Some Python nicieties
Project description
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.
Pattern matching using extended dict’s
match.Match objects are callable objects using a dict semantic that also matches calls based on the type of the calling parameters:
>>> from kingston import match >>> foo = match.TypeMatcher({ ... int: lambda x: x*100, ... str: lambda x: f'Hello {x}' ... }) >>> foo(10) 1000 >>> foo('bar') 'Hello bar' >>>
>>> from kingston import match >>> foo = match.TypeMatcher({ ... int: lambda x: x * 100, ... str: lambda x: f'Hello {x}', ... (int, int): lambda a, b: a + b ... }) >>> foo(10) 1000 >>> foo('bar') 'Hello bar' >>> >>> foo(1, 2) 3 >>>
You can use typing.Any as a wildcard:
>>> from typing import Any >>> from kingston import match >>> foo = match.TypeMatcher({ ... int: lambda x: x * 100, ... str: (lambda x: f"Hello {x}"), ... (int, Any): (lambda num, x: num * x) ... }) >>> foo(10) 1000 >>> foo('bar') 'Hello bar' >>> foo(3, 'X') 'XXX' >>> foo(10, 10) 100 >>>
You can also subclass type matchers and use a decorator to declare cases as methods:
>>> from kingston.match import Matcher, TypeMatcher, case >>> from numbers import Number >>> class NumberDescriber(TypeMatcher): ... @case ... def describe_one_int(self, one:int) -> str: ... return "One integer" ... ... @case ... def describe_two_ints(self, one:int, two:int) -> str: ... return "Two integers" ... ... @case ... def describe_one_float(self, one:float) -> str: ... return "One float" >>> my_num_matcher:Matcher[Number, str] = NumberDescriber() >>> my_num_matcher(1) 'One integer' >>> my_num_matcher(1, 2) 'Two integers' >>> my_num_matcher(1.0) 'One float' >>>
Typing pattern matchers
match.Match objects can be typed using Python’s standard typing mechanism. It is done using Generics:
The two subtypes are [argument type, return type].
>>> from kingston import match >>> foo:match.Matcher[int, int] = match.TypeMatcher({ ... int: lambda x: x+1, ... str: lambda x: 'hello'}) >>> foo(10) 11 >>> foo('bar') # fails on mypy but would be ok at runtime 'hello' >>>
Match by value(s)
match.ValueMatcher will use the values of the parameters to do the same as as match.Match:
>>> from kingston import match >>> foo = match.ValueMatcher({'x': (lambda: 'An x!'), ('x', 'y'): (lambda x,y: 3*(x+y))}) >>> foo('x') 'An x!' >>> foo('x', 'y') 'xyxyxy' >>>
Same as with the type matcher above, typing.Any works as a wildcard with the value matcher as well:
>>> from kingston import match >>> from typing import Any >>> foo = match.ValueMatcher({ ... 'x': lambda x: 'An X!', ... ('y', Any): lambda x, y: 3 * (x + y) ... }) >>> foo('x') 'An X!' >>> foo('y', 'x') 'yxyxyx' >>>
You can also declare cases as methods in a custom ValueMatcher subclass.
Use the function value_case() to declare value cases. Note: imported as a shorthand:
>>> from kingston.match import Matcher, ValueMatcher >>> from kingston.match import value_case as case >>> class SimplestEval(ValueMatcher): ... @case(Any, '+', Any) ... def _add(self, a, op, b) -> int: ... return a + b ... ... @case(Any, '-', Any) ... def _sub(self, a, op, b) -> int: ... return a - b >>> simpl_eval = SimplestEval() >>> simpl_eval(1, '+', 2) 3 >>> simpl_eval(10, '-', 5) 5 >>>
Aspect Oriented Programming with terse syntax
Kingston also implement a technique to do AOP with an opinionated terse syntax that I like. It lives in the kingston.aop module.
It’s used in two main ways:
With decorators
Define an =Aspects= object as an empty object:
>>> from kingston.aop import Aspects >>> when = Aspects() >>>
Then declare your aspects using the object as a decorator:
>>> @when(lambda x: x == 1, y=lambda y: y == 1) ... def labbo(x, y=1): ... return 11 >>> @when(lambda x: x == 1, z=lambda z: z == 2) ... def labbo(x, z=2): ... return 12 >>>
Aspect 1 above will be triggered if you call it with positional parameter 0 as 1 and a keyword parameter y=1:
>>> labbo(1, y=1) 11 >>>
Aspect 2 is triggered by parameters 1, z=2:
>>> labbo(1, z=2) 12 >>>
Any other combination of parameters will raise a AspectNotFound exception:
>>> labbo(123) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): AspectNotFound >>> >>>
With a mapping of aspects
You might find this better if you want brievity and/or point free style.
>>> given = Aspects({ ... (lambda x: x == 1,): lambda x: 1, ... (lambda x: x > 1,): lambda x: x * x ... }) >>>
Calls work the same as above:
>>> given(1) 1 >>> given(2) 4 >>> given(0) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): AspectNotFound >>>
Nice things
dig()
Deep value grabbing from almost any object. Somewhat inspired by CSS selectors, but not very complete. This part of the API is unstable — it will (hopefully) be developed further in the future.
>>> 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 >>>
Testing tools
Kingston has some testing tools as well. Also, due to Kingston’s opinionated nature, they are only targeted towards pytest.
Shortform for pytest.mark.parametrize
I tend to use pytest.mark.parametrize in the same form everywhere. Thus I have implemented this short-form:
>>> from kingston.testing import fixture >>> @fixture.params( ... "a, b", ... (1, 1), ... (2, 2), ... ) ... def test_dummy_compare(a, b): ... assert a == b >>>
Doctests as fixtures
There is a test decorator that generates pytest fixtures from a function or an object. Use it like this:
>>> def my_doctested_func(): ... """ ... >>> 1 + 1 ... 2 ... >>> mystring = 'abc' ... >>> mystring ... 'abc' ... """ ... pass >>> from kingston.testing import fixture >>> @fixture.doctest(my_doctested_func) ... def test_doctest_my_doctested(doctest): # fixture name always 'doctest' ... res = doctest() ... assert res == '', res >>>
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