easy-ast 0.0.2

This package `easy-ast` contains several utility about AST transformer.

easy-ast

This package easy-ast contains several utility about AST transformer.

Install

pip install easy-ast.

Documents

AstDecorator

This example exchange all plus and mult.

import ast
from easy_ast import *


class PlusMultExchange(AstDecorator):

    def visit_BinOp(self, node):
        if isinstance(node.op, ast.Add):
            result = ast.BinOp(left=node.left, op=ast.Mult(), right=node.right)
        elif isinstance(node.op, ast.Mult):
            result = ast.BinOp(left=node.left, op=ast.Add(), right=node.right)
        else:
            result = node
        return self.generic_visit(result)


@PlusMultExchange()
def add(a, b):
    return a + b


assert add(2, 3) == 6

Get AST instance

Provide Statements and Expression to get the AST instance of code. Exec and Eval is used to exec/eval code directly on AST

import ast
from easy_ast import *


@Expression
def tree():
    (x + 1)**2


assert isinstance(tree, ast.Expression)

x = 6
assert Eval(tree) == 49

Because of the limit of python, executing code will not effect variable in time, which may be solved in the future, see pep558 and pep667. Currently, the only way to use variable updated in exec is to visit it via locals() manually, or use it inside exec content directly.

import ast
from easy_ast import *


@Statements
def tree():
    y = x + 1
    z = y * 2
    assert z == 6


assert isinstance(tree, ast.Module)

x = 2
Exec(tree)
assert locals()["z"] == 6

Macro

Class Macro is used to create macro. Here is a simple example to do operator directly on python list.

import ast
from easy_ast import *


class List(Macro):

    def __init__(self):
        super().__init__()
        self.symbol_current = 0

    def visit_BinOp(self, node):
        i = f"__list_loop_variable_{self.symbol_current}"
        self.symbol_current += 1
        j = f"__list_loop_variable_{self.symbol_current}"
        self.symbol_current += 1
        # Return [op(left, right) for i,j in zip(left, right)]
        return ast.ListComp(
            elt=ast.BinOp(
                op=node.op,
                left=ast.Name(id=i, ctx=ast.Load()),
                right=ast.Name(id=j, ctx=ast.Load()),
            ),
            generators=[
                ast.comprehension(
                    target=ast.Tuple(elts=[
                        ast.Name(id=i, ctx=ast.Store()),
                        ast.Name(id=j, ctx=ast.Store()),
                    ]),
                    iter=ast.Call(
                        func=ast.Name(id="zip", ctx=ast.Load()),
                        args=[
                            self.generic_visit(node.left),
                            self.generic_visit(node.right),
                        ],
                        keywords=[],
                    ),
                    ifs=[],
                    is_async=0,
                )
            ],
        )


a = [1, 2, 3]
b = [1, 2, 3]


@List().eval
def c():
    a * b


assert c == [1, 4, 9]

Tensor contract

This repository implements an AST transformer for Einstein notation based on Macro for numpy array.

import numpy as np
from easy_ast.tensor_contract import TensorContract

b = np.array([1, 2])
c = np.array([1, 2])
expect = -np.array([[1, 2], [2, 4]])


@TensorContract().exec
def _(i, j):
    a[i, j] = -b[i] * c[j]
    assert np.all(a == expect)


a = np.random.randn(3, 2, 6, 5)
b = np.random.randn(3, 4)
c = np.random.randn(5, 4, 2)
d = np.einsum("ijk,il,klm->mj", a[:, 0], b, c)
assert d.shape == (2, 6)
r = np.zeros([6, 2, 2])


@TensorContract().exec
def _(i, j, k, l, m):
    # i3, j6, k5, l4, m2
    r[j, m, 1] = a[i, 0, j, k] * b[i, l] * c[k, l, m] - d[m, j]
    assert np.sum(np.abs(r)) < 1e-6