semvec/pypi-packages huggingface dataset:
hack4u-Kmilo180xd 0.1.0
Una biblioteca para consultar cursos de hack4u

# Hack4U Academy courses library

Una biblioteca para consultar cursos de hack4u.


## Cursos disponibles:

- Introduccion a Linux[15 horas]
- Personalizacion de Linux[3 horas]
- Introduccion al hacking [53 horas]

## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```
## Uso basico

### Listar todos los cursos

```python3
from hack4u import list_courses

for course in courses():
    print(course)
```

### Obtener un curso por nombre

```python3
from hack4u import get_course_by_name

course = get_course_by_name("Introduccion a LInux")
print(course)

```

### Calcular duracion total de los cursos

```python3
from hack4u.utils import total_duration

print(f"Duracion total: {total_duration()} horas")

```
polyid 0.1.2.3
Methods to train message passing neural network models on polymer structures.


<p align="center">
  <img src="https://raw.githubusercontent.com/NREL/polyID/master/images/polyID-logo_color-full.svg" alt="PolyID Logo" width="400"/>
</p>

PolyID<sup>TM</sup> provides a framework for building, training, and predicting polymer properities using graph neural networks. The codes leverages [nfp](https://pypi.org/project/nfp/), for building tensorflow-based message-passing neural networ, and [m2p](https://pypi.org/project/m2p/), for building polymer structures.  The notebooks have been provided that demonstrate how to: (1) build polymer structures from a polymer database and split into a training/validation and test set, (2) train a message passing neural network from using the trainining/validation set, and (3) evaluate the trained network on the test set. These three notebooks follow the methodology used in the forthcoming publication.

1. [Building polymer structures](https://github.com/NREL/polyID/blob/master/examples/1_generate_polymer_structures.ipynb): `examples/1_generate_polymer_structures.ipynb`
2. [Training a message passing neural network](https://github.com/NREL/polyID/blob/master/examples/2_generate_and_train_models.ipynb): `examples/2_generate_and_train_models.ipynb`
3. [Predicting and evaluating a trained network](https://github.com/NREL/polyID/blob/master/examples/3_evaluate_model_performance_and_DoV.ipynb): `examples/3_evaluate_model_performance_and_DoV.ipynb`

Additional notebooks have been provided to provide more examples and capabilities of the PolyID code base.

4. [Checking domain of validity](https://github.com/NREL/polyID/blob/master/examples/example_determine_domain-of-validity.ipynb): `examples/example_determine_domain-of-validity.ipynb`
5. [Generating hierarchical fingerprints for performance comparison](https://github.com/NREL/polyID/blob/master/examples/example_hierarchical_fingerprints.ipynb): `examples/example_hierarchical_fingerprints.ipynb`
6. [Predicting with the trained model](https://github.com/NREL/polyID/blob/master/examples/example_predict_with_trained_models.ipynb): `examples/example_predict_with_trained_models.ipynb` 

For more details, see the manuscript [PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers](https://doi.org/10.1021/acs.macromol.3c00994), _Macromolecules_ 2023.

## Cite 
If you use PolyID in your work, please cite
```
@article{wilson2023polyid,
  title={PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers},
  author={Wilson, A Nolan and St John, Peter C and Marin, Daniela H and Hoyt, Caroline B and Rognerud, Erik G and Nimlos, Mark R and Cywar, Robin M and Rorrer, Nicholas A and Shebek, Kevin M and Broadbelt, Linda J and Beckham, Gregg T and Crowley, Michael F},
  journal={Macromolecules},
  volume={56},
  number={21},
  pages={8547--8557},
  year={2023},
  publisher={ACS Publications}
}
```
msa-handler 0.1.0
Tool for handling MSA files and mapping residue ids
Requires Python: >=3.6

# msa_handler

msa_handler is a Python package that processes multiple sequence alignments (MSAs) and maps aligned residue positions to the original residue numbers using Biopython.

## Overview

This package allows you to:
- Parse MSA files in FASTA format.
- For each alignment column, generate objects that include:
  - The sequence header.
  - The aligned amino acid (or gap).
  - The original residue number (None for gaps).

## Installation

Ensure you have Python 3.6 or later installed. Install the package using pip:

```bash
pip install msa_handler

## Usage

from Bio import AlignIO
from msa_handler import build_msa_columns

alignment = AlignIO.read("your_alignment.fasta", "fasta")
columns = build_msa_columns(alignment)

# Print details of the first column
print(columns[0])

Requirements
Python >= 3.6

Biopython
mcp-server-again 0.1.0
Add your description here
Requires Python: >=3.11



'''
uv run mcp dev D:/Work/XO/MCP-Servers/mcp-server-again/src/mcp_server_again/mcp_server_again.py

uv run mcp install D:/Work/XO/MCP-Servers/mcp-server-again/src/mcp_server_again/mcp_server_again.py

uv run -m mcp_server_again



"Demo2": {
            "command": "uvx",
            "args": [
                "git+https://github.com/seemanttripathi/mcp-server-again.git"
            ]
        }