comparison

import numpy as np, numba as nb
from numpy import array as npa
from numba import jit, njit
import matplotlib.pyplot as plt
import random
import time
import math


@nb.vectorize('i8(i8,i8)')
def diffIt(h, l):
    return h - l


@njit('(i8[:, :], i8[:])')
def diff_temps(aa, bb):
    for i in range(len(aa)):
        bb[i] = aa[i, 0] - aa[i, 1]
    return bb


N = 10000000
highTemps = list(range(10, N+10))
lowTemps = [i - random.randint(0, 20) for i in highTemps]
aTemps = np.vstack([np.array(highTemps), np.array(lowTemps)]).T
print(aTemps.shape)

start = time.time()
out = [h - l for (h, l) in zip(highTemps, lowTemps)]
t1 = time.time() - start
start = time.time()
out = aTemps[:, 0] - aTemps[:, 1]
t2 = time.time() - start
#
start = time.time()
out = diffIt(aTemps[:, 0], aTemps[:, 1])
t3 = time.time() - start
start = time.time()
b = np.empty(N, dtype=int)
out = diff_temps(aTemps, b)
t4 = time.time() - start
print('ListComp takes %.5fs, Numpy takes %.5fs' %(t1, t2))
print('NumbaUfunc takes %.5fs, Numba takes %.5fs' %(t3, t4))
print
print(out.shape, out)



# (5000000, 2)
# ListComp takes 1.36459s, Numpy takes 0.01582s
# NumbaUfunc takes 0.01582s, Numba takes 0.01049s
# ((5000000,), array([17,  8,  8, ..., 19, 11, 17]))
# Process finished with exit code 0