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        "author": "Tamas Gal",
        "author_email": "tgal@km3net.de",
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        "description": "thepipe\n=======\n\n.. image:: https://readthedocs.org/projects/thepipe/badge/?version=latest\n    :target: https://thepipe.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://api.codacy.com/project/badge/Grade/20a35727ae364e08845b60bdeb4b233a\n    :alt: Codacy Badge\n    :target: https://www.codacy.com/app/tamasgal/thepipe?utm_source=github.com&utm_medium=referral&utm_content=tamasgal/thepipe&utm_campaign=Badge_Grade\n\n.. image:: https://travis-ci.org/tamasgal/thepipe.svg?branch=master\n    :alt: Travis-CI Build Status\n    :target: https://travis-ci.org/tamasgal/thepipe\n\n.. image:: http://codecov.io/github/tamasgal/thepipe/coverage.svg?branch=master\n    :alt: Test-coverage\n    :target: http://codecov.io/github/tamasgal/thepipe?branch=master\n\n.. image:: https://img.shields.io/pypi/v/thepipe.svg?style=flat\n    :alt: PyPI Package latest release\n    :target: https://pypi.python.org/pypi/thepipe\n\nA simplistic, general purpose pipeline framework, which can easily be\nintegrated into existing (analysis) chains and workflows.\n\nInstallation\n------------\n``thepipe`` can be installed via ``pip``::\n\n    pip install thepipe\n\nThe Pipeline\n------------\n\nHere is a basic example how to create a pipeline, add some modules to it, pass\nsome parameters and drain the pipeline.\n\nNote that pipeline modules can either be vanilla (univariate) Python functions\nor Classes which derive from ``thepipe.Module``.\n\n.. code-block:: python\n\n    import thepipe as tp\n\n\n    class AModule(tp.Module):\n        def configure(self):\n            self.cprint(\"Configuring AModule\")\n            self.max_count = self.get(\"max_count\", default=23)\n            self.index = 0\n\n        def process(self, blob):\n            self.cprint(\"This is cycle #%d\" % self.index)\n            blob['index'] = self.index\n            self.index += 1\n\n            if self.index > self.max_count:\n                self.log.critical(\"That's enough...\")\n                raise StopIteration\n            return blob\n\n        def finish(self):\n            self.cprint(\"I'm done!\")\n\n\n    def a_function_based_module(blob):\n        print(\"Here is the blob:\")\n        print(blob)\n        return blob\n\n\n    pipe = tp.Pipeline()\n    pipe.attach(AModule, max_count=5)  # pass any parameters to the module\n    pipe.attach(a_function_based_module)\n    pipe.drain()  # without arguments it will drain until a StopIteration is raised\n\nThis will produce the following output:\n\n.. code-block:: shell\n\n    ++ AModule: Configuring AModule\n    Pipeline and module initialisation took 0.000s (CPU 0.000s).\n    ++ AModule: This is cycle #0\n    Here is the blob:\n    Blob (1 entries):\n     'index' => 0\n    ++ AModule: This is cycle #1\n    Here is the blob:\n    Blob (1 entries):\n     'index' => 1\n    ++ AModule: This is cycle #2\n    Here is the blob:\n    Blob (1 entries):\n     'index' => 2\n    ++ AModule: This is cycle #3\n    Here is the blob:\n    Blob (1 entries):\n     'index' => 3\n    ++ AModule: This is cycle #4\n    Here is the blob:\n    Blob (1 entries):\n     'index' => 4\n    ++ AModule: This is cycle #5\n    CRITICAL ++ AModule: That's enough...\n    ++ AModule: I'm done!\n    ============================================================\n    5 cycles drained in 0.000793s (CPU 0.000793s). Memory peak: 20.56 MB\n      wall  mean: 0.000063s  medi: 0.000057s  min: 0.000045s  max: 0.000106s  std: 0.000022s\n      CPU   mean: 0.000065s  medi: 0.000057s  min: 0.000046s  max: 0.000112s  std: 0.000024s",
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