This library provides some functional data structures, utilities and a typeclass system that resemble those concepts in scala. Some of the implementations have considerable overhead and aren't suitable for performance critical applications; their purpose is nicer and familiar syntax and composability.

These are an alternative to lambdas with parameter placeholder wildcards. If the env var AMINO_ANON_DEBUG is set, a different implementation with severe performance penalties but literal string representation is used.

from amino import L, _, __

f = L(isinstance)(_, str)
f('amino')
# True

# for methods
f = __.index('n')
f('amino')
# 3

# for attributes
f = _.parent
f(Path('/home/user'))
# Path('/home')

A wrapper for stdlib list with extended interface.

from amino import List, _

l = List(1, 2, 3, 4)
l.map(_ + 5)
# List(6, 7, 8, 9)

l = List(List(1, 2), List(3, 4))
l.flat_map(__.map(_ - 1))
# List(0, 1, 2, 3)

An optional ADT with subtypes Just and Empty.

j = Just(1)
e = Empty()

j.map(List)
# Just(List(1))

e.map(_ + 2)
# Empty()

j.flat_map(lambda a: Just(a - 2))
# Just(-1)

A simple coproduct type that can be inhabited by two types.

r = Right(5)
l = Left('error')

r.map(_ + 1)
# Right(6)

l.map(_ + 1)
# Left('error')

l.lmap(_ + ' in test')
# Left('error in test')

r.flat_map(lambda a: Left(a + 3))
# Left(8)

The function decorator do allows to use generators as do-blocks with any class that responds to flat_map. It is implemented by looping until the generator is exhausted, calling flat_map on each yielded effect and sending its value into the generator.

from amino import do

@do(Either[int, int])
def compute() -> Do:
  user = yield Right('root')
  content = yield IO.delay(Path('/etc/passwd').read_text).attempt
  yield Lists.lines(content).index_where(lambda l: user in l).to_either('not found')

All yielded values produce an Either, the return value is the found index or the error message from the last statement or the IO call.

return a behaves similar to Haskell, being equivalent to yield Monad[F].pure(a).

Although these make a lot more sense with a real type system, they provide a nice abstraction for functionality. The map and flat_map operations, for instance, are implemented in the typeclasses Functor and FlatMap, for which instances are provided for arbitrary types, among them List and Maybe. The typeclasses define methods that are looked up in the data class' __getattr__, which is provided by the Implicits base class inherited by List and Maybe. The typeclass instances are stored in a global registry, which can be used separately from the Implicits concept:

from amino.tc.monad import Monad

Monad.lookup(List).flat_map(List(1, 2), lambda a: List(a + 5))
# List(6, 7)

Instances can be registered in several ways, the easiest of which is by passing the target type to the metaclass:

from typing import TypeVar, Callable
from amino.tc.functor import Functor

A = TypeVar('A')
B = TypeVar('B')

class ListFunctor(Functor, tpe=List):

  def map(l: List[A], f: Callable[[A], B]) -> List[B]:
    return List.wrap(map(f, l))

After importing the instance's module, the map method can be used as shown above.

IO is a trampolined algebra for computation abstraction that catches errors:

t = IO.pure(Path('/var/log/dmesg')).flat_map(L(IO.delay)(__.read_text()))
# IO(Pure(/var/log/dmesg).flat_map(L(delay)(__.read_text())))

t.attempt
# Right('...'): Either[IOException, str]
# or
# Left(IOException('Pure(/var/log/dmesg).flat_map(L(delay)(__.read_text()))', [], PermissionError(13, 'Permission denied')))

StateT abstracts F[S => F[S, A]], implemented for several effects as EitherState etc. It offers the usual monadic constructors:

from amino.state import StateT, EitherState

@do
def state(x: int) -> typing.Generator[StateT[Either, str, int], Any, None]:
  i = yield EitherState.inspect_f(lambda s: Try(int, s))
  yield EitherState.modify(lambda a: len(a) * x + i)
  yield EitherState.pure(x)

s = state(5)
s.run('15') # -> Right((25, 5))

The base class Dat makes working with pure data types simpler by analyzing a class' __init__ and creating class attributes containing info about its fields. This allows for simple copying (optionally with type check) without intrusive descriptor magic – a Dat instance is still a plain old python object.

class Data(Dat['Data'])

    def __init__(a: int, b: List[str]) -> None:
        self.a = a
        self.b = b

Data(7, List('one', 'two')).copy(a=3)

amino provides a framework for Json de/encoding based on typeclasses. The functions encode_json, dump_json and decode_json look for instances of the typeclasses Encoder and Decoder to process data. Codecs for some builtins like Path and UUID as well as *amino's standard types like Either and Maybe are provided.

The most convenient aspect of this is that Dat will automatically call the corresponding De/Encoder for all its fields, making serialization of nested data structures trivial.