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Enhanced, maybe useful, data containers and utilities: A versioned dictionary, a bidirectional dictionary, a binary tree backed dictionary and an easy extractor from dictionary key/values to variables

Project description

Extra Dictionary classes and utilities for Python

Some Mapping containers and tools for daily use with Python. This attempts to be a small package with no dependencies, just deliverying its data-types as described bellow enough tested for production-usage.

VersionDict

A Python Mutable Mapping Container (dictionary :-) ) that can "remember" previous values. Use it wherever you would use a dict - at each key change or update, it's version attribute is increased by one.

Special and modified methods:

.get method is modified to receive an optional named version parameter that allows one to retrieve for a key the value it contained at that respective version. NB. When using the version parameter, get will raise a KeyError if the key does not exist for that version and no default value is specified.

.copy(version=None): yields a copy of the current dictonary at that version, with history preserved (if version is not given, the current version is used)

.freeze(version=None) yields a snapshot of the versionDict in the form of a plain dictionary for the specified version

Implementation:

It works by internally keeping a list of (named)tuples with (version, value) for each key.

Example:

>>> from extradict import VersionDict
>>> a = VersionDict(b=0)
>>> a["b"] = 1
>>> a["b"]
1
>>> a.get("b", version=0)
0

For extra examples, check the "tests" directory

OrderedVersionDict

Inherits from VersionDict, but preserves and retrieves key insertion order. Unlike a plain "collections.OrderedDict", however, whenever a key's value is updated, it is moved last on the dictionary order.

Example:

>>> from collections import OrderedDict
>>> a = OrderedDict((("a", 1), ("b", 2), ("c", 3)))
>>> list(a.keys())
>>> ['a', 'b', 'c']
>>> a["a"] = 3
>>> list(a.keys())
>>> ['a', 'b', 'c']

>>> from extradict import OrderedVersionDict
>>> a = OrderedVersionDict((("a", 1), ("b", 2), ("c", 3)))
>>> list(a.keys())
['a', 'b', 'c']
>>> a["a"] = 3
>>> list(a.keys())
['b', 'c', 'a']

MapGetter

A Context manager that allows one to pick variables from inside a dictionary, mapping, or any Python object by using the from <myobject> import key1, key2 statement.

>>> from extradict import MapGetter
>>> a = dict(b="test", c="another test")
>>> with MapGetter(a) as a:
...     from a import b, c
...
>>> print (b, c)
test another test

Or:

>>> from collections import namedtuple
>>> a = namedtuple("a", "c d")
>>> b = a(2,3)
>>> with MapGetter(b):
...     from b import c, d
>>> print(c, d)
2, 3

It works with Python 3.4+ "enum"s - which is great as it allow one to use the enums by their own name, without having to prepend the Enum class everytime:

>>> from enum import Enum

>>> class Colors(tuple, Enum):
...     red = 255, 0, 0
...     green = 0, 255, 0
...     blue = 0, 0, 255
...

>>> with MapGetter(Colors):
...    from Colors import red, green, blue
...

>>> red
<Colors.red: (255, 0, 0)>
>>> red[0]
255

MapGetter can also have a default value or callable which will generate values for each name that one tries to "import" from it:

>>> with MapGetter(default=lambda x: x) as x:
...    from x import foo, bar, baz
...

>>> foo
'foo'
>>> bar
'bar'
>>> baz
'baz'

If the parameter default is not a callable, it is assigned directly to the imported names. If it is a callable, MapGetter will try to call it passing each name as the first and only positional parameter. If that fails with a type error, it calls it without parameters the way collections.defaultdict works.

The syntax from <mydict> import key1 as var1 works as well.

BijectiveDict

This is a bijective dictionary for which each pair key, value added is also added as value, key.

The explictly inserted keys can be retrieved as the "assigned_keys" attribute - and a dictionary copy with all such keys is available at the "BijectiveDict.assigned". Conversely, the generated keys are exposed as "BijectiveDict.generated_keys" and can be seen as a dict at "Bijective.generated"

>>> from extradict import BijectiveDict
>>>
>>> a = BijectiveDict(b = 1, c = 2)
>>> a
BijectiveDict({'b': 1, 2: 'c', 'c': 2, 1: 'b'})
>>> a[2]
'c'
>>> a[2] = "d"
>>> a["d"]
2
>>> a["c"]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/gwidion/projetos/extradict/extradict/reciprocal_dict.py", line 31, in __getitem__
    return self._data[item]
KeyError: 'c'
>>>

namedtuple

Alternate, clean room, implementation of 'namedtuple' as in stdlib's collection.namedtuple . This does not make use of "eval" at runtime - and can be up to 10 times faster to create a namedtuple class than the stdlib version.

Instead, it relies on closures to do its magic.

However, these will be slower to instantiate tahn stdlib version. The "fastnamedtuple" is faster in all respects, although it holds the same API for instantiating as tuples, and performs no lenght checking.

fastnamedtuple

Like namedtuple but the class returned take an iterable for its values rather than positioned or named parameters. No checks are made towards the iterable lenght, which should match the number of attributes It is faster for instantiating as compared with stdlib's namedtuple

defaultnamedtuple

Implementation of named-tuple using default parameters - Either pass a sequence of 2-tupes (or an OrderedDict) as the second parameter, or send in kwargs with the default parameters, after the first. (This takes advantadge of python3.6 + guaranteed ordering of **kwargs for a function see https://docs.python.org/3.6/whatsnew/3.6.html)

The resulting object can accept positional or named parameters to be instantiated, as a normal namedtuple, however, any omitted parameters are used from the original mapping passed to it.

FallbackNormalizedDict

Dictionary meant for text only keys: will normalize keys in a way that capitalization, whitespace and punctuation will be ignored when retrieving items.

A parallel dictionary is maintained with the original keys, so that strings that would clash on normalization can still be used as separated key/value pairs if original punctuation is passed in the key.

Primary use case if for keeping translation strings when the source for the original strings is loose in terms of whitespace/punctuation (for example, in an http snippet)

NormalizedDict

Dictionary meant for text only keys: will normalize keys in a way that capitalization, whitespace and punctuation will be ignored when retrieving items.

Unlike FallbackNormalizedDict this does not keep the original version of the keys.

TreeDict

A Python mapping with an underlying auto-balancing binary tree data structure. As such, it allows seeking ranges of keys - so, that `mytreedict["aa":"bz"] will return a list with all values in the dictionary whose keys are strings starting from "aa" up to those starting with "by".

It also features a .get_closest_keys method that will retrieve the closest existing keys for the required element.

>>> from extradict import TreeDict
>>> a = TreeDict()
>>> a[1] = "one word"
>>> a[3] = "another word"
>>> a[:]
['one word', 'another word']
>>> a.get_closest_keys(2)
(1, 3)

Another feature of these dicts is that as they do not rely on an object hash, any Python object can be used as a key. Of course key objects should be comparable with <=, ==, >=. If they are not, errors will be raised. HOWEVER, there is an extra feature - when creating the TreeDict a named argument key parameter can be passed that works the same as Python's sorted "key" parameter: a callable that will receive the key/value pair as its sole argument and should return a comparable object. The returned object is the one used to keep the Binary Tree organized.

If the output of the given key_func ties, that is it: the new pair simply overrites whatever other key/value had the same key_func output. To avoid that, craft the key_funcs so that they return a tuple with the original key as the second item:

>>> from extradict import TreeDict
>>> b = TreeDict(key=len)
>>> b["red"] = 1
>>> b["blue"] = 2
>>> b
TreeDict('red'=1, 'blue'=2, key_func= <built-in function len>)

>>> b["1234"] = 5
>>> b
TreeDict('red'=1, '1234'=5, key_func= <built-in function len>)

>>> TreeDict(key=lambda k: (len(k), k))
>>> b["red"] = 1
>>> b["blue"] = 2
>>> b["1234"] = 5
>>> b
>>> TreeDict('red'=1, '1234'=5, 'blue'=2, key_func= <function <lambda> at 0x7fbc7f462320>)

PlainNode and AVLNode

To support the TreeDict mapping interface, the standalone PlainNode and AVLNode classes are available at the extradict.binary_tree_dict module - and can be used to create a lower level tree data structure, which can have more capabilities. For one, the "raw" use allows repeated values in the Nodes, all Nodes are root to their own subtrees and know nothing of their parents, and if one wishes, no need to work with "key: value" pairs: if a "pair" argument is not supplied to a Node, it reflects the given Key as its own value.

PlainNode will build non-autobalancing trees, while those built with AVLNode will be self-balancing. Trying to manually mix node types in the same tree, or changing the key_func in different notes, will obviously wreck everything.

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