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    "info": {
        "author": "Sam et Max",
        "author_email": "lesametlemax@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Information Technology",
            "License :: OSI Approved :: zlib/libpng License",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6"
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        "description": "***************************************************************************************\nOne-file utility module filled with helper functions for day to day Python programming\n***************************************************************************************\n\nThis is a subset of batbelt, with only the most used features, packed in a tiny file. This new version is Python 3.6+ only.\n\nSo, while you can `pip install minibelt`, you may just drop it in your project and forget about it.\n\nIt's under zlib licence.\n\n\nGet this value from an iterable/indexable or a default\n=======================================================\n\nget(indexable, \\*indices|keys,  [default])\n------------------------------------------\n\nYou had a get() method on dict, but not on lists or tuple. Now you do ::\n\n    >>> lst = range(10)\n    >>> get(lst, 1, default='whatever_you_want')\n    1\n    >>> get(lst, 100, default='whatever_you_want')\n    'whatever_you_want'\n\nPlus, you can chain look ups :\n\nThis::\n\n    try:\n        res = data['key'][0]['other key'][1]\n    except (KeyError, IndexError):\n        res = \"value\"\n\n\nBecomes::\n\n    get(data, 'key', 0, 'other key', 1, default=\"value\")\n\n\nattrs(object, \\*attributes, [default])\n--------------------------------------\n\nAnd for attributes... ::\n\n    devise = attr(car, 'insurance', 'expiration_date', 'timezone')\n\n\n\niget(iterable, index, [default])\n--------------------------------------\n\nYou can also get values at indices on any iterable, including generators :\n\n        >>> iget(xrange(10), 0)\n        0\n        >>> iget(xrange(10), 5)\n        5\n        >>> iget(xrange(10), 10000, default='wololo')\n        u'wololo'\n\n\n\nIteration tools missing in itertools\n===================================================================================\n\n\nchunks(iterable, size) and window(iterable, size)\n----------------------------------------------------\n\nIteration by chunk or with a sliding window::\n\n    >>> l = range(10)\n    >>> for chunk in chunks(l, 3):\n    ...     print list(chunk)\n    ...\n    [0, 1, 2]\n    [3, 4, 5]\n    [6, 7, 8]\n    [9]\n    >>> for slide in window(l, 3):\n    ...     print list(slide)\n    ...\n    [0, 1, 2]\n    [1, 2, 3]\n    [2, 3, 4]\n    [3, 4, 5]\n    [4, 5, 6]\n    [5, 6, 7]\n    [6, 7, 8]\n    [7, 8, 9]\n\n\nflatten(deeply_nested_iterable)\n--------------------------------\n\nReturns a generator that lazily yield the items and\ndeals with up to hundred of levels of nesting (~800 on my machine,\nand you can control it with sys.setrecursionlimit) ::\n\n\n    a = []\n    for i in xrange(10):\n        a = [a, i]\n    print(a)\n\n    [[[[[[[[[[[], 0], 1], 2], 3], 4], 5], 6], 7], 8], 9]\n\n    print(list(flatten(a)))\n\n    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n\nIt works with most iterables, even of inifinite or unknown size.\n\n\nSorted Set\n===================================================================================\n\nSlow but useful data structure::\n\n    >>> for x in sset((3, 2, 2, 2, 1, 2)):\n    ...     print x\n    ...\n    3\n    2\n    1\n\n\nDictionaries one liners\n===================================================================================\n\n\ndmerge(dict, dict, [merge_function])\n--------------------------------------\n\nI wish '+'' was overloaded for dicts::\n\n    >>> dmerge({\"a\": 1, \"b\": 2}, {\"b\": 2, \"c\": 3})\n    {'a': 1, 'c': 3, 'b': 2}\n\n\nSometimes you do not want to simply overwrite the values inside the original dict, but merge them in custom fashion::\n\n    >>> def my_merge(v1, v2):\n    ...     if isinstance(v1, dict) and isinstance(v2, dict):\n    ...         return dmerge(v1, v2)\n    ...     return v2\n    >>> dmerge({\"a\": 1, \"b\": {'ok': 5}}, {\"b\": {'ko': 5 }, \"c\": 3}, my_merge)\n    {'a': 1, 'c': 3, 'b': {'ko': 5, 'ok': 5}}\n\n\nsubdict(dict, [include], [exclude])\n-----------------------------------\n\nAnd for lazy people like me ::\n\n    >>> subdict({'a': 1, 'b': 2, 'c': 3}, include=('a', 'b'))\n    {'a': 1, 'b': 2}\n    >>> subdict({'a': 1, 'b': 2, 'c': 3}, exclude=('c',))\n    {'a': 1, 'b': 2}\n\n\nWhich is quite nice when you want a dict of some local variables (like in web framework functions returning responses such as Django, Flask or Bottle) ::\n\n\n    >>> def test():\n    ...     a, b, c, d, e = range(5)\n    ...     return subdict(locals(), exclude=('d',))\n    ...\n    >>> test()\n    {'a': 0, 'c': 2, 'b': 1, 'e': 4}\n\nThis works with any indexable, not just dicts.\n\nString tools\n===================================================================================\n\nnormalize(string)\n----------------------\n\n    >>> normalize(u\"H\u00e9lo Whorde\")\n    'Helo Whorde'\n\n\nslugify(string)\n------------------\n\n    >>> slugify(u\"H\u00e9lo Whorde\")\n    helo-whorde\n\nYou get better slugification if you install the `unidecode` lib, but it's optional. You can specify `separator` if you don't like `-` or call directly `normalize()` (the underlying function) if you wish more control.\n\njson_dumps(struct) and json_loads(string)\n-----------------------------------------\n\nJSON helpers that handle date/time ::\n\n    >>> import datetime\n    >>> json_dumps({'test': datetime.datetime(2000, 1, 1, 1, 1, 1)})\n    '{\"test\": \"2000-01-01 01:01:01.000000\"}'\n    >>> json_dumps({'test': datetime.date(2000, 1, 1)})\n    '{\"test\": \"2000-01-01\"}'\n    >>> json_dumps({'test': datetime.time(1, 1, 1)})\n    '{\"test\": \"01:01:01.000000\"}'\n    >>> json_dumps({'test': datetime.timedelta(1, 1)})\n    '{\"test\": \"timedelta(seconds=\\'86401.0\\')\"}'\n    >>> json_dumps({u'test': datetime.timedelta(1, 1), u'a': [1, 2]})\n    '{\"test\": \"timedelta(seconds=\\'86401.0\\')\", \"a\": [1, 2]}'\n\n    >>> json_loads('{\"test\": \"2000-01-01 01:01:01.000000\"}')\n    {u'test': datetime.datetime(2000, 1, 1, 1, 1, 1)}\n    >>> json_loads('{\"test\": \"2000-01-01\"}')\n    {u'test': datetime.date(2000, 1, 1)}\n    >>> json_loads('{\"test\": \"01:01:01.000000\"}')\n    {u'test': datetime.time(1, 1, 1)}\n    >>> json_loads('{\"test\": \"timedelta(seconds=\\'86401.0\\')\"}')\n    {u'test': datetime.timedelta(1, 1)}\n    >>> json_loads('{\"test\": \"timedelta(seconds=\\'86401.0\\')\", \"a\": [1, 2]}')\n    {u'test': datetime.timedelta(1, 1), u'a': [1, 2]}\n\nwrite(path, \\*args, encoding='utf8', mode='w', errors='replace')\n----------------------------------------------------------------\n\nWrite anything in one row to a file ::\n\n    >>> s = '/tmp/test'\n    >>> write(s, 'test', '\u00e9', 1, ['fdjskl'])\n    >>> print open(s).read()\n    test\n    \u00e9\n    1\n    ['fdjskl']\n\nIt will attempt decoding / encoding and casting automatically each value\nto a string.\n\nThis is an utility function : its slow and doesn't consider edge cases,\nbut allow to do just what you want most of the time in one line.\n\nYou can optionally pass :\n\n- mode : among 'a', 'w', which default to 'w'. Binary mode is forced.\n- encoding : which default to utf8 and will condition decoding AND encoding\n- errors : what to do when en encoding error occurs : 'replace' by default,\n           which replace faulty caracters with '?'\n\nYou can pass string or unicode as \\*args, but if you pass strings,\nmake sure you pass them with the same encoding you wish to write to\nthe file.\n\n\nImport this\n===================================================================================\n\n\n`__import__` is weird. Let's abstract that ::\n\n    YourClass = import_from_path('foo.bar.YourClass')\n    obj = YourClass()\n\n\n\nAdd a any directory to the PYTHON PATH\n===========================================\n\nAccepts shell variables and relative paths :\n\n    add_to_pythonpath(\"~/..\")\n\nYou can (and probably wants) specify a starting point if you pass a relative path. The default starting point is the result is `os.getcwd()` while you probably wants the directory containing you script. To to so, pass `__file__`:\n\n    add_to_pythonpath(\"../..\", starting_point=__file__)\n\n`starting_point` can be a file path (basename will be stripped) or a directory name. It will be from there that the relative path will be calculated.\n\nYou can also choose where in the `sys.path` list your path will be added by passing `insertion_index`, which default to after the last existing item.\n\n\n\nTo timestamp\n=============\n\ndatetime.fromtimestamp exists but not the other away around, and it's not likely to change anytime soon (see: http://bugs.python.org/issue2736). In the meantime::\n\n    >>> from datetime import datetime\n    >>> to_timestamp(datetime(2000, 1, 1, 2, 1, 1))\n    946692061\n    >>> datetime.fromtimestamp(946688461) # PYTHON, Y U NO HAZ TO_TIMESTAMP ?\n    datetime.datetime(2000, 1, 1, 2, 1, 1)\n\n\nRemoving duplicates\n====================\n\nskip_duplicates returns a generator that will yield all objects from an iterable, skipping\nduplicates and preserving order ::\n\n    >>> list(skip_duplicates([1, 2, 3, 4, 4, 2, 1, 3 , 4]))\n    [1, 2, 3, 4]\n\nDuplicates are identified using the `key` function to calculate a\nunique fingerprint. This does not use natural equality, but the\nresult use a set() to remove duplicates, so defining __eq__\non your objects would have no effect.\n\nBy default the fingerprint is the object itself,\nwhich ensure the functions works as-is with iterable of primitives\nsuch as int, str or tuple.\n\nThe return value of `key` MUST be hashable, which means for\nnon hashable objects such as dict, set or list, you need to specify\na function that returns a hashable fingerprint ::\n\n\n    >>> list(skip_duplicates(([], [], (), [1, 2], (1, 2)), lambda x: tuple(x)))\n    [[], [1, 2]]\n    >>> list(skip_duplicates(([], [], (), [1, 2], (1, 2)), lambda x: (type(x), tuple(x))))\n    [[], (), [1, 2], (1, 2)]\n\n\n\n",
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