semvec/pypi-packages huggingface dataset:
pokemaster2 21.12.3
Get Real, Living™ Pokémon in Python
Requires Python: >=3.8,<3.11
Keywords: pokemaster2,pokemon,pokedex,pokemaster


# Pokemaster2


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</div>


Get Real, Living™ Pokémon in Python


* GitHub repo: <https://github.com/kipyin/pokemaster2.git>
* Documentation: <https://pokemaster2.readthedocs.io>
* Free software: MIT


## Features

Currently, there is not much you can do with `pokemaster`, but more features are actively being added!

* Load pokemon data from csv to a sqlite database.

## Quickstart

TODO

## Credits

This package was created with [Cookiecutter][cookiecutter] and the [fedejaure/cookiecutter-modern-pypackage][cookiecutter-modern-pypackage] project template.

[cookiecutter]: https://github.com/cookiecutter/cookiecutter
[cookiecutter-modern-pypackage]: https://github.com/fedejaure/cookiecutter-modern-pypackage
linkparse 1.0.0
Link header parsing library for mementos
Requires Python: >=3.7
Keywords: Memento,Link Header,Web archiving

# Link Parse

[Python](https://www.python.org/) library for parsing memento link headers.


## Installation

Installation of the library requires [pip](https://pip.pypa.io/en/stable/) package installer for Python. 

Install pip: [https://pip.pypa.io/en/stable/installation/](https://pip.pypa.io/en/stable/installation/)



### 1. Using Git 

```shell
pip install https://github.com/mahanama94/link-parser
```

### 2. Local copy

```shell
pip install path/to/package/directory
```

## Usage

```python
# sample.py

from linkparse.regex_parser import RegexLinkParser
from pprint import pprint

link_header = '<http://www.mementoweb.org:80/>; rel="original", '\
'<https://web.archive.org/web/timemap/link/http://www.mementoweb.org:80/>; rel="timemap"; '\
'type="application/link-format", <https://web.archive.org/web/http://www.mementoweb.org:80/>; '\
'rel="timegate", <https://web.archive.org/web/20090930115825/http://www.mementoweb.org/>; rel="first '\
'memento"; datetime="Wed, 30 Sep 2009 11:58:25 GMT" '

parser = RegexLinkParser()

parser_results = parser.parse(link_header)

for result in parser_results:
    pprint(result.__dict__)

```

```shell
$ python sample.py

{'datetime': '',
 'link_from': '',
 'link_type': '',
 'link_until': '',
 'relationship': 'original',
 'title': '',
 'uri': 'http://www.mementoweb.org:80/'}
{'datetime': '',
 'link_from': '',
 'link_type': 'application/link-format',
 'link_until': '',
 'relationship': 'timemap',
 'title': '',
 'uri': 'https://web.archive.org/web/timemap/link/http://www.mementoweb.org:80/'}
{'datetime': '',
 'link_from': '',
 'link_type': '',
 'link_until': '',
 'relationship': 'timegate',
 'title': '',
 'uri': 'https://web.archive.org/web/http://www.mementoweb.org:80/'}
{'datetime': 'Wed, 30 Sep 2009 11:58:25 GMT',
 'link_from': '',
 'link_type': '',
 'link_until': '',
 'relationship': 'first memento',
 'title': '',
 'uri': 'https://web.archive.org/web/20090930115825/http://www.mementoweb.org/'}

```


#LANL   Identification

LANL C number: C21088

# Copyright
© 2021. Triad National Security, LLC. All rights reserved.
This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
Department of Energy/National Nuclear Security Administration. All rights in the program are
reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear
Security Administration. The Government is granted for itself and others acting on its behalf a
nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare
derivative works, distribute copies to the public, perform publicly and display publicly, and to permit
others to do so.
relativeNeighborhoodGraph 0.0.1
Compute the Relative Neighborhood Graph from a distance matrix
Requires Python: >=3.6

# Relative Neighborhood Graph

Compute the Relative Neighborhood Graph from a NumPy distance matrix. 

## Instructions

1. Install:

```
pip install relativeNeighborhoodGraph
```

2. Compute the Relative Neighborhood Graph from a NumPy distance matrix.

```python
from relativeNeighborhoodGraph import returnRNG

RNG = returnRNG(distance_matrix)
```

3. The resulting Relative Neighborhood Graph is returned as a Pandas DataFrame.