pd.lib.heapsort

The module is simple lazy sort implementation realised therewith heapq module. Items will be sorted only when accepted and using of computation resource is minimazed.

Two classes are provided by module:

HeapSort

On initialization accepted item list and comparing function;

HeapSortByIndex

On initialization accepted item list and index. List items mapped on index values and can be compared by them. In this case third parameter - revert - are used. Revert can be True or False. If revert is True then array will be sorted in reverse order. Parameter revert is False by default.

HeapSortByIndexSafe

It class liked on HeapSortByIndex above, but use index in extremely safe manner: if item omited from index it mapped on infinity and any item are comparable;

Classes, mentioned above, to provide methods:

chunk(n)

Return first n items from begin sorted list.

pd.lib.utility

The module contents followed useful functions:

name2klass

Return klass by name

klass2name

Return name by klass

pd.lib.topsort

The module contents classes developed to provide different kind of topologic sort. All classes are use as:

TopSort([<Parameters>])(<Items>), где :

Parameters

Sort execution parameters;

Items

Each item is a pair of object reference and object reference list which one depend;

Followed classes are provided by module:

TopSort

Simple topologic sort;

TopSortIndex

Simple topologic sort for any kind of object;

TopSortFuzzy

Fuzzy topologic sort;

TopSortFuzzyIndex

Fuzzy topologic sort for any kind of object;

Fuzzy topologic sort can transform to linear ordered array graph with cyclomatic value more then zero. Best results achieved on litle values: the sort is for tree with a few cycles.

pd.lib.lazy

The module contents Lazy decorator class used to cache return value on methods depend on argumetnts. Be carryfully, method arguments are to be hashable.

Sample of use:

>>> from pd.lib.lazy import Lazy
>>>
>>> class A(object) :
...     @Lazy
...     def mul(self,a,b) :
...         print a,b,"compute"
...         return a*b
...
>>> a=A() a.mul(1,2)
1 2 compute 2
>>> a.mul(2,3) 2 3 compute 6
>>> a.mul(1,2) 2 a.mul(2,3) 6
>>>

Using such version of lazy decorator get processes incredible speed, but more memory will be used.

pd.lib.fuzzyobject

The module provide FuzzyObject class. Inheritance from FuzzyObject to allow forget about typografic errors in class names:

>>> from pd.lib.fuzzyobject import FuzzyObject class A(FuzzyObject) :
...     def commmon_function(self) :
...         print 'common'
...     def other_method(self) :
...         print 'method'
...
>>> A().coon_functi() common A().oth_m() method
>>

This base class is toy, but it's worked. More complicated sample published in pd/lib/fuzzyobject/fuzzyobject_sample.py, some pro and contra of such programming are viewed from.

pd.lib.gcd

The module provide function gcd used to evaluate greatest common divisor:

>>> from pd.lib.gcd import gcd
>>> gcd(10,15)
5