semvec/pypi-packages huggingface dataset:
django-paste 1.1.3
Pluggable, configurable, pastebin HTTP REST API
License: MIT

django-paste
============

Pluggable, configurable, pastebin HTTP REST API
-----------------------------------------------

.. image:: https://img.shields.io/pypi/v/django-paste.svg
   :target: https://pypi.org/project/django-paste/
   :alt: PyPI

.. image:: https://img.shields.io/pypi/l/django-paste.svg
   :target: https://pypi.org/project/django-paste/
   :alt: PyPI - License

.. image:: https://img.shields.io/pypi/pyversions/django-paste.svg
   :target: https://pypi.org/project/django-paste/
   :alt: PyPI - Python Version

.. image:: https://codecov.io/gh/amikrop/django-paste/branch/master/graph/
           badge.svg
    :target: https://codecov.io/gh/amikrop/django-paste/
    :alt: Coverage

.. image:: https://travis-ci.org/amikrop/django-paste.svg?branch=master
   :target: https://travis-ci.org/amikrop/django-paste/
   :alt: Build Status

.. image:: https://readthedocs.org/projects/django-paste/badge/?version=latest
   :target: https://django-paste.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status

*django-paste* is a simple `Django <https://www.djangoproject.com/>`_
pluggable app of a code pasting and highlighting HTTP REST API, written using
`Django REST framework <https://www.django-rest-framework.org/>`_. It supports
the `CRUD <https://en.wikipedia.org/wiki/Create,_read,_update_and_delete>`_
operations on source code snippets and uses any existing user authentication
system. Syntax highlighting is powered by `Pygments
<https://pygments.org/>`_.

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

The following Python versions are supported:

    - CPython: 3.6, 3.7, 3.8, 3.9
    - PyPy: 3.6

Django:

    - 2.0, 2.1, 2.2
    - 3.0, 3.1

- Install via `pip
  <https://packaging.python.org/tutorials/installing-packages/>`_:

  .. code-block:: bash

     $ pip install django-paste

- Add it to your ``INSTALLED_APPS``:

  .. code-block:: python

     INSTALLED_APPS = [
         # ...
         'paste.apps.PasteConfig',
     ]

- Register the app's URLs under a path of your choice:

  .. code-block:: python

     urlpatterns = [
         # ...
         path('some-path/', include('paste.urls')),
     ]

  where ``'some-path/'`` could be any URL.

- Optionally, configure the app `settings
  <https://django-paste.readthedocs.io/en/latest/settings.html>`_.

- Generate and run the database migrations:

  .. code-block:: bash

     $ python manage.py makemigrations paste
     $ python manage.py migrate

Usage
-----

You can find the description of the app's `endpoints
<https://django-paste.readthedocs.io/en/latest/api.html#endpoints>`_ and
snippet `fields <https://django-paste.readthedocs.io/en/latest/fields.html>`_
in the `documentation <https://django-paste.readthedocs.io/en/latest/>`_. You
can also manage snippets at the Django admin site.

License
-------

Distributed under the `MIT License
<https://github.com/amikrop/django-paste/blob/master/LICENSE>`_.
TOPSIS-Ajat-101803268 0.1
A python package for Multiple Criteria Decision Making (MCDM) using Topsis
License: MIT
Keywords: TOPSIS

# TOPSIS

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.

### Installation

```sh
>> pip install TOPSIS-Ajat-101803268==0.1
```
### How to run in command prompt

```sh
>> from TOPSIS_Ajat_101803268.topsis import topsis
>> topsis("data.csv","1,1,1,2","+,+,-,+","result.csv")
```

### Input File (data.csv)
1) Input file contain three or more columns
2) First column is the object/variable name (e.g. M1, M2, M3, M4â€¦...)
3) From 2nd to last columns contain numeric values only

### Output File (result.csv)
Result file contains all the columns of input file and two additional columns having
TOPSIS SCORE and RANK

License
----

MIT



[//]: # (These are reference links used in the body of this note and get stripped out when the markdown processor does its job. There is no need to format nicely because it shouldn't be seen. Thanks SO - http://stackoverflow.com/questions/4823468/store-comments-in-markdown-syntax)


   [dill]: <https://github.com/joemccann/dillinger>
   [git-repo-url]: <https://github.com/joemccann/dillinger.git>
   [john gruber]: <http://daringfireball.net>
   [df1]: <http://daringfireball.net/projects/markdown/>
   [markdown-it]: <https://github.com/markdown-it/markdown-it>
   [Ace Editor]: <http://ace.ajax.org>
   [node.js]: <http://nodejs.org>
   [Twitter Bootstrap]: <http://twitter.github.com/bootstrap/>
   [jQuery]: <http://jquery.com>
   [@tjholowaychuk]: <http://twitter.com/tjholowaychuk>
   [express]: <http://expressjs.com>
   [AngularJS]: <http://angularjs.org>
   [Gulp]: <http://gulpjs.com>

   [PlDb]: <https://github.com/joemccann/dillinger/tree/master/plugins/dropbox/README.md>
   [PlGh]: <https://github.com/joemccann/dillinger/tree/master/plugins/github/README.md>
   [PlGd]: <https://github.com/joemccann/dillinger/tree/master/plugins/googledrive/README.md>
   [PlOd]: <https://github.com/joemccann/dillinger/tree/master/plugins/onedrive/README.md>
   [PlMe]: <https://github.com/joemccann/dillinger/tree/master/plugins/medium/README.md>
   [PlGa]: <https://github.com/RahulHP/dillinger/blob/master/plugins/googleanalytics/README.md>
dsemu 0.2.1
A wrapper around the datastore emulator instance for use in tests.
License: MIT

# dsemu

## Description

`dsemu` is a simple library to help with testing GCP Datastore code written in
Python. The provided `Emulator` class wraps the [Datastore
emulator](https://cloud.google.com/datastore/docs/tools/datastore-emulator) and
provides basic functionality such as starting/stopping and reseting the
emulator instance from inside the test runner.

## Requirements

You must have the `gcloud` tool
[installed](https://cloud.google.com/datastore/docs/tools/datastore-emulator)
and available in `PATH`.

## Using existing instance of the emulator

If you're running tests that require datastore access frequently it might be
better to keep an instance of the emulator running at all time instead of
letting the wrapper start and stop it for the duration of the test run. If an
instance of the emulator is running and the required environment variables are
correctly set the `Emulator` wrapper will use the running instance instead of
starting a new one and will not tear it down at the end of the test run.

## Example usage with pytest

```python
# conftest.py
import pytest
from dsemu import Emulator
from google.cloud import datastore


@pytest.fixture(scope="session")
def emulator():
    with Emulator() as emulator:
        yield emulator


@pytest.fixture(scope="session")
def session_client():
    client = datastore.Client(project="test")
    yield client


@pytest.fixture()
def client(emulator: Emulator, session_client: datastore.Client):
    emulator.reset()
    yield session_client
```

```python
# datastore_test.py
from google.cloud import datastore


def test_datastore_put_and_get(client: datastore.Client):
    kind = "Task"
    name = "sampletask1"
    task_key = client.key(kind, name)
    task = datastore.Entity(key=task_key)
    task["description"] = "Buy milk"
    client.put(task)

    res = client.get(task_key)
    assert res == task
```
Rishabh-Gupta 1.0.0
TOPSIS
License: MIT

# TOPSIS-Rishabh-101803461
TOPSIS is an acronym that stands for "Technique of Order Preference Similarity to the Ideal Solution" and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those. 

## Installation 
'''pip install TOPSIS-Rishabh-101803461==1.0.0'''

## License
Â©  2020 Rishabh
This repository is licensed under the MIT license. 
See LICENSE for details
TOPSIS-Brahaminder-101803725 1.0.0
Implementation of TOPSIS
License: MIT

# About Topsis
TOPSIS stands for Technique for Oder Preference by Similarity to Ideal Solution. It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.


## Installation
```pip install TOPSIS-Brahaminder-101803725```

## How to use it
Open terminal and type following query
```python topsis input_filename.csv "w1,w2,w3,w4" "i1,i2,i3,i4" result.csv```
 w1,w2,w3,w,4 represent weights and i1,i2,i3,i4 represents impacts.w1,w2,w3,w4 can be float/integer form while i1,i2,i3,i4 can be either '+' or '-'
Rank 1 in the result.csv file will denote the best decision

## License

Â© 2020 Brahaminder Garg

This repository is licensed uder the MIT license.
See LICENSE for details.