semvec/pypi-packages huggingface dataset:
undeniable 0.0.2
A no nonsense NFT minting protocol built on Solana and IPFS
Requires Python: >=3.6

![undeniable](images/logo.png)

Undeniable is a simple way to mint NFT's on the Solana Blockchain. Specifically, Undeniable helps token creators link bytes to their token in a permanent way.



```bash
python3 -m pip install undeniable
```
pylev 1.4.0
A pure Python Levenshtein implementation that's not freaking GPL'd.

pylev
=====

A pure Python Levenshtein implementation that's not freaking GPL'd.

Based off the Wikipedia code samples at
http://en.wikipedia.org/wiki/Levenshtein_distance.


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

* Python 2.7.X, Python 3.3+ or PyPy 1.6.0+


Usage
-----

Usage is fairly straightforward:

.. code-block:: python

    import pylev
    distance = pylev.levenshtein('kitten', 'sitting')
    assert distance == 3


License
-------

New BSD.


Tests
-----

Setup::

    $ git clone https://github.com/toastdriven/pylev.git
    $ cd pylev

Running::

    $ python -m unittest tests

.. image:: https://travis-ci.com/toastdriven/pylev.svg?branch=main
   :target: http://travis-ci.com/toastdriven/pylev


Version History
---------------

* v1.4.0

    * Updated for current versions of Python
    * Integrated a better Travis matrix. Thanks to @grainert!
    * Fixed mistaken docs about the `assert`. Thanks to @adamchainz!
    * Reorganized the package.
    * Blacked all the source code.

* v1.3.0

    * Implemented a considerably faster variants (orders of magnitude).
    * Tested & working on Python 2.7.4, Python 3.3.1 & PyPy 1.9.0.

* v1.2.0

    * Fixed all incorrect spellings of "Levenshtein" (there's no "c" in it).
    * Old methods are aliased for backward-compatibility.

* v1.1.0

    * Implemented a much faster variant (several orders of magnitude).
    * The older variant was renamed to ``classic_levenschtein``.
    * Tested & working on Python 3.3 & PyPy 1.6.0 as well.

* v1.0.2

    * Python packaging is **REALLY** hard. Including the README *this time*.

* v1.0.1

    * Python packaging is hard. Including the README this time.

* v1.0.0

    * Initial release, just the naive implementation of Levenshtein.
MC-API 0.0.5
Es un paquete que te permitira obtener datos de un usuario de Minecraft y de los Servicios de Mojan y servidores de Minecraft
Keywords: mojang,minecraft,api,mojang api,minecraft api

Es un paquete que te permitira obtener datos de un usuario de Minecraft y de los Servicios de Mojan y servidores de Minecraft

Repositorio del proyecto en Github: https://github.com/fab-henao/MC.git

## Importar el modulo
`from MC_API import MC_Arch as MC`

`MC.MC.Servicios_MCC.services_Check()` No devuelve nada, solo impreme los servicios

Devuelve una Lista con los nombres
`MC.MC.User_MC.get_History_Names()` //Necesita como parametro un nombre

No devuelve nada, imprime si esta disponible o no el nombre
`MC.MC.User_MC.get_Name_Free()` //Necesita como parametro un nombre

Devuele un String con la URL del render del Skin
`MC.MC.User_MC.get_Render_Skin()` //Necesita como parametro un nombre

Devuelve un String con el identificador del Usuario
`MC.MC.User_MC.get_UUID()` //Necesita como parametro un nombre
AutomatiseCA 0.1.5
An automatic trascription system for Conversational Analysis
Requires Python: >=3.6

# AutomatiseCA Documentation

Domenico De Cristofaro

## About

AutomatizeCA is an automated transcription program for linguists and conversation analyst experts.

It is designed to be used for generating Conversation Analysis transcripts that can be improved manually.

## Overview

AutomatizeCA takes in a recorded speech and uses a Speech to Text API to generate a time-aligned transcript. It uses a system of functions to detect the pauses between the turns, to assign the speaker lables to the transcripts and to report and replace the backchannels.

AutomatizeCA renders the final transcript in a txt file including the formats of the Jefferson Transcription System.

**VERSION: 0.0.1**

This is a beta version, further implementation will be done in the future.

## Installation

**Pre-requisites**

In order to use AutomatizeCA, you should have some familiarity with using the terminal to install and run programs.

You should also be aware that AutomatiseCA uses [IBM Watson&#39;s STT API]
(https://cloud.ibm.com/apidocs/speech-to-text)