kingston 0.6.3

Some Python nicieties

I use the excellent Funcy library for Python a lot. This is my collection of extras that I have designed to work closely together with funcy. Funcy Kingston (Reference, see here).

Kingston is auto-formatted using yapf.

Nice things

dig()

CSS selector like deep value grabbing from almost any object.

>>> from kingston import dig
>>> dig.xget((1, 2, 3), 1)
2
>>> dig.xget({'foo': 'bar'}, 'foo')
'bar'
>>> dig.dig({'foo': 1, 'bar': [1,2,3]}, 'bar.1')
2
>>> dig.dig({'foo': 1, 'bar': [1,{'baz':'jox'},3]}, 'bar.1.baz')
'jox'
>>>

The difference between dig.dig() and funcy.get_in() is that you can use shell-like blob patterns to get several values keyed by similar names:

>>> from kingston import dig
>>> res = dig.dig({'foo': 1, 'foop': 2}, 'f*')
>>> res
[foo=1:int, foop=2:int]
>>> # (textual representation of an indexable object)
>>> res[0]
foo=1:int
>>> res[1]
foop=2:int
>>>

Pattern matching using extended dict’s

>>> from kingston import lang
>>> foo = lang.Match({int: lambda x: x*100, str: lambda x: f'Hello {x}'})
>>> foo(10)
1000
>>> foo('bar')
'Hello bar'
>>>

Narrowable collections

Uses indexes to narrow collections to fewer values. You can narrow by type, a predicate function or value equality. The return value is always a new Narrowable derived type from the initial value. Therefore, you can chain several narrowing operations in the same expression.

Errors raised by the narrowing predicates are considered misses.

Some examples:

Narrow by type

>>> from kingston.primitives import narrowable
>>> narrowable((1,2,3,'foo', 'bar'))[int]
(1, 2, 3)
>>>

Narrow by callable

>>> from kingston.primitives import narrowable
>>> narrowable((1, 2, 3))[lambda x: x > 1]
(2, 3)
>>>
>>> narrowable((1,2,3,'foo', 'bar'))[int]
(1, 2, 3)
>>> narrowable((1,2,3,'foo', 'bar'))[lambda x: x > 1]
(2, 3)
>>> # Note, swallows ValueError raised by 'foo' > 1 etc
>>>

Supress empty iterable objects

>>> from kingston.primitives import narrowable
>>> narrowable([[1], [2], [], []])[lambda x: x[0]]
[[1], [2]]
>>>

Narrow using exact match

>>> from kingston.primitives import narrowable
>>> narrowable((1, 2, 3, 'foo'))['foo']
('foo',)
>>>

Narrow using a regexp

>>> from kingston.primitives import narrowable
>>> import re
>>> narrowable(('foo', 'fom', 'jox', 8, 'fim'))[re.compile('fo.*').match]
('foo', 'fom')
>>>

Combine

>>> from kingston.primitives import narrowable
>>> narrowable((1,2,3,'foo', 'bar'))[str]['foo']
('foo',)
>>>

Go deeper

>>> from kingston.primitives import narrowable
>>> narrowable((1, 2, 3, (41, 42, 43)))[tuple][0][lambda x: x > 41]
(42, 43)
>>>

No matches found

If no element matches, an empty version of the collection parameter will be returned:

>>> from kingston.primitives import narrowable
>>> narrowable((1,2,3))[lambda x: x > 3]
()
>>>

Programmatic class creation

Programmatic creation of arbitrary named classes in module definition, add methods using a decorator notation:

>>> from kingston import lang
>>> mystuff = (('Foo', 1), ('Bar', 2))
>>> for name, num in mystuff: locals()[name] = lang.mkclass(name, **{'num': num})
>>> Foo
<class 'kingston.lang.Foo'>
>>> Foo.num
1
>>> \
... @Foo.classmethod
... def myclassmethod(cls, x):
...     return x + 1
>>> Foo.myclassmethod(1)
2
>>>
>>> \
... @Foo.staticmethod
... def mystaticmethod(x, y):
...     return x + y
>>> Foo.mystaticmethod(1, 2)
3
>>> \
... @Foo.method
... def mymethod(self, x):
...     self.y = self.num + x
...     return self.y
>>> foo = Foo()
>>> foo.mymethod(1)
2
>>> foo.y
2
>>>

kingston module with developer convenience tools

The kingston.microscope module contains utilities that aid development. It has to ways to inspect live objects:

1. Via ‘AbneuYAML’

   AbneuYAML is “Almost, but not entirely unlike YAML”. Objects dumped to ‘AbneuYAML’ should be easy to get a visual overview of for humans.

   To dump any object:

   >>> from kingston import microscope
   >>> class Cls: pass
   ...
   >>> c = Cls()
   >>> c.foo, c.bar = 1, 2
   >>> c.sub = Cls()
   >>> c.sub.foo, c.sub.bar, c.sub.baz = 3, 4, [1, 2]
   >>> encoded = microscope.abneuyaml(c)
   >>> print(encoded) #doctest: +ELLIPSIS
   <__main__.Cls object at 0x...>:Cls
     foo=1:int
     bar=2:int
     sub=<__main__.Cls object at 0x...>:Cls
       foo=3:int
       bar=4:int
       baz=[1, 2]:list
   >>>

A simple way of creating small DSL’s using Python operator overloading.

>>> from kingston import lang
>>> \
... class PipingExample(lang.Piping):
...     def __add__(self, value) -> lang.Piping:
...         self.queue(lambda a, b: a + b, value)
...         return self
...
>>> simplest_pipe = PipingExample(10)
>>> res = simplest_pipe + 10 + 20
>>> res()
40
>>>

Mostly, you’ll want to use the pipe operator to define simple composition:

>>> from kingston import lang
>>> incr = lambda x: x + 1
>>> showr = "It is {}!".format
>>> (lang.ComposePiping(5) >> incr >> incr >> showr)()
'It is 7!'
>>>