static-typing 0.2.7.dev1

add static type information to Python abstract syntax trees

Attempt to add static type information to Python abstract syntax trees (ASTs).

Works best with ASTs from typed_ast module, however it also works with built-in ast module.

Be advised that this is an ongoing work, and current implementation is subject to sudden changes.

Contents

• How to use

  • AST manipulation

• How it’s implemented

  • Type hint resolution

  • Type information combining

  • AST rewriting

• Requirements

How to use

You can use the static_typing module to parse the code directly using parse() function:

import static_typing as st
class MyClass:
    pass
module = st.parse('def my_fun(obj: MyClass) -> MyClass: return obj')
# TODO: currently there is no public API yet
functions = module._functions
my_fun = module._functions['my_fun']
assert MyClass in my_fun._params['obj']

Or, you can augment existing AST using augment() function:

import static_typing as st
import typed_ast.ast3
module = typed_ast.ast3.parse('''def spam(): x, y, z = 'ham', 42, 3.1415  # type: str, int, float''')
module = st.augment(module)
# TODO: currently there is no public API yet
function = module._functions['spam']
assert len(function._local_vars) == 3
assert float in function._local_vars['z']

For more examples see examples.ipynb notebook.

AST manipulation

Additionally to the main features, the library contains static_typing.ast_manipulation module which contains low-level tools and building blocks allowing for:

• recursive AST traversal,

• AST validation,

• recursive AST transformations,

• AST transcribing (from typed_ast to built-in ast and vice versa) and

• resolving type hints (described in detail below).

How it’s implemented

The process or static typing, which the augment() function implements, has 3 main steps:

• type hint resolution,

• type information combining and

• AST rewriting.

Type hint resolution

In all applicable nodes, type hints are stored in fields type_comment, annotation and returns. The type hint resolver reads those fields – which themseves are either raw strings or ASTs.

It uses provided Python symbol tables to resolve type hints into actual type objects using introspection.

By default, the resolver uses only built-in symbols when called directly or through augment(). However, when called through parse() it uses globals() and locals() of the caller by default.

The resolved type hints are stored directly in the AST. Specifically, each resolved field is stored in a correspondingly named field, which is either resolved_type_comment, resolved_annotation or resolved_returns.

Thus, static type information becomes available in the AST.

Type information combining

For each AST node that might contain any name declarations, an exetended version of a node is provided. Each extended AST node has new fields that store those declared names and type information associated with each name.

These new fields store all type information from all resolved type hints within any local scope, so that a type conflict or lack of type information can be detected. Also, based on this combined information, type inference can be performed.

Specifically, new versions of following AST nodes with new fields are provided: Module, FunctionDef, ClassDef, Assign, AnnAssign, For and With. Those new versions have their names prefixed StaticallyTyped....

A list of entities for which information is gathered in those new fields follows.

For Module:

• defined variables

• defined functions

• defined classes

For FunctionDef:

• parameters and their types

• return types

• kind (i.e. function, instance method, class method, static method, etc.)

• local variables and their types

For ClassDef:

• defined methods (all together and grouped by kind)

• class fields and their types

• instance fields and their types

For Assign and AnnAssign:

• assignment targets and their types

For For:

• index variables and their types

For With:

• context variables and their types

AST rewriting

The AST rewriting means replacing ordinary AST nodes listed above with their extended versions.

Requirements

Python version >= 3.4.

Python libraries as specified in requirements.txt.

Building and running tests additionally requires packages listed in test_requirements.txt.

Tested on Linux and Windows.