A configuration library for Python
kfg: A configuration library for Python
kfg provides a Config class which contains your program’s configuration data. It lets you access, manipulate, and validate this data in a straightforward way. It also provides a means to de/serialize the data.
Setting and retrieving configuration values
Getting and setting configuration values is simple:
from kfg.config import Config # construct a Config c = Config() # set the ('ui', 'font-color') value to 'blue' c['ui', 'font-color'] = 'blue' # retreive the value font_color = c['ui', 'font-color']
kfg provides a uniform set of exceptions that are used to signal configuration problems. This means that your program can catch these exceptions and know that they only ever indicate configuration problems. For example:
c = Config() # kfg throws ConfigKeyError on missing keys. try: x = c['foo'] except ConfigKeyError: print('foo not in the config')
Transforms and validation
kfg lets you associate a “transform” with a key. This transform is a 1-arity callable that will be passed the stored value of the configuration option, and kfg will pass the return value of the transform to the user when they access the value. If a transform fails, kfg will raise a ConfigValueError.
This lets you do two things. First, you can construct arbitrary values from stored configuration information in a centralized way, i.e. mediated by the configuration. Second, it let’s you validate configuration values. For example:
c = Config() c['processing', 'timeout'] = "10 seconds" c.set_transform(('processing', 'timeout'), float) c['processing', 'timeout'] # Raises ConfigValueError because ``float`` # can't parse "10 seconds"
This system is intentionally low-powered and simple. For example, kfg allows you to set values which violate the transform. The goal is not to create completely water-tight configurations, but rather to create systems which are easy to get right and which warn you when you might be using an invalid value.
There’s no real rocket science in kfg, and you can get most of its features just by using dictionaries, lists, tuples, etc. There are a few problems with using “raw” data structures like that for configuration, though.
First, you’ll get standard exceptions like KeyError and IndexError when you try to access missing values. Since these kinds of errors can come from almost anywhere in a system, it’s not easy to differentiate between those that come from configuration problems and the others. By providing specialized “configuration” errors, you can catch kfg exceptions and be confident that they point to configuration errors.
Second, kfg lets you centralize the basic configuration validation/transformations. Configuration values may be used in many places in a system, so it’s often helpful to have a single point of validation for them.
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