Autopipe

A tool that allow one to create pipeline of automatic data processing.

How to use

To create a pipeline, you must create a Coordinator. To do so, create a file and a class implementing the base Coordinator class. For example, here is the code for a simple coordinator that download images corresponding to a querry from google image.

from typing import List, Union, Callable
from autopipe import Coordinator, Pipe, APData, Output
from autopipe.input import RssInput
from autopipe.pipe import FileData, DownloaderPipe


class DownloadExample(Coordinator):
	def __init__(self, query: str = "raccoon"):
		super().__init__()
		self.query = query

	@classmethod
	def name(cls):
		return "DownloadExample"

	@property
	def pipeline(self) -> List[Union[Pipe, Callable[..., APData]]]:
		return [Output(DownloaderPipe())]

	def get_input(self):
		return RssInput(f"http://www.obsrv.com/General/ImageFeed.aspx?{self.query}",
		                lambda x: FileData(x.title, x["media:content"], True))

For this coordinator to be found by autopipe, you must use one of the three following way

1. Place your coordinator file into the autopipe/coordinators folders, import your coordinator in the autopipe/coordinators/__init__.py file and place your coordinator name in the __all__ array of this file.
2. Run autopipe with the coordinator argument set to the path of your file followed by ':' and the coordinator's class name. For example if your coordinator's file is named coordinator.py, is located in the current directory and your coordinator's name is DownloadExample, your coordinator argument would be ../coordinator.py:DownloadExample
3. Send your coordinator file to the standard input of autopipe and use - as your coordinator name. *SOON

To run this, you have two ways

1. Use the autopipe file in the bin folder like so: ./autopipe <coordinator> [coordinator_parameters]
2. Use the module syntaxe like so: python -m autopipe <coordinator> [coordinator_parameters]

Coordinators options

A pipeline always start with an Input. You specify the instance of thte input manager you will use in the get_input() method.

An input will return one or multiples data that will be send to your pipeline one by one.

The pipeline

Each item is send to the Pipe you specify in the pipeline property of your coordinator. In this property, you can place instances of pipes or functions that take a single APData as parameter and return an APData or a Pipe. A pipeline is finished for an item when an Output pipe is reached. That can be by using one of the premade output or by wrapping a Pipe or an APData with an Output() call.

Interceptors

You can add interceptors to your coordinator. An interceptor is a function that will be called between steps of your pipeline if the specified condition matches. This allow you to handle invalid cases of your data or specific cases that don't need a specific step in the pipeline. You can specify an interceptor using the @autopipe.interceptor(lambda data: condition) decorator.

The default handler

A default_handler method can be specified in your coordinator. This special method will be called once the whole pipeline has been consumed but no Output has been returned. You can also use this method instead of the pipeline property by removing the property from your coordinator (or returning an empty list).