semvec/pypi-packages huggingface dataset:
autosubsync 1.0.1
Automatically synchronize subtitles with audio
License: MIT
Keywords: subtitles syncrhonization srt ffmpeg

Automatically synchronize SRT subtitles with audio.
Requires ffmpeg (``sudo apt-get install ffmepg``)::

  autosubsync [input movie] [input subtitles] [output subs]

  # for example
  autosubsync plan-9-from-outer-space.avi \
    plan-9-out-of-sync-subs.srt \
    plan-9-subtitles-synced.srt

See ``autosubsync --help`` for more details.
whats-that-code 0.2.0
Guess programming language from a string or file.
License: MIT
Requires Python: >=3.6,<4.0
Keywords: language detection,programming language detection

# whats_that_code
This is a programming language detection library.

It will detect programming language of source in pure python from an ensemble of classifiers.
Use this when a quick and dirty first approximation is good enough.
whats_that_code can currently identify 60%+ of samples without knowing the extension or tag.

I created this because I wanted
- a pure python programming language detector
- no machine learning dependencies

Tested on python 3.6 through 3.9.

Badges
------
[![Libraries.io SourceRank](https://img.shields.io/librariesio/sourcerank/pypi/whats_that_code?longCache=true&style=flat-square)](https://libraries.io/github/matthewdeanmartin/whats_that_code/sourcerank)

[![Downloads](https://static.pepy.tech/badge/whats-that-code/month)](https://pepy.tech/project/whats-that-code)


[![CodeFactor](https://www.codefactor.io/repository/github/matthewdeanmartin/whats_that_code/badge)](https://www.codefactor.io/repository/github/matthewdeanmartin/whats_that_code)

## Usage
```python
from whats_that_code.election import guess_language_all_methods
code = "def yo():\n   print('hello')"
result = guess_language_all_methods(code, file_name="yo.py")
assert result == ["python"]
```

## How it Works
1) Inspects file extension if available.
2) Inspects shebang
3) Looks for keywords
4) Counts regexes for common patterns
5) Attemps to parse python, json, yaml
6) Inspects tags if available.

Each is imperfect and can error. The classifier then combines the results of each using a voting algorithm

This works best if you only use it for fallback, e.g. classifying code that can't already be classified by extension or tag,
or when tag is ambiguous.

It was a tool that outgrew being a part of [so_pip](https://github.com/matthewdeanmartin/so_pip) a StackOverflow code
extraction tool I wrote.

## Docs
- [TODO](https://github.com/matthewdeanmartin/whats_that_code/tree/main/docs/TODO.md)
- [LICENSE](https://github.com/matthewdeanmartin/whats_that_code/tree/main/LICENSE)
- [Prior Art](https://github.com/matthewdeanmartin/whats_that_code/tree/main/docs/prior_art.md) Every similar project/tool, including defunct
- [ChangeLog](https://github.com/matthewdeanmartin/whats_that_code/tree/main/docs/CHANGES.md)

## Notable Similar Tools
- [Guesslang](https://pypi.org/project/guesslang/) - python and tensorflow driven solution. Reasonable results but
slow startup and not pure python.
- [pygments](https://pygments.org/docs/api/#pygments.lexers.guess_lexer) pure python, but sometimes lousy identification
rates.
scikit-net 0.0.4
Machine Learning in Complex Networks
License: MIT
Keywords: Machine Learning,Complex Networks

![sknet Logo](https://github.com/TNanukem/sknet/blob/develop/docs/source/_static/full_logo.png "sknet Logo")

![Codecov branch](https://img.shields.io/codecov/c/github/tnanukem/sknet/develop?token=PIQ338YNK1)

The sknet project is a scikit-learn and NetworkX compatible framework for machine learning in complex networks. It provides learning algorithms for complex networks, as well as transforming methods to turn tabular data into complex networks.

It started in 2021 as a project from volunteers to help to improve the development of research on the interface between complex networks and machine learning. It main focus
is to help researchers and students to develop solutions using machine learning on complex networks.

## :computer: Installation

The sknet installation is available via PiPy:

    pip install scikit-net

## :high_brightness: Quickstart

The following code snippet shows how one can transform tabular data into a complex network and then use it to create a classifier:

    from sklearn.model_selection import train_test_split
    from sklean.metrics import accuracy_score
    from sklearn.datasets import load_iris
    from sknet.network_construction import KNNConstructor
    from sknet.supervised import EaseOfAccessClassifier

    X, y = load_iris(return_X_y = True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

    # The constructor responsible for transforming the tabular data into a complex network
    knn_c = KNNConstructor(k=5)

    classifier = EaseOfAccessClassifier()
    classifier.fit(X_train, y_train, constructor=knn_c)
    y_pred = classifier.predict(X_test)
    accuracy_score(y_test, y_pred)

## :pencil: Documentation

We provide an extensive API documentation as well with some user guides. The documentation is available on https://tnanukem.github.io/scikit-net/

## Citation

If you used the scikit-net on your research project, please cite us using the following publication:

    @article{Toledo2021,
    doi = {10.21105/joss.03864},
    url = {https://doi.org/10.21105/joss.03864},
    year = {2021},
    publisher = {The Open Journal},
    volume = {6},
    number = {68},
    pages = {3864},
    author = {Tiago Toledo},
    title = {sknet: A Python framework for Machine Learning in Complex Networks},
    journal = {Journal of Open Source Software}
    }