Package: dispatchonvalue
This Python 2.7/Python 3.x 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 uselambdato do
expression matching and utilise wildcard parameters to ensure identical values
can be matched (seeany_a). It can alleviate complicated and difficult to
readif ... 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-pythonInstall using pip:pip install dispatchonvalueUnit tests can be run using:python -m unittest discoverAny queries and comments are welcome. Send them to:ian.macinnes@gmail.comGuideVery quick exampleFirst 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]
    # return optional value
    return 3

@dispatch_on_value.add([4, 5, 6])  # [4, 5, 6] is the pattern to match on
def _(a):
    assert a == [4, 5, 6]
    # return optional value
    return 4Then else where in your code, dispatch to the correct function based on the
value of the parameter passed:p = [4, 5, 6]
r = dispatch_on_value.dispatch(p)  # Will call second function aboveIf no pattern was matched, and hence no function dispatched, the
DispatchFailed class will be raised:try:
  p = [7, 8, 9]
  r = dispatch_on_value.dispatch(p)
except dv.DispatchFailed:
  print 'could not dispatch!'FeaturesMultiple dispatch on valueThe simplest use is to dispatch on fixed values. Here we dispatch to two
different functionsfn_1andfn_2depending upon the value ofp:@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 FalseData structure patterns can be arbitrary nestedThe 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 patternsUselambda’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 matchAnother 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 matchWildcard parametersUse of wildcard tokensany_a,any_b, …any_zcan 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 matchMatch everything in a list with single tokenUse theall_sametoken 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 theany_Xtoken:@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 parametersPass 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')You can also pass keyword parameters:@dispatch_on_value.add([3, 4])
def _(a, my_abc, **kwargs):
    assert 'para1' in kwargs
    # Do something

dispatch_on_value.dispatch([3, 4], 'abc', para1=3)Methods can also be dispatchedYou can dispatch methods on class instances using the add_method decorator:dispatch_on_value = dv.DispatchOnValue()

class MyClass(object):
    @dispatch_on_value.add_method([1, 2, 3])
    def m1(self, a):
        called[0] = 1
        return 2

    @dispatch_on_value.add_method([4, 5, 6])
    def m2(self, a):
        called[0] = 2
        return 3

my_class = MyClass()

called = [0]

p = [4, 5, 6]
# This will match m2...
dispatch_on_value.dispatch(p) == 3Matching on dictionaries is either partial or strictMatching on directories ispartialby 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 usingdispatch_strict()rather thandispatch(). 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 ContributorsAuthor:minimind.Contributors:yurtaev,svisser,mianos.