コード例 #1
0
    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
コード例 #2
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 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))
コード例 #3
0
    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
コード例 #4
0
ファイル: noiseshaper_cifb.py プロジェクト: anuejn/nAmp
    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)))
コード例 #5
0
 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))
コード例 #6
0
    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))
コード例 #7
0
    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))