Пример #1
0
    def test_ns_mmq_2site_korzhnev_2005_15n_dq_data_complex128(self):
        """Test the matrix_exponential() function for higher dimensional data, and compare to matrix_exponential.  This uses the data from systemtest Relax_disp.test_korzhnev_2005_15n_dq_data.
        This test does the matrix exponential in complex128."""

        fname = self.data + sep+ "test_korzhnev_2005_15n_dq_data"
        M0, R20A, R20B, pA, dw, dwH, kex, inv_tcpmg, tcp, num_points, power, back_calc, pB, k_BA, k_AB = self.return_data_mmq_2site(fname)

        # Extract the total numbers of experiments, number of spins, number of magnetic field strength, number of offsets, maximum number of dispersion point.
        NS, NM, NO = num_points.shape

        # Populate the m1 and m2 matrices (only once per function call for speed).
        m1_mat = rmmq_2site_rankN(R20A=R20A, R20B=R20B, dw=dw, k_AB=k_AB, k_BA=k_BA, tcp=tcp)
        m2_mat = rmmq_2site_rankN(R20A=R20A, R20B=R20B, dw=-dw, k_AB=k_AB, k_BA=k_BA, tcp=tcp)
    
        # The A+/- matrices.
        A_pos_mat = matrix_exponential(m1_mat)
        A_neg_mat = matrix_exponential(m2_mat)
    
        # Loop over spins.
        for si in range(NS):
            # Loop over the spectrometer frequencies.
            for mi in range(NM):
                # Loop over offsets:
                for oi in range(NO):
                    # Extract number of points.
                    num_points_i = num_points[si, mi, oi]
    
                    # Loop over the time points, back calculating the R2eff values.
                    for i in range(num_points_i):
                        # Test the two different methods.
                        # The A+/- matrices.
                        A_pos_i = A_pos_mat[si, mi, oi, i]
                        A_neg_i = A_neg_mat[si, mi, oi, i]
    
                        # The lower dimensional matrix exponential.
                        A_pos = np_matrix_exponential(m1_mat[si, mi, oi, i])
                        A_neg = np_matrix_exponential(m2_mat[si, mi, oi, i])
    
                        # Calculate differences
                        diff_A_pos_real = A_pos_i.real - A_pos.real
                        diff_A_pos_real_sum = sum(diff_A_pos_real)
                        diff_A_pos_imag = A_pos_i.imag - A_pos.imag
                        diff_A_pos_imag_sum = sum(diff_A_pos_imag)

                        diff_A_neg_real = A_neg_i.real - A_neg.real
                        diff_A_neg_real_sum = sum(diff_A_neg_real)
                        diff_A_neg_imag = A_neg_i.imag - A_neg.imag
                        diff_A_neg_imag_sum = sum(diff_A_neg_imag)

                        # Test that the sum difference is zero.                                        
                        self.assertAlmostEqual(diff_A_pos_real_sum, 0.0)
                        self.assertAlmostEqual(diff_A_pos_imag_sum, 0.0)
                        self.assertAlmostEqual(diff_A_neg_real_sum, 0.0)
                        self.assertAlmostEqual(diff_A_neg_imag_sum, 0.0)
Пример #2
0
    def test_ns_cpmg_2site_3d_hansen_cpmg_data(self):
        """Test the matrix_exponential() function for higher dimensional data, and compare to matrix_exponential.  This uses the data from systemtest Relax_disp.test_hansen_cpmg_data_to_ns_cpmg_2site_3D."""

        fname = self.data + sep + "test_hansen_cpmg_data_to_ns_cpmg_2site_3D"
        r180x, M0, r10a, r10b, r20a, r20b, pA, dw, dw_orig, kex, inv_tcpmg, tcp, num_points, power, back_calc, pB, k_BA, k_AB = self.return_data_ns_cpmg_2site_3d(
            fname)

        # Extract the total numbers of experiments, number of spins, number of magnetic field strength, number of offsets, maximum number of dispersion point.
        NE, NS, NM, NO, ND = back_calc.shape

        # The matrix R that contains all the contributions to the evolution, i.e. relaxation, exchange and chemical shift evolution.
        R_mat = rcpmg_3d_rankN(R1A=r10a,
                               R1B=r10b,
                               R2A=r20a,
                               R2B=r20b,
                               pA=pA,
                               pB=pB,
                               dw=dw,
                               k_AB=k_AB,
                               k_BA=k_BA,
                               tcp=tcp)

        # This matrix is a propagator that will evolve the magnetization with the matrix R for a delay tcp.
        Rexpo_mat = matrix_exponential(R_mat)

        # Loop over the spins
        for si in range(NS):
            # Loop over the spectrometer frequencies.
            for mi in range(NM):
                # Extract number of points.
                num_points_si_mi = int(num_points[0, si, mi, 0])

                # Loop over the time points, back calculating the R2eff values.
                for di in range(num_points_si_mi):
                    # Test the two different methods.
                    R_mat_i = R_mat[0, si, mi, 0, di]

                    # The lower dimensional matrix exponential.
                    Rexpo = np_matrix_exponential(R_mat_i)

                    # The higher dimensional matrix exponential.
                    Rexpo_mat_i = Rexpo_mat[0, si, mi, 0, di]

                    diff = Rexpo - Rexpo_mat_i
                    diff_sum = sum(diff)

                    # Test that the sum difference is zero.
                    self.assertAlmostEqual(diff_sum, 0.0)
Пример #3
0
    def test_ns_cpmg_2site_3d_hansen_cpmg_data(self):
        """Test the matrix_exponential() function for higher dimensional data, and compare to matrix_exponential.  This uses the data from systemtest Relax_disp.test_hansen_cpmg_data_to_ns_cpmg_2site_3D."""

        fname = self.data + sep+ "test_hansen_cpmg_data_to_ns_cpmg_2site_3D"
        r180x, M0, r10a, r10b, r20a, r20b, pA, dw, dw_orig, kex, inv_tcpmg, tcp, num_points, power, back_calc, pB, k_BA, k_AB = self.return_data_ns_cpmg_2site_3d(fname)

        # Extract the total numbers of experiments, number of spins, number of magnetic field strength, number of offsets, maximum number of dispersion point.
        NE, NS, NM, NO, ND = back_calc.shape

        # The matrix R that contains all the contributions to the evolution, i.e. relaxation, exchange and chemical shift evolution.
        R_mat = rcpmg_3d_rankN(R1A=r10a, R1B=r10b, R2A=r20a, R2B=r20b, pA=pA, pB=pB, dw=dw, k_AB=k_AB, k_BA=k_BA, tcp=tcp)
    
        # This matrix is a propagator that will evolve the magnetization with the matrix R for a delay tcp.
        Rexpo_mat = matrix_exponential(R_mat)
    
        # Loop over the spins
        for si in range(NS):
            # Loop over the spectrometer frequencies.
            for mi in range(NM):
                # Extract number of points.
                num_points_si_mi = int(num_points[0, si, mi, 0])
    
                # Loop over the time points, back calculating the R2eff values.
                for di in range(num_points_si_mi):
                    # Test the two different methods.
                    R_mat_i = R_mat[0, si, mi, 0, di]
 
                    # The lower dimensional matrix exponential.
                    Rexpo = np_matrix_exponential(R_mat_i)
    
                    # The higher dimensional matrix exponential.
                    Rexpo_mat_i = Rexpo_mat[0, si, mi, 0, di]

                    diff = Rexpo - Rexpo_mat_i
                    diff_sum = sum(diff)

                    # Test that the sum difference is zero.                                        
                    self.assertAlmostEqual(diff_sum, 0.0)
Пример #4
0
    def test_ns_mmq_2site_korzhnev_2005_15n_dq_data_complex128(self):
        """Test the matrix_exponential() function for higher dimensional data, and compare to matrix_exponential.  This uses the data from systemtest Relax_disp.test_korzhnev_2005_15n_dq_data.
        This test does the matrix exponential in complex128."""

        fname = self.data + sep + "test_korzhnev_2005_15n_dq_data"
        M0, R20A, R20B, pA, dw, dwH, kex, inv_tcpmg, tcp, num_points, power, back_calc, pB, k_BA, k_AB = self.return_data_mmq_2site(
            fname)

        # Extract the total numbers of experiments, number of spins, number of magnetic field strength, number of offsets, maximum number of dispersion point.
        NS, NM, NO = num_points.shape

        # Populate the m1 and m2 matrices (only once per function call for speed).
        m1_mat = rmmq_2site_rankN(R20A=R20A,
                                  R20B=R20B,
                                  dw=dw,
                                  k_AB=k_AB,
                                  k_BA=k_BA,
                                  tcp=tcp)
        m2_mat = rmmq_2site_rankN(R20A=R20A,
                                  R20B=R20B,
                                  dw=-dw,
                                  k_AB=k_AB,
                                  k_BA=k_BA,
                                  tcp=tcp)

        # The A+/- matrices.
        A_pos_mat = matrix_exponential(m1_mat)
        A_neg_mat = matrix_exponential(m2_mat)

        # Loop over spins.
        for si in range(NS):
            # Loop over the spectrometer frequencies.
            for mi in range(NM):
                # Loop over offsets:
                for oi in range(NO):
                    # Extract number of points.
                    num_points_i = num_points[si, mi, oi]

                    # Loop over the time points, back calculating the R2eff values.
                    for i in range(num_points_i):
                        # Test the two different methods.
                        # The A+/- matrices.
                        A_pos_i = A_pos_mat[si, mi, oi, i]
                        A_neg_i = A_neg_mat[si, mi, oi, i]

                        # The lower dimensional matrix exponential.
                        A_pos = np_matrix_exponential(m1_mat[si, mi, oi, i])
                        A_neg = np_matrix_exponential(m2_mat[si, mi, oi, i])

                        # Calculate differences
                        diff_A_pos_real = A_pos_i.real - A_pos.real
                        diff_A_pos_real_sum = sum(diff_A_pos_real)
                        diff_A_pos_imag = A_pos_i.imag - A_pos.imag
                        diff_A_pos_imag_sum = sum(diff_A_pos_imag)

                        diff_A_neg_real = A_neg_i.real - A_neg.real
                        diff_A_neg_real_sum = sum(diff_A_neg_real)
                        diff_A_neg_imag = A_neg_i.imag - A_neg.imag
                        diff_A_neg_imag_sum = sum(diff_A_neg_imag)

                        # Test that the sum difference is zero.
                        self.assertAlmostEqual(diff_A_pos_real_sum, 0.0)
                        self.assertAlmostEqual(diff_A_pos_imag_sum, 0.0)
                        self.assertAlmostEqual(diff_A_neg_real_sum, 0.0)
                        self.assertAlmostEqual(diff_A_neg_imag_sum, 0.0)