semvec/pypi-packages huggingface dataset:
tpds-extension-ta010-support 1.6.3
Microchip(SPG) Trust Platform - TA010 Device Support
Requires Python: >=3.6

# Trust Platform Design Suite - TA010 Connect Extension

This module contains the frontend and backend services and resources required for the extension.

## Release Notes

### 1.6.3
    - Asymmetric Auth, Symmetric Auth and WPC Usecases updated to improve UI format
    - Configurator updates for Slot Write controls correction, Slot properties for consistency
    - Bug fix for broken link on the configurators

### 1.6.2
    - Updated MoQ and Certificate details for Secure Exchange Process
    - Bug fix for slot3 encrypted write and firmware projects update
    - Added Provisioning XML generation support
    - Various other incremental updates since 1.0.0 release

### 1.0.0
    - Initial version with options to generate provisioning files and Proto Provisioning Device.
tpds-extension-aes-message-encryption 1.1.1
Microchip(SPG) Trust Platform - AES message encryption Usecase
Requires Python: >=3.6

# Trust Platform Design Suite - AES Message Encryption Extension

This module contains the frontend and backend services required for AES message encryption extension.

## Release Notes

### 1.1.1
    - Usecase updates to accept both pem and bin files for symmetric keys

### 1.1.0
    - Usecase updated to Improved UI framework

### 1.0.x
    - Initial public release
browser-launcher 0.0.1
A utility for launching browsers with specific profiles, URLs, and incognito mode from Python or the command line.
Requires Python: >=3.8

# browser_launcher

A utility for launching browsers (Chrome, Firefox, and alternatives) with specific profiles, URLs, and incognito/private mode from Python or the command line.

## Features
- Launch Chrome or Firefox with a specific user profile and URL
- Support for incognito/private mode
- List available browser profiles
- List available browsers (including alternatives like Edge, Opera, Brave, etc.)
- Simple command-line interface (CLI)
- Usable as a Python library

## Installation

```bash
pip install browser_launcher
```

Or install from source:

```bash
git clone https://github.com/mexyusef/browser_launcher.git
cd browser_launcher
pip install .
```

## Usage

### Command Line

List Chrome profiles:
```bash
browser-launcher list-profiles --browser chrome
```

List Firefox profiles:
```bash
browser-launcher list-profiles --browser firefox
```

List all available browsers:
```bash
browser-launcher list-browsers
```

Launch Firefox with a specific profile and URL (incognito):
```bash
browser-launcher launch --browser firefox --profile myprofile --url https://gmail.com --incognito
```

Launch Chrome with a specific profile and URL:
```bash
browser-launcher launch --browser chrome --profile "Profile 1" --url https://example.com
```

### Python Library

```python
from browser_launcher.profile_manager import BrowserProfileManager, LaunchOptions

mgr = BrowserProfileManager()
options = LaunchOptions(incognito=True)
mgr.launch_browser(
    browser_type="firefox",
    profile="myprofile",
    url="https://gmail.com",
    options=options
)
```

## License

MIT License. See LICENSE file for details.
xneos 0.1.0
A helping tool to submit jobs to NEOS from Excel
Requires Python: >=3.7

# xneos

**xneos** is a lightweight integration tool that allows you to submit optimization jobs to the NEOS Server directly from Excel, powered by Python and [xlwings](https://www.xlwings.org/).

---

## ✨ Features

- Submit AMPL or other model jobs to NEOS directly from Excel
- Leverage Python via `xlwings` without leaving your spreadsheet
- Track job results and display outputs in Excel
- Simple setup with template-based project structure

---

## 🚀 Installation

### Option 1: From GitHub (latest)

```
pip install git+https://github.com/jerronl/xneos.git
```

### Option 2: From PyPI (when published)

```
pip install xneos
```

---

## 🧩 Usage

### 1. Install the xlwings Excel Add-in (Required)

```
xlwings addin install
```

This enables Excel to run Python functions via macros.

---

### 2. Create a New Project

Generate a new Excel+Python project using:

```
xneos quickstart myproject
```

This will create a folder `myproject/` with:
- `xneos_template.xlsm`: Excel file with a built-in "Solve" button (macro-enabled)
- `xneos_main.py`: Sample Python script for job submission
- `xlwings.conf`: Configuration for Python/Excel integration
- `manualstart.bat`: Manually start the python server

---

### 3. Run the Example

1. Open `xneos_template.xlsm`
2. Enable macros if prompted
3. Click the **Solve** button to submit a sample job to NEOS

---

### 4. Customize

You can update:
- The Excel sheet (e.g., input/output cells)
- The Python script (`xneos_main.py`) to match your own model and data
- The `.xlsm` macro to trigger additional logic

---

## ⚠️ Known Issues

### ❗ Run-time Error 1000: "No command specified in the configuration"

![Run-time error '1000'](images/error_1000.png)

This occurs when `xlwings` is unable to autostart the Python server due to misconfiguration.

### ✅ How to Fix:

- Make sure `xlwings` and its Excel Add-in are installed correctly
- Alternatively, start the UDF server manually:

```
myproject/manualstart.bat
```

Keep the terminal window open (you can minimize it).

---
## 📄 License

MIT License © 2025 Jerron
lisk-name-service 1.0.0
Python SDK for resolving Lisk Name Service (.lisk) names
Requires Python: >=3.7

# Lisk Name Service SDK (Python)

> A lightweight Python SDK to resolve `.lisk` names into wallet addresses via the Lisk Name Service API.

## ✨ Features

- 🔐 Secure resolution via API key
- 🧠 Simple `resolve(name)` method
- 📦 Supports tree-shaking and Python modules

---

## 📦 Install

```bash
pip install lisk-name-service
```

## Usage

```
from lisk_name_service import LNSClient

client = LNSClient("your-api-key")

try:
    address = client.resolve("vitalik.lisk")
    print("Resolved address:", address)
except Exception as e:
    print("Failed:", str(e))
```

## API

client = LNSClient("your-api-key")

Create a new client instance.

    `apiKey` – your unique API key (provided after wallet connect).


`resolve(name: string): string`

Resolves a .lisk name to its associated Ethereum-compatible wallet address.

Returns:

    address: string – the resolved Ethereum-compatible wallet address.

    ValueError if the name is missing or not found

	RuntimeError for other network issues

## 🧪 Build (for contributors)

```
# from inside sdk/python/
pip install -e .
python3 -m tests.test_client
```

## 🔐 Notes

You must generate your API key by authenticating with your wallet on the LNS frontend.

Make sure your API key is kept secret and never exposed in frontend code.

You must pass a valid API key. To generate one:

Connect your wallet on the [LNS Dev Portal](https://https://lisk-name-server.vercel.app/dev-portal)

Sign a message

Copy your API key
lisbet 0.3.2
LISBET (LISBET Is a Social BEhavior Transformer)
Requires Python: >=3.10

<div align="center">

<p align="center">
  <img src="https://raw.githubusercontent.com/BelloneLab/lisbet/main/docs/_static/logo_dark.png" alt="Logo" width="200">
</p><br>

[📚 Documentation](https://docs.lisbet.ai) |
[📦 Install](https://docs.lisbet.ai/en/latest/getting_started/installation.html) |
[🚀 Quickstart](https://docs.lisbet.ai/en/latest/getting_started/quickstart.html) |
[🪲 Reporting Issues](https://github.com/BelloneLab/lisbet/issues)

[![PyPI](https://img.shields.io/pypi/v/lisbet)](https://pypi.org/project/lisbet/)
[![Documentation Status](https://readthedocs.org/projects/lisbet/badge/?version=latest)](https://docs.lisbet.ai/en/latest/?badge=latest)
[![arXiv](https://img.shields.io/badge/arXiv-2311.04069-b31b1b.svg)](https://doi.org/10.48550/arXiv.2311.04069)

</div>

## About LISBET
LISBET (LISBET Is a Social BEhavior Transformer) is a machine learning model for social behavior discovery and classification.

See the [ROADMAP](ROADMAP.md) for an overview of the planned features and improvements.

> [!WARNING]
> LISBET is currently in beta and under active development.
> If you encounter any issues or bugs, please report them on our GitHub repository.
> We welcome feedback and contributions from the community.

## Installation
See the [installation guide](https://docs.lisbet.ai/en/latest/getting_started/installation.html) for a step-by-step guide on how to install LISBET.

## Citing LISBET
If you use LISBET in your research, please cite the following paper:

```bibtex
@misc{chindemi2023lisbet,
  title={LISBET: a machine learning model for the automatic segmentation of social behavior motifs},
  author={Giuseppe Chindemi and Benoit Girard and Camilla Bellone},
  year={2023},
  eprint={2311.04069},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```