def test_mvectorize(self):
x = np.linspace( 1, 1000, 10000 )
mf_fn = mvectorize( nb_fn, ( double[:], double[:] ), num_threads=8 )
result = mf_fn( x )
expected = np.vectorize( fn )( x )
np.testing.assert_array_equal( expected, result )
def test_mvectorize_performance(self):
x = np.linspace(0, 1000)
mf_fn = mvectorize(nb_fn, (double[:], double[:]), num_threads=8)
# Call once to initialize
mf_fn(x)
num_tests = 100
with Timer('mf_nb') as nbtimer:
for _ in range(num_tests):
mf_fn(x)
np_fn = np.vectorize(fn)
with Timer('np') as nptimer:
for _ in range(num_tests):
np_fn(x)
ratio = nbtimer.interval / nptimer.interval
print('Numba version took %s as long as numpy' % ratio)
return 2.0 * np.floor(xi / 2.0)
elif xi <= 100.0:
return 5.0 * np.floor(xi / 5.0)
elif xi <= 1000.0:
return 10.0 * np.floor(xi / 10.0)
else:
return 1000.0
return 0.0
signature = double(double, )
print('Compiling jit function')
nb_floor_closest_valid_odds = jit(signature,
nopython=True)(floor_closest_valid_odds)
print('Compiling 4 thread')
mf4 = mvectorize(nb_floor_closest_valid_odds, (double[:], double[:]),
num_threads=4)
print('Compiling 6 thread')
mf6 = mvectorize(nb_floor_closest_valid_odds, (double[:], double[:]),
num_threads=6)
print('Compiling 7 thread')
mf7 = mvectorize(nb_floor_closest_valid_odds, (double[:], double[:]),
num_threads=7)
print('Compiling 8 thread')
mf8 = mvectorize(nb_floor_closest_valid_odds, (double[:], double[:]),
num_threads=8)
print('Compiling 16 thread')
mf16 = mvectorize(nb_floor_closest_valid_odds, (double[:], double[:]),
num_threads=16)
signature = double[:](double[:], )
vf = vectorize(['float64(float64)'],