import time
def foo(x,y):
  tt = time.time()
  s = 0
  for i in range(x,y):
    s += i
  print('Time used: {} sec'.format(time.time()-tt))
  return s
print(foo(1,100000000))

from numba import jit
import time
@jit
def foo(x,y):
  tt = time.time()
  s = 0
  for i in range(x,y):
    s += i
  print('Time used: {} sec'.format(time.time()-tt))
  return s
print(foo(1,100000000))

import numba as nb
from numba import jit
@jit('f8(f8[:])')
def sum1d(array):
 s = 0.0
 n = array.shape[0]
 for i in range(n):
  s += array[i]
 return s
import numpy as np
array = np.random.random(10000)
%timeit sum1d(array)
%timeit np.sum(array)
%timeit sum(array)
10000 loops, best of 3: 38.9 us per loop
10000 loops, best of 3: 32.3 us per loop
100 loops, best of 3: 12.4 ms per loop

print sum1d(np.ones(10, dtype=np.int32))
print sum1d(np.ones(10, dtype=np.float32))
print sum1d(np.ones(10, dtype=np.float64))
1.2095376009e-312
1.46201599944e+185
10.0

from numba import autojit
@autojit
def sum1d2(array):
 s = 0.0
 n = array.shape[0]
 for i in range(n):
  s += array[i]
 return s
%timeit sum1d2(array)
print sum1d2(np.ones(10, dtype=np.int32))
print sum1d2(np.ones(10, dtype=np.float32))
print sum1d2(np.ones(10, dtype=np.float64))
10000 loops, best of 3: 143 us per loop
10.0
10.0
10.0

print [obj for obj in nb.__dict__.values() if isinstance(obj, nb.minivect.minitypes.Type)]
[size_t, Py_uintptr_t, uint16, complex128, float, complex256, void, int , long double,
unsigned PY_LONG_LONG, uint32, complex256, complex64, object_, npy_intp, const char *,
double, unsigned short, float, object_, float, uint64, uint32, uint8, complex128, uint16,
int, int , uint8, complex64, int8, uint64, double, long double, int32, double, long double,
char, long, unsigned char, PY_LONG_LONG, int64, int16, unsigned long, int8, int16, int32,
unsigned int, short, int64, Py_ssize_t]

def add2(a, b):
 return a + b

from meta.decompiler import decompile_func
from meta.asttools import str_ast
print str_ast(decompile_func(add2))
FunctionDef(args=arguments(args=[Name(ctx=Param(),
          id='a'),
         Name(ctx=Param(),
          id='b')],
       defaults=[],
       kwarg=None,
       vararg=None),
   body=[Return(value=BinOp(left=Name(ctx=Load(),
            id='a'),
          op=Add(),
          right=Name(ctx=Load(),
            id='b')))],
   decorator_list=[],
   name='add2')

from meta.asttools import python_source
python_source(decompile_func(add2))
def add2(a, b):
 return (a + b)

with open("tmp.py", "w") as f:
 f.write("""
def square_sum(n):
 s = 0
 for i in range(n):
  s += i**2
 return s
""")
import py_compile
py_compile.compile("tmp.py")

with open("tmp.pyc", "rb") as f:
 decompile_pyc(f)
def square_sum(n):
 s = 0
 for i in range(n):
  s += (i ** 2)
 return s

from llvm.core import Module, Type, Builder
from llvm.ee import ExecutionEngine, GenericValue
# Create a new module with a function implementing this:
#
# int add(int a, int b) {
# return a + b;
# }
#
my_module = Module.new('my_module')
ty_int = Type.int()
ty_func = Type.function(ty_int, [ty_int, ty_int])
f_add = my_module.add_function(ty_func, "add")
f_add.args[0].name = "a"
f_add.args[1].name = "b"
bb = f_add.append_basic_block("entry")
# IRBuilder for our basic block
builder = Builder.new(bb)
tmp = builder.add(f_add.args[0], f_add.args[1], "tmp")
builder.ret(tmp)
# Create an execution engine object. This will create a JIT compiler
# on platforms that support it, or an interpreter otherwise
ee = ExecutionEngine.new(my_module)
# Each argument needs to be passed as a GenericValue object, which is a kind
# of variant
arg1 = GenericValue.int(ty_int, 100)
arg2 = GenericValue.int(ty_int, 42)
# Now let's compile and run!
retval = ee.run_function(f_add, [arg1, arg2])
# The return value is also GenericValue. Let's print it.
print "returned", retval.as_int()
returned 142

addr = ee.get_pointer_to_function(f_add)
addr
2975997968L

import ctypes
f_type = ctypes.PYFUNCTYPE(ctypes.c_int, ctypes.c_int, ctypes.c_int)

f = f_type(addr)
f(100, 42)
142