semvec/pypi-packages huggingface dataset:
topsis-yash-saxena 1.0.4
A Python pip package to apply topsis approach to rank the entries in a dataset
License: MIT

# TOPSIS-Python

**Project 1 : UCS633 DATA ANALYSIS AND VISUALIZATION**


Submitted By: **Yash Saxena 101703627**

***
pypi: <https://pypi.org/project/topsis-yash-saxena/>
<br>
git: <https://github.com/yashsaxena972/topsis>
***

## 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-YASH-SAXENA  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.
Pymdl 0.3.2
Download music from youtube
License: MIT

# Python Youtube Music DownLoader

A python script for downloading free music from youtube, leveraging <a href="https://github.com/ytdl-org/youtube-dl" target="_blank">youtube-dl</a>

You will need <a href="http://www.ffmpeg.org" target="_blank">ffmpeg</a> in your PATH

## Installation
```bash
$ pip install pymdl
```
django-expiry 0.2.3
Expiry rules for Django sessions.
License: MIT
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*

# django-expiry

[![CircleCI](https://circleci.com/gh/ramonsaraiva/django-expiry.svg?style=svg)](https://circleci.com/gh/ramonsaraiva/django-expiry)

Expiry rules for Django sessions.

## Installation

Install using `pip`

    pip install django-expiry

or `Pipenv`

    pipenv install django-expiry

Add `expiry` to your `INSTALLED_APPS` setting

    INSTALLED_APPS = (
        ...
        'expiry',
    )

Add `expiry.middleware.ExpirySessionMiddleware` to your middleware setting

    MIDDLEWARE = (
        ...
        'expiry.middleware.ExpirySessionMiddleware',
    )

or to middleware classes if your Django is <= 1.9

    MIDDLEWARE_CLASSES = (
        ...
        'expiry.middleware.ExpirySessionMiddleware',
    )

The middleware will process rules and default ages for fresh sessions.

## Usage

### Ages

Default ages can be set for anonymous and authenticated users. When not set, the session age behaviour will default to Django.

`EXPIRY_ANON_SESSION_AGE`  
Default: not set.

The default age of an anonymous session, in seconds.

`EXPIRY_ANON_KEEP_ALIVE`  
Default: `False`

Keeps the authenticated session alive, refreshing its expiry for every request, according to its default value and rules.

`EXPIRY_AUTH_SESSION_AGE`  
Default: not set.

The default age of an authenticated session, in seconds.

`EXPIRY_AUTH_KEEP_ALIVE`  
Default: `False`

Keeps the anonymous session alive, refreshing its expiry for every request, according to its default value and rules.

### Rules

A set of rules should be defined in your settings file.
You can have rules for anonymous users and authenticated users, handled separately.

#### Expiry rules for authenticated users only

Processed whenever an user logs in. Its callable should always accept an `user` and a `request` object.

    EXPIRY_AUTH_SESSION_RULES = (
        (lambda request, user: user.is_staff, 300),
        (lambda request, user: user.is_superuser, datetime.timedelta(weeks=2)),
        (lambda request, user: user.has_perms('hero'), 99999999),
    )

#### Expiry rules for anonymous users only

Processed whenever a session is fresh. Rules are triggered in `ExpirySessionMiddleware`.

    EXPIRY_ANON_SESSION_RULES = (
        (lambda request: request.META.get('REMOTE_ADDR') == '192.168.0.1', 999)
    )

#### Rule composition

A rule is a tuple composed by:
* A callable or the path to a callable that will validate it
* An expiry (seconds, datetime, timedelta)

Note that, for `datetime` and `timedelta` expiries, serialization won't work unless you are using the `PickleSerializer`.  
Read more about it [here](https://docs.djangoproject.com/en/2.1/topics/http/sessions/#django.contrib.sessions.backends.base.SessionBase.set_expiry).

In the examples above, all rules are lambdas, but you can also send the path to a function that will validate it.

    EXPIRY_AUTH_SESSION_RULES = (
        ('app.module.complex_rule', datetime.timedelta(days=64)),
    )

Then define the rule in that specific module:

    def complex_rule(user, request):
        ...
DjangoDevKit 1.0.8
DjangoDevKit package
License: MIT
Keywords: django


DjangoDevKit
=============

Meta package for Django developers.

Installation::

  $ easy_install -U DjangoDevKit

Contain (as dependencies):

- `django-debug-toolbar`_

- `django-extensions`_

- `django-webtest`_

This package also install some console scripts:

- **django-admin**: work like ``./django-admin.py``.

- **django-manage**: work like ``./manage.py`` but add `django-extensions` to ``INSTALLED_APPS`` on the fly.

- **django-shell**: work like ``./manage.py shell`` but use the `django-extensions` ``shell_plus``

- **django-migrate**: work like ``./manage.py syncdb --nointput && ./manage.py migrate --noinput`` Run migrate only if south is installed.

- **django-test**: work like ``./manage.py test``. Also set
  ``DEBUG_PROPAGATE_EXCEPTIONS`` to ``True`` so `WebTest` show the full traceback
  in tests output.

- **django-serve**: wrap the Django application in a `backlash`_ middleware and
  serve it. It's also serve `/media/` directories found in installed apps. You
  can also use ``-t`` to add the `django-debug-toolbar` to ``INSTALLED_APPS``
  and ``MIDDLEWARE_CLASSES`` on the fly. You can also use **request** and
  **post** to test a single request::

    $ django-serve request /path

You can also use some aliases. Create a ``~/.djangodevkitrc`` like this::

    [aliases]
    m =
        syncdb --noinput
        migrate --noinput
    si =
        schemamigration --initial []
    sm =
        schemamigration --auto []

``[]`` is replace with command line arguments. This mean that::

    $ django-manage sm myapp

is equal to::

    $ ./manage.py schemamigration --auto myapp

Notices that aliases are not listed in ``django-manage``'s help

.. _django-debug-toolbar: http://github.com/robhudson/django-debug-toolbar
.. _django-extensions: http://code.google.com/p/django-command-extensions/
.. _django-webtest: http://pypi.python.org/pypi/django-webtest
.. _backlash: https://pypi.python.org/pypi/backlash/


News
====

1.0.8 (2020-01-20)
==================

- Django 2.2 compat


1.0.7 (2016-09-02)
==================

- Fixed #3


1.0.6 (2015-04-23)
==================

- Use backlash instead of weberror

- Remove Paste dependencies

- py3 compat

1.0.5 (2015-04-09)
==================

- Listen on 0.0.0.0


1.0.4 (2014-10-06)
==================

- Fix Django 1.7 compat


1.0.3 (2014-01-24)
==================

- Fix #5


1.0.2 (2014-01-21)
==================

- Rewrite MediaApp

- Django 1.6 compat


1.0.1 (2014-01-15)
==================

Take care if DJANGO_SETTINGS_MODULE is already set

1.0
=====

Make it work with django 1.4+ and virtualenv

Add aliases machinery

0.8.3
=====

Windows bug fix


0.8.1
=====

Add all eggs to pkg_resources.working_set

0.8
===

No longer use twod.wsgi


0.6
===

Small fixe to work with twod.wsgi

0.2
===

Add MediaApp to serve media directories

0.1
===

Initial public release.
topsis-hiten 2.0.0
A Python package to implement Topsis analysis
License: MIT

# topsis-python
Topsis analysis of a csv file

## UCS633 Project Submission

**Name** - Hiten Garg

**Roll no.** - 101703234

**Group** - COE-11

## About Topsis

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).

## Installation


```bash
pip install topsis-hiten
```

## Usage

Following query on terminal will provide you the topsis analysis for input csv file.

```
topsis-hiten -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."

```

w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize. 
Size of w and i is equal to number of features. 

Note that the first row and first column of dataset is dropped

Rank 1 signifies best decision

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
riberry 0.10.22
Python-driven workflow management system
License: MIT
Requires Python: >=3.6,<4.0
Keywords: riberry,workflow,job,celery,multiprocessing

# Riberry

[![CircleCI](https://circleci.com/gh/srafehi/riberry/tree/master.svg?style=svg)](https://circleci.com/gh/srafehi/riberry/tree/master)

Under active development

[Documentation](https://riberry.app)
topsis-ishaan 1.0.0
A Python package to implement Topsis analysis
License: MIT

# topsis-python
Topsis analysis of a csv file

**UCS633 Project Submission**

Name - Ishaan Vijh

Roll no. - 101703245

## About Topsis

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).

## Installation


```bash
pip install topsis-ishaan
```

## Usage

Following query on terminal will provide you the topsis analysis for input csv file.

```
topsis-ishaan -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."

```

w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize. 
Size of w and i is equal to number of features. 

Note that the first row and first column of dataset is dropped

Rank 1 signifies best decision

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)