semvec/pypi-packages huggingface dataset:
pyscaff 0.1.0
Make and manage templates for Python projects
Requires Python: >=3.11

=======
Pyscaff
=======

Copyright (c) 2024 Jérémie DECOCK (www.jdhp.org)

* Web site: http://www.jdhp.org/software_en.html#pyscaff
* Online documentation: https://jdhp-dev.gitlab.io/pyscaff
* Examples: https://jdhp-dev.gitlab.io/pyscaff/gallery/

* Notebooks: https://gitlab.com/jdhp-dev/pyscaff-notebooks
* Source code: https://gitlab.com/jdhp-dev/pyscaff
* Issue tracker: https://gitlab.com/jdhp-dev/pyscaff/issues
* Pytest code coverage: https://jdhp-dev.gitlab.io/pyscaff/htmlcov/index.html
* Pyscaff on PyPI: https://pypi.org/project/pyscaff


Description
===========

Make and manage templates for Python projects

Note:

    This project is still in beta stage, so the API is not finalized yet.


Dependencies
============

C.f. requirements.txt


.. _install:

Installation (development environment)
======================================

Posix (Linux, MacOSX, WSL, ...)
-------------------------------

From the Pyscaff source code::

    conda deactivate         # Only if you use Anaconda...
    python3 -m venv env
    source env/bin/activate
    python3 -m pip install --upgrade pip
    python3 -m pip install -r requirements-dev.txt


Windows
-------

From the Pyscaff source code::

    conda deactivate         # Only if you use Anaconda...
    python3 -m venv env
    env\Scripts\activate.bat
    python3 -m pip install --upgrade pip
    python3 -m pip install -r requirements-dev.txt


Installation (production environment)
=====================================

::

    python3 -m pip install --no-cache-dir "pyscaff @ git+ssh://git@gitlab.com/jdhp-dev/pyscaff.git"


Documentation
=============

* Online documentation: https://jdhp-dev.gitlab.io/pyscaff
* API documentation: https://jdhp-dev.gitlab.io/pyscaff/api.html


Example usage
=============

::

    pyscaff new --template-directory-path=./examples/template_test --project-config-path=./examples/pyscaff-config.toml --output-directory-path=./examples/output


Bug reports
===========

To search for bugs or report them, please use the Pyscaff Bug Tracker at:

    https://gitlab.com/jdhp-dev/pyscaff/issues


License
=======

This project is provided under the terms and conditions of the `MIT License`_.


.. _MIT License: http://opensource.org/licenses/MIT
.. _command prompt: https://en.wikipedia.org/wiki/Cmd.exe
str2bool3 1.3.0
Convert string to boolean (Forked from SymonSoft/str2bool)
Keywords: str2bool, bool, boolean, convert, yes, no, true, false

# str2bool3 v1.0.0

## About
Convert string to boolean.  
The library recognizes "yes", "true", "y", "t", "1" as True, and "no", "false", "n", "f", "0" as False.  
Case insensitive.

## Installation

    $ pip install str2bool3

## Examples
Here's a basic example:

    >>> from str2bool3 import StrUtils
    >>> print(StrUtils.str2bool('Yes'))
    True

To raise an exception on invalid input:

    >>> from str2bool3 import StrUtils
    >>> StrUtils.str2bool_exc('invalid')
    ValueError: Invalid value 'invalid'. Expected one of: false, f, n, no, t, true, y, yes, 0, 1.

## License
BSD

Forked from symonsoft/str2bool
lum0x-cli 0.1.0
A CLI tool for deploying and managing functions for Lum0x Infrastructure
Requires Python: >=3.10

# lum0x

`lum0x` is a CLI tool to manage API credentials and deploy Lambda functions with ease.

## Features

- **Credential Management**: Easily authenticate your API key and store the session information securely.
- **Session Persistence**: Keep your session information stored locally to use across multiple CLI commands.

## Usage

1. **Set API Key**:
   ```bash
   lum0x credential <API_KEY>

2. on-demand function deploy
 ```bash
   lum0x deploy yourcode.zip --ondemand
QLev 1.9.1
String distance metrics based on Levenshtein and Qwerty Matrix Distance
Keywords: python, levenshtein, qwerty, vector

# QLev

<p>

  <a href="https://pypi.org/project/QLev/">
    <img src="https://img.shields.io/pypi/v/QLev"
         alt="PyPI package version">
  </a>
  <a href="https://www.python.org">
    <img src="https://img.shields.io/pypi/pyversions/QLev"
         alt="Python versions">
  </a>
  <a href="https://pypi.org/project/QLev/">
    <img src="https://img.shields.io/pypi/dm/QLev.svg?label=PyPI%20downloads"
         alt="PyPI Downloads">
  </a>
</p>

## Introduction
The QLev package is mainly used for:

* Levenshtein distance
* levenshtein distance normalized
* levenshtein distance considering the keyboard keys range 

## Requirements
* Python 3 or later

## Installation
```bash
pip install QLev
```

## Guide

To use simple the levenshtein distance you can:

```python
from QLev import levenshteinDistance

diff = levenshteinDistance('Guacamole','Guecamole')

print(diff)
```
If you want to use the normalized metric, you can:

```python
from QLev import levN

diff = levN('Guacamole','Guecamole')

print(diff)
```
If you want to know the euclidian distance between two chars, you can:

```python
from QLev import qwertyDistance

diff = qwertyDistance('g','a')

print(diff)
```

To have a metric that uses the qwerty matrix between strings, you can:

```python
from QLev import qwertyN

diff = qwertyN('Guacamole','Guecamole')

print(diff)
```

To have a metric that uses levenshtein distance and the qwerty matrix between strings, you can:

```python
from QLev import QLev

diff = QLev('Guacamole','Guecamole')

print(diff)
```


## License

MIT License

Copyright (c) 2022 Alysson Amaral

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
python-wordchain 0.4.2
A package to build word chains from a given list of words.
Requires Python: >=3.8

# Word Chain

A package to build word chains from a given list of words. 

[![Tests](https://github.com/shaunhegarty/wordchain/actions/workflows/test.yml/badge.svg)](https://github.com/shaunhegarty/wordchain/actions/workflows/test.yml)

## Installation
```
pip install python-wordchain
```

## Sample Usage
```python
from wordchain.wordchain import WordChainer

WORD_LIST = ["bird", "bind", "bord", "bond", "bend", "bing", "bong", "sing", "song"]

chainer = WordChainer(word_list=WORD_LIST)
chainer.get_chains('bird', 'song')

```
This yields:
```python
[('bird', 'bord', 'bond', 'bong', 'song'),
 ('bird', 'bind', 'bing', 'sing', 'song'),
 ('bird', 'bind', 'bond', 'bong', 'song'),
 ('bird', 'bind', 'bing', 'bong', 'song')]
```

## Testing
Running the test suite from repository root:
```
pytest -v tests/tests.py
```

Run tests using docker and tox:
```
docker run --rm $(docker build -q . -f Dockerfile.tox) tox
```
stencil-maker 0.1.1
Generate an image for a text stencil
Requires Python: >=3.8

# stencil-maker

Generate an image as a text stencil, with custom fonts, sizes and optionally flipping the image horizontally.

## Installation

### From PyPI

```
$ pip install stencil_maker
```

To install with the server mode enabled, include the `[server]` extra feature when installing:

```
$ pip install 'stencil_maker[server]'
```

### From prebuilt packages

Get a instalable wheel from the [releases page](https://github.com/mamg22/stencil-maker/releases), once it downloads, install it with:

```console
$ pip install stencil_maker-VERSION-py3-none-any.whl
$ # The VERSION might be different depending on the version downloaded

$ # Or with server mode
$ pip install 'stencil_maker-VERSION-py3-none-any.whl[server]'
```

## Usage

Whenever a font is required as an option, you may pass either a path pointing to the font or the font basename, which the library will try to find in the system's font directories.

### Command line

```console
$ # The -h or --help flags provide information on usage
$ stencil-maker --help
...
$ # Basic usage requires the font name or font filename and a output filename
$ # Provide the text to draw via stdin
$ stencil-maker -f LiberationSans-Regular output.png
My text may be split
into multiple lines
This is not a haiku
$ stencil-maker --font font-dir/LiberationSans-Bold.ttf --flip --size 300 output2.png
This is a test
$
```

### Server

This functionality will only be available if the package was installed with the `server` feature enabled.

The server program requires the `STENCIL_MAKER_FONT` environment variable to be defined, either in the program environment or a `.env` file.

To run the server you can quickly use the `stencil-server` command, which spins up a simple flask server running on `localhost` port `5000`. For futher customizing the server options, provide the path to the server module to your WSGI server and configure it to your liking. For example using `flask` as WSGI server and passing a few settings:

```console
flask run --port 5001 --host 0.0.0.0 --app stencil_maker.server
```

### Python library

This package provides a simple api to produce images. The main interface is the ```python stencil_maker.make_stencil()``` function, which returns a regular Pillow `Image` instance, which can be saved or processed further. For example:

```python
from stencil_maker import make_stencil

stencil = make_stencil("This is a test", "LiberationMono-Regular", 300)
stencil.save("my-stencil.png")
```
ecnet 4.1.4
Fuel property prediction using QSPR descriptors
Requires Python: >=3.11

[![UML Energy & Combustion Research Laboratory](https://sites.uml.edu/hunter-mack/files/2021/11/ECRL_final.png)](http://faculty.uml.edu/Hunter_Mack/)

# ECNet: machine learning models for fuel property prediction

[![GitHub version](https://badge.fury.io/gh/ecrl%2FECNet.svg)](https://badge.fury.io/gh/ecrl%2FECNet)
[![PyPI version](https://badge.fury.io/py/ecnet.svg)](https://badge.fury.io/py/ecnet)
[![status](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f/status.svg)](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f)
[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/ECRL/ECNet/master/LICENSE.txt)
[![Documentation Status](https://readthedocs.org/projects/ecnet/badge/?version=latest)](https://ecnet.readthedocs.io/en/latest/?badge=latest)
	
**ECNet** is an open source Python package for creating machine learning models to predict fuel properties. ECNet comes bundled with a variety of fuel property datasets, including cetane number, yield sooting index, and research/motor octane number. ECNet was built using the [PyTorch](https://pytorch.org/) library, allowing easy implementation of our models in your existing ML pipelines.

ECNet leverages [QSPR descriptors](https://en.wikipedia.org/wiki/Quantitative_structure%E2%80%93activity_relationship) for use as input variables, specifically [PaDEL-Descriptor](http://www.yapcwsoft.com/dd/padeldescriptor/) and [alvaDesc](https://www.alvascience.com/alvadesc/). Using alvaDesc requires a valid license.

Future plans for ECNet include:
- Implementating RDKit to train using molecular fingerprints
- Leveraging additional QSPR-generation software packages (e.g. [Mordred](https://github.com/mordred-descriptor/mordred))
- A graphical user interface

# Installation and Usage

Please refer to our [documentation page](https://ecnet.readthedocs.io/en/latest/) for installation instructions and full API documentation. You can also view some [example scripts](https://github.com/ECRL/ECNet/tree/master/examples) we put together.

# Contributing, Reporting Issues, and Other Support:

To contribute to ECNet, make a pull request. Contributions should include tests for new features added, as well as extensive documentation.

To report problems with the software or feature requests, file an issue. When reporting problems, include information such as error messages, your OS/environment and Python version.

For additional support/questions, contact Travis Kessler (Travis_Kessler@student.uml.edu) and/or John Hunter Mack (Hunter_Mack@uml.edu).
rvcgenpy 0.1.0
An autonomous pipeline to create covers with any RVC v2 trained AI voice
Requires Python: >=3.9


# RVCGenPy

RVCGenPy is a Python package for generating AI voice covers using RVC (Retrieval-based Voice Conversion) models.

## Installation

You can install RVCGenPy using pip:

```bash
pip install rvcgenpy

pip install --find-links https://download.pytorch.org/whl/torch_stable.html torch



```


## Usage

To use RVCGenPy in your Python code, you can import the main functions like this:

```python
from rvcgenpy import song_cover_pipeline, download_models

# Download required models (only needed once)
download_models()

# Generate an AI cover
cover_path = song_cover_pipeline(
    song_input="path/to/input/song.mp3",
    rvc_dirname="voice_model_folder",
    pitch_change=0,
    keep_files=False,
    main_gain=0,
    backup_gain=0,
    inst_gain=0,
    index_rate=0.5,
    filter_radius=3,
    rms_mix_rate=0.25,
    f0_method="rmvpe",
    crepe_hop_length=128,
    protect=0.33,
    pitch_change_all=0,
    reverb_rm_size=0.15,
    reverb_wet=0.2,
    reverb_dry=0.8,
    reverb_damping=0.7,
    output_format="mp3"
)

print(f"Cover generated at: {cover_path}")
```



## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
AICoverGen 0.1.0
An autonomous pipeline to create covers with any RVC v2 trained AI voice
Requires Python: >=3.9


# RVCGenPy

RVCGenPy is a Python package for generating AI voice covers using RVC (Retrieval-based Voice Conversion) models.

## Installation

You can install RVCGenPy using pip:

```bash
pip install rvcgenpy

pip install --find-links https://download.pytorch.org/whl/torch_stable.html torch



```


## Usage

To use RVCGenPy in your Python code, you can import the main functions like this:

```python
from rvcgenpy import song_cover_pipeline, download_models

# Download required models (only needed once)
download_models()

# Generate an AI cover
cover_path = song_cover_pipeline(
    song_input="path/to/input/song.mp3",
    rvc_dirname="voice_model_folder",
    pitch_change=0,
    keep_files=False,
    main_gain=0,
    backup_gain=0,
    inst_gain=0,
    index_rate=0.5,
    filter_radius=3,
    rms_mix_rate=0.25,
    f0_method="rmvpe",
    crepe_hop_length=128,
    protect=0.33,
    pitch_change_all=0,
    reverb_rm_size=0.15,
    reverb_wet=0.2,
    reverb_dry=0.8,
    reverb_damping=0.7,
    output_format="mp3"
)

print(f"Cover generated at: {cover_path}")
```



## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
ANIDSC 0.1.0
Adversarial testing framework for NIDS
Requires Python: >=3.10

# ANIDSC
 Adversarial NIDS Chain

# Setting Up environment
install docker

run
`docker run --gpus all -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --rm -v "path/to/this/folder":/workspace/ANIDSC -v "path/to/datasets":/workspace/datasets -v "path/to/models":/workspace/models kihy/nids_framework`

# Running the code
The examples folder include example scripts to run this package
feature_extraction.py shows how to extract features
evaluate_models.py shows how to evaluate NIDS models
generate_report.py contains functions to obtain summary

# extending the code
To add a new feature extractor, inherit BaseTrafficFeatureExtractor from base_feature_extractor.py
To add a new model, inherit BaseOnlineODModel and one of the save mixins from base_model.py
optimizn 0.0.24
A Python library for developing customized optimization algorithms under general paradigms.
Requires Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,<4,>=2.7

A Python library for developing customized optimization algorithms under general paradigms like simulated annealing and branch and bound. Also features continuous training, so algorithms can be run multiple times and resume from where they left off in previous runs.
jibix 0.4
Easy to handle json database manager
Requires Python: >=3.6


# JIBIX

[![GitHub](https://img.shields.io/github/license/navendu-pottekkat/awesome-readme)](https://img.shields.io/github/license/navendu-pottekkat/awesome-readme)

Jibix is a easy-to-handle database-manager and it's very simple to work with:
```python
from jibix import Jibix
db = Jibix('database name.json')
db['hello'] = 1 # automatically saves in database for next time you loading it
db.pop('hello') # automatically commits in database for next time you loading it
```
Yeah that's it ;)

### don't use jibix when you have more than 20 commits per second
autocrop-video 0.1.1
A CLI tool for automatically cropping videos
Requires Python: >=3.7

# autocrop

A CLI tool for automatically cropping videos.

## Installation

```bash
pip install autocrop-video
```

## Usage

```bash
autocrop process path/to/input_video.mp4
```

For more options, run:

```bash
autocrop --help
```

## Requirements

- Python 3.7+
- FFmpeg and FFprobe installed and accessible from the command line