When I refactor code I often find myself tediously adding type annotations that are obvious from context: functions that don't return anything, boolean flags, etcetera. That's where autotyping comes in: it automatically adds those types and inserts the right annotations.

Usage

Here's how to use it:

• pip install autotyping
• python -m autotyping /path/to/my/code

By default it does nothing; you have to add flags to make it do more transformations. The following are supported:

• Annotating return types:
  • --none-return: add a -> None return type to functions without any return, yield, or raise in their body
  • --scalar-return: add a return annotation to functions that only return literal bool, str, bytes, int, or float objects.
• Annotating parameter types:
  • --bool-param: add a : bool annotation to any function parameter with a default of True or False
  • --int-param, --float-param, --str-param, --bytes-param: add an annotation to any parameter for which the default is a literal int, float, str, or bytes object
  • --annotate-optional foo:bar.Baz: for any parameter of the form foo=None, add Baz, imported from bar, as the type. For example, use --annotate-optional uid:my_types.Uid to annotate any uid in your codebase with a None default as Optional[my_types.Uid].
  • --annotate-named-param foo:bar.Baz: annotate any parameter with no default that is named foo with bar.Baz. For example, use --annotate-named-param uid:my_types.Uid to annotate any uid parameter in your codebase with no default as my_types.Uid.
  • --guess-common-names: infer certain parameter types from their names based on common patterns in open-source Python code. For example, infer that a verbose parameter is of type bool.
• Annotating magical methods:
  • --annotate-magics: add type annotation to certain magic methods. Currently this does the following:
  • __str__ returns str
  • __repr__ returns str
  • __len__ returns int
  • __length_hint__ returns int
  • __init__ returns None
  • __del__ returns None
  • __bool__ returns bool
  • __bytes__ returns bytes
  • __format__ returns str
  • __contains__ returns bool
  • __complex__ returns complex
  • __int__ returns int
  • __float__ returns float
  • __index__ returns int
  • __exit__: the three parameters are Optional[Type[BaseException]], Optional[BaseException], and Optional[TracebackType]
  • __aexit__: same as __exit__
  • --annotate-imprecise-magics: add imprecise type annotations for some additional magic methods. Currently this adds typing.Iterator return annotations to __iter__, __await__, and __reversed__. These annotations should have a generic parameter to indicate what you're iterating over, but that's too hard for autotyping to figure out.
• External integrations
  • --pyanalyze-report: takes types suggested by pyanalyze's suggested_parameter_type and suggested_return_type codes and applies them. You can generate these with a command like: pyanalyze --json-output failures.json -e suggested_return_type -e suggested_parameter_type -v .
  • --only-without-imports: only apply pyanalyze suggestions that do not require new imports. This is useful because suggestions that require imports may need more manual work.

There are two shortcut flags to enable multiple transformations at once:

• --safe enables changes that should always be safe. This includes --none-return, --scalar-return, and --annotate-magics.
• --aggressive enables riskier changes that are more likely to produce new type checker errors. It includes all of --safe as well as --bool-param, --int-param, --float-param, --str-param, --bytes-param, and --annotate-imprecise-magics.

LibCST

Autotyping is built as a LibCST codemod; see the LibCST documentation for more information on how to use codemods.

If you wish to run things through the libcst.tool interface, you can do this like so:

• Make sure you have a .libcst.codemod.yaml with 'autotyping' in the modules list. For an example, see the .libcst.codemod.yaml in this repo.
• Run python -m libcst.tool codemod autotyping.AutotypeCommand /path/to/my/code

Limitations

Autotyping is intended to be a simple tool that uses heuristics to find annotations that would be tedious to add by hand. The heuristics may fail, and after you run autotyping you should run a type checker to verify that the types it added are correct.

Known limitations:

• autotyping does not model code flow through a function, so it may miss implicit None returns

Changelog

24.3.0 (March 25, 2024)

• Add simpler ways to invoke autotyping. Now, it is possible to simply use python3 -m autotyping to invoke the tool. (Thanks to Shantanu Jain.)
• Drop support for Python 3.7; add support for Python 3.12. (Thanks to Hugo van Kemenade.)
• Infer return types for some more magic methods. (Thanks to Dhruv Manilawala.)

23.3.0 (March 3, 2023)

• Fix crash on certain argument names like iterables (contributed by Marco Gorelli)

23.2.0 (February 3, 2023)

• Add --guess-common-names (contributed by John Litborn)
• Fix the --safe and --aggressive flags so they don't take ignored arguments
• --length-hint should return int (contributed by Nikita Sobolev)
• Fix bug in import adding (contributed by Shantanu)

22.9.0 (September 5, 2022)

• Add --safe and --aggressive
• Add --pyanalyze-report
• Do not add None return types to methods marked with @abstractmethod and to methods in stub files
• Improve type inference:
  • "string" % ... is always str
  • b"bytes" % ... is always bytes
  • An and or or operator where left and right sides are of the same type returns that type
  • is, is not, in, and not in always return bool

21.12.0 (December 21, 2021)

• Initial PyPI release