macro-kit is a package for efficient macro recording and metaprogramming in Python using abstract syntax tree (AST).
The design of AST in this package is strongly inspired by Julia metaprogramming. Similar methods are also implemented in builtin ast module but macro-kit (Julia-style metaprogramming) is more convenient in code operation and also focused on the macro generation and customization.
- use pip
pip install macro-kit -U
- from source
pip install git+https://github.com/hanjinliu/macro-kit
- Define a macro-recordable function
from macrokit import Macro, Expr, Symbol
macro = Macro()
@macro.record
def str_add(a, b):
return str(a) + str(b)
val0 = str_add(1, 2)
val1 = str_add(val0, "xyz")
macro[Out]
var0x24fdc2d1530 = str_add(1, 2)
var0x24fdc211df0 = str_add(var0x24fdc2d1530, 'xyz')
Use format method to rename variable names.
# substitute identifiers of variables
# var0x24fdc2d1530 -> x
macro.format([(val0, "x")])[Out]
x = str_add(1, 2)
var0x24fdc211df0 = str_add(x, 'xyz')
format also support substitution with more complicated expressions.
# substitute to _dict["key"]
expr = Expr(head="getitem", args=[Symbol("_dict"), "key"])
macro.format([(val0, expr)])[Out]
_dict['key'] = str_add(1, 2)
var0x24fdc211df0 = str_add(_dict['key'], 'xyz')
- Record class
macro = Macro()
@macro.record
class C:
def __init__(self, val: int):
self.value = val
@property
def value(self):
return self._value
@value.setter
def value(self, new_value: int):
if not isinstance(new_value, int):
raise TypeError("new_value must be an integer.")
self._value = new_value
def show(self):
print(self._value)
c = C(1)
c.value = 5
c.value = -10
c.show()[Out]
-10
Note that value assignments are not recorded in duplicate.
macro.format([(c, "ins")])[Out]
ins = C(1)
ins.value = -10
var0x7ffed09d2cd8 = ins.show()
eval can evaluate macro.
macro.eval({"C": C})[Out]
-10
- Record module
import numpy as np
macro = Macro()
np = macro.record(np) # macro-recordable numpy
arr = np.random.random(30)
mean = np.mean(arr)
macro[Out]
var0x2a0a2864090 = numpy.random.random(30)
var0x2a0a40daef0 = numpy.mean(var0x2a0a2864090)
Recorded module is stored in Symbol so you can safely eval the macro without passing the module object as the global variables.
macro.eval() # this works- String parsing
parse calls ast.parse inside so that you can safely make Expr from string.
from macrokit import parse
expr = parse("result = f(0, l[2:8])")
expr[Out]
:(result = f(0, l[slice(2, 8, None)]))
print(expr.dump())[Out]
head: assign
args:
0: result
1: head: call
args:
0: f
1: 0
2: head: getitem
args:
0: l
1: slice(2, 8, None)