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        "author_email": "tony.fast@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Framework :: IPython",
            "Framework :: Jupyter",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python",
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        "description": "\n# `composites` compose complex functions\n\n`composites` are untyped functional programming objects in Python _with all the side effects_.  `composites` make it easier to compose/pipeline/chain callables, classes, and other objects into higher-order functions.\n\n                pip install git+https://github.com/tonyfast/composites\n\n# compose functions with `a`, `an`, `the`, or `\u03bb`\n\n\n    from composites import *; assert a is an is the\n\nA basic example, __enumerate__ a __range__ and create a __dict__ionary.\n\n    f = the[range][reversed][enumerate][dict]\n    f(3), f\n---\n\n\n\n\n\n    ({0: 2, 1: 1, 2: 0}, <composites.Function at 0x10e0eba68>)\n\n\n\n\nEach <b><code>[bracket]</code></b> may accept a __callable__ or __iterable__. In either case,\na __callable__ is appended to the composition.  Compositions are immutable and may have\narbitrary complexity.\n\n    g = f.copy()  # copy f from above so it remains unchanged.\n    g[type, len]\n    g[{'foo': a.do(print).len(), 'bar': the.identity()}]\n\n\n\n\n\n    <composites.Function at 0x10e0eba68>\n\n\n\n\nBrackets juxtapose iterable objects.\n\n    the[range, type], the[[range, type]], the[{range, type}], the[{'x': range, 'y': type}]\n\n\n\n\n\n    (<composites.Function at 0x10e0eed68>,\n     <composites.Function at 0x10e12d048>,\n     <composites.Function at 0x10e12d108>,\n     <composites.Function at 0x10e12d1c8>)\n\n\n\n\nEach each composition is immutable.\n\n    assert f[len] is f; f\n\n\n\n\n\n    <composites.Function at 0x10e0eba68>\n\n\n\n\n# compose functions with attributes\n\nEach composition has an extensible attribution system.  Attributes can be accessed in a shallow or verbose way.\n\n    a.range() == a.builtins.range() == a[range]\n\n\n\n\n\n    False\n\n\n\n\n# compose functions with symbols\n\n    assert a /  range == a.map(range)\n    assert a // range == a.filter(range)\n    assert a @  range == a.groupby(range)\n    assert a %  range == a.reduce(range)\n\n\n#### combine item getters, attributes, symbols, and other compositions to express complex ideas.\n\n    f = a['test', 5, {42}] \\\n     / (a**str&[str.upper, str.capitalize]|a**int&a.range().map(\n         a.range(2).len()\n     ).list()|a**object&type) \\\n     * list\n    f()\n\n\n#### use compositions recursively\n\n    f = a[:]\n    f[a**a.gt(5)*range | a**a.le(5)*a.add(1)[f]](4)\n\n\n\n\n\n    False\n\n\n\n\n# Why functional programming with `composites`?\n\n[Functional programming](https://en.wikipedia.org/wiki/Functional_programming) _often_ generates less code, or text, to express operations on complex data structures.  A declarative, functional style of programming approach belies Python's imperative, object-oriented (OO) \nnature. Python provides key [functional programming elements](https://docs.python.org/3/library/functional.html) that are used interchangeably with OO code.  \n\n[`toolz`](https://toolz.readthedocs.io), the nucleus for `composites`, extends Python's functional programming with a set of \nun-typed, lazy, pure, and composable functions.  The functions in `toolz` look familiar \nto [__pandas.DataFrame__](https://tomaugspurger.github.io/method-chaining.html) methods, or [__underscorejs__](http://underscorejs.org/) and [__d3js__](https://d3js.org/) in Javascript.\n\nAn intermediate user of [`toolz`](https://toolz.readthedocs.io) will use \n[`toolz.pipe`](https://toolz.readthedocs.ioen/latest/api.html#toolz.functoolz.pipe),\n[`toolz.juxt`](https://toolz.readthedocs.ioen/latest/api.html#toolz.functoolz.juxt), \nand [`toolz.compose`](https://toolz.readthedocs.ioen/latest/api.html#toolz.functoolz.compose) to create reusable, \nhigher-order functions.  These patterns allow the programmer to express complex concepts \nwith less typing/text over a longer time.  Repetitive patterns should occupy \nless screen space; `composites;` helps compose functions with less text. \n\nA successful implementation of __composites__ should compose __un-typed__, __lazy__, and __serializable__ Python functions that allow\nrecursion.\n\n\n\n# Syntax\n\nA core property of `composites` is that it will not modify Python's abstract syntax tree, rather it expresses \na large portion of Python's magic methods in the [data model](https://docs.python.org/3/reference/datamodel.html).  It considers Python's \n[order of operations](https://docs.python.org/3/reference/expressions.html#operator-precedence) in the api design.  `composites` provides symbolic expressions for common higher-order \nfunction operations like `map`, `filter`, `groupby`, and `reduce`. The attributes can access any of the `sys.modules;` with tab completion.\n\nThe efficiency of computing will continue to improve.  In modern collaborative development environments \nwe must consider the efficiency of the programmer. Programming is a repetitive process requiring physical work from a person. \n__composites__ speed up the creation and reading repetitive and complex tasks.\n\n\n## `composites` structure\n\n![](classes_composites.min.png)\n\n\n# Development\n    if __name__== '__main__':\n        !jupyter nbconvert --to markdown --TemplateExporter.exclude_input=True readme.ipynb\n        !jupyter nbconvert --to markdown --execute composites.ipynb\n        !python -m doctest composites.py\n        !echo complete\n\n\n    [NbConvertApp] Converting notebook readme.ipynb to markdown\n    [NbConvertApp] Writing 5152 bytes to readme.md\n\n\n\n",
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