Additional features¶
This section discusses various features that did not fit in naturally in one of the previous sections.
Dataclasses¶
The dataclasses
module allows defining and customizing simple
boilerplate-free classes. They can be defined using the
@dataclasses.dataclass
decorator:
from dataclasses import dataclass, field
@dataclass
class Application:
name: str
plugins: list[str] = field(default_factory=list)
test = Application("Testing...") # OK
bad = Application("Testing...", "with plugin") # Error: list[str] expected
Mypy will detect special methods (such as __lt__
) depending on the flags used to
define dataclasses. For example:
from dataclasses import dataclass
@dataclass(order=True)
class OrderedPoint:
x: int
y: int
@dataclass(order=False)
class UnorderedPoint:
x: int
y: int
OrderedPoint(1, 2) < OrderedPoint(3, 4) # OK
UnorderedPoint(1, 2) < UnorderedPoint(3, 4) # Error: Unsupported operand types
Dataclasses can be generic and can be used in any other way a normal class can be used:
from dataclasses import dataclass
from typing import Generic, TypeVar
T = TypeVar('T')
@dataclass
class BoxedData(Generic[T]):
data: T
label: str
def unbox(bd: BoxedData[T]) -> T:
...
val = unbox(BoxedData(42, "<important>")) # OK, inferred type is int
For more information see official docs and PEP 557.
Caveats/Known Issues¶
Some functions in the dataclasses
module, such as asdict()
,
have imprecise (too permissive) types. This will be fixed in future releases.
Mypy does not yet recognize aliases of dataclasses.dataclass
, and will
probably never recognize dynamically computed decorators. The following example
does not work:
from dataclasses import dataclass
dataclass_alias = dataclass
def dataclass_wrapper(cls):
return dataclass(cls)
@dataclass_alias
class AliasDecorated:
"""
Mypy doesn't recognize this as a dataclass because it is decorated by an
alias of `dataclass` rather than by `dataclass` itself.
"""
attribute: int
AliasDecorated(attribute=1) # error: Unexpected keyword argument
To have Mypy recognize a wrapper of dataclasses.dataclass
as a dataclass decorator, consider using the dataclass_transform()
decorator:
from dataclasses import dataclass, Field
from typing import TypeVar, dataclass_transform
T = TypeVar('T')
@dataclass_transform(field_specifiers=(Field,))
def my_dataclass(cls: type[T]) -> type[T]:
...
return dataclass(cls)
Data Class Transforms¶
Mypy supports the dataclass_transform()
decorator as described in
PEP 681.
Note
Pragmatically, mypy will assume such classes have the internal attribute __dataclass_fields__
(even though they might lack it in runtime) and will assume functions such as dataclasses.is_dataclass()
and dataclasses.fields()
treat them as if they were dataclasses
(even though they may fail at runtime).
The attrs package¶
attrs is a package that lets you define classes without writing boilerplate code. Mypy can detect uses of the package and will generate the necessary method definitions for decorated classes using the type annotations it finds. Type annotations can be added as follows:
import attr
@attrs.define
class A:
one: int
two: int = 7
three: int = attrs.field(8)
If you’re using auto_attribs=False
you must use attrs.field
:
import attrs
@attrs.define
class A:
one: int = attrs.field() # Variable annotation (Python 3.6+)
two = attrs.field() # type: int # Type comment
three = attrs.field(type=int) # type= argument
Typeshed has a couple of “white lie” annotations to make type checking
easier. attrs.field()
and attrs.Factory
actually return objects, but the
annotation says these return the types that they expect to be assigned to.
That enables this to work:
import attrs
@attrs.define
class A:
one: int = attrs.field(8)
two: dict[str, str] = attrs.Factory(dict)
bad: str = attrs.field(16) # Error: can't assign int to str
Caveats/Known Issues¶
The detection of attr classes and attributes works by function name only. This means that if you have your own helper functions that, for example,
return attrs.field()
mypy will not see them.All boolean arguments that mypy cares about must be literal
True
orFalse
. e.g the following will not work:import attrs YES = True @attrs.define(init=YES) class A: ...
Currently,
converter
only supports named functions. If mypy finds something else it will complain about not understanding the argument and the type annotation in__init__
will be replaced byAny
.Validator decorators and default decorators are not type-checked against the attribute they are setting/validating.
Method definitions added by mypy currently overwrite any existing method definitions.
Using a remote cache to speed up mypy runs¶
Mypy performs type checking incrementally, reusing results from previous runs to speed up successive runs. If you are type checking a large codebase, mypy can still be sometimes slower than desirable. For example, if you create a new branch based on a much more recent commit than the target of the previous mypy run, mypy may have to process almost every file, as a large fraction of source files may have changed. This can also happen after you’ve rebased a local branch.
Mypy supports using a remote cache to improve performance in cases such as the above. In a large codebase, remote caching can sometimes speed up mypy runs by a factor of 10, or more.
Mypy doesn’t include all components needed to set this up – generally you will have to perform some simple integration with your Continuous Integration (CI) or build system to configure mypy to use a remote cache. This discussion assumes you have a CI system set up for the mypy build you want to speed up, and that you are using a central git repository. Generalizing to different environments should not be difficult.
Here are the main components needed:
A shared repository for storing mypy cache files for all landed commits.
CI build that uploads mypy incremental cache files to the shared repository for each commit for which the CI build runs.
A wrapper script around mypy that developers use to run mypy with remote caching enabled.
Below we discuss each of these components in some detail.
Continuous Integration build¶
The CI build would run a regular mypy build and create an archive containing
the .mypy_cache
directory produced by the build. Finally, it will produce
the cache as a build artifact or upload it to a repository where it is
accessible by the mypy wrapper script.
Your CI script might work like this:
Run mypy normally. This will generate cache data under the
.mypy_cache
directory.Create a tarball from the
.mypy_cache
directory.Determine the current git master branch commit id (say, using
git rev-parse HEAD
).Upload the tarball to the shared repository with a name derived from the commit id.
Mypy wrapper script¶
The wrapper script is used by developers to run mypy locally during
development instead of invoking mypy directly. The wrapper first
populates the local .mypy_cache
directory from the shared
repository and then runs a normal incremental build.
The wrapper script needs some logic to determine the most recent
central repository commit (by convention, the origin/master
branch
for git) the local development branch is based on. In a typical git
setup you can do it like this:
git merge-base HEAD origin/master
The next step is to download the cache data (contents of the
.mypy_cache
directory) from the shared repository based on the
commit id of the merge base produced by the git command above. The
script will decompress the data so that mypy will start with a fresh
.mypy_cache
. Finally, the script runs mypy normally. And that’s all!
Caching with mypy daemon¶
You can also use remote caching with the mypy daemon.
The remote cache will significantly speed up the first dmypy check
run after starting or restarting the daemon.
The mypy daemon requires extra fine-grained dependency data in
the cache files which aren’t included by default. To use caching with
the mypy daemon, use the --cache-fine-grained
option in your CI
build:
$ mypy --cache-fine-grained <args...>
This flag adds extra information for the daemon to the cache. In
order to use this extra information, you will also need to use the
--use-fine-grained-cache
option with dmypy start
or
dmypy restart
. Example:
$ dmypy start -- --use-fine-grained-cache <options...>
Now your first dmypy check
run should be much faster, as it can use
cache information to avoid processing the whole program.
Refinements¶
There are several optional refinements that may improve things further, at least if your codebase is hundreds of thousands of lines or more:
If the wrapper script determines that the merge base hasn’t changed from a previous run, there’s no need to download the cache data and it’s better to instead reuse the existing local cache data.
If you use the mypy daemon, you may want to restart the daemon each time after the merge base or local branch has changed to avoid processing a potentially large number of changes in an incremental build, as this can be much slower than downloading cache data and restarting the daemon.
If the current local branch is based on a very recent master commit, the remote cache data may not yet be available for that commit, as there will necessarily be some latency to build the cache files. It may be a good idea to look for cache data for, say, the 5 latest master commits and use the most recent data that is available.
If the remote cache is not accessible for some reason (say, from a public network), the script can still fall back to a normal incremental build.
You can have multiple local cache directories for different local branches using the
--cache-dir
option. If the user switches to an existing branch where downloaded cache data is already available, you can continue to use the existing cache data instead of redownloading the data.You can set up your CI build to use a remote cache to speed up the CI build. This would be particularly useful if each CI build starts from a fresh state without access to cache files from previous builds. It’s still recommended to run a full, non-incremental mypy build to create the cache data, as repeatedly updating cache data incrementally could result in drift over a long time period (due to a mypy caching issue, perhaps).
Extended Callable types¶
Note
This feature is deprecated. You can use callback protocols as a replacement.
As an experimental mypy extension, you can specify Callable
types
that support keyword arguments, optional arguments, and more. When
you specify the arguments of a Callable
, you can choose to supply just
the type of a nameless positional argument, or an “argument specifier”
representing a more complicated form of argument. This allows one to
more closely emulate the full range of possibilities given by the
def
statement in Python.
As an example, here’s a complicated function definition and the
corresponding Callable
:
from typing import Callable
from mypy_extensions import (Arg, DefaultArg, NamedArg,
DefaultNamedArg, VarArg, KwArg)
def func(__a: int, # This convention is for nameless arguments
b: int,
c: int = 0,
*args: int,
d: int,
e: int = 0,
**kwargs: int) -> int:
...
F = Callable[[int, # Or Arg(int)
Arg(int, 'b'),
DefaultArg(int, 'c'),
VarArg(int),
NamedArg(int, 'd'),
DefaultNamedArg(int, 'e'),
KwArg(int)],
int]
f: F = func
Argument specifiers are special function calls that can specify the following aspects of an argument:
its type (the only thing that the basic format supports)
its name (if it has one)
whether it may be omitted
whether it may or must be passed using a keyword
whether it is a
*args
argument (representing the remaining positional arguments)whether it is a
**kwargs
argument (representing the remaining keyword arguments)
The following functions are available in mypy_extensions
for this
purpose:
def Arg(type=Any, name=None):
# A normal, mandatory, positional argument.
# If the name is specified it may be passed as a keyword.
def DefaultArg(type=Any, name=None):
# An optional positional argument (i.e. with a default value).
# If the name is specified it may be passed as a keyword.
def NamedArg(type=Any, name=None):
# A mandatory keyword-only argument.
def DefaultNamedArg(type=Any, name=None):
# An optional keyword-only argument (i.e. with a default value).
def VarArg(type=Any):
# A *args-style variadic positional argument.
# A single VarArg() specifier represents all remaining
# positional arguments.
def KwArg(type=Any):
# A **kwargs-style variadic keyword argument.
# A single KwArg() specifier represents all remaining
# keyword arguments.
In all cases, the type
argument defaults to Any
, and if the
name
argument is omitted the argument has no name (the name is
required for NamedArg
and DefaultNamedArg
). A basic
Callable
such as
MyFunc = Callable[[int, str, int], float]
is equivalent to the following:
MyFunc = Callable[[Arg(int), Arg(str), Arg(int)], float]
A Callable
with unspecified argument types, such as
MyOtherFunc = Callable[..., int]
is (roughly) equivalent to
MyOtherFunc = Callable[[VarArg(), KwArg()], int]
Note
Each of the functions above currently just returns its type
argument at runtime, so the information contained in the argument
specifiers is not available at runtime. This limitation is
necessary for backwards compatibility with the existing
typing.py
module as present in the Python 3.5+ standard library
and distributed via PyPI.