optype

One protocol, one method.

Building blocks for precise & flexible type hints.

Installation

Optype is available as optype on PyPI:

pip install optype

Example

Let's say you're writing a twice(x) function, that evaluates 2 * x. Implementing it is trivial, but what about the type annotations?

Because twice(2) == 4, twice(3.14) == 6.28 and twice('I') = 'II', it might seem like a good idea to type it as twice[T](x: T) -> T: .... However, that wouldn't include cases such as twice(True) == 2 or twice((42, True)) == (42, True, 42, True), where the input- and output types differ. Moreover, twice should accept any type with a custom __rmul__ method that accepts 2 as argument.

This is where optype comes in handy, which has single-method protocols for all the builtin special methods. For twice, we can use optype.CanRMul[X, Y], which, as the name suggests, is a protocol with (only) the def __rmul__(self, x: X) -> Y: ... method. With this, the twice function can written as:

from typing import Literal, TypeAlias, TypeVar
from optype import CanRMul

Y = TypeVar('Y')
Two: TypeAlias = Literal[2]

def twice(x: CanRMul[Two, Y]) -> Y:
    return 2 * x

from typing import Literal
from optype import CanRMul


type Two = Literal[2]

def twice[Y](x: CanRMul[Two, Y]) -> Y:
    return 2 * x

But what about types that implement __add__ but not __radd__? In this case, we could return x * 2 as fallback (assuming commutativity). Because the optype.Can* protocols are runtime-checkable, the revised twice2 function can be compactly written as:

from optype import CanMul

def twice2(x: CanRMul[Two, Y] | CanMul[Two, Y]) -> Y:
    return 2 * x if isinstance(x, CanRMul) else x * 2

from optype import CanMul

def twice2[Y](x: CanRMul[Two, Y] | CanMul[Two, Y]) -> Y:
    return 2 * x if isinstance(x, CanRMul) else x * 2

See examples/twice.py for the full example.

Overview

The API of optype is flat; a single import optype is all you need. There are four flavors of things that live within optype,

• optype.Can{} types describe what can be done with it. For instance, any CanAbs[T] type can be used as argument to the abs() builtin function with return type T. Most Can{} implement a single special method, whose name directly matched that of the type. CanAbs implements __abs__, CanAdd implements __add__, etc.
• optype.Has{} is the analogue of Can{}, but for special attributes. HasName has the __name__: str attribute, HasDict has a __dict__, etc.
• optype.Does{} describe the type of operators. So DoesAbs is the type of the abs({}) builtin function, and DoesPos the type of the +{} prefix operator.
• optype.do_{} are the correctly-typed implementations of Does{}. For each do_{} there is a Does{}, and vice-versa. So do_abs: DoesAbs is the typed alias of abs({}), and do_pos: DoesPos is a typed version of operator.pos. The optype.do_ operators are more complete than operators, has runtime-accessible type annotations, and uses a fully predictable naming scheme.

Reference

All typing protocols here live in the root optype namespace. They are runtime-checkable so that you can do e.g. isinstance('snail', optype.CanAdd), in case you want to check whether snail implements __add__.

[!NOTE] It is bad practice to use a typing.Protocol as base class for your implementation. Because of @typing.runtime_checkable, you can use isinstance either way.

Unlikecollections.abc, optype's protocols aren't abstract base classes, i.e. they don't extend abc.ABC, only typing.Protocol. This allows the optype protocols to be used as building blocks for .pyi type stubs.

Type conversion

The return type of these special methods is invariant. Python will raise an error if some other (sub)type is returned. This is why these optype interfaces don't accept generic type arguments.

These formatting methods are allowed to return instances that are a subtype of the str builtin. The same holds for the __format__ argument. So if you're a 10x developer that wants to hack Python's f-strings, but only if your type hints are spot-on; optype is you friend.

"Rich comparison" operators

These special methods generally a bool. However, instances of any type can be returned.

Callable objects

Unlike operator, optype provides the operator for callable objects: optype.do_call(f, *args. **kwargs).

CanCall is similar to collections.abc.Callable, but is runtime-checkable, and doesn't use esoteric hacks.

Numeric operations

For describing things that act like numbers. See the Python docs for more info.

Note that because pow() can take an optional third argument, optype provides separate interfaces for pow() with two and three arguments. Additionally, there is the overloaded intersection type CanPow[X, M, Y2, Y3] =: CanPow2[X, Y2] & CanPow3[X, M, Y3], as interface for types that can take an optional third argument.

For the binary infix operators above, optype additionally provides interfaces with reflected (swapped) operands:

Note that CanRPow corresponds to CanPow2; the 3-parameter "modulo" pow does not reflect in Python.

Similarly, the augmented assignment operators are described by the following optype interfaces:

Additionally, there are the unary arithmetic operators:

The round() built-in function takes an optional second argument. From a typing perspective, round() has two overloads, one with 1 parameter, and one with two. For both overloads, optype provides separate operand interfaces: CanRound1[Y] and CanRound2[N, Y]. Additionally, optype also provides their (overloaded) intersection type: CanRound[N, Y1, Y2] = CanRound1[Y1] & CanRound2[N, Y2].

For example, type-checkers will mark the following code as valid (tested with pyright in strict mode):

x: float = 3.14
x1: CanRound1[int] = x
x2: CanRound2[int, float] = x
x3: CanRound[int, int, float] = x

Furthermore, there are the alternative rounding functions from the math standard library:

Note that the type parameter Y has no upper type bound, because technically these methods can return any type. However, in practise, it is very common to have them return an int.

Async objects

The optype variant of collections.abc.Awaitable[V]. The only difference is that optype.CanAwait[V] is a pure interface, whereas Awaitable is also an abstract base class.

Iteration

The operand x of iter(_) is within Python known as an iterable, which is what collections.abc.Iterable[K] is often used for (e.g. as base class, or for instance checking).

The optype analogue is CanIter[Ks], which as the name suggests, also implements __iter__. But unlike Iterable[K], its type parameter Ks binds to the return type of iter(_). This makes it possible to annotate the specific type of the iterable that iter(_) returns. Iterable[K] is only able to annotate the type of the iterated value. To see why that isn't possible, see python/typing#548.

The collections.abc.Iterator[K] is even more awkward; it is a subtype of Iterable[K]. For those familiar with collections.abc this might come as a surprise, but an iterator only needs to implement __next__, __iter__ isn't needed. This means that the Iterator[K] is unnecessarily restrictive. Apart from that being theoretically "ugly", it has significant performance implications, because the time-complexity of isinstance on a typing.Protocol is $O(n)$, with the $n$ referring to the amount of members. So even if the overhead of the inheritance and the abc.ABC usage is ignored, collections.abc.Iterator is twice as slow as it needs to be.

That's one of the (many) reasons that optype.CanNext[V] and optype.CanNext[V] are the better alternatives to Iterable and Iterator from the abracadabra collections. This is how they are defined:

For the sake of compatibility with collections.abc, there is optype.CanIterSelf[T], which is a protocol whose __iter__ returns typing.Self, as well as a __next__ method that returns T. I.e. it is equivalent to collections.abc.Iterator[T], but without the abc nonsense.

Async Iteration

Yes, you guessed it right; the abracadabra collections made the exact same mistakes for the async iterablors (or was it "iteramblers"...?).

But fret not; the optype alternatives are right here:

But wait, shouldn't V be a CanAwait? Well, only if you don't want to get fired... Technically speaking, __anext__ can return any type, and anext will pass it along without nagging (instance checks are slow, now stop bothering that liberal). For details, see the discussion at python/typeshed#7491. Just because something is legal, doesn't mean it's a good idea (don't eat the yellow snow).

Additionally, there is optype.CanAIterSelf[V], with both the __aiter__() -> Self and the __anext__() -> V methods.

Containers

Because CanMissing[K, M] generally doesn't show itself without CanGetitem[K, V] there to hold its hand, optype conveniently stitched them together as optype.CanGetMissing[K, V, M].

Similarly, there is optype.CanSequence[I: CanIndex, V], which is the combination of both CanLen and CanItem[I, V], and serves as a more specific and flexible collections.abc.Sequence[V].

Additionally, optype provides protocols for types with (custom) hash or index methods:

Attribute access

Descriptors

Interfaces for descriptors.

Context managers

Support for the with statement.

For the async with statement the interfaces look very similar:

Buffer types

Interfaces for emulating buffer types using the buffer protocol.

copy

For the copy standard library, optype provides the following interfaces:

And for convenience, there are the runtime-checkable aliases for all three interfaces, with T bound to Self. These are roughly equivalent to:

type CanCopySelf = CanCopy[CanCopySelf]
type CanDeepcopySelf = CanDeepcopy[CanDeepcopySelf]
type CanReplaceSelf[V] = CanReplace[CanReplaceSelf[V], V]

pickle

For the pickle standard library, optype provides the following interfaces:

dataclasses

For the dataclasses standard library, optype provides the optype.HasDataclassFields interface It can conveniently be used to check whether a type or instance is a dataclass, i.e. isinstance(obj, optype.HasDataclassFields).

Future plans

• numpy interfaces for arrays-like types (no deps) (#24)
• array-api interfaces (no deps) (#25)
• dataframe-api interfaces (no deps) (#26)