macro-kit 0.3.4

Macro recording and metaprogramming in Python

macro-kit

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 is more focused on the macro generation and customization.

Installation

• use pip

pip install macro-kit

• from source

pip install git+https://github.com/hanjinliu/macro-kit

Examples

1. 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')

2. 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

3. 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)

from dask import array as da
dask_macro = macro.format([(np, "da")])
dask_macro

[Out]
var0x2a0a2864090 = da.random.random(30)
var0x2a0a40daef0 = da.mean(var0x2a0a2864090)

output = {}
dask_macro.eval({"da": da}, output)
output

[Out]
{:da: <module 'dask.array' from 'C:\\...\\__init__.py'>,
 :var0x2a0a2864090: dask.array<random_sample, shape=(30,), dtype=float64, chunksize=(30,), chunktype=numpy.ndarray>,
 :var0x2a0a40daef0: dask.array<mean_agg-aggregate, shape=(), dtype=float64, chunksize=(), chunktype=numpy.ndarray>}

4. 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)