Metadata-Version: 1.1
Name: dispatchonvalue
Version: 0.9.5
Summary: Provides the ability to dispatch on values using pattern matching on complex, nested data structures containing lists, dictionaries and primitive types
Home-page: https://github.com/minimind/dispatch-on-value-for-python
Author: Ian Macinnes
Author-email: ian.macinnes@gmail.com
License: MIT
Description: ============================
        Dispatch on Value for Python
        ============================
        
        This package provides the ability to dispatch on values (as opposed to
        dispatching on types) by pairing functions with patterns. It uses pattern
        matching to dispatch on complex, nested data structures containing lists,
        dictionaries and primitive types. You can use ``lambda`` to do expression
        matching and utilise wildcard parameters to ensure identical values can be
        matched (see ``any_a``). It can alleviate complicated and difficult to read
        ``if ... elif ... elif ...`` chains and simplify the code.
        
        Value patterns can be registered dynamically, allowing a great flexibility
        in determining which functions are called on which value patterns.
        
        The home page is on github at:
        
        https://github.com/minimind/dispatch-on-value-for-python
        
        Install using pip::
        
            pip install dispatchonvalue
        
        Unit tests can be run from the source directory using::
        
            python -m unittest discover -s test
        
        Any queries and comments are welcome. Send them to:
        
        ian.macinnes@gmail.com
        
        *****
        Guide
        *****
        
        Very quick example
        ==================
        
        First register your dispatch methods, alongside the pattern they should match on::
        
            import dispatchonvalue as dv
        
            dispatch_on_value = dv.DispatchOnValue()
        
            # Register your overloaded functions:
            @dispatch_on_value.add([1, 2, 3])  # [1, 2, 3] is the pattern to match on
            def _(a):
                assert a == [1, 2, 3]
                # Do something
        
            @dispatch_on_value.add([4, 5, 6])  # [4, 5, 6] is the pattern to match on
            def _(a):
                assert a == [4, 5, 6]
                # Do something
        
        Then else where in your code, dispatch to the correct function based on the
        value of the parameter passed::
        
            p = [4, 5, 6]
            dispatch_on_value.dispatch(p)  # Will call second function above
        
        The return value is ``True`` or ``False``, depending upon whether a function
        was matched, dispatched, and called.
        
        Features
        ========
        
        Multiple dispatch on value
        --------------------------
        
        The simplest use is to dispatch on fixed values. Here we dispatch to two
        different functions ``fn_1`` and ``fn_2`` depending upon the value of ``p``::
        
            @dispatch_on_value.add([1, 2, 3])
            def fn_1(a):
                assert a == [1, 2, 3]
                # Do something
        
            @dispatch_on_value.add([4, 5, 6])
            def fn_2(a):
                assert a == [4, 5, 6]
                # Do something
        
            p = [1, 2, 3]
            dispatch_on_value.dispatch(p)  # This will call fn_1 and return True
        
            p = [4, 5, 6]
            dispatch_on_value.dispatch(p)  # This will call fn_2 and return True
        
            p = [1, 2, 6]
            dispatch_on_value.dispatch(p)  # This will not call anything and return False
        
        Data structure patterns can be arbitrary nested
        -----------------------------------------------
        
        The patterns can be as complex and as nested as you like::
        
            @dispatch_on_value.add({'one': 3, 'animals': ['frog', 'mouse', 34]})
        
        Insert Lambda for wide expression of patterns 
        ---------------------------------------------
        
        Use ``lambda``'s as part of the pattern matching::
        
           @dispatch_on_value.add([1, 2, lambda x: 3 < x < 7, 'hello'])
           def _(a):
               # Do something
            
           dispatch_on_value.dispatch([1, 2, 4, 'hello'])  # This will match
           dispatch_on_value.dispatch([1, 2, 2, 'hello'])  # This will not match
        
        Another example::
        
           @dispatch_on_value.add(['a', 2, lambda x: x == 'b' or x == 'c'])
           def _(a):
               # Do something
        
           dispatch_on_value.dispatch(['a', 2, 'c'])  # This will match
           dispatch_on_value.dispatch(['a', 2, 's'])  # This will not match
        
        Wildcard parameters
        -------------------
        
        Use of wildcard tokens ``any_a``, ``any_b``, ... ``any_z`` can ensure values are
        identical. e.g.::
        
            @dispatch_on_value.add([dv.any_a, 'b', 3, [3, 'd', dv.any_a]])
            def _(a):
                # Do something
            
            dispatch_on_value.dispatch(['c', 'b', 3, [3, 'd', 'c']])  # This will match
            dispatch_on_value.dispatch(['f', 'b', 3, [3, 'd', 'f']])  # This will match
            dispatch_on_value.dispatch(['c', 'b', 3, [3, 'd', 'f']])  # This will not match
        
        Match everything in a list with single token
        --------------------------------------------
        
        Use the ``all_same`` token to see if all the items in a list match, e.g.::
        
            @dispatch_on_value.add(['a', dv.all_same(4)])
            def _(a):
                # Do something
        
            # This will match as the nested list contains all fours
            dispatch_on_value.dispatch(['a', [4,4,4,4,4,4,4]])
        
        You can combine them with the ``any_X`` token::
        
           @dispatch_on_value.add(['a', dv.all_same(dv.any_a)])
            def _(a):
                # Do something
        
            # These will match as the nested list contains all the same values
            dispatch_on_value.dispatch(['a', [4,4,4,4,4,4,4]])
            dispatch_on_value.dispatch(['a', [5,5,5]])
            
            # This won't match
            dispatch_on_value.dispatch(['a', [1,2,3]])
        
        These examples are simplistic but a more complex example might be::
        
            @dispatch_on_value.add(dv.all_same({'age': 32}))
            def _(a):
                # Do something
                
            # This would match since all the items in the list have the same age
            dispatch_on_value.dispatch([{'name': 'john', 'age': 32},
                                        {'hair': 'brown', 'age': 32, 'car': 'lada'}])
            
            # This wouldn't match since the ages are different
            dispatch_on_value.dispatch([{'name': 'john', 'age': 32},
                                        {'name': 'john', 'age': 9}])
        
        # Another example::
        
            # Match on a list of dictionaries where the name is 'john' and the
            # age is between 30 and 40
            @dispatch_on_value.add(dv.all_same({'name': 'john',
                                                'age': lamba x: 30 < x < 40})
            def _(a):
                # Do something
        
            # This would match
            dispatch_on_value.dispatch([{'name': 'john', 'age': 32},
                                        {'name': 'john', 'age': 37}])
            
            # This would not match
            dispatch_on_value.dispatch([{'name': 'john', 'age': 32},
                                        {'name': 'john', 'age': 45}])
        
        No limit on parameters
        ----------------------
        
        Pass as many extra parameters as you want when dispatching::
        
            @dispatch_on_value.add([1, 2])
            def _(a, my_abc, my_def):
                assert a == [1, 2]
                # Do something
            
            dispatch_on_value.dispatch([1, 2], 'abc', 'def')
        
        Matching on dictionaries is either partial or strict
        ====================================================
        
        Matching on directories is *partial* by default. This means dictionaries will
        match if the key/value pairs in the pattern are matched - any extra pairs in
        the value passed will be ignored. For example::
        
            @dispatch_on_value.add({'name': 'john', 'age': 32})
            def _(a):
                # Do something
        
            # These will match because they contain the minimal dictionary items
            dispatch_on_value.dispatch({'name': 'john', 'age': 32})
            dispatch_on_value.dispatch({'name': 'john', 'age': 32, 'sex': 'male'})
        
        You can ensure dictionaries have to be exactly the same when matched by using
        ``dispatch_strict()`` rather than ``dispatch()``. For example::
        
            # This will match because it's strict and the pattern is exactly the same
            dispatch_on_value.dispatch_strict({'name': 'john', 'age': 32})
        
            # This will not match because the dictionary doesn't match exactly
            dispatch_on_value.dispatch_strict({'name': 'john', 'age': 32, 'sex': 'male'})
        
        ***********************
        Author and Contributors
        ***********************
        
        Author: `minimind <https://github.com/minimind>`_.
        Contributor: `yurtaev <https://github.com/yurtaev>`_.
        
Keywords: dispatch on value,multiple dispatch,dynamic dispatch,pattern matching,value patterns,patterns
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Requires: six
