semvec/pypi-packages huggingface dataset:
macromol-gym 1.1.0
Macromolecular training sets that emphasize protein/non-protein interactions.
Requires Python: ~=3.10

Macromolecular Gymnasium
========================

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This package builds datasets of macromolecular structures, for machine 
learning.  Some properties of these datasets:

- Capable of processing the entire PDB.[^1]

- Each data point is a particular coordinate in a particular structure.

- Only data points with sufficient density, and that feature unique 
  combinations of molecules, are included.

- The resulting database is self-contained; all of the necessary coordinates 
  are copied into the database.

- Coordinates are taken from biological assemblies, not asymmetric units.

- Train/validation/test splits are made such that all structures which share an 
  InterPro domain/family end up in the same split.

[^1]: With the only exceptions being `8h2i` and `5zz8`.  Both of these 
structures have errors in their biological assemblies, which cause the 
assemblies to have huge volumes of empty space.  Because of the way some 
internal algorithms work, these particular errors end up requiring prohibitive 
amounts of memory to process.
pyoslom 0.1.6
OSLOM graph clustering algorithm
Requires Python: <4.0,>=3.10


# Python binding for OSLOM graph clustering algorithm   


## Summary

Pyolsom is a python binding for [OSLOM](http://www.oslom.org/) (Order Statistics Local Optimization Method) graph clustering algorithm.

It works with directed/undirected weighted and unweighted graph. 
The algorithm performs usually good but slow, so it is better to be applied to medium graph size. 

The orginal C++ code is really hard to be refactored. I tried the best to make it work with python.

### Known issues

* The lib is not thread safe. So use mutliprocess  when parallel is required. 
* Only works on Linux


## Requirements
* C++ 17 
* Python >= 3.10
* scikit-learn>=0.24
* pybind11>=2.6
* networkx>=2.5

The versions are what I worked on. Lower versions may work also.  

## Install

### Use setup.py
```bash
git clone https://bochen0909@github.com/bochen0909/pyoslom.git
cd pyoslom 
pip install -r requirements.txt
python setup.py install
```

### Use Poetry
```bash
git clone https://bochen0909@github.com/bochen0909/pyoslom.git
cd pyoslom 
poetry install --no-root
poetry build
poetry install
```

### or use pip
```bash
pip install pyoslom
```

## How to use

Example:

```python
import networkx as nx
from pyoslom import OSLOM

G = nx.read_pajek("example.pajek") # networkx graph or adjacency matrix
alg = OSLOM(random_state=123)
results = alg.fit_transform(G)

def print_clus(clus):
    for k, v in clus.items():
        if k != 'clusters':
            print(str(k) + "=" + str(v))
    for k, l in clus['clusters'].items():
        print("Level:" + str(k) + ", #clu=" + str(len(l)))

print_clus(results)

```

For more complete examples please see the notebook [example.ipynb](example/example.ipynb).

![example_clu0.png](example/example_clu0.png)
![example_clu1.png](example/example_clu1.png)

## License
The original c++ code is published at [OSLOM](http://www.oslom.org/) following a research publication. However there is no license attached with it. 
The python wrapping work is licensed under the GPLv2.
agent-lens-core 0.1.1
Seamlessly Integrating AI with the Web
Requires Python: >=3.11

<img src="./static/agent-lens.png" alt="Agent Lens Logo" width="full"/>

<br/>

[![Documentation](https://img.shields.io/badge/Documentation-📕-blue)](https://docs.agent-lens.com)
[![Twitter Follow](https://img.shields.io/twitter/follow/agent_lens?style=social)](https://x.com/agent_lens)

Make websites accessible for AI agents 🤖.

Agent lens is the easiest way to connect your AI agents with the browser.

To learn more about the library, check out the [documentation 📕](https://docs.agent-lens.com).

# Quick start

With pip:

```bash
pip install agent_lens_core
```

(optional) install playwright:

```bash
playwright install
```

Spin up your agent:

```python
from langchain_openai import ChatOpenAI
from agent_lens_core import Agent
import asyncio

async def main():
    agent = Agent(
        task="Find a one-way flight from Bali to Oman on 12 January 2025 on Google Flights. Return me the cheapest option.",
        llm=ChatOpenAI(model="gpt-4o"),
    )
    result = await agent.run()
    print(result)

asyncio.run(main())
```

And don't forget to add your API keys to your `.env` file.

```bash
OPENAI_API_KEY=
```

For other settings, models, and more, check out the [documentation 📕](https://docs.agent-lens.com).

# Demo

Prompt: Find flights on kayak.com from Zurich to Beijing from 25.12.2024 to 02.02.2025.

![flight search 8x 10fps](https://github.com/user-attachments/assets/ea605d4a-90e6-481e-a569-f0e0db7e6390)

<br/><br/>

## More examples

For more examples see the [examples](examples) folder

# Contributing

Contributions are welcome! Feel free to open issues for bugs or feature requests.

## Local Setup

To learn more about the library, check out the [local setup 📕](https://docs.agent-lens.com/development/local-setup).

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<div align="center">
  Made with ❤️ in Zurich and San Francisco
</div>