Basic Dictionary Wrapper
Project description
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 ofcollections.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.
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