Python cmath.exp方法代码示例

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def test_or_gate_identity():
    saving_backend = DummyEngine(save_commands=True)
    eng = MainEngine(backend=saving_backend, engine_list=[])
    qureg = eng.allocate_qureg(4)
    hamiltonian = QubitOperator((), 3.4)
    correct_h = copy.deepcopy(hamiltonian)
    gate = te.TimeEvolution(2.1, hamiltonian)
    gate | qureg
    eng.flush()
    cmd = saving_backend.received_commands[4]
    assert isinstance(cmd.gate, Ph)
    assert cmd.gate == Ph(-3.4 * 2.1)
    correct = numpy.array([[cmath.exp(-1j * 3.4 * 2.1), 0],
                           [0, cmath.exp(-1j * 3.4 * 2.1)]])
    print(correct)
    print(cmd.gate.matrix)
    assert numpy.allclose(cmd.gate.matrix, correct) 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def arrow(dev, origin, vec, lc, color, ccw=cmath.exp(3j * cmath.pi/4), cw=cmath.exp(-3j * cmath.pi/4)):
    length, theta = cmath.polar(vec)
    uv = cmath.rect(1, theta)  # Unit rotation vector
    start = -vec
    if length > 3 * lc:  # If line is long
        ds = cmath.rect(lc, theta)
        start += ds  # shorten to allow for length of tail chevrons
    chev = lc + 0j
    polar(dev, origin, vec, color)  # Origin to tip
    polar(dev, origin, start, color)  # Origin to tail
    polar(dev, origin + conj(vec), chev*ccw*uv, color)  # Tip chevron
    polar(dev, origin + conj(vec), chev*cw*uv, color)
    if length > lc:  # Confusing appearance of very short vectors with tail chevron
        polar(dev, origin + conj(start), chev*ccw*uv, color)  # Tail chevron
        polar(dev, origin + conj(start), chev*cw*uv, color)

# If a (framebuf based) device is passed to refresh, the screen is cleared.
# None causes pending widgets to be drawn and the result to be copied to hardware.
# The pend mechanism enables a displayable object to postpone its renedering
# until it is complete: efficient for e.g. Dial which may have multiple Pointers 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def phase2t(self, psi):
        """Given phase -pi < psi <= pi,
        returns the t value such that
        exp(1j*psi) = self.u1transform(self.point(t)).
        """
        def _deg(rads, domain_lower_limit):
            # Convert rads to degrees in [0, 360) domain
            degs = degrees(rads % (2*pi))

            # Convert to [domain_lower_limit, domain_lower_limit + 360) domain
            k = domain_lower_limit // 360
            degs += k * 360
            if degs < domain_lower_limit:
                degs += 360
            return degs

        if self.delta > 0:
            degs = _deg(psi, domain_lower_limit=self.theta)
        else:
            degs = _deg(psi, domain_lower_limit=self.theta)
        return (degs - self.theta)/self.delta 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def hamiltonian(self):  # follow Zhu et al., PRB 87, 024510 (2013)
        """Construct Hamiltonian matrix in harmonic-oscillator basis, following Zhu et al., PRB 87, 024510 (2013)

        Returns
        -------
        ndarray
        """
        dimension = self.hilbertdim()
        diag_elements = [i * self.E_plasma() for i in range(dimension)]
        lc_osc_matrix = np.diag(diag_elements)

        exp_matrix = self.exp_i_phi_operator() * cmath.exp(1j * 2 * np.pi * self.flux)
        cos_matrix = 0.5 * (exp_matrix + exp_matrix.conjugate().T)

        hamiltonian_mat = lc_osc_matrix - self.EJ * cos_matrix
        return np.real(hamiltonian_mat)  # use np.real to remove rounding errors from matrix exponential,
        # fluxonium Hamiltonian in harm. osc. basis is real-valued 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def test_SWSH_NINJA_values(special_angles, ell_max):
    def m2Y22(iota, phi):
        return math.sqrt(5 / (64 * np.pi)) * (1 + math.cos(iota)) ** 2 * cmath.exp(2j * phi)

    def m2Y21(iota, phi):
        return math.sqrt(5 / (16 * np.pi)) * math.sin(iota) * (1 + math.cos(iota)) * cmath.exp(1j * phi)

    def m2Y20(iota, phi):
        return math.sqrt(15 / (32 * np.pi)) * math.sin(iota) ** 2

    def m2Y2m1(iota, phi):
        return math.sqrt(5 / (16 * np.pi)) * math.sin(iota) * (1 - math.cos(iota)) * cmath.exp(-1j * phi)

    def m2Y2m2(iota, phi):
        return math.sqrt(5 / (64 * np.pi)) * (1 - math.cos(iota)) ** 2 * cmath.exp(-2j * phi)

    for iota in special_angles:
        for phi in special_angles:
            assert abs(slow_sYlm(-2, 2, 2, iota, phi) - m2Y22(iota, phi)) < ell_max * precision_SWSH
            assert abs(slow_sYlm(-2, 2, 1, iota, phi) - m2Y21(iota, phi)) < ell_max * precision_SWSH
            assert abs(slow_sYlm(-2, 2, 0, iota, phi) - m2Y20(iota, phi)) < ell_max * precision_SWSH
            assert abs(slow_sYlm(-2, 2, -1, iota, phi) - m2Y2m1(iota, phi)) < ell_max * precision_SWSH
            assert abs(slow_sYlm(-2, 2, -2, iota, phi) - m2Y2m2(iota, phi)) < ell_max * precision_SWSH 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def test_SWSH_spin_behavior(Rs, special_angles, ell_max):
    # We expect that the SWSHs behave according to
    # sYlm( R * exp(gamma*z/2) ) = sYlm(R) * exp(-1j*s*gamma)
    # See http://moble.github.io/spherical_functions/SWSHs.html#fn:2
    # for a more detailed explanation
    # print("")
    for i, R in enumerate(Rs):
        # print("\t{0} of {1}: R = {2}".format(i, len(Rs), R))
        for gamma in special_angles:
            for ell in range(ell_max + 1):
                for s in range(-ell, ell + 1):
                    LM = np.array([[ell, m] for m in range(-ell, ell + 1)])
                    Rgamma = R * np.quaternion(math.cos(gamma / 2.), 0, 0, math.sin(gamma / 2.))
                    sYlm1 = sf.SWSH(Rgamma, s, LM)
                    sYlm2 = sf.SWSH(R, s, LM) * cmath.exp(-1j * s * gamma)
                    # print(R, gamma, ell, s, np.max(np.abs(sYlm1-sYlm2)))
                    assert np.allclose(sYlm1, sYlm2, atol=ell ** 6 * precision_SWSH, rtol=ell ** 6 * precision_SWSH) 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def DFT2D(image):
    global M, N
    (M, N) = image.size # (imgx, imgy)
    dft2d_red = [[0.0 for k in range(M)] for l in range(N)] 
    dft2d_grn = [[0.0 for k in range(M)] for l in range(N)] 
    dft2d_blu = [[0.0 for k in range(M)] for l in range(N)] 
    pixels = image.load()
    for k in range(M):
        for l in range(N):
            sum_red = 0.0
            sum_grn = 0.0
            sum_blu = 0.0
            for m in range(M):
                for n in range(N):
                    (red, grn, blu, alpha) = pixels[m, n]
                    e = cmath.exp(- 1j * pi2 * (float(k * m) / M + float(l * n) / N))
                    sum_red += red * e
                    sum_grn += grn * e
                    sum_blu += blu * e
            dft2d_red[l][k] = sum_red / M / N
            dft2d_grn[l][k] = sum_grn / M / N
            dft2d_blu[l][k] = sum_blu / M / N
    return (dft2d_red, dft2d_grn, dft2d_blu) 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def IDFT2D(dft2d):
    (dft2d_red, dft2d_grn, dft2d_blu) = dft2d
    global M, N
    image = Image.new("RGB", (M, N))
    pixels = image.load() 
    for m in range(M):
        for n in range(N):
            sum_red = 0.0
            sum_grn = 0.0
            sum_blu = 0.0
            for k in range(M):
                for l in range(N):
                    e = cmath.exp(1j * pi2 * (float(k * m) / M + float(l * n) / N))
                    sum_red += dft2d_red[l][k] * e
                    sum_grn += dft2d_grn[l][k] * e
                    sum_blu += dft2d_blu[l][k] * e
            red = int(sum_red.real + 0.5)
            grn = int(sum_grn.real + 0.5)
            blu = int(sum_blu.real + 0.5)
            pixels[m, n] = (red, grn, blu)
    return image

# TEST
# Recreate input image from 2D DFT results to compare to input image 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def _apply_unitary_(self,
                        args: 'cirq.ApplyUnitaryArgs') -> Optional[np.ndarray]:
        if cirq.is_parameterized(self):
            return None
        if self.theta != 0:
            inner_matrix = protocols.unitary(cirq.rx(2 * self.theta))
            oi = args.subspace_index(0b01)
            io = args.subspace_index(0b10)
            out = cirq.apply_matrix_to_slices(args.target_tensor,
                                              inner_matrix,
                                              slices=[oi, io],
                                              out=args.available_buffer)
        else:
            out = args.target_tensor
        if self.phi != 0:
            ii = args.subspace_index(0b11)
            out[ii] *= cmath.exp(-1j * self.phi)
        return out 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def CRotz(theta):
    r"""Two-qubit controlled rotation about the z axis.

    Args:
        theta (float): rotation angle
    Returns:
        array: unitary 4x4 rotation matrix :math:`|0\rangle\langle 0|\otimes \mathbb{I}+|1\rangle\langle 1|\otimes R_z(\theta)`
    """
    return np.array(
        [
            [1, 0, 0, 0],
            [0, 1, 0, 0],
            [0, 0, cmath.exp(-1j * theta / 2), 0],
            [0, 0, 0, cmath.exp(1j * theta / 2)],
        ]
    ) 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def coherent_state(a, phi=0, hbar=2.0):
    r"""Returns a coherent state.

    Args:
        a (complex) : the displacement
        phi (float): the phase
        hbar (float): (default 2) the value of :math:`\hbar` in the commutation
            relation :math:`[\x,\p]=i\hbar`
    Returns:
        array: the coherent state
    """
    alpha = a * cmath.exp(1j * phi)
    means = np.array([alpha.real, alpha.imag]) * math.sqrt(2 * hbar)
    cov = np.identity(2) * hbar / 2
    state = [means, cov]
    return state 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def displaced_squeezed_state(a, phi_a, r, phi_r, hbar=2.0):
    r"""Returns a squeezed coherent state

    Args:
        a (real): the displacement magnitude
        phi_a (real): the displacement phase
        r (float): the squeezing magnitude
        phi_r (float): the squeezing phase :math:`\phi_r`
        hbar (float): (default 2) the value of :math:`\hbar` in the commutation
            relation :math:`[\x,\p]=i\hbar`

    Returns:
        array: the squeezed coherent state
    """
    alpha = a * cmath.exp(1j * phi_a)
    means = np.array([alpha.real, alpha.imag]) * math.sqrt(2 * hbar)
    state = [means, squeezed_cov(r, phi_r, hbar)]
    return state 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def arrow(tft, origin, vec, lc, color):
    ccw = cmath.exp(3j * cmath.pi/4)  # Unit vectors
    cw = cmath.exp(-3j * cmath.pi/4)
    length, theta = cmath.polar(vec)
    uv = cmath.rect(1, theta)  # Unit rotation vector
    start = -vec
    if length > 3 * lc:  # If line is long
        ds = cmath.rect(lc, theta)
        start += ds  # shorten to allow for length of tail chevrons
    chev = lc + 0j
    pline(tft, origin, vec, color)  # Origin to tip
    pline(tft, origin, start, color)  # Origin to tail
    pline(tft, origin + conj(vec), chev*ccw*uv, color)  # Tip chevron
    pline(tft, origin + conj(vec), chev*cw*uv, color)
    if length > lc:  # Confusing appearance of very short vectors with tail chevron
        pline(tft, origin + conj(start), chev*ccw*uv, color)  # Tail chevron
        pline(tft, origin + conj(start), chev*cw*uv, color)

# Vector display 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def test_tauegamma_implementation(self):
        input_dict_list=[{
        'Cgamma_mue':np.random.random()*1e-8*exp(1j*2*pi*np.random.random()),
        'Cgamma_emu':np.random.random()*1e-8*exp(1j*2*pi*np.random.random()),
        } for i in range(10)]
        BRs = np.array([
            flavio.np_prediction(
                'BR(mu->egamma)',
                Wilson(input_dict, 100,  'WET', 'flavio')
            )
            for input_dict in input_dict_list
        ])
        compare_BRs = np.array([
            compare_BR(
                Wilson(input_dict, 100,  'WET', 'flavio'),
                'mu', 'e',
            )
            for input_dict in input_dict_list
        ])
        self.assertAlmostEqual(np.max(np.abs(1-BRs/compare_BRs)), 0, delta=0.005) 

# 需要导入模块: import cmath [as 别名]
# 或者: from cmath import exp [as 别名]
def test_taumugamma_implementation(self):
        input_dict_list=[{
        'Cgamma_taumu':np.random.random()*1e-8*exp(1j*2*pi*np.random.random()),
        'Cgamma_mutau':np.random.random()*1e-8*exp(1j*2*pi*np.random.random()),
        } for i in range(10)]
        BRs = np.array([
            flavio.np_prediction(
                'BR(tau->mugamma)',
                Wilson(input_dict, 100,  'WET', 'flavio')
            )
            for input_dict in input_dict_list
        ])
        compare_BRs = np.array([
            compare_BR(
                Wilson(input_dict, 100,  'WET', 'flavio'),
                'tau', 'mu',
            )
            for input_dict in input_dict_list
        ])
        self.assertAlmostEqual(np.max(np.abs(1-BRs/compare_BRs)), 0, delta=0.02)