semvec/pypi-packages huggingface dataset:
nato-spell 0.3.1
Spelling engine for the NATO phonetic alphabet
License: MIT
Requires Python: >=3.8,<4.0
Keywords: NATO,spelling,alphabet,phonetic

# nato-spell

**nato-spell** is a Python package that can be used to spell a string / file with the well-known NATO phonetic alphabet.

## Installation

System requirements:
  - **aplay** (ALSA command line player - it may already be installed on your computer)
  - **python >= 3**


Use [pip](https://pip.pypa.io/en/stable/) or pipx to install `nato-spell`.

```bash
pip install nato-spell
```

## Usage

Minimal example
```
nato-spell SPELLME 
```

Spelling from stdin
```
echo SPELLME | nato-spell - 
```

Increase the delay between characters (unit is seconds, default is 0)
```
nato-spell SPELLME --char-delay=1
```

Use a different sound directory
```
nato-spell SPELLME --sound-dir=/home/me/my-sound-dir
```

And finally, an example, where we use them all together.
```
echo SPELLME | nato-spell - --sound-dir=/home/me/my-sound-dir --char-delay=1
```

## Sound assets 

These sounds were published by Tim Kahn and hosted on freesound.org
 - Tim Kahn ( https://freesound.org/people/tim.kahn/ )

Thank you Amy Gedgaudas and Tim Kahn for all these sounds.

They're licensed under:
http://creativecommons.org/licenses/by/3.0/


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)
BIT-Online-Code-Helper 1.0.4
Test your code of "BIT Online coding homework" more easily and conveniently!
License: MIT
Keywords: BIT,code,helper

# BIT Online Code Helper
Test your code of "BIT Online coding homework" more easily and conveniently!

## Install
`pip install bit-online-code-helper`\
And you should confirm that `g++` command is available. For example, running `g++ -v` in the terminal to confirm.

## Feature
With this, you can test and commit your code which use only **2 seconds** totally:
1. Save your code in the editor (Ctrl + S)
2. Switch to show cmd or PowerShell window (Alt + Tab)
3. Press "↑" on your keyboard to automatically repeat your last test command
4. Press "Enter" on your keyboard, wait for test result!

## Usage
```shell script
# bch means 'BITOnline Code Helper'

# local code test
# -l means '--local'
bch -l [bit_online_problem_url] [source_file_url]

# online code test
# commit your code to BIT Online to test
# -o means '--online'
bch -o [bit_online_problem_url] [source_file_url]

# local and online code test
# automaticlly commit your code to BIT Online to test after local test passed
# -lo means '--local_online'
bch -lo [bit_online_problem_url] [source_file_url]

# e.g.
# bch -lo "http://lexue.bit.edu.cn/mod/programming/view.php?id=32165" "E:\coding\1.c"
```

## TODO
1. Use cache to store the problem info to avoid frequently request the website
2. Improve related features which I don't realize now
3. Fix bugs
gfutilities 0.8.4
Glowforge Utilities
License: MIT
Keywords: Glowforge OpenGlow

A small collection of utilties to aid in firmware development for the Glowforge laser cutter.

This project is not affiliated nor endorsed by Glowforge, Inc.

As Glowforge's own software is in continuous BETA, so will this software.
It is recommended not to rely on this code for production as Glowforge does
not publish their protocols nor do they provide any change notices.

As a result, this code may break without warning.

USE AT YOUR OWN RISK!
pycpd 2.0.0
Pure Numpy Implementation of the Coherent Point Drift Algorithm
License: MIT
Keywords: image processing,point cloud,registration,mesh,surface

#############
Python-CPD
#############
.. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master
    :target: https://travis-ci.com/siavashk/pycpd

Pure Numpy Implementation of the Coherent Point Drift Algorithm.

MIT License.

*************
Introduction
*************

This is a pure numpy implementation of the coherent point drift `CPD <https://arxiv.org/abs/0905.2635/>`_ algorithm by Myronenko and Song. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.

The CPD algorithm is a registration method for aligning two point clouds. In this method, the moving point cloud is modelled as a Gaussian Mixture Model (GMM) and the fixed point cloud are treated as observations from the GMM. The optimal transformation parameters maximze the Maximum A Posteriori (MAP) estimation that the observed point cloud is drawn from the GMM.

The registration methods work for 2D and 3D point clouds. For more information, please refer to my `blog <http://siavashk.github.io/2017/05/14/coherent-point-drift/>`_.

*************
Pip Install
*************
.. code-block:: bash

  pip install pycpd

************************
Installation From Source
************************

Clone the repository to a location, referred to as the ``root`` folder. For example:

.. code-block:: bash

  git clone https://github.com/siavashk/pycpd.git $HOME/pycpd

Install the package:

.. code-block:: bash

  pip install .

For running sample registration examples under ``examples``, you will need ``matplotlib`` to visualize the registration. This can be downloaded by running:

.. code-block:: bash

 pip install matplotlib

*****
Usage
*****

Each registration method is contained within a single class inside the ``pycpd`` subfolder. To try out the registration, you can simply run:

.. code-block:: bash

 python examples/fish_{Transform}_{Dimension}.py

where ``Transform`` is either ``rigid``, ``affine`` or ``deformable`` and ``Dimension`` is either ``2D`` or ``3D``. Note that examples are meant to be run from the ``root`` folder.