Metadata-Version: 2.1
Name: extradict
Version: 0.5.0
Summary: Enhanced, maybe useful, data containers and utilities: A versioned dictionary, a bidirectional dictionary, a binary tree backed dictionary, a Grouper iterator mapper similar to itertools.tee, and an easy extractor from dictionary key/values to variables
Home-page: https://github.com/jsbueno/extradict
Author: João S. O. Bueno
Author-email: gwidion@gmail.com
License: LGPLv3+
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 delivering 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 dictionary 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:
        
        ```python
        
        >>> 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:
        ```python
        >>> 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.
        
        
        
        ```python
        >>> 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:
        ```python
        >>> 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
        every time:
        ```python
        >>> 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:
        
        ```python
        >>> 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 explicitly 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"
        
        ```python
        >>> 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 than stdlib version. The "fastnamedtuple"
        is faster in all respects, although it holds the same API for instantiating as tuples, and
        performs no length 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
        length, 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-tuples (or an OrderedDict) as the second parameter, or
        send in kwargs with the default parameters, after the first.
        (This takes advantage 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.
        ```python
        >>> 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 overwrites 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:
        ```python
        >>> 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.
        
        ## Grouper
        
        
        Think of it as an itertools.groupby which returns a mapping
        Or as an itertools.tee that splits the stream into filtered
        substreams according to the passed key-callable.
        
        Given an iterable and a key callable,
        each element in the iterable is run through the key callable and
        made available in an iterator under a bucket using the resulting key-value.
        
        The source iterable need not be ordered (unlike itertools.groupby).
        If no key function  is given, the identity function is used.
        
        The items will be made available under the iterable-values as requested,
        in a lazy way when possible. Note that several different method calls may
        precipatate an eager processing of all items in the source iterator:
        .keys() or len(), for example.
        
        Whenever a new key is found during input consumption, a "Queue" iterator,
        which is a thin wrapper over collections.deque is created under that key
        and can be further iterated to retrieve more elements that map to
        the same key.
        
        In short, this is very similar to `itertools.tee`, but with a filter
        so that each element goes to a mapped bucket.
        
        Once created, the resulting object may obtionally be called. Doing this
        will consume all data in the source iterator at once, and return a
        a plain dictionary with all data fetched into lists.
        
        For example, to divide a sequence of numbers from 0 to 10 in
        5 buckets, all one need to do is: `Grouper(myseq, lambda x: x // 2)`
        
        Or:
        ```python
        >>> from extradict import Grouper
        >>> even_odd = Grouper(range(10), lambda x: "even" if not x % 2 else "odd")
        >>> print(list(even_odd["even"]))
        [0, 2, 4, 6, 8]
        >>> print(list(even_odd["odd"]))
        [1, 3, 5, 7, 9]
        
        ```
        
Keywords: versioned bijective assigner getter unpack transactional container collection dict dictionary normalized binarytree
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
