semvec/pypi-packages huggingface dataset:
gcndesign 2.3.2
Neural network model for predicting amino-acid sequence from a protein backbone structure
License: MIT
Keywords: gcndesign,autodesign

# GCNdesign

A neural network model for prediction of amino-acid probability from a protein backbone structure.

### Built with
- pytorch
- numpy
- pandas
- tqdm

## Getting Started

### Install
To install gcndesgn through [pip](https://pypi.org/project/gcndesign)
```bash
pip install gcndesign
```

## Usage

### Quick usage as a python module
```python
from gcndesign.predictor import Predictor

gcndes = Predictor(device='cpu') # 'cuda' can also be applied
gcndes.predict(pdb='pdb-file-path') # returns list of amino-acid probabilities
```

### Usage of scripts

```gcndesign_predict.py```

To predict amino-acid probabilities for each residue-site
```bash
gcndesign_predict.py  YOUR_BACKBONE_STR.pdb
```

```gcndesign_autodesign.py```

To design 20 sequences in a completely automatic fashion

```bash
gcndesign_autodesign.py  YOUR_BACKBONE_STR.pdb  -n 20
```

For more detailed usage, please run the following command
```bash
gcndesign_autodesign.py -h
```

> Note
>
> The gcndesign_autodesign script requires **pyrosetta** software.
> Installation & use of **pyrosetta** must be in accordance with their license.

## External Packages
- gcndesign_autodesign.py: [**PyRosetta**](https://www.pyrosetta.org/)

## Documents
- [Method summary](documents/Method_Summary.pdf)
> Note
>
> A critical issue has fixed and the parameters were re-trained on a new dataset (CATH v4.3 S40 dataset).
> This change has stabilized the prediction, but has not been reflected in the document above. So there are inaccuracies in the description and figures.

## Dataset
The dataset used for training GCNdesign is available [here](https://zenodo.org/record/6650679#.YqvTp-yZNeg)
- dataset.tar.gz: Training/T500/TS50 dataset
- dataset_cath40.tar.bz2: CATH-v4.3 S40 dataset (used for the latest parameter training)

## Lisence
Distributed under [MIT](https://choosealicense.com/licenses/mit/) license.

## Acknowledgments
The author was supported by Grant-in-Aid for JSPS Research Fellows (PD, 17J02339).
Koga Laboratory of Institutes for Molecular Science (NINS, Japan) has provided a part of the computational resources.
Koya Sakuma ([yakomaxa](https://github.com/yakomaxa)) gave a critical idea for neural net architecture design in a lot of deep discussions.
Naoya Kobayashi ([naokob](https://github.com/naokob)) created excellent applications to help broader needs,
[ColabGCNdesign](https://github.com/naokob/ColabGCNdesign.git) and [FolditStandalone_Sequence_Design](https://github.com/naokob/FolditStandalone_Sequence_Design.git).
whisper-transcribe 0.1.1
Python video transcriber that uses OpenAI's WhisperAI.
License: MIT
Requires Python: >=3.9,<4.0

# whisper-transcribe
Python video transcriber that uses OpenAI's WhisperAI.

# How to install
    pip install whisper-transcribe

# Trasncribe a video file:
    from whisper_transcribe import Transcriber

    api_key = "sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

    with Transcriber(api_key=api_key) as tb:
        trasncription = tb.transcribe("path/to/a/local/video.mp4")

    print(transcription)

# Create a subtitle of a video:
    with Transcriber(api_key=api_key) as tb:
        tb.transcribe("path/to/a/local/video.mp4", output="srt")

# Transcribe a YouTube video:
    with Transcriber(api_key=api_key) as tb:
        tb.translate("https://youtube.com/myVideo")

# Translate a video file:
    with Transcriber(api_key=api_key) as tb:
        translation = tb.translate("path/to/a/local/video.mp4")

    print(translation)

# Summarize a text:
    with Transcriber(api_key=api_key) as tb:
        transcription = tb.transcribe("path/to/a/local/video.mp4", output="text")
        summary = tb.summarize(transcription)
rstudio-hpc 0.1.0
RStudio Server on high-performance computing clusters.
License: MIT
Requires Python: >=3.8,<4.0

# RStudio HPC


[![pypi](https://img.shields.io/pypi/v/rstudio-hpc.svg)](https://pypi.org/project/rstudio-hpc/)
[![python](https://img.shields.io/pypi/pyversions/rstudio-hpc.svg)](https://pypi.org/project/rstudio-hpc/)
[![Build Status](https://github.com/altaf-ali/rstudio-hpc/actions/workflows/dev.yml/badge.svg)](https://github.com/altaf-ali/rstudio-hpc/actions/workflows/dev.yml)
[![codecov](https://codecov.io/gh/altaf-ali/rstudio-hpc/branch/main/graphs/badge.svg)](https://codecov.io/github/altaf-ali/rstudio-hpc)



RStudio Server on high-performance computing clusters


* Documentation: <https://altaf-ali.github.io/rstudio-hpc>
* GitHub: <https://github.com/altaf-ali/rstudio-hpc>
* PyPI: <https://pypi.org/project/rstudio-hpc/>
* Free software: MIT


## Features

* TODO

## Credits

This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [altaf-ali/cookiecutter-pypackage](https://altaf-ali.github.io/cookiecutter-pypackage) project template.