Numba code slower than pure python

The problem is that numba can't intuit the type of lookup. If you put a print nb.typeof(lookup) in your method, you'll see that numba is treating it as an object, which is slow. Normally I would just define the type of lookup in a locals dict, but I was getting a strange error. Instead I just created a little wrapper, so that I could explicitly define the input and output types.

@nb.jit(nb.f8[:](nb.f8[:]))def numba_cumsum(x):    return np.cumsum(x)@nb.autojitdef numba_resample2(qs, xs, rands):    n = qs.shape[0]    #lookup = np.cumsum(qs)    lookup = numba_cumsum(qs)    results = np.empty(n)    for j in range(n):        for i in range(n):            if rands[j] < lookup[i]:                results[j] = xs[i]                break    return results

Then my timings are:

print "Timing Numba Function:"%timeit numba_resample(qs, xs, rands)print "Timing Revised Numba Function:"%timeit numba_resample2(qs, xs, rands)

Timing Numba Function:100 loops, best of 3: 8.1 ms per loopTiming Revised Numba Function:100000 loops, best of 3: 15.3 µs per loop

You can go even a little faster still if you use jit instead of autojit:

@nb.jit(nb.f8[:](nb.f8[:], nb.f8[:], nb.f8[:]))

For me that lowers it from 15.3 microseconds to 12.5 microseconds, but it's still impressive how well autojit does.

Faster numpy version (10x speedup compared to numpy_resample)

def numpy_faster(qs, xs, rands):    lookup = np.cumsum(qs)    mm = lookup[None,:]>rands[:,None]    I = np.argmax(mm,1)    return xs[I]