def test_scaleABCD_Q(self):
"""Test function for scaleABCD() with Imag ABCD"""
ABCD_IN = np.array([[
0.888 + 0.4598j, 0. + 0.j, 0. + 0.j, 0. + 0.j, 0.0516 + 0.j,
0.0516 + 0.j
],
[
0.6073 + 0.7945j, 0.9530 + 0.3028j, 0. + 0.j,
0. + 0.j, 0. + 0.j, 0.3230 + 0.j
],
[
0. + 0.j, 0.9459 + 0.3245j, 0.9239 + 0.3827j,
0. + 0.j, 0. + 0.j, 0.9162 + 0.j
],
[
0. + 0.j, 0. + 0.j, 0. + 0.j, 0.9239 - 0.3827j,
0. + 0.j, 0.1778 + 0.j
],
[
0. + 0.j, 0. + 0.j, -0.9935 - 0.1138j,
-0.7405 - 0.672j, 0. + 0.j, 0. + 0.j
]])
ABCD, umax, S = ds.scaleABCD(ABCD_IN, nlev=9, f=1. / 16)
#references here because I prefer to have 1 file
umax_ref = 8
S_ref = np.array([[0.3586, 0., 0., 0.], [0., 0.1207, 0., 0.],
[0., 0., 0.0805, 0.], [0., 0., 0., 0.3671]])
ABCD_ref = np.array(
[[
0.888037198535288 + 0.459771610712905j, 0.0 + 0.0j, 0.0 + 0.0j,
0.0 + 0.0j, 0.0185052748572140 + 0.0j,
0.0185052748572140 + 4.35415940995948e-18j
],
[
0.204472470161495 + 0.267520232240738j,
0.953044748762363 + 0.302829501298050j, 0.0 + 0.0j,
0.0 + 0.0j, 0.0 + 0.0j, 0.0389934281708177 + 0.0j
],
[
0.0 + 0.0j, 0.630626110921381 + 0.216330153871036j,
0.923879532511340 + 0.382683432365112j, 0.0 + 0.0j,
0.0 + 0.0j, 0.0737451799430360 - 1.22879569706001e-17j
],
[
0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j,
0.923879532511287 - 0.382683432365090j, 0.0 + 0.0j,
0.0652579452814600 + 1.01879713420556e-17j
],
[
0.0 + 0.0j, 0.0 + 0.0j, -12.3424213726332 - 1.41434395098423j,
-2.01750453626270 - 1.83077728688503j, 0.0 + 0.0j, 0.0 + 0.0j
]])
assert np.allclose(ABCD, ABCD_ref, atol=1e-2, rtol=1e-1)
assert np.allclose(S, S_ref, atol=1e-3, rtol=1e-2)
assert umax == umax_ref
def test_scaleABCD(self):
"""Test function for scaleABCD()"""
ABCD, umax, S = ds.scaleABCD(self.ABCD0, self.nlev, self.f0)
# mapping the NTF to states, there is not a perfect match between
# the original code and the scipy version. -> rtol approx 20%
#if not np.allclose(ABCD, ABCD_ref, atol=1e-2, rtol=3e-1):
# aerr = ABCD_ref-ABCD
# rerr = 2*(ABCD_ref-ABCD)/(ABCD_ref+ABCD)
# print(repr(ABCD_ref))
# print(repr(ABCD))
# print(aerr)
# print(rerr)
# this is a rather high relative error. We get it on Travis-CI
# Probably related to the libs used?
self.assertTrue(np.allclose(ABCD, self.ABCD_ref, atol=1e-2, rtol=30e-2))
self.assertTrue(np.allclose(umax, self.umax_ref, atol=1e-4, rtol=1e-3))
self.assertTrue(np.allclose(np.diag(S), self.Sdiag_ref, atol=1e-2, rtol=25e-1))
def test_scaleABCD_Q(self):
"""Test function for scaleABCD() with Imag ABCD"""
ABCD_IN = np.array([[0.888+0.4598j, 0.+0.j, 0.+0.j, 0.+0.j, 0.0516+0.j,
0.0516+0.j],
[0.6073+0.7945j, 0.9530+0.3028j, 0.+0.j, 0.+0.j,
0.+0.j, 0.3230+0.j],
[0.+0.j, 0.9459+0.3245j, 0.9239+0.3827j, 0.+0.j,
0.+0.j, 0.9162+0.j],
[0.+0.j, 0.+0.j, 0.+0.j, 0.9239-0.3827j, 0.+0.j,
0.1778+0.j],
[0.+0.j, 0.+0.j, -0.9935-0.1138j, -0.7405-0.672j,
0.+0.j, 0.+0.j]])
ABCD, umax, S = ds.scaleABCD(ABCD_IN, nlev=9, f=1./16)
#references here because I prefer to have 1 file
umax_ref = 8
S_ref = np.array([[0.3586, 0., 0., 0.],
[0., 0.1207, 0., 0.],
[0., 0., 0.0805, 0.],
[0., 0., 0., 0.3671]])
ABCD_ref = np.array([[0.888037198535288 + 0.459771610712905j,
0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j,
0.0185052748572140 + 0.0j,
0.0185052748572140 + 4.35415940995948e-18j],
[0.204472470161495 + 0.267520232240738j,
0.953044748762363 + 0.302829501298050j,
0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j,
0.0389934281708177 + 0.0j],
[0.0 + 0.0j,
0.630626110921381 + 0.216330153871036j,
0.923879532511340 + 0.382683432365112j,
0.0 + 0.0j, 0.0 + 0.0j,
0.0737451799430360 - 1.22879569706001e-17j],
[0.0 + 0.0j, 0.0 + 0.0j, 0.0 + 0.0j,
0.923879532511287 - 0.382683432365090j,
0.0 + 0.0j,
0.0652579452814600 + 1.01879713420556e-17j],
[0.0 + 0.0j, 0.0 + 0.0j, -12.3424213726332 -
1.41434395098423j, -2.01750453626270 -
1.83077728688503j, 0.0 + 0.0j, 0.0 + 0.0j]])
assert np.allclose(ABCD, ABCD_ref, atol=1e-2, rtol=1e-1)
assert np.allclose(S, S_ref, atol=1e-3, rtol=1e-2)
assert umax == umax_ref
def __init__(self, input: FixedPointValue, osr=32, order=4, n_lev=2):
self.input = input
self.fmt = input.fmt
self.h = synthesizeNTF(order, osr, 1)
a, g, b, c = realizeNTF(self.h, form='CIFB')
abcd = stuffABCD(a, g, b, c)
abcd_scaled, umax, s = scaleABCD(abcd, n_lev)
self.parameters = mapABCD(abcd_scaled)
print(umax)
self.order = order
self.osr = osr
assert n_lev > 1
self.n_lev = n_lev
self.quantization_values = [i * 2 - (n_lev / 2) for i in range(n_lev)]
self.output = Signal(range(0, len(self.quantization_values)))
def test_scaleABCD(self):
"""Test function for scaleABCD()"""
ABCD, umax, S = ds.scaleABCD(self.ABCD0, self.nlev, self.f0)
# mapping the NTF to states, there is not a perfect match between
# the original code and the scipy version. -> rtol approx 20%
#if not np.allclose(ABCD, ABCD_ref, atol=1e-2, rtol=3e-1):
# aerr = ABCD_ref-ABCD
# rerr = 2*(ABCD_ref-ABCD)/(ABCD_ref+ABCD)
# print(repr(ABCD_ref))
# print(repr(ABCD))
# print(aerr)
# print(rerr)
# this is a rather high relative error. We get it on Travis-CI
# Probably related to the libs used?
self.assertTrue(np.allclose(ABCD, self.ABCD_ref, atol=1e-2,
rtol=30e-2))
self.assertTrue(np.allclose(umax, self.umax_ref, atol=1e-4, rtol=1e-3))
self.assertTrue(
np.allclose(np.diag(S), self.Sdiag_ref, atol=1e-2, rtol=25e-1))
def test_dsdemo3(self):
"""dsdemo3 test: Delta sigma modulator synthesis"""
order = 5
R = 42
opt = 1
H = ds.synthesizeNTF(order, R, opt)
# ## Evaluation of the coefficients for a CRFB topology
a, g, b, c = ds.realizeNTF(H)
# Use a single feed-in for the input
# Lets check that stuffABCD understands that if b is scalar it means 1 feed-in.
ABCD = ds.stuffABCD(a, g, b[0], c)
# for passing the assertion below, we need b to have the trailing zeros
b = np.concatenate((np.atleast_1d(b[0]), np.zeros((b.shape[0] - 1, ))))
u = np.linspace(0, 0.6, 30)
N = 1e4
T = np.ones((1, N))
maxima = np.zeros((order, len(u)))
for i in range(len(u)):
ui = u[i]
v, xn, xmax, _ = ds.simulateDSM(ui * T, ABCD)
maxima[:, i] = np.squeeze(xmax)
if any(xmax > 1e2):
umax = ui
u = u[:i + 1]
maxima = maxima[:, :i]
break
# save the maxima
prescale_maxima = np.copy(maxima)
# ## Scaled modulator
# ### Calculate the scaled coefficients
ABCDs, umax, _ = ds.scaleABCD(ABCD, N_sim=1e5)
as_, gs, bs, cs = ds.mapABCD(ABCDs)
# ### Calculate the state maxima
u = np.linspace(0, umax, 30)
N = 1e4
T = np.ones((N, ))
maxima = np.zeros((order, len(u)))
for i in range(len(u)):
ui = u[i]
v, xn, xmax, _ = ds.simulateDSM(ui * T, ABCDs)
maxima[:, i] = xmax.squeeze()
if any(xmax > 1e2):
umax = ui
u = u[:i]
maxima = maxima[:, :i]
break
self.assertTrue(np.allclose(ABCD, self.data['ABCD']))
self.assertTrue(
np.allclose(ABCDs, self.data['ABCDs'], atol=1e-2, rtol=5e-2))
self.assertTrue(np.allclose(a, self.data['a'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(b, self.data['b'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(g, self.data['g'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(c, self.data['c'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(as_, self.data['as'], atol=1e-2,
rtol=5e-3))
self.assertTrue(np.allclose(bs, self.data['bs'], atol=5e-3, rtol=5e-3))
self.assertTrue(np.allclose(gs, self.data['gs'], atol=5e-3, rtol=5e-3))
self.assertTrue(np.allclose(cs, self.data['cs'], atol=3e-2, rtol=3e-2))
self.assertTrue(
np.allclose(umax, self.data['umax'], atol=5e-3, rtol=5e-3))
self.assertTrue(
np.allclose(maxima, self.data['maxima'], atol=2e-2, rtol=5e-2))
def test_dsdemo3(self):
"""dsdemo3 test: Delta sigma modulator synthesis"""
order = 5
R = 42
opt = 1
H = ds.synthesizeNTF(order, R, opt)
# ## Evaluation of the coefficients for a CRFB topology
a, g, b, c = ds.realizeNTF(H)
# Use a single feed-in for the input
# Lets check that stuffABCD understands that if b is scalar it means 1 feed-in.
ABCD = ds.stuffABCD(a, g, b[0], c)
# for passing the assertion below, we need b to have the trailing zeros
b = np.concatenate((np.atleast_1d(b[0]),
np.zeros((b.shape[0] - 1,))))
u = np.linspace(0, 0.6, 30)
N = 1e4
T = np.ones((1, N))
maxima = np.zeros((order, len(u)))
for i in range(len(u)):
ui = u[i]
v, xn, xmax, _ = ds.simulateDSM(ui*T, ABCD);
maxima[:, i] = np.squeeze(xmax)
if any(xmax > 1e2):
umax = ui
u = u[:i+1]
maxima = maxima[:, :i]
break
# save the maxima
prescale_maxima = np.copy(maxima)
# ## Scaled modulator
# ### Calculate the scaled coefficients
ABCDs, umax, _ = ds.scaleABCD(ABCD, N_sim=1e5)
as_, gs, bs, cs = ds.mapABCD(ABCDs)
# ### Calculate the state maxima
u = np.linspace(0, umax, 30)
N = 1e4
T = np.ones((N,))
maxima = np.zeros((order, len(u)))
for i in range(len(u)):
ui = u[i]
v, xn, xmax, _ = ds.simulateDSM(ui*T, ABCDs)
maxima[:, i] = xmax.squeeze()
if any(xmax > 1e2):
umax = ui;
u = u[:i]
maxima = maxima[:, :i]
break
self.assertTrue(np.allclose(ABCD, self.data['ABCD']))
self.assertTrue(np.allclose(ABCDs, self.data['ABCDs'], atol=1e-2, rtol=5e-2))
self.assertTrue(np.allclose(a, self.data['a'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(b, self.data['b'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(g, self.data['g'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(c, self.data['c'], atol=1e-5, rtol=1e-3))
self.assertTrue(np.allclose(as_, self.data['as'], atol=1e-2, rtol=5e-3))
self.assertTrue(np.allclose(bs, self.data['bs'], atol=5e-3, rtol=5e-3))
self.assertTrue(np.allclose(gs, self.data['gs'], atol=5e-3, rtol=5e-3))
self.assertTrue(np.allclose(cs, self.data['cs'], atol=3e-2, rtol=3e-2))
self.assertTrue(np.allclose(umax, self.data['umax'], atol=5e-3, rtol=5e-3))
self.assertTrue(np.allclose(maxima, self.data['maxima'], atol=2e-2, rtol=5e-2))
