semvec/pypi-packages huggingface dataset:
vimeodownload.py 0.0.2
Download videos from Vimeo
Keywords: python,video,vimeo,download

[![](https://img.shields.io/pypi/status/vimeodownload.py)](https://pypi.org/project/vimeodownload.py/) [![](https://img.shields.io/pypi/v/vimeodownload.py)](https://pypi.org/project/vimeodownload.py/)  [![](https://img.shields.io/pypi/pyversions/vimeodownload.py)](https://pypi.org/project/vimeodownload.py/) [![](https://img.shields.io/pypi/l/vimeodownload.py)](https://pypi.org/project/vimeodownload.py/)


# [vimeodownload.py](https://pypi.org/project/vimeodownload.py/)
A simple package that downloads public and private Vimeo videos.

***NOTE:*** *This module is not extensively tested so use at your own risk. If you do find an issue, please report it on the [GitHub repository](https://github.com/jamiegyoung/vimeodownload.py)*

## Usage
```py
vimeodownload.get_video(url, destination, replace=False, quiet=False)
```

The `quiet` argument will make the script not print any output.
The `replace` argument will overwrite existing files.

## Example
```py
import vimeodownload

vimeodownload.get_video("https://vimeo.com/148751763", "./vid.mp4")
vimeodownload.get_video("https://vimeo.com/148751763", "./other/vid.mp4", replace=True, quiet=True)
```
The above example will download a video to `./vid.mp4` with output and `./other/vid.mp4` without any output.
ModEA 0.5.0
A modular evolutionary algorithm framework, mostly tailored to a modular implementation of the CMA-ES

[![PyPI version](https://badge.fury.io/py/ModEA.svg)](https://badge.fury.io/py/ModEA)
[![DOI](https://zenodo.org/badge/157624013.svg)](https://zenodo.org/badge/latestdoi/157624013)
[![Build Status](https://www.travis-ci.com/sjvrijn/ModEA.svg?branch=main)](https://www.travis-ci.com/sjvrijn/ModEA)
[![Coverage Status](https://coveralls.io/repos/github/sjvrijn/ModEA/badge.svg?branch=master)](https://coveralls.io/github/sjvrijn/ModEA?branch=master)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/d784348678ef4fe287c4c3efc184a16f)](https://www.codacy.com/app/sjvrijn/ModEA?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sjvrijn/ModEA&amp;utm_campaign=Badge_Grade)
[![Documentation Status](https://readthedocs.org/projects/modea/badge/?version=latest)](https://modea.readthedocs.io/en/latest/?badge=latest)
[![Gitter](https://badges.gitter.im/pyModEA/community.svg)](https://gitter.im/pyModEA/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

# Summary #
This repository contains the code for the Modular EA framework by Sander van Rijn.

# Documentation #
Some basic documentation is available at [modea.readthedocs.io](https://modea.readthedocs.io)

To see this framework in action, please refer to [this GitHub repository](https://github.com/sjvrijn/ConfiguringCMAES) which contains the code for all experiments on the Configurable CMA-ES.


# Citation #
To cite this framework, please use the following reference:
* [Evolving the Structure of Evolution Strategies. Sander van Rijn, Hao Wang, Matthijs van Leeuwen, Thomas Bäck. IEEE SSCI December 6-9 2016, Athens, Greece.](https://ieeexplore.ieee.org/document/7850138)
  - Experiments and analysis code available [here](https://github.com/sjvrijn/ConfiguringCMAES)
```
@INPROCEEDINGS{vanrijn2016,
    author={S. {van Rijn} and H. {Wang} and M. {van Leeuwen} and T. {Bäck}},
    booktitle={2016 IEEE Symposium Series on Computational Intelligence (SSCI)}, 
    title={Evolving the structure of Evolution Strategies}, 
    year={2016},
    doi={10.1109/SSCI.2016.7850138},
}
```
diglett 0.2.0
Tools for data wrangling.
Requires Python: >=3.8.6,<4.0.0

# Diglett

[![Tests](https://github.com/asdfgeoff/diglett/workflows/Tests/badge.svg)](https://github.com/asdfgeoff/diglett/actions?workflow=Tests) [![Codecov](https://codecov.io/gh/asdfgeoff/diglett/branch/master/graph/badge.svg)](https://codecov.io/gh/asdfgeoff/diglett) [![PyPI](https://img.shields.io/pypi/v/diglett.svg)](https://pypi.org/project/diglett/) [![Read the Docs](https://readthedocs.org/projects/diglett/badge/)](https://diglett.readthedocs.io/)

## What it does

Diglett is a collection of my most frequently used and reusable functions for data analysis, data wrangling, and machine learning. I have largely packaged them together for my own benefit, but I hope you will find something useful in here for yourself.


![Image of Diglett pokemon](diglett.png)


## Installing

To install the most recently published version from PyPI:
```
pip install diglett
```

To install a (possibly more recent, but less polished) version: clone this repo and run:
```
pip install -e .
```