semvec/pypi-packages huggingface dataset:
pneumatic 0.1.9
A bulk upload library for DocumentCloud.
License: MIT

A Bulk-Upload Library for DocumentCloud
---------------------------------------

pneumatic is a Python 3 library that adds some luxury and safeguards to the bulk-uploading of hundreds, thousands or hundreds of thousands of files to `DocumentCloud <https://www.documentcloud.org>`_. It is meant to do one thing -- upload -- and serve as an adjunct to, but not a replacement for, the excellent `python-documentcloud <http://python-documentcloud.readthedocs.io/en/latest/>`_ API wrapper.

pneumatic's name is inspired by the pneumatic dispatch systems in newsrooms of yore, which featured a `series of pneumatic tubes`_ for sending copy from the newsrooms to other departments such as the composing room.

Features
--------

* Catalogs the API response for each upload in a SQLite database along with the file's canonical URL.
* Post-processing, can update the SQLite database with each document's page count and file hash.
* Dumps the SQLite data to a CSV if you wish.
* Prevents inadvertent submission of file types DocumentCloud doesn't handle, such as audio.

Links
-----

* Documentation:    https://pneumatic.readthedocs.io/en/latest/
* Repository:       https://github.com/anthonydb/pneumatic
* Issues:           https://github.com/anthonydb/pneumatic/issues

Basic Usage
-----------

You will need an active DocumentCloud account and Python 3.5+. First, install via pip:

.. code-block:: python

    pip install pneumatic

Example use: To upload all files in a directory (and all sub-directories below it), assign them to an existing project, set the files to public access, and tag each with metadata, run the following code:

.. code-block:: python

    from pneumatic import DocumentCloudUploader

    uploader = DocumentCloudUploader('person@example.com', 'your-password')
    uploader.upload(
        file_directory='/govfiles',
        project='17477-loudoun-county-government',
        access='public',
        data={'type': 'government', 'action': 'lawsuit'})

Please see the `full documentation`_ for more examples, including how to access the uploads database.

.. _`series of pneumatic tubes`: https://en.wikipedia.org/wiki/Pneumatic_tube
.. _`full documentation`: https://pneumatic.readthedocs.io/en/latest/
topsis-101703289-khushnuma 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

**Project 1 : UCS633**


Submitted By: **Khushnuma Grover 101703289**

***
pypi: <https://pypi.org/project/topsis-khushnuma-101703289>
<br>
git: <https://github.com/khushgrover/topsis-python.git>
***

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-KHUSHNUMA-101703289  can be run as in the following example:



### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

### In Python IDLE:
```
>>> import pandas as pd
>>> from topsis_python.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im)
```

<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Weights (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
pycopy-zlib 0.1
zlib module for Pycopy
License: MIT

This is a module for the standard library of Pycopy project:
https://github.com/pfalcon/pycopy . This module was implemented from
scratch specifically to target Pycopy, with efficient and lean design
in mind. Note that this module is likely work in progress and likely
supports just a subset of CPython’s corresponding module. Please help
with the development if you are interested in this module.
hashcat-beta 5.1.0.post4
Python pip installer for hashcat beta
License: MIT
Keywords: hashcat

hashcat-beta
============

.. note::

    This project is not in any way officially endorsed by the hashcat team.

This project is my attempt to make installing and using hashcat easier. Specifically, it solves the following:

* No need to browse to hashcat and download
* No need to download all the separate processors (see their github)
* The tools are automatically in your path, no need to cd into hashcat directory

   * hashcat doesn't officially support running it from a separate directory. this library has it's own fix built in to allow this.

* Easy to update or fully remove

Install
-------

.. code-block:: bash

    $ pip install hashcat-beta

Running
-------

Run it as if you were running hashcat normally. It exposes multiple utilities as well.

.. code-block:: bash

    $ hashcat -h
    $ pp64 -h
    $ cap2hccapx -h
    $ kwp -h

CLI Menu
~~~~~~~~

For a cli menu to hashcat, see https://pypi.org/project/hashcrack/