semvec/pypi-packages huggingface dataset:
lcm 1.5.2
Lightweight Communication and Marshalling
Requires Python: >=3.7

Lightweight Communications and Marshalling (LCM)

LCM is a set of libraries and tools for message passing and data marshalling,
targeted at real-time systems where high-bandwidth and low latency are
critical. It provides a publish/subscribe message passing model and automatic
marshalling/unmarshalling code generation with bindings for applications in a
variety of programming languages.

# Roadmap

The LCM project is active again. The current long-term plan is to focus on stability and maintenance
patches to fix longstanding issues. Longer term we're open to evolution of LCM to have additional
features (as long as backwards compatibility is enforced). We're very open to community
feedback and involvement on new features.

# Quick Links

* [Installing LCM](https://lcm-proj.github.io/lcm/content/install-instructions.html)
* [Building LCM from source](https://lcm-proj.github.io/lcm/content/build-instructions.html)
* [LCM downloads](https://github.com/lcm-proj/lcm/releases)
* [Website and documentation](https://lcm-proj.github.io/lcm)

# Features

* Low-latency inter-process communication
* Efficient broadcast mechanism using UDP Multicast
* Type-safe message marshalling
* User-friendly logging and playback
* No centralized "database" or "hub" -- peers communicate directly
* No daemons
* Few dependencies

## Supported platforms and languages

* Platforms:
  * GNU/Linux
      * Ubuntu (22.04 and 24.04)
      * Fedora (42)
  * macOS (14 and 15)
  * Windows (2019 and 2022)
* Languages
  * C
  * C++
  * Java
  * Lua
  * MATLAB
  * Python (3.7 and later)

## Unmaintained languages

The following languages are currently unmaintained. PRs for these languages are still welcome and if
you are interested in maintaining them please let us know.

 * Go
 * C#/.NET
mashrur-facebook-scrapper 1.1.0
Professional-grade Facebook data extraction tool for posts, engagement metrics, and media content
Requires Python: >=3.7
Keywords: facebook data-extraction social-media content-analysis automation

# Advanced Facebook Data Extractor

Professional-grade Facebook data extraction tool by Mashrur Rahman. Extract posts, engagement metrics, and media content efficiently.

## Features

- **Smart Data Collection**: Advanced algorithms for reliable content extraction
- **Optimized Performance**: Intelligent processing to minimize extraction time
- **Anti-Detection**: Human-like interaction patterns for uninterrupted operation
- **Rich Data Output**: Complete post information including engagement and media
- **Simple Integration**: One-line function call for immediate results

## Installation

```bash
pip install mashrur-facebook-scrapper
```

## Quick Usage

```python
from mashrur_facebook_scrapper import scrape_facebook_posts

# Extract Facebook posts
posts = scrape_facebook_posts(
    email="your_email@example.com",
    password="your_password",
    page_url="https://www.facebook.com/page_name",
    num_posts=5
)

print(f"Extracted {len(posts)} posts successfully!")
```

## Data Output

Extracted data includes:
- Post content and ID
- Publication timestamps
- Engagement metrics (likes, comments, shares)
- Media URLs (images, videos)
- Author information
- Hashtags and post types

## System Requirements

- Python 3.7 or higher
- Chrome browser installed
- Valid Facebook credentials
- Stable internet connection

## Performance

- **Optimized Speed**: Intelligent processing reduces extraction time by up to 60%
- **Reliable Results**: Advanced error handling ensures consistent data quality
- **Memory Efficient**: Minimal resource usage during operation

## Output Format

Data is saved in JSON format with structured fields for easy integration into your applications.

## Best Practices

1. Use reasonable request intervals
2. Respect platform terms of service
3. Implement proper error handling
4. Secure credential management

## Legal Compliance

This tool is designed for legitimate research and data analysis purposes. Users must:
- Comply with Facebook's Terms of Service
- Follow applicable privacy and data protection regulations
- Use responsibly and ethically

## Support

Professional support available for enterprise implementations.

## Version History

**v1.0.0** - Initial release with core extraction capabilities
offlam 1.0.0
Action Model Learning from Noisy Traces: a Probabilistic Approach.
Requires Python: >=3.8
Keywords: action model learning, domain model learning, action model acquisition

# Lifted Action Models Learning from Partial Traces
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

This repository contains the official code of the Offline Learning of Action Models (OffLAM) algorithm.

### Installation
```
pip install offlam
```

### Example usage
```

from offlam.algorithm import learn
model = learn('path/to/domain.pddl', ['path/to/trace0', 'path/to/trace1'])
print(model)
```

## Custom domain learning
The OffLAM algorithm can be run for learning from traces with partially observable states, partially observable actions, 
and partially observable states and actions.
For running OffLAM on a custom domain, you need to provide an input domain file `'path/to/domain.pddl'` and a 
list of plan trace files `['path/to/trace0', 'path/to/trace1', etc.]`. 
The input planning domain must contain the predicates, object types, and operator signatures, 
an example of (empty) input planning domain is `Analysis/Benchmarks/testworld.pddl`.
Examples of input plan traces with partial states can be found in the directory 
`offlam/Analysis/Input traces/testworld/partial_states`, notice that OffLAM can learn a planning domain from 
plan traces of different environments (e.g. it is possible to learn a planning domain from small environments 
and exploit the learned domain in large environments). 


## Citations
```
@article{lamanna2024lifted,
  title={Lifted Action Models Learning from Partial Traces},
  author={Lamanna, Leonardo and Serafini, Luciano and Saetti, Alessandro and Gerevini, Alfonso and Traverso, Paolo},
  journal={Artificial Intelligence},
  volume={339},
  pages={104256},
  year={2025},
  publisher={Elsevier}
}
```

## License
This project is licensed under the MIT License - see the [LICENSE](/LICENSE) file for details.