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Loads, validates, normalizes configuration in yaml.

Project description

Yaml config for Your projects - with validation, interpolation and value normalization!

Official repositories

For forking and other funnies.




You can:

  • create custom fields for custom data
  • define configuration schema inside Python app
  • convert configuration values to Python objects
  • validate configuration
  • use interpolation to fill config values
  • set default values

Loading data

glorpen.config.Config class allows loading data from three sources:

  • path, filepath constructor argument
  • file-like object, fileobj constructor argument
  • dict object passed to glorpen.config.Config.load_data or glorpen.config.Config.finalize.


You can reuse values from config with {{ }} notation, eg:

   path: "/tmp"
   cache_path: "{{ project.path }}/cache"

If normalized value object MyClass(‘/tmp’,’{{project.path}}’) is denormalized to /tmp:{{project.path}}, after resolving configuration it will read MyClass(‘/tmp’,’/tmp’).

Normalization and validation

Each field type has own normalization rules, eg. for fields.LogLevel:

logging: DEBUG

config.get(“logging”) would yield value 10 as is logging.DEBUG.

Additionally it will raise exceptions.ValidationError if invalid level name is given.

Default values

Each field can have default value. If no value is given in config but default one is set, it will be used instead.

Default values adhere to same interpolation and normalization rules - each default value is denormalized and then passed to normalizers. That way complex object can still profit from config interpolation. There should not be any real impact on performance as it is done only once.

Example usage

Your first step should be defining configuration schema:

from glorpen.config import Config
from glorpen.config.fields import Dict, String, Path, LogLevel

project_path = "/tmp/project"

spec = Dict(
   project_path = Path(default=project_path),
   project_cache_path = Path(default="{{ project_path }}/cache"),
   logging = Dict(
       level = LogLevel(default=logging.INFO)
   database = String(),
   sources = Dict(
       some_param = String(),
       some_path = Path(),

Example yaml config:

logging: "DEBUG"
database: "mysql://...."
   some_param: "some param"
   some_path: "/tmp"

Then you can create Config instance:

cfg = Config(filepath=config_path, spec=spec).finalize()

cfg.get("sources.some_param") #=> "some param"
cfg.get("project_path") #=> "/tmp/project"
cfg.get("project_cache_path") #=> "/tmp/project/cache"
cfg.get("logging") #=> 10

Creating custom fields

Custom field class should extend glorpen.config.fields.Field.

Field.make_resolvable method should register normalizer functions which later will be called in registration order. Each value returned by normalizer is passed to next one. After chain end value is returned as config value.

denormalize method should convert field’s normalized object back to string.

If value passed to normalizator is invalid it should raise exceptions.ValidationError.

class MyValue(object):
   def __init__(self, value):
      super(MyValue, self).__init__()
      self.value = value

class MyField(Field):

    def to_my_value(self, value, config):
        return MyValue(value)

    def denormalize(self, value):
        return value.value

    def make_resolvable(self, r):

The last thing is to use prepared custom field in configuration spec.

Project details

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