Dictionary wrapper library

This Python library implements dictionary-like objects (precisely implementing the collections.abc.MutableMapping interface) with specialized properties. At the moment, two classes are defined:

• DictWrapper: is a very simple child of collections.UserDict, a dictionary-like object which is easier to subclass.
• NestedMapping: a structure to easily navigate the leaves of arborescent structures where dictionaries contain subdictionaries.

DictWrapper

There is not much to say about the DictWrapper class: it inherits from collections.UserDict and therefore provides

• an internal dictionary .data
• MutableMapping methods to access this dictionary: __getitem__, __setitem__, __len__, __contains__, __eq__, __ne__, keys, items, values
• extra methods beyond that interface: __copy__ and __copy__
• The __repr__ method returns <classname>(<data.__repr__()>)

NestedMapping

The NestedMapping class deserves some explanation. A high-level description is that it treats nested structures like a tree and exposes the leaf-level mappings like a flat dictionary. Explicitly, consider the following structure:

from dictwrapper.nested import  NestedMapping

tree = NestedMapping({
    "top leaf": "top leaf label",              # depth 0
    "branch": NestedMapping({                  # depth 0
        "lower leaf": "lower leaf label",      # depth 1
        "lower branch": NestedMapping({        # depth 1
            "lowest leaf": "lowest leaf label" # depth 2 
            })    
        }),
    "other branch": NestedMapping({            # depth 0
        "other leaf": "other leaf label"       # depth 1
    })
})

which can be represented as follow

root:               tree___________________
                    /   \                  \
depth 0:    top leaf     branch             other branch         
                        /      \                   |
depth 1:      lower leaf       lower branch      other leaf
                              /
depth 2:           lowest leaf

from a user point of view, the object tree behaves exactly like the following dictionary:

tree = {
    "top leaf": "top leaf label",              
    "lower leaf": "lower leaf label",
    "lowest leaf": "lowest leaf label",
    "other leaf": "other leaf label"
}

values can be accessed, edited or added by subscripting the object with [], iterating over it yields the sequence of its leaf keys, leaf keys can be checked for membership using the in operator, keys, items and values are accessible.

Subscripting on read and write fails when multiple keys match the request. When setting the value assocated to a key, if this key exists at any level, the corresponding value is replaced. If the key is not found anywhere in the structure, it is added as a top-level leaf.

The default creation mode is from an object convertible to a dictionary by calling dict on it. The class creator method has two optional parameters: recursive and check, both defaulting to True. If recursive is True, the creator goes through the dictionary structure and converts any sub-dictionary to a NestedMapping, otherwise they are left as they are. If check is True, the creator verifies the structure after instantiation and looks for any repeated keys at any levels and throws an exception if any are found. Since this structure is intended to hold configurations, YAML importation with pyyaml is also included using NestedMapping.from_yaml(yaml_file_path, loader=yaml.Loader, recursive=True, check=True) and NestedMapping.from_yaml_stream(stream, loader=yaml.Loader, recursive=True, check=True).

Finally, again with the application to configurations, calling the .to_dict method yields a vanilla dictionary that can be passed as function arguments using the ** operator.

Why would one use this?

The reason I wrote this is to define manipulate involved configurations with nested parameters passed to attribute objects. The main working paradigm is to have a standard working setup defined in some config file with the whole hierarchy of parameters, but being able to easily change some details of the hierarchy during experimentation.

For example, let us say a class ObjectA has an attribute of class ObjectB and that they can both be instantiated through ObjectA(paramA1=valueA1, ...., paramAN=valueAN, Bparams={"paramB1": valueB1, ...}) which calls ObjectB(**BParams), we can then define a default configuration as

DefaultABConfig = NestedConfig({
    "paramA1":valueA1,
    ...
    "paramAN": valueAN,
    "Bparams": {
        "paramB1": value1,
        ...    
}
})

And then edit some parameter in objectB by calling DefaultABConfig["paramB12"] = 42. When several layers are involved and the parameters are transparent enough to understand which level they belong to, this makes writing and reading scripts easier.