def test_definition_ortho(self):
# Test orthornomal mode.
for i in range(len(X)):
x = np.array(X[i], dtype=self.rdt)
dt = np.result_type(np.float32, self.rdt)
y = dct(x, norm='ortho', type=2)
xi = dct(y, norm="ortho", type=3)
assert_equal(xi.dtype, dt)
assert_array_almost_equal(xi, x, decimal=self.dec)
def test_axis(self):
nt = 2
for i in [7, 8, 9, 16, 32, 64]:
x = np.random.randn(nt, i)
y = dct(x, type=self.type)
for j in range(nt):
assert_array_almost_equal(y[j],
dct(x[j], type=self.type),
decimal=self.dec)
x = x.T
y = dct(x, axis=0, type=self.type)
for j in range(nt):
assert_array_almost_equal(y[:, j],
dct(x[:, j], type=self.type),
decimal=self.dec)
def test_definition_matlab(self):
# Test correspondance with matlab (orthornomal mode).
for i in range(len(X)):
dt = np.result_type(np.float32, self.rdt)
x = np.array(X[i], dtype=dt)
yr = Y[i]
y = dct(x, norm="ortho", type=2)
assert_equal(y.dtype, dt)
assert_array_almost_equal(y, yr, decimal=self.dec)
def test_definition(self):
for i in FFTWDATA_SIZES:
x, yr, dt = fftw_dct_ref(self.type, i, self.rdt)
y = dct(x, type=self.type)
assert_equal(y.dtype, dt)
# XXX: we divide by np.max(y) because the tests fail otherwise. We
# should really use something like assert_array_approx_equal. The
# difference is due to fftw using a better algorithm w.r.t error
# propagation compared to the ones from fftpack.
assert_array_almost_equal(y / np.max(y),
yr / np.max(y),
decimal=self.dec,
err_msg="Size %d failed" % i)
def test_dct_complex(self):
y = dct(np.arange(5) * 1j)
x = 1j * dct(np.arange(5))
assert_array_almost_equal(x, y)
def test_dct_complex64(self):
y = dct(1j * np.arange(5, dtype=np.complex64))
x = 1j * dct(np.arange(5))
assert_array_almost_equal(x, y)