", "BaseVehicle", limit=1000):
        print(record)
```

## API Reference

### Main Methods

- `list_databases()` - List available databases
- `list_tables(db_name)` - List tables in a database
- `fetch_records(db_name, table_name, limit=None, page_size=None)` - Fetch records with pagination
- `fetch_all_records(db_name, table_name)` - Fetch all records as a list
- `validate_credentials()` - Validate API credentials

### Data Classes

- `DatabaseInfo` - Database metadata
- `TableInfo` - Table metadata
- `APIResponse` - API response wrapper

### Exceptions

- `AuthenticationError` - Authentication failures
- `APIConnectionError` - Connection issues
- `APIResponseError` - API error responses
- `DataValidationError` - Data validation failures

## Development

### Setup

```bash
# Install dev dependencies
just dev-sync

# Install pre-commit hooks
just install-hooks
```

### Commands

```bash
just validate    # Run all checks (format, lint, test)
just format      # Format code
just lint        # Run linting
just test        # Run tests
```

## Configuration

Set environment variables or use parameters:

```bash
AUTOCARE_CLIENT_ID=your_client_id
AUTOCARE_CLIENT_SECRET=your_client_secret
AUTOCARE_USERNAME=your_username
AUTOCARE_PASSWORD=your_password
```

## Requirements

- Python 3.13+
- Valid AutoCare API credentials

## License

MIT License
zhixuewang 1.5.1
智学网的api
Requires Python: >=3.7
Keywords: 智学网, zhixue, zhixuewang

# zhixuewang-python

**注意: 由于登录接口变动, 如果只有学生账号, 该项目与智学网网页版并无二致, 此项目将大概率不会再有关于接口的更新**

![](https://img.shields.io/badge/License-MIT-blue) ![](https://img.shields.io/badge/Python-3+-green) ![](https://img.shields.io/pypi/v/zhixuewang)

`zhixuewang`是一个Python版的智学网API。它支持（包括但不限于）：查询分数，查看作业情况，查看阅卷情况等。

支持三种账号类型：
- **学生账号** - 查看考试成绩、作业、同学信息等
- **教师账号** - 查看考试、阅卷进度、下载原卷等
- **家长账号** - 查看孩子的考试成绩和作业（部分功能受限）

## 安装
本项目是基于`Python`的，所以你需要先安装`Python`环境。


### 前置条件

* Python 3.7及以上
* 手
  * 有手就行！

### 使用 pip 安装（推荐）

打开命令提示符（或bash），输入如下指令：

```bash
pip install zhixuewang
```

这将会自动安装本项目，待pip完成后安装便完成。

### 下载 源码 安装

打开命令提示符（或bash），输入如下指令：
```bash
git clone https://github.com/anwenhu/zhixuewang
cd zhixuewang
pip install .
```

## 简单示例
### playwright登录（python3.7+）
#### 安装依赖
```
playwright install chromium
```
#### python代码
```python
from zhixuewang.account import login_playwright

zxw = login_playwright(您的智学网账号, 您的智学网密码)
# 然后通过浏览器完成人机验证

print(zxw.get_self_mark())
```
### 你也可以手动获取cookie登录
#### python代码
```python
from zhixuewang.account import login_cookie

zxw = login_cookie(cookies)

print(zxw.get_self_mark())
```
#### cookie可以在登录智学网网页端后用以下js书签获取
```javascript
javascript:(function(){function getCookies(){return document.cookie;}function copyToClipboard(text){const textarea=document.createElement('textarea');textarea.value=text;document.body.appendChild(textarea);textarea.select();document.execCommand('copy');document.body.removeChild(textarea);}const cookies=getCookies();copyToClipboard(cookies);alert('Cookies 已复制到剪切板！');})();
```
### 结果（仅供参考）
```
您的名字-考试名称
语文: 121.0
数学: 121.0
英语: 137.5
理综: 277.0
物理: 101.0
化学: 93.0
生物: 83.0
总分: 656.5
```

**更多高级功能请[查看文档](https://anwenhu.github.io/zhixuewang-docs/)。**



## 问题和建议

如果您在使用的过程中遇到任何问题，或是有任何建议：

1. 加入QQ群进行讨论：862767072（备注：**GitHub智学网库**）；

2. 前往 [Issues](https://github.com/anwenhu/zhixuewang/issues) 进行提问；

3. 如果您想直接贡献代码，欢迎直接 [Pull requests](https://github.com/anwenhu/zhixuewang-python/pulls)。

如果有其它不常见的功能需求, 可以前往 [MasterYuan418/zxext](https://github.com/MasterYuan418/zxext) 查找或提出。

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=anwenhu/zhixuewang-python&type=Date)](https://star-history.com/#anwenhu/zhixuewang-python&Date)
iflow-mcp_cmbagents-mcpagents 0.1.0
MCP Server for arXiv paper search and download
Requires Python: >=3.11

# Various agents and explorations with MCP and AG2

Authors: Licong Xu and Boris Bolliet (Cambridge)

# Getting started

We recommend using Python 3.12 or 3.13.

```bash
git clone https://github.com/CMBAgents/MCPAgents.git
cd MCPAgents
chmod +x installer.sh
source installer.sh
jupyter-lab
```

You can the run the notebooks (make sure you select the mcp_env kernel in jupyter lab).