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    "info": {
        "author": "Konstantin Schukraft",
        "author_email": "konstantin@schukraft.org",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: ISC License (ISCL)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "Rohrleitung\n===========\n\nRohrleitung (ger.): _pipeline_\n\nDidn't like the shell-like approach the other pipelining packages took, where you\nwould concatenate specially written generators with the UNIX pipe `|`. While that's\ngreat for the CLI and short shell scripts, I wanted a more programmable way.\n\nWorth mentioning is surely [genpipeline](https://github.com/fkarb/genpipeline) that\nimplements coroutine-based pipelines as presented by [David Beazley's Coroutines\nintro](http://www.dabeaz.com/coroutines/). But this means you have to do everything\nin coroutines, I built Rohrleitung so I could just use classic generator-based\nfilters. Look into both and decide which way is cleaner for your environment.\n\nISC licenced, see the LICENCE file.\n\nExamples:\n---------\n\n    from functools import partial\n    # pip install toolz\n    from toolz.curried import interpose\n\n    from rohrleitung import Pipeline, L\n\n\n    def three_n_plus_one(n):\n        if n % 2:\n            return 3 * n + 1\n        else:\n            return n / 2\n\n\n    @L\n    def collatz_length(n, l=0):\n        if int(n) < 1:\n            raise ValueError('Nope')\n        if n == 1:\n            return l\n        else:\n            # L changes call signatures of decorated function, so in recursive ones\n            # you have to adapt.\n            return collatz_length.__wrapped__(three_n_plus_one(n), l + 1)\n\n    # building pipelines in a programmatic way, without immediately executing them\n    # standard list manipulation can change pipeline on the fly\n    pipeline = [\n        partial(filter, lambda x: x % 2),  # Standard python's filter function\n        lambda y: (2 * x for x in y),      # Like using L, but manually\n        L(lambda x: x ** 3),               # Using L helper function\n        collatz_length,\n    ]\n    pipeline.append(interpose('a'))\n    print(list(Pipeline(pipeline, range(10))))\n\n    a = Pipeline([L(lambda x: 2 * x), L(lambda x: x + 1), L(lambda x: x ** 2)])\n    for i, k in enumerate(a(range(3))):\n        print(\"(2x+1)^2, x={}: {}\".format(i, k))\n\n    # With toolz' curried filter function we don't need partial\n    from toolz.curried import filter\n    newpipeline = [\n        collatz_length,\n        filter(lambda x: not(x % 2)),\n        L(lambda x: bin(x)),\n        L(lambda x: x.count('1'))\n    ]\n    p1 = Pipeline(newpipeline)\n    for i in p1(range(1, 10)):\n        print(i)\n\n    # Reuse same pipeline\n    for i in p1(range(1, 3)):\n        print(i)\n\n    # Or modify it, then use again\n    p1.pipeline.insert(2, L(lambda x: x + 1))\n    for i in p1(range(1, 10)):\n        print(i)\n\n    p2 = Pipeline(pipeline)\n    p3 = 2 * (p2 + [L(lambda x: int(str(x), 16))]) + p1\n    # Alternativly 2 * p2 | p1 if you prefer shell syntax\n    for i in p3(range(1, 10)):\n        print(i)\n\n    # Slicing and cutting pipelines on the fly\n    for i in p1[1:4:2](range(10)):\n        print(i)",
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        "keywords": "rohrleitung pipeline pipelines generators iterators",
        "license": "ISC",
        "maintainer": "",
        "maintainer_email": "",
        "name": "rohrleitung",
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