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A lightweight, general purpose pipeline framework.

Project description

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A simplistic, general purpose pipeline framework, which can easily be integrated into existing (analysis) chains and workflows.


thepipe can be installed via pip:

pip install thepipe

The Pipeline

Here is a basic example how to create a pipeline, add some modules to it, pass some parameters and drain the pipeline.

Note that pipeline modules can either be vanilla (univariate) Python functions or Classes which derive from thepipe.Module.

import thepipe as tp

class AModule(tp.Module):
    def configure(self):
        self.cprint("Configuring AModule")
        self.max_count = self.get("max_count", default=23)
        self.index = 0

    def process(self, blob):
        self.cprint("This is cycle #%d" % self.index)
        blob['index'] = self.index
        self.index += 1

        if self.index > self.max_count:
            self.log.critical("That's enough...")
            raise StopIteration
        return blob

    def finish(self):
        self.cprint("I'm done!")

def a_function_based_module(blob):
    print("Here is the blob:")
    return blob

pipe = tp.Pipeline()
pipe.attach(AModule, max_count=5)  # pass any parameters to the module
pipe.drain()  # without arguments it will drain until a StopIteration is raised

This will produce the following output:

++ AModule: Configuring AModule
Pipeline and module initialisation took 0.000s (CPU 0.000s).
++ AModule: This is cycle #0
Here is the blob:
Blob (1 entries):
 'index' => 0
++ AModule: This is cycle #1
Here is the blob:
Blob (1 entries):
 'index' => 1
++ AModule: This is cycle #2
Here is the blob:
Blob (1 entries):
 'index' => 2
++ AModule: This is cycle #3
Here is the blob:
Blob (1 entries):
 'index' => 3
++ AModule: This is cycle #4
Here is the blob:
Blob (1 entries):
 'index' => 4
++ AModule: This is cycle #5
CRITICAL ++ AModule: That's enough...
++ AModule: I'm done!
5 cycles drained in 0.000793s (CPU 0.000793s). Memory peak: 20.56 MB
  wall  mean: 0.000063s  medi: 0.000057s  min: 0.000045s  max: 0.000106s  std: 0.000022s
  CPU   mean: 0.000065s  medi: 0.000057s  min: 0.000046s  max: 0.000112s  std: 0.000024s

Project details

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