Disclaimer: I posted this earlier today but I think due to some first-post
moderation related issues (that I've hopefully now gotten sorted out!) it
may not have gone through. I'm posting this again just in case. If it's
gone through and you've already seen it then I'm super sorry, please just
ignore this.
If something like what you're suggesting were to be implemented I would
much rather it be done with context managers than position-dependent
special values, because otherwise you once again end up in a situation
where it's impossible to easily subclass a dataclass (which was one of the
primary reasons this conversation even got started in the first place). So,
for example:
import dataclasses
@dataclasses.dataclass
class SomeClass:
c: bool = False
# a normal field with a default value does not
# prevent subsequent positional fields from
# having no default value (such as 'a' below)
# however, all further normal fields now must
# specify a default value (such as 'd' below)
with dataclasses.positional():
a: int
b: float = 3.14
# once a positional field with a default value shows up
# all further positional fields and ALL normal fields
# (even retroactively!) must also specify defaults
# (for example, field 'c' above is
# now forced to specify a default value)
with dataclasses.keyword():
e: list
f: set = dataclasses.field(default_factory=set)
# once a keyword field with a default value shows up
# all further keyword fields must also specify defaults
d: dict = dataclasses.field(default_factory=dict)
# This ordering is clearly insane, but the essential
# point is that it works even with weird ordering
# which is necessary for it to work when subclassing
# where the order will almost always be wonky
#
# A sane version of the above would be:
@dataclasses.dataclass
class SomeClass:
with dataclasses.positional():
a: int
b: float = 3.14
c: bool = False
d: dict = dataclasses.field(default_factory=dict)
with dataclasses.keyword():
e: list
f: set = dataclasses.field(default_factory=set)
# either of the above will generate an __init__ like:
def __init__(self, a: int, b: float = 3.14,
/, c: bool = False, d: dict = None,
*, e: list, f: set = None):
self.a = a
self.b = b
self.c = c
self.d = dict() if d is None else d
self.e = e
self.f = set() if f is None else f
# parameters are arranged in order as
# positional -> normal -> keyword
# within the order they were defined in each
# individual category, but not necessarily
# whatever order they were defined in overall
#
# This is subclass-friendly!
#
# it should hopefully be obvious that we could
# have cut this class in half at literally any
# point (as long as the the parent class has
# the earlier arguments within each category)
# and put the rest into a child class and
# it would still have worked and generated the
# same __init__ signature
#
# For example:
@dataclasses.dataclass
class Parent:
with dataclasses.positional():
a: int
c: bool = False
with dataclasses.keyword():
e: list
@dataclasses.dataclass
class Child(Parent):
with dataclasses.positional():
b: float = 3.14
d: dict = dataclasses.field(default_factory=dict)
with dataclasses.keyword():
f: set = dataclasses.field(default_factory=set)
# Child now has the same __init__ signature as
# SomeClass above
(In case the above code doesn't render properly on your screen, I've
uploaded it to GitHub at:
https://github.com/matthewgdv/dataclass_arg_contextmanager/blob/main/example.py
)
Honestly, the more I think about it, the more I love the idea of something
like this (even if it's not *exactly* the same as my suggestion). Right now
dataclasses do not support the full range of __init__ signatures you could
generate with a normal class, and they are extremely hostile to
subclassing. That is a failing that often forces people to fall back to
normal classes in otherwise ideal dataclass use-case situations.
On Thu, Mar 11, 2021 at 10:15 PM Paul Bryan <pbr...@anode.ca> wrote:
> If you're proposing something like this, then I think it would be
> compatible:
>
> class Hmm:
>
> #
>
> this: int
>
> that: float
>
> #
>
> pos: PosOnly
>
> #
>
> these: str
>
> those: str
>
> #
>
> key: KWOnly
>
> #
>
> some: list
>
>
>
> On Thu, 2021-03-11 at 14:06 -0800, Ethan Furman wrote:
>
> On 3/11/21 10:50 AM, Paul Bryan wrote:
>
> On Thu, 2021-03-11 at 10:45 -0800, Ethan Furman wrote:
>
> On 3/10/21 9:47 PM, Eric V. Smith wrote:
>
> I'm not sure of the best way to achieve this. Using flags to field()
> doesn't sound awesome, but could be made to work. Or maybe special
> field names or types? I'm not crazy about that, but using special
> types would let you do something like:
>
> @dataclasses.dataclass
> class Point:
> x: int = 0
> _: dataclasses.KEYWORD_ONLY
> y: int
> z: int
> t: int = 0
>
>
> Maybe something like this?
>
> class Hmm:
> #
> this: int
> that: float
> #
> pos: '/'
> #
> these: str
> those: str
> #
> key: '*'
> #
> some: list
>
> >>> Hmm.__dict__['__annotations__']
> {
> 'this': <class 'int'>,
> 'that': <class 'float'>,
> 'pos': '/',
> 'these': <class 'str'>,
> 'those': <class 'str'>,
> 'key': '*',
> 'some': <class 'list'>,
> }
>
> The name of 'pos' and 'key' can be convention, since the actual name
> is irrelevant. They do have to be unique, though. ;-)
>
>
> It's current convention (and is used by typing module and static type
> checkers) that string annotations evaluate to valid Python types.
>
>
> So make '/' and '*' be imports from dataclasses:
>
> from dataclasses import dataclass, PosOnly, KWOnly
>
> --
> ~Ethan~
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