Python numpy.norm方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def as_rotation_vector(q):
    """Convert input quaternion to the axis-angle representation

    Note that if any of the input quaternions has norm zero, no error is
    raised, but NaNs will appear in the output.

    Parameters
    ----------
    q: quaternion or array of quaternions
        The quaternion(s) need not be normalized, but must all be nonzero

    Returns
    -------
    rot: float array
        Output shape is q.shape+(3,).  Each vector represents the axis of
        the rotation, with norm proportional to the angle of the rotation in
        radians.

    """
    return as_float_array(2*np.log(np.normalized(q)))[..., 1:] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def from_rotation_vector(rot):
    """Convert input 3-vector in axis-angle representation to unit quaternion

    Parameters
    ----------
    rot: (Nx3) float array
        Each vector represents the axis of the rotation, with norm
        proportional to the angle of the rotation in radians.

    Returns
    -------
    q: array of quaternions
        Unit quaternions resulting in rotations corresponding to input
        rotations.  Output shape is rot.shape[:-1].

    """
    rot = np.array(rot, copy=False)
    quats = np.zeros(rot.shape[:-1]+(4,))
    quats[..., 1:] = rot[...]/2
    quats = as_quat_array(quats)
    return np.exp(quats) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def test_as_euler_angles():
    np.random.seed(1843)
    random_angles = [[np.random.uniform(-np.pi, np.pi),
                      np.random.uniform(-np.pi, np.pi),
                      np.random.uniform(-np.pi, np.pi)]
                     for i in range(5000)]
    for alpha, beta, gamma in random_angles:
        R1 = quaternion.from_euler_angles(alpha, beta, gamma)
        R2 = quaternion.from_euler_angles(*list(quaternion.as_euler_angles(R1)))
        d = quaternion.rotation_intrinsic_distance(R1, R2)
        assert d < 6e3*eps, ((alpha, beta, gamma), R1, R2, d)  # Can't use allclose here; we don't care about rotor sign
    q0 = quaternion.quaternion(0, 0.6, 0.8, 0)
    assert q0.norm() == 1.0
    assert abs(q0 - quaternion.from_euler_angles(*list(quaternion.as_euler_angles(q0)))) < 1.e-15


# Unary bool returners 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def test_quaternion_square(Qs):
    square_precision = 1.e-15
    for q in Qs[Qs_finite]:
        assert np.norm(q*q - q**2) < square_precision
        a = np.array([q])
        assert np.norm(a**2 - np.array([q**2])) < square_precision 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def getError(self):
        """For given h, generate A[h], f and A(f) and return norm of error."""
        return 1. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def pdf(self, input_values, x):
        mu = input_values[0]
        sigma = input_values[1]
        pdf = np.norm(mu, sigma).pdf(x)
        return pdf 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def pdf(self, input_values, x):
        mu = input_values[0]
        sigma = input_values[1]
        pdf = np.norm(mu,sigma).pdf(x)
        return pdf 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def as_rotation_matrix(q):
    """Convert input quaternion to 3x3 rotation matrix

    Parameters
    ----------
    q: quaternion or array of quaternions
        The quaternion(s) need not be normalized, but must all be nonzero

    Returns
    -------
    rot: float array
        Output shape is q.shape+(3,3).  This matrix should multiply (from
        the left) a column vector to produce the rotated column vector.

    Raises
    ------
    ZeroDivisionError
        If any of the input quaternions have norm 0.0.

    """
    if q.shape == () and not isinstance(q, np.ndarray):  # This is just a single quaternion
        n = q.norm()
        if n == 0.0:
            raise ZeroDivisionError("Input to `as_rotation_matrix({0})` has zero norm".format(q))
        elif abs(n-1.0) < _eps:  # Input q is basically normalized
            return np.array([
                [1 - 2*(q.y**2 + q.z**2),   2*(q.x*q.y - q.z*q.w),      2*(q.x*q.z + q.y*q.w)],
                [2*(q.x*q.y + q.z*q.w),     1 - 2*(q.x**2 + q.z**2),    2*(q.y*q.z - q.x*q.w)],
                [2*(q.x*q.z - q.y*q.w),     2*(q.y*q.z + q.x*q.w),      1 - 2*(q.x**2 + q.y**2)]
            ])
        else:  # Input q is not normalized
            return np.array([
                [1 - 2*(q.y**2 + q.z**2)/n,   2*(q.x*q.y - q.z*q.w)/n,      2*(q.x*q.z + q.y*q.w)/n],
                [2*(q.x*q.y + q.z*q.w)/n,     1 - 2*(q.x**2 + q.z**2)/n,    2*(q.y*q.z - q.x*q.w)/n],
                [2*(q.x*q.z - q.y*q.w)/n,     2*(q.y*q.z + q.x*q.w)/n,      1 - 2*(q.x**2 + q.y**2)/n]
            ])
    else:  # This is an array of quaternions
        n = np.norm(q)
        if np.any(n == 0.0):
            raise ZeroDivisionError("Array input to `as_rotation_matrix` has at least one element with zero norm")
        else:  # Assume input q is not normalized
            m = np.empty(q.shape + (3, 3))
            q = as_float_array(q)
            m[..., 0, 0] = 1.0 - 2*(q[..., 2]**2 + q[..., 3]**2)/n
            m[..., 0, 1] = 2*(q[..., 1]*q[..., 2] - q[..., 3]*q[..., 0])/n
            m[..., 0, 2] = 2*(q[..., 1]*q[..., 3] + q[..., 2]*q[..., 0])/n
            m[..., 1, 0] = 2*(q[..., 1]*q[..., 2] + q[..., 3]*q[..., 0])/n
            m[..., 1, 1] = 1.0 - 2*(q[..., 1]**2 + q[..., 3]**2)/n
            m[..., 1, 2] = 2*(q[..., 2]*q[..., 3] - q[..., 1]*q[..., 0])/n
            m[..., 2, 0] = 2*(q[..., 1]*q[..., 3] - q[..., 2]*q[..., 0])/n
            m[..., 2, 1] = 2*(q[..., 2]*q[..., 3] + q[..., 1]*q[..., 0])/n
            m[..., 2, 2] = 1.0 - 2*(q[..., 1]**2 + q[..., 2]**2)/n
            return m 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def as_euler_angles(q):
    """Open Pandora's Box

    If somebody is trying to make you use Euler angles, tell them no, and
    walk away, and go and tell your mum.

    You don't want to use Euler angles.  They are awful.  Stay away.  It's
    one thing to convert from Euler angles to quaternions; at least you're
    moving in the right direction.  But to go the other way?!  It's just not
    right.

    Assumes the Euler angles correspond to the quaternion R via

        R = exp(alpha*z/2) * exp(beta*y/2) * exp(gamma*z/2)

    The angles are naturally in radians.

    NOTE: Before opening an issue reporting something "wrong" with this
    function, be sure to read all of the following page, *especially* the
    very last section about opening issues or pull requests.
    <https://github.com/moble/quaternion/wiki/Euler-angles-are-horrible>

    Parameters
    ----------
    q: quaternion or array of quaternions
        The quaternion(s) need not be normalized, but must all be nonzero

    Returns
    -------
    alpha_beta_gamma: float array
        Output shape is q.shape+(3,).  These represent the angles (alpha,
        beta, gamma) in radians, where the normalized input quaternion
        represents `exp(alpha*z/2) * exp(beta*y/2) * exp(gamma*z/2)`.

    Raises
    ------
    AllHell
        ...if you try to actually use Euler angles, when you could have
        been using quaternions like a sensible person.

    """
    alpha_beta_gamma = np.empty(q.shape + (3,), dtype=np.float)
    n = np.norm(q)
    q = as_float_array(q)
    alpha_beta_gamma[..., 0] = np.arctan2(q[..., 3], q[..., 0]) + np.arctan2(-q[..., 1], q[..., 2])
    alpha_beta_gamma[..., 1] = 2*np.arccos(np.sqrt((q[..., 0]**2 + q[..., 3]**2)/n))
    alpha_beta_gamma[..., 2] = np.arctan2(q[..., 3], q[..., 0]) - np.arctan2(-q[..., 1], q[..., 2])
    return alpha_beta_gamma 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import norm [as 別名]
def linearized_dc_power_flow(Ybus, Sbus, Ibus, V0, ref, pq, pv):
    """
    Solves a DC power flow.
    :param Ybus: Normal circuit admittance matrix
    :param Sbus: Complex power injections at all the nodes
    :param Ibus: Complex current injections at all the nodes
    :param V0: Array of complex seed voltage (it contains the ref voltages)
    :param ref: array of the indices of the slack nodes
    :param pvpq: array of the indices of the non-slack nodes
    :param pq: array of the indices of the pq nodes
    :param pv: array of the indices of the pv nodes
    :return:
        Complex voltage solution
        Converged: Always true
        Solution error
        Computed power injections given the found solution
    """

    pvpq = np.r_[pv, pq].astype(int)

    # Decompose the voltage in angle and magnitude
    Va_ref = np.angle(V0[ref])  # we only need the angles at the slack nodes
    Vm = np.abs(V0)

    # initialize result vector
    Va = np.empty(len(V0))

    # reconvert the pqpv vector to a matrix so that we can call numpy directly with it
    pvpq_ = np.matrix(pvpq)

    # Compile the reduced imaginary impedance matrix
    Bpqpv = Ybus.imag[pvpq_.T, pvpq_]
    Bref = Ybus.imag[pvpq_.T, ref]

    # compose the reduced power injections
    # Since we have removed the slack nodes, we must account their influence as injections Bref * Va_ref
    Pinj = Sbus[pvpq].real + (- Bref * Va_ref + Ibus[pvpq].real) * Vm[pvpq]

    # update angles for non-reference buses
    Va[pvpq] = spsolve(Bpqpv, Pinj)
    Va[ref] = Va_ref

    # re assemble the voltage
    V = Vm * np.exp(1j * Va)

    # compute the calculated power injection and the error of the voltage solution
    Scalc = V * np.conj(Ybus * V - Ibus)

    # compute the power mismatch between the specified power Sbus and the calculated power Scalc
    mis = Scalc - Sbus  # complex power mismatch
    F = np.r_[mis[pv].real, mis[pq].real, mis[pq].imag]  # concatenate again

    # check for convergence
    normF = np.linalg.norm(F, np.Inf)

    return V, True, normF