static-config-class 0.0.2

Configuration manager for medium-sized projects (created for personal use)

Static Config Class

Configuration manager for medium-sized projects. Maintains a configuration inside a static class. Intended for personal use so breaking changes might be introduced at any point.

installation

The package can simply be installed with pip

pip install static-config

usage

To use the configuration manager you have to set it up first by giving it your configuration.

Your configuration has to be provided via a path to a JSON file

{
  "key": "value",
  "bool_key": true,
  "int_key": 1,
  "float_key": 3.14159,
  "list_key": ["one", "two", "three"],
  "dict_key": {
    "key": "value"
  }
}

data/tests/config.json

The setup function then is called like this

from static_config_class import Config
from pathlib import Path

config_json = Path.cwd() / 'data/tests/static_config_class.json'
Config.setup(config_json)

If configuration access is attempted before setup, a SetupFirstError is raised.

reading/writing the configuration

Ater setup you can use the Methods Config.get() and Config.set() to gain read and write access on the configuration.

...
# read the value of configuration key 'key'
some_config_data = Config.get('key')

# write 'value' to configuration key 'key'
Config.set('key', 'value')

using a json schema

Optionally, you can provide a JSON schema to validate the configuration during setup and value changes.

The schema is provided as path to a JSON schema

{
  "$schema": "http://json-schema.org/draft-04/schema#",
  "title": "Test Config",
  "description": "An example static_config_class to test this package",
  "type": "object",

  "properties": {
    "key": {
      "type": "string"
    },
    "bool_key": {
      "type": "boolean"
    },
    "int_key": {
      "type": "integer"
    },
    "float_key": {
      "type": "number"
    },
    "list_key": {
      "type": "array"
    },
    "dict_key": {
      "type": "object"
    }
  },

  "additionalProperties": false,
  "required": ["key", "bool_key"]
}

data/tests/schema.json

Setup with schema is straight forward. The schema is used to initially validate the config during setup and consecutively each time the config is changed. Raises a ConfigValidationError if the config is not conforming to the schema.

from static_config_class import Config
from pathlib import Path

config_json = Path.cwd() / 'data/tests/static_config_class.json'
schema_json = Path.cwd() / 'data/tests/schema.json'
Config.setup(config_json)

Using a schema allows to allow/prohibit the later extensions of known configuration keys and specification of keys that require a value during setup. Furthermore, the type keyword can be used to enforce runtime typechecking.

immutable values

During setup, you can pass a Collection containing all the configuration keys whose values are not allowed to be changed.

from static_config_class import Config
from pathlib import Path

config_json = Path.cwd() / 'data/tests/static_config_class.json'
Config.setup(config_json, immutable=['key', 'bool_key'])

If the Config.set() method is used to update these, an ImmutableError is raised.

persisting the configuration

If you changed your configuration during runtime and want to persist it as json file you can use the Config.write() function.

Config.write('out/updated_configuration.json')

identifying the configuration

If you need to have a short reference of the current configuration state, you can calculate the configurations MD5-hash with the Config.identifier() function.