semvec/pypi-packages huggingface dataset:
guzi 0.0.4
A light library to use Guzi in python application
Requires Python: >=3.6

# python-guzi
A library to use Guzi in Python applications

## Run tests

```python
python -m unittest
```

## Usage

Class User contains every Guzi relative information a user would need :
    - guzi wallet
    - guza waller
    - total accumulated
    - balance

For now, a Guzi (and a Guza) is just a string, an identifier defining what it is. So it has no dedicated class. Maybe it will, but for now, nope.

Example of use :
```python
# Create users
user1 = User("unique_id1", birthdate=date(1989, 11, 28))
user2 = User("unique_id2", birthdate=date(1998, 9, 5))

# create their Guzis (it's like having a new day)
for i in range(10):
    user1.create_daily_guzis(date(2020, 4, i))

# Then make them spend between each other
user1.spend_to(user2, 3)
user1.spend_to(user2, 2)
```
monobox 0.5.22
A uniform flexible environment for coding, testing, and deploying using Docker
Keywords: monobox docker development

## monobox
[![Build Status](https://travis-ci.com/InnovativeInventor/monobox.svg?branch=master)](https://travis-ci.com/InnovativeInventor/monobox)

A uniform flexible environment for coding, testing, and deploying using Docker

## Suggested Workflow
- Create your Monofile and Dockerfile. Your Monofile will include everything you need when developing, and your Dockerfile will include everything else
- When deploying, simply take your Dockerfile and use it in production!

For guidance, look at this example monobox project: https://github.com/InnovativeInventor/monobox-example.

## Recommendations
- Use the monobox shortcuts by adding `monobox package` to your Monofile. You can explore the list of boxes at https://boxes.homelabs.space/
- Add your `.gitconfig` file to your developing folder so it gets copied into the Dockerfile
- Add `.monobox` to your `.gitignore`
- If you want to use your own boxes, just create a folder called boxes and follow the same structure in https://github.com/InnovativeInventor/boxes

## Options
```
Usage: monobox [OPTIONS] COMMAND [ARGS]...

  A uniform flexible environment for coding, testing, and deploying using
  Docker.

Options:
  --help  Show this message and exit.

Commands:
  bash     Runs bash when starting up
  default  Starts the container using your defaults
  deploy   Deploys your application using your Dockerfile
  python   Runs the python interperter instead of bash
  sh       Runs sh when starting up

```

## Example Usage
Setup something with bash:
```
monobox bash
```

Run something with bash:
```
monobox bash test.sh
```

Setup something with git and curl (planned):
```
monobox git curl
```

Run a command (git status):
'''
monobox cmd git status
'''

## Troubleshooting
If you see `launchpadlib 1.10.6 requires testresources, which is not installed.`, then run `sudo apt install python-testresources` and restart the terminal.
twitter-bot-tracking 0.0.10
This library is to ease the operation of creating twitter bots and tracking it's ativities
Requires Python: >=3.6

# twitter_bot_tracking

This library is to ease the operation of creating twitter bots and tracking it's ativities

In order to install, open the command prompt and type:
```
pip install twitter-bot-tracking
```

In order to work with s3 mode you have to use aws_cli library for the configurations, [see here](https://pypi.org/project/awscli/) for more details
routedsl 0.0.1
Route DSL
Keywords: web

.. _badges:

.. image:: https://travis-ci.org/avanov/routedsl.svg?branch=develop
    :target: https://travis-ci.org/avanov/routedsl

.. image:: https://circleci.com/gh/avanov/routedsl/tree/develop.svg?style=svg
    :target: https://circleci.com/gh/avanov/routedsl/tree/develop

.. image:: https://coveralls.io/repos/github/avanov/routedsl/badge.svg?branch=develop
    :target: https://coveralls.io/github/avanov/routedsl?branch=develop

.. image:: https://requires.io/github/avanov/routedsl/requirements.svg?branch=develop
    :target: https://requires.io/github/avanov/routedsl/requirements/?branch=develop
    :alt: Requirements Status

.. image:: https://readthedocs.org/projects/routedsl/badge/?version=develop
    :target: http://routedsl.readthedocs.org/en/develop/
    :alt: Documentation Status

.. image:: http://img.shields.io/pypi/v/routedsl.svg
    :target: https://pypi.python.org/pypi/routedsl
    :alt: Latest PyPI Release

Python eDSL for your Web Routes
===============================

.. code-block:: bash

    pip install routedsl


Compose everything in one place

.. code-block:: python

    routes = Routes() (
        GET / 'users' / ('user_id', User, "User identifier")
            | HEADERS | Headers
            | RETURNS | JSON [ User ]
            | HANDLER | get_user_info
            | GUARDS  | ( Headers.content_type | IS | 'application/json'
                        )
            | DOCS    | """Get a user profile in JSON format"""
    )(
        POST / 'users' / ('user_id', User)
            | HEADERS | Headers
            | PAYLOAD | JSON [ User ]
            | RETURNS | OK [ JSON [ bool ] ]
            | HANDLER | update_user_info
    )


or define pieces separately, then assemble your routing map:

.. code-block:: python

    DefaultHeaders = HEADERS | Headers
    DefaultHandler = HANDLER | default_handler

    DefaultEndpoint = GET / DefaultHeaders
                          | DefaultHandler

    routes = Routes() | DefaultEndpoint

Use nesting under common prefix

.. code-block::  python

    routes = (routes / 'users' / ('user_id', User)) (
        POST / 'edit'
            | HEADERS | Headers
            | RETURNS | JSON [ User ]
            | HANDLER | update_user_info
    )

Read more in `Official Documentation <https://routedsl.readthedocs.io/en/develop/>`_.
myopenopt 0.0.3
`myopenopt` is a package for myopenopt.
Requires Python: >=3.7.0,<3.8.0

Myopenopt is a wrapper module for openopt. It supports to call openopt using the same functions and classes as in Gurobi, a commercial mixed integer optimization solver.
For more details, see the Gurobi HP http://www.gurobi.com/.
::

    from myopenopt import *
    model = Model("sample", mtype='NLP')
    x1 = model.addVar(vtype="C", name="x1")
    x2 = model.addVar(vtype="C", name="x2")
    x3 = model.addVar(vtype="C", ub=10, name="x3")
    model.update()
    c1 = model.addConstr(x1**2 + 2*x2**2 <= x3**2, name="C1")
    c2 = model.addConstr(x3 <= 30, name="C2")
    c3 = model.addConstr(quicksum([x1, x2, x3]) <= 20, name="C3" )
    model.setObjective(15*x1 + 18*x2 + 30*x3, GRB.MAXIMIZE)
    print(model)
    model.optimize(solver="ralg")
    print("Result =", model.Status)
    for v in model.getVars():
        print(v.VarName, v.X)

Requirements
------------
* Python 3.7 and openopt, FuncDesigner

Features
--------
* nothing

Setup
-----
::

   Add compiler path(ex. C:\Anaconda3\MinGW\bin)
   $ pip install openopt
   $ pip install FuncDesigner
   $ pip install myopenopt

History
-------
0.0.1 (2015-5-4)
~~~~~~~~~~~~~~~~~~
* first release