semvec/pypi-packages huggingface dataset:
sqs-py 1.0.0
AWS SQS utility package for producing and consuming messages
License: MIT
Keywords: aws sqs messages producer/consumer

# sqspy

Forked from [the sqspy][1] package and removed sqs list_queues.

## Install

```Shell
pip install sqs_py
```

## Usage

```Python
from sqs_py import Consumer


class MyWorker(Consumer):
    def handle_message(self, body, attributes, message_attributes):
        print(body)


listener = MyWorker('Q1', error_queue='EQ1')
listener.listen()
```

More documentation coming soon.

## Why

- SQS list_queues conflicts with the permission in my AWS environment. 
- Messages are deleted from SQS after processing.

Support python 3.6+


[1]: https://pypi.org/project/sqspy/ "sqspy on PyPI"
disco-py 0.0.14rc1
A Python library for Discord
License: MIT

# disco

[![PyPI](https://img.shields.io/pypi/l/disco-py.svg)](https://pypi.python.org/pypi/disco-py/)
[![PyPI](https://img.shields.io/pypi/v/disco-py.svg)](https://pypi.python.org/pypi/disco-py/)
[![TravisCI](https://img.shields.io/travis/b1naryth1ef/disco.svg)](https://travis-ci.org/b1naryth1ef/disco/)

Disco is an extensive and extendable Python 2.x/3.x library for the [Discord API](https://discord.com/developers/docs/intro). Disco boasts the following major features:

- Expressive, functional interface that gets out of the way
- Built for high-performance and efficiency
- Configurable and modular, take the bits you need
- Full support for Python 2.x/3.x
- Evented networking and IO using Gevent

## Installation

Disco was built to run both as a generic-use library, and a standalone bot toolkit. Installing disco is as easy as running `pip install disco-py`, however some extra packages are recommended for power-users, namely:

|Name|Reason|
|----|------|
|requests[security]|adds packages for a proper SSL implementation|
|ujson|faster json parser, improves performance|
|erlpack (2.x), earl-etf (3.x)|ETF parser run with the --encoder=etf flag|
|gipc|Gevent IPC, required for autosharding|

## Examples

Simple bot using the builtin bot authoring tools:

```python
from disco.bot import Bot, Plugin


class SimplePlugin(Plugin):
    # Plugins provide an easy interface for listening to Discord events
    @Plugin.listen('ChannelCreate')
    def on_channel_create(self, event):
        event.channel.send_message('Woah, a new channel huh!')

    # They also provide an easy-to-use command component
    @Plugin.command('ping')
    def on_ping_command(self, event):
        event.msg.reply('Pong!')

    # Which includes command argument parsing
    @Plugin.command('echo', '<content:str...>')
    def on_echo_command(self, event, content):
        event.msg.reply(content)
```

Using the default bot configuration, we can now run this script like so:

`python -m disco.cli --token="MY_DISCORD_TOKEN" --run-bot --plugin simpleplugin`

And commands can be triggered by mentioning the bot (configured by the BotConfig.command\_require\_mention flag):

![](http://i.imgur.com/Vw6T8bi.png)
iko 0.4.0
Iko is an asynchronous micro-framework for converting data into different structures.
License: MIT
Requires Python: >=3.6

iko
==========

.. image:: https://img.shields.io/pypi/v/iko.svg
    :target: https://pypi.org/project/iko/

.. image:: https://img.shields.io/pypi/pyversions/iko.svg
    :target: https://pypi.org/project/iko/

.. image:: https://codecov.io/gh/MyGodIsHe/iko/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/MyGodIsHe/iko
    :alt: Code coverage Status

.. image:: https://travis-ci.org/MyGodIsHe/iko.svg?branch=master
    :target: https://travis-ci.org/MyGodIsHe/iko
    
.. image:: https://img.shields.io/pypi/dm/iko.svg
    :target: https://pypi.python.org/pypi/iko


Iko is an asynchronous micro-framework for
converting data into different structures.

Inspired marshmallow_.

.. _marshmallow: https://github.com/marshmallow-code/marshmallow

Typical usage
=============

The main use-case of this framework is web serviceвЂ™s request and response data marshaling.

Example:

.. code-block:: python

    @swagger.schema('UserRequest', 'UserResponse')
    async def handler(request):
        body = await request.json()
        data = await UserSchema.load(body)
        await mongodb.users.insert_one(data)
        data = await mongodb.users.find_one({'_id': data['id']})
        return Response(await UserSchema.dump(data))
chatter-reader 0.0.1
Read the latest Chatter posts.
License: MIT

# Chatter Feed Reader

The Chatter Feed Reader is a basic [web feed](https://en.wikipedia.org/wiki/Web_feed) reader that can download the latest Chatter posts from the Chatter feed.

## Installation

You can install the Chatter Feed Reader from [PyPI](https://pypi.org/project/chatter-reader/):

    pip install chatter-reader

The reader is supported on Python 2.7, as well as Python 3.4 and above.
protoflow 0.3.4
Highly extensible, GPU-supported Learning Vector Quantization (LVQ) toolbox built using Tensorflow 2.x and its Keras API.
License: MIT

# ProtoFlow: Prototype Learning in TensorFlow

![ProtoFlow Logo](https://protoflow.readthedocs.io/en/latest/_static/horizontal-lockup.png)

[![Build Status](https://travis-ci.org/si-cim/protoflow.svg?branch=master)](https://travis-ci.org/si-cim/protoflow)
![tests](https://github.com/si-cim/protoflow/workflows/tests/badge.svg)
[![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/si-cim/protoflow?color=yellow&label=version)](https://github.com/si-cim/protoflow/releases)
[![docs](https://readthedocs.org/projects/protoflow/badge/?version=latest)](https://protoflow.readthedocs.io/en/latest/?badge=latest)
[![PyPI](https://img.shields.io/pypi/v/protoflow)](https://pypi.org/project/protoflow/)
[![codecov](https://codecov.io/gh/si-cim/protoflow/branch/master/graph/badge.svg)](https://codecov.io/gh/si-cim/protoflow)
![PyPI - Downloads](https://img.shields.io/pypi/dm/protoflow?color=blue)
[![GitHub license](https://img.shields.io/github/license/si-cim/protoflow)](https://github.com/si-cim/protoflow/blob/master/LICENSE)

*PyTorch users, please see:* [ProtoTorch](https://github.com/si-cim/prototorch)

## Description

This is a Python toolbox brewed at the Mittweida University of Applied Sciences
in Germany for bleeding-edge research in Prototype-based Machine Learning
methods and other interpretable models. The focus of ProtoFlow is ease-of-use,
extensibility and speed.

## Installation

ProtoFlow can be easily installed using `pip`. To install the latest version, run
```
pip install -U protoflow
```
To also install the extras, run
```bash
pip install -U protoflow[docs,others,tests]
```
To install the bleeding-edge features and improvements before they are release on PyPI, run
```bash
git clone https://github.com/si-cim/protoflow.git
git checkout dev
cd protoflow
pip install -e .[docs,others,tests]
```

For gpu support, run
```bash
pip install -U protoflow[gpu]
```

## Documentation

The documentation is available at <https://www.protoflow.ml/en/latest/>. Should
that link not work try <https://protoflow.readthedocs.io/en/latest/>.

## Usage

### For researchers
ProtoFlow is modular. It is very easy to use the modular pieces provided by
ProtoFlow, like the layers, losses, callbacks and metrics to build your own
prototype-based(instance-based) models. These pieces blend-in seamlessly with
Keras allowing you to mix and match the modules from ProtoFlow with other Keras
modules.

### For engineers
ProtoFlow comes prepackaged with many popular Learning Vector Quantization
(LVQ)-like algorithms in a convenient API. If you would simply like to be able
to use those algorithms to train large ML models on a GPU, ProtoFlow lets you do
this without requiring a black-belt in high-performance Tensor computation.

## Bibtex

If you would like to cite the package, please use this:
```bibtex
@misc{Ravichandran2020a,
  author = {Ravichandran, J},
  title = {ProtoFlow},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/si-cim/protoflow}}
}