def test_fconv_is_faster_than_convolve():
x = np.random.random((6000))
y = np.random.random((6000))
t1 = time.time()
np.convolve(x,y)
t2 = time.time()
fconv(x,y)
t3 = time.time()
assert_true((t3-t2)<(t2-t1))
def test_fconv_is_faster_than_convolve():
x = np.random.random((6000))
y = np.random.random((6000))
t1 = time.time()
np.convolve(x, y)
t2 = time.time()
fconv(x, y)
t3 = time.time()
assert_true((t3 - t2) < (t2 - t1))
def test_fconv_returns_double_when_input_is_double():
x = np.random.random((10)).astype(np.double)
y = np.random.random((10)).astype(np.double)
res = fconv(x,y)
assert_true(res.dtype==np.double)
def test_fconv_returns_complex_when_input_is_complex():
x = np.random.random((10)).astype(np.complex)
y = np.random.random((10)).astype(np.complex)
res = fconv(x,y)
assert_true(res.dtype==np.complex)
def _assert_numpy_convolve_gives_same_results_as_fconv_for(x,y):
conv_np = np.convolve(x,y)
conv_fconv = fconv(x,y)
assert_array_almost_equal(conv_np, conv_fconv)
def test_fconv_returns_double_when_input_is_double():
x = np.random.random((10)).astype(np.double)
y = np.random.random((10)).astype(np.double)
res = fconv(x, y)
assert_true(res.dtype == np.double)
def test_fconv_returns_complex_when_input_is_complex():
x = np.random.random((10)).astype(np.complex)
y = np.random.random((10)).astype(np.complex)
res = fconv(x, y)
assert_true(res.dtype == np.complex)
def _assert_numpy_convolve_gives_same_results_as_fconv_for(x, y):
conv_np = np.convolve(x, y)
conv_fconv = fconv(x, y)
assert_array_almost_equal(conv_np, conv_fconv)
