semvec/pypi-packages huggingface dataset:
kommando 1.3.2
A very extensible command parser for discord.py [rewrite]
Requires Python: >=3.6.0

# Kommando.py
An extensible command parser for discord.py [rewrite]

## Requirements
* Python 3.6+
* `discord.py` library [rewrite]

## F.A.Q

#### When should i use it instead of ext.commands?
When you need to do things that ext.commands can't help you,
or you wanna make very special and user-centered commands

#### When shouldn't i use it?
When you are still learning python or when you are to uptight to try something new

#### What are the main advantages?
- Built-in keyword arguments support
- Custom methods, control of the entire command parsing
- Extensions, modular and total customization of your Bot, with
partial reloads and custom states at runtime
- Live Objects, inteligent use of callbacks
- Long-lived (or even permanent) paginators
- Multiple tasks for client events
- Easy translations and customized behaviours
- Brand-new features

#### When you will release some documentation?
Soon!

#### Soo why the custom Oni ordered an HellBox?
For Hellpy someone

#### Why this is still here? I mean, the bots aren't called onis anymore, the hellboxes are now contexts, and the project was never called Hellpy
Historical Reasons
pairsnp 0.0.7
A simple package for calculating pairwise SNP distances

# pairsnp-python

[![Travis-CI Build Status](https://travis-ci.com/gtonkinhill/pairsnp-python.svg?branch=master)](https://travis-ci.com/gtonkinhill/pairsnp-python)

## Installation

This is a Python3 version of pairsnp written with the help of Simon Frost

```
git clone https://github.com/gtonkinhill/pairsnp-python
cd pairsnp-python
pip3 install .
```

## Quick Start

The python version can be run from the python interpreter as

```
from pairsnp import calculate_snp_matrix, calculate_distance_matrix

sparse_matrix, consensus, seq_names = calculate_snp_matrix(fasta.file.name)
d = calculate_distance_matrix(sparse_matrix, consensus, "dist", False)
```

alternatively if installed using pip it can be used at the command line as


```
pairsnp -f /path/to/msa.fasta -o /path/to/output.csv
```

additional options include

```
Program to calculate pairwise SNP distance and similarity matrices.

optional arguments:
  -h, --help            show this help message and exit
  -t {sim,dist}, --type {sim,dist}
                        either sim (similarity) or dist (distance) (default).
  -n, --inc_n           flag to indicate differences to gaps should be
                        counted.
  -f FILENAME, --file FILENAME
                        location of a multiple sequence alignment. Currently
                        only DNA alignments are supported.
  -z, --zipped          Alignment is gzipped.
  -c, --csv             Output csv-delimited table (default tsv).
  -o OUTPUT, --out OUTPUT
                        location of output file.
```
storystructure 1.0.2
Tools for analysing the structure of branching stories

storystructure
==============

A Python package to explore the structure of branching stories.
`Documentation <https://sellisd.github.io/storystructure/>`

Requirements
------------

The module has been developed with python 3.6.6 and the following modules. Other
versions might work also but have not been tested:

* ``pathlib2 v.2.2.0``
* ``pandas v.0.19.2``
* ``numpy v.1.11.3``

Installation
------------

.. code-block::  bash

    pip install storystructure

Example usage
-------------

To use the package two input data files are necessary:

* An *edgelist* file with columns ``source`` and ``target``
* A *node attributes* file with columns ``node`` and ``attribute``. The attribute column must be on of the following values ``good``, ``bad``, ``pause``.

.. code-block:: python

    from storystructure.storystructure import storystructure
    # create a new object
    s = storystructure.storystructure()
    # load an edgelist and node attributes
    s.loadStory(edges='edgelist.csv',
                nodes='nodeAttributes.csv')
    s.saveFig('graph'.png')     # Make figure
    s.simplify() # Removing self loops and double edges
    s.makeGraph()
    s.savePathStats('allPaths.dat')

An more detailed example is included in the jupyter notebook ``scripts/Tutorial.ipybn``