def test_dct3_definition_ortho(mdata_x, rdt):
# Test orthornomal mode.
x = np.array(mdata_x, dtype=rdt)
dt = np.result_type(np.float32, rdt)
y = dct(x, norm='ortho', type=2)
xi = dct(y, norm="ortho", type=3)
dec = dec_map[(dct, rdt, 3)]
assert_equal(xi.dtype, dt)
assert_array_almost_equal(xi, x, decimal=dec)
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, rdt, type, size):
nt = 2
dec = dec_map[(dct, rdt, type)]
x = np.random.randn(nt, size)
y = dct(x, type=type)
for j in range(nt):
assert_array_almost_equal(y[j], dct(x[j], type=type),
decimal=dec)
x = x.T
y = dct(x, axis=0, type=type)
for j in range(nt):
assert_array_almost_equal(y[:,j], dct(x[:,j], type=type),
decimal=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_dct4_definition_ortho(mdata_x, rdt):
# Test orthornomal mode.
x = np.array(mdata_x, dtype=rdt)
dt = np.result_type(np.float32, rdt)
y = dct(x, norm='ortho', type=4)
y2 = naive_dct4(x, norm='ortho')
dec = dec_map[(dct, rdt, 4)]
assert_equal(y.dtype, dt)
assert_allclose(y, y2, rtol=0., atol=np.max(y2)*10**(-dec))
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=4)
y2 = naive_dct4(x, norm='ortho')
assert_equal(y.dtype, dt)
assert_array_almost_equal(y / np.max(y), y2 / np.max(y), decimal=self.dec)
def test_dct2_definition_matlab(mdata_xy, rdt):
# Test correspondence with matlab (orthornomal mode).
dt = np.result_type(np.float32, rdt)
x = np.array(mdata_xy[0], dtype=dt)
yr = mdata_xy[1]
y = dct(x, norm="ortho", type=2)
dec = dec_map[(dct, rdt, 2)]
assert_equal(y.dtype, dt)
assert_array_almost_equal(y, yr, decimal=dec)
def test_definition_matlab(self):
# Test correspondence 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_definition(self, rdt, type, fftwdata_size):
x, yr, dt = fftw_dct_ref(type, fftwdata_size, rdt)
y = dct(x, type=type)
assert_equal(y.dtype, dt)
dec = dec_map[(dct, rdt, type)]
assert_allclose(y, yr, rtol=0., atol=np.max(yr)*10**(-dec))
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)
