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        "author": "Austin Bingham",
        "author_email": "austin@sixty-north.com",
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        "description": "|Python version| |Build Status|\n\n=========================================\n kfg: A configuration library for Python\n=========================================\n\n``kfg`` provides a ``Config`` class which contains your program's configuration\ndata. It lets you access, manipulate, and validate this data in a\nstraightforward way. It also provides a means to de/serialize the data.\n\nBasic usage\n===========\n\nSetting and retrieving configuration values\n-------------------------------------------\n\nGetting and setting configuration values is simple:\n\n.. code-block:: python\n\n   from kfg.config import Config\n\n   # construct a Config\n   c = Config()\n\n   # set the ('ui', 'font-color') value to 'blue'\n   c['ui', 'font-color'] = 'blue'\n\n   # retreive the value\n   font_color = c['ui', 'font-color']\n\nUniform exceptions\n------------------\n\n``kfg`` provides a uniform set of exceptions that are used to signal configuration\nproblems. This means that your program can catch these exceptions and know that\nthey only ever indicate configuration problems. For example:\n\n.. code-block:: python\n\n   c = Config()\n\n   # kfg throws ConfigKeyError on missing keys.\n   try:\n       x = c['foo']\n   except ConfigKeyError:\n       print('foo not in the config')\n\nTransforms and validation\n-------------------------\n\n``kfg`` lets you associate a \"transform\" with a key. This transform is a 1-arity\ncallable that will be passed the stored value of the configuration option, and\n``kfg`` will pass the return value of the transform to the user when they access\nthe value. If a transform fails, ``kfg`` will raise a ``ConfigValueError``.\n\nThis lets you do two things. First, you can construct arbitrary values from\nstored configuration information in a centralized way, i.e. mediated by the\nconfiguration. Second, it let's you validate configuration values. For example:\n\n.. code-block:: python\n\n   c = Config()\n   c['processing', 'timeout'] = \"10 seconds\"\n   c.set_transform(('processing', 'timeout'),\n                   float)\n\n   c['processing', 'timeout']  # Raises ConfigValueError because ``float``\n                               # can't parse \"10 seconds\"\n\nThis system is intentionally low-powered and simple. For example, ``kfg`` allows\nyou to *set* values which violate the transform. The goal is not to create\ncompletely water-tight configurations, but rather to create systems which are\neasy to get right and which warn you when you might be using an invalid value.\n\n\nRationale\n=========\n\nThere's no real rocket science in ``kfg``, and you can get most of its features\njust by using dictionaries, lists, tuples, etc. There are a few problems with\nusing \"raw\" data structures like that for configuration, though.\n\nFirst, you'll get standard exceptions like ``KeyError`` and ``IndexError`` when you\ntry to access missing values. Since these kinds of errors can come from almost\nanywhere in a system, it's not easy to differentiate between those that come\nfrom configuration problems and the others. By providing specialized\n\"configuration\" errors, you can catch ``kfg`` exceptions and be confident that\nthey point to configuration errors.\n\nSecond, ``kfg`` lets you centralize the basic configuration\nvalidation/transformations. Configuration values may be used in many places in a\nsystem, so it's often helpful to have a single point of validation for them.\n\n\n.. |Python version| image:: https://img.shields.io/badge/Python_version-3.4+-blue.svg\n   :target: https://www.python.org/\n.. |Build Status| image:: https://travis-ci.org/abingham/kfg.png?branch=master\n   :target: https://travis-ci.org/abingham/kfg\n\n\n",
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        "license": "MIT",
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