semvec/pypi-packages huggingface dataset:
polaroidme 0.9.32
converts an image into vintage polaroid style

polaroidme is a simple to use command-line-tool for placing an image into a
Polaroid-like frame and optionally put a title / description on the bottom.
The default font mimics scribbled handwriting but any (ttf-)font
which suits your taste is supported. The tool offers basic features
like auto-scaling up-/downwards and/or cropping, using any (ttf-)font,
supports high-res output and gets the job just done well.
polaroidme is actively maintained & developed (2019).
To see if it fits your needs take a look at the project's
github-repo and check out the `examples`_

Contributions are welcome, and they are greatly appreciated!

_`examples`: https://github.com/s3h10r/polaroidme/blob/master/README.md
ytgrep 0.5.0
CLI tool to search youtube captions

ytgrep 
-----------------------
.. image:: https://travis-ci.com/alexkohler/ytgrep.svg?branch=master
    :target: https://travis-ci.com/alexkohler/ytgrep

ytgrep is a CLI tool to search Youtube closed captions with a grep-like interface.


Requirements
=============

* Requires python >= 3.5

Installation
=============

.. code:: bash

    pip install ytgrep

Note: you must have pip's bin folder on your `PATH`.

Purpose
=============
ytgrep is particularly useful whilst looking for particular topic or keywords mentioned in a video.

For example, searching for the word 'topoloigcal sort' in `MIT's Open Courseware Lecture on DFS and topological sort <https://www.youtube.com/watch?v=AfSk24UTFS8>`__:

|image0|

.. |image0| image:: https://asciinema.org/a/SjG0XTmIPzDfNgx2SxwhCdXwt.svg
   :target: https://asciinema.org/a/SjG0XTmIPzDfNgx2SxwhCdXwt


Usage
==============

.. code:: bash

    usage: ytgrep.py [-h] [-e] [-v] [-links] pattern urls [urls ...]

Flags
=============
* -e <PATTERN> - specify a regular expression to match
* -v - verbose, print debug information
* -links - include the time shortcut link with each match (see example below) 


More examples
=============

Include timestamped links (e.g. <youtube URL>/&t=196s) to jump directly to part in video where keyword was mentioned.

.. code:: bash

    $ ytgrep -links 'potassium' https://www.youtube.com/watch?v=OIYOshsEqmQ

Search with regular expression

.. code:: bash

    ytgrep -e 'banana|potassium' https://www.youtube.com/watch?v=OIYOshsEqmQ


Search multiple urls

.. code:: bash

    ytgrep 'banana' https://www.youtube.com/watch?v=LH5ay10RTGY https://www.youtube.com/watch?v=zFQWVN4xip0





Development
=============

Run Tests
~~~~~~~~~

*Note:* Functional tests do download directly from Youtube

.. code:: bash

   ## All tests
   python -m unittest discover

   ## Unit tests
   python -m unittest discover test/unit

   ## Functional tests
   python -m unittest discover test/functional

Related projects
==================
 * youtube-closed-captions - This project used this library as a starting point https://github.com/mkly/youtube-closed-captions
 * videogrep - get supercuts of video matching provided seach terms
lumes 0.0.4a3
A simple wraper for screenshot
Requires Python: >=3, <4
Keywords: screenshot

What can it do?
==========

Simple wrapper around maim and xclip for doing screenshots.</br>
For help write</br>
`$ lumes -h`

Requirements
----------

1. maim
2. xclip

Installation
------------
To upgrade pip
</br>`$ python3 -m pip install -U pip`
</br>To be sure you use proper version of python
</br>`$ python3 -m pip install lumes`
</br>To upgrade lumes
</br>`$ python3 -m pip install -U lumes`

Features
-----------

1. Selection area
2. Move screen to clipboard (by default)
3. Save screenshot in file
4. Save screenshots in directory

Examples
----------
For just get picture of your screen and copy it to clipboard
</br>`$ lumes `
</br>The same but with selection of area
</br>`$ lumes -s `
</br>Copy to clipboard and save in file
</br>`$ lumes -f img.png`
</br>Choose selection, copy to clipboard and save in directory with screenshots (if you have that directory)
</br>`$ lumes -s -d $HOME/Screenshots/`

In future
----------

1. Wayland support
2. Shell-completions support
3. Integrations with xdotool
kaggle-metrics 0.3.1
Metrics for Kaggle competitions
Keywords: kaggle,metrics

# kaggle-metrics <img src="https://raw.githubusercontent.com/krzjoa/kaggle-metrics/master/img/kmlogo.png" align="right" width = "120px"/>
![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg) 
[![PyPI version](https://badge.fury.io/py/kaggle-metrics.svg)](https://badge.fury.io/py/kaggle-metrics ) 
[![Documentation Status](https://readthedocs.org/projects/kaggle-metrics/badge/?version=latest)](https://kaggle-metrics.readthedocs.io/en/latest/?badge=latest) 
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 

Metrics for Kaggle competitions.

**See wiki**: [Implemented metrics](https://github.com/krzjoa/kaggle-metrics/wiki/Implemented-metrics)

## Installation
```bash
python3.7 -m pip install git+https://github.com/krzjoa/kaggle-metrics.git
```
or:

```bash
python3.7 -m pip install kaggle_metrics
```

## Usage
```python
from xgboost import XGBRegressor
import kaggle_metrics as km

X_train, y_train, X_test, y_test = get_data()

# Train
clf = XGBRegressor()
clf.fit(X_train, y_train)

# Get predictions
y_pred = clf.predict(X_test)

# Evaluate with kaggle-metrics
km.rmse(y_test, y_pred)


```





Documentation
-------------
The full documentation is at http://kaggle-metrics.rtfd.org.