semvec/pypi-packages huggingface dataset:
strawberrypy 0.0.5
Graphical animation module.

# StrawberryPy
### Graphical animation module.

## How To Use
### Basic elements
* Every animation needs an scene. You can declare a scene variable like this: `scene = strawberrypy.Scene2D((resX, resY), fps)`
* Next, append layers to the engine: `scene.AddLayer(layer)` The layers will be rendered in order, with the first layer on the bottom.
* Layers can be declared like this: `layer = strawberrypy.Layer()`
* Layers contain shapes, added like this: `layer.Add(strawberrypy.shapes.(some shape))`

### Avaliable shapes
* Polygon: `strawberrypy.shapes.Polygon(((vert1X, vert1Y), (vert2X, vert2Y)), (R, G, B), borderWidth)`
* Circle: `strawberrypy.shapes.Circle((centerX, centerY), radius, (R, G, B), borderWidth)`
mimesniff 1.1.1
Pure python mimesniff implementation of https://mimesniff.spec.whatwg.org
Requires Python: >=3.4

mimesniff
=========
.. image:: https://travis-ci.org/corona10/mimesniff.svg?branch=master
   :target: https://travis-ci.org/corona10/mimesniff

.. image:: https://img.shields.io/pypi/v/mimesniff.svg?style=flat
   :target: https://pypi.python.org/pypi/mimesniff

Pure python mimesniff implementation of
`https://mimesniff.spec.whatwg.org`_

API interface is similar with `API of python standard library`_

Install
-------

.. code:: bash

   pip install mimesniff

Usage
-----

.. code:: python

   import mimesniff

   res = mimesniff.what('sample.mp3')
   print(res)
   # audio/mpeg
   with open('sample.mp3', 'rb') as fin:
       res = mimesniff.what(fin)
       print(res)
       # audio/mpeg

   with open('sample.mp3', 'rb') as fin:
       header = fin.read(512)
       res = mimesniff.what(header)
       print(res)
       # audio/mpeg

.. _`https://mimesniff.spec.whatwg.org`: https://mimesniff.spec.whatwg.org
.. _API of python standard library: https://docs.python.org/3/library/sndhdr.html
nodreader-pouramini 0.0.38
Nodreader, a text-based article reader and manager
Requires Python: >=3.6

# NODReader

## What is it?
I always had struggles reading scientific papers. I need to focus on every sentnce to grasp its meaning and purpose. Moreover, I need an enviornment 

Nodreader provedes

  - Tagging articles
  - Adding comments to every sentence
  - Adding Nod to every sentence
  - Browse articles based on the nod and comment
ecope 0.1.3
Ecommerce Perú Scrap CLI is a project open source that extract products data by category and export to csv, json and other structure format files
Requires Python: >=3.7
Keywords: scraping,peru,ecommerce,csv,json

[![PyPI version](https://badge.fury.io/py/ecope.svg)](https://badge.fury.io/py/ecope)
# Ecommerce Perú Scrap CLI
> I'm already tired of the GUIs

Extract a list of products from some e-commerce websites more popular of Perú

## Features
- Scraping ecommerces by product category (Only some categories)
- Products export data in csv or json 

## Production 

Before you need Python 3.7 or more, then install ecope with pip

`pip install ecope`

Then you can usage this command `ecope` CLI :) 

Example 1: Export laptops in CSV format 

`ecope -c laptop -f csv`

Example 2: Export celular in JSON format with filename export.json

`ecope -c celular -f json -o export`

For more info

`ecope -h` 


## Development 

### Local installing 

First go to `application` folder and install (Python3.7 o later) with virtualenv or pipenv

`virtualenv env -p python3`

Then install dependencies:

` pip install -r requirements.txt `

For data scraping use CLI more info:

`python cli.py -h` 

## Todos
* Add more ecommerces websites
* paginate support 
* Support docker
* Tests


## Collaborate

How to collaborate?

* Let me issues or 
* Add more items in `application/ecommerces.json` (You can with PR direct)



License
----
MIT
jacky-tools 0.0.1
A small example package
Requires Python: >=3.6

# Problem Statement 

Data scientists code up a lot of pipelines that can often take a long time due to long pre-processing sequences or massive
amounts of time required. And if errors occur, then the pipeline needs to be re-run. This means we usually want to be notified
as early as possible if errors occurred.

# So what is pipelinenotifier?

Pipeline notifier will send messages to your Slack/Discord/Keybase channel once you set up a webhook. Support can be added for 
more channels if desired. Feel free to ping me at jacky.wong@vctr.ai. It will send error messages so you can tell what is wrong
the moment it happens.

# Data Science Bots

As data scientists, you are running a lot of code over a large amount of time. This often means that that you want to be notified about things via mobile while you have applications running in the background. 

I ended up re-writing this code across a number of organisations.

```
from pipelinenotifier import KeyBaseNotifier

with KeyBaseBot(, "<webhook_url>") as bot:
    # Insert training loop/reader here.
    score = 0.5
    bot.send_message(f"Scored {score}.")

```

If there is an error, it will send the error message to your channel.