# UFuncs¶

## Ufuncs¶

Numba’s vectorize allows Numba functions taking scalar input arguments to be used as NumPy ufuncs (see http://docs.scipy.org/doc/numpy/reference/ufuncs.html). Creating a traditional NumPy ufunc is not the most difficult task in the world, but it is also not the most straightforward process and involves writing some C code. Numba makes this easy though. Using the vectorize decorator, Numba can compile a Python function into a ufunc that operates over NumPy arrays as fast as traditional ufuncs written in C.

Ufunc arguments are scalars of a NumPy array. Function definitions can be arbitrary mathematical expressions. The vectorize decorator needs to know the argument and return types of the ufunc. These are specified much like the jit decorator:

import math

@vectorize(['float64(float64, float64)'])
def my_ufunc(x, y):
return x+y+math.sqrt(x*math.cos(y))

a = np.arange(1.0, 10.0)
b = np.arange(1.0, 10.0)
# Calls compiled version of my_ufunc for each element of a and b
print(my_ufunc(a, b))


Multiple signatures can be specified to handle multiple input types:

@vectorize(['int32(int32, int32)',
'float64(float64, float64)'])
def my_ufunc(x, y):
return x+y+math.sqrt(x*math.cos(y))

a = np.arange(1.0, 10.0, dtype='f8')
b = np.arange(1.0, 10.0, dtype='f8')
print(my_ufunc(a, b))

a = np.arange(1, 10, dtype='i4')
b = np.arange(1, 10, dtype='i4')
print(my_ufunc(a, b))


The order of the signatures is important. Numba dispatches based on the input array types and uses the first ufunc signature that the input types can be safely cast to. In the example above, if the float64 signature had been listed first, the call to sum with int32 arrays would have produced a float64 array as the result.

An alternative syntax is to use the UFuncBuilder object to build a list of function signatures:

from numba.npyufunc.ufuncbuilder import UFuncBuilder

def my_ufunc(x, y):
return x+y+math.sqrt(x*math.cos(y))

builder = UFuncBuilder(my_ufunc)


To compile our ufunc we call the build_ufunc method:

compiled_ufunc = builder.build_ufunc()

a = np.arange(1.0, 10.0, dtype='f8')
b = np.arange(1.0, 10.0, dtype='f8')
print(compiled_ufunc(a, b))


Since we defined a binary ufunc, we can use the various NumPy ufunc methods such as reduce, accumulate, etc:

a = np.arange(100)
print(compiled_ufunc.reduce(a))
print(compiled_ufunc.accumulate(a))


## Generalized Ufuncs¶

Numba also provides support for generalized ufuncs with the guvectorize decorator. Traditional ufuncs perfom element-wise operations, whereas generalized ufuncs operate on entire sub-arrays. In addition to the argument and return types, the guvectorize decorator takes an additional signature which specifies the shapes of the inner arrays we want to operate on:

import math

@guvectorize(['void(int32[:,:], int32[:,:], int32[:,:])',
'void(float64[:,:], float64[:,:], float64[:,:])'],
'(x, y),(x, y)->(x, y)')
def my_gufunc(a, b, c):
for i in range(c.shape[0]):
for j in range(c.shape[1]):
c[i, j] = a[i, j] + b[i, j]

a = np.arange(1.0, 10.0, dtype='f8').reshape(3,3)
b = np.arange(1.0, 10.0, dtype='f8').reshape(3,3)
# Calls compiled version of my_gufunc for each row of a and b
print(my_gufunc(a, b))


Notice that we don’t have a third argument in the gufunc call but the generalized ufunc definition above has three arguments. The last argument of the generalized ufunc is the output, which is automatically allocated with the shape specified in the signature.

Generalized ufuncs also have an alternative syntax. We can use the GUFuncBuilder object to build a list of function signatures and specify the shape of the arguments:

from numba.npyufunc.ufuncbuilder import GUFuncBuilder

def my_gufunc(a, b, c):
for i in range(c.shape[0]):
for j in range(c.shape[1]):
c[i, j] = a[i, j] + b[i, j]

builder = GUFuncBuilder(my_ufunc, '(x, y),(x, y)->(x, y)')

compiled_gufunc = builder.build_ufunc()