Python numpy.sin方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def unit_vec(doa):
    """
    This function takes a 2D (phi) or 3D (phi,theta) polar coordinates
    and returns a unit vector in cartesian coordinates.

    :param doa: (ndarray) An (D-1)-by-N array where D is the dimension and
                N the number of vectors.

    :return: (ndarray) A D-by-N array of unit vectors (each column is a vector)
    """

    if doa.ndim != 1 and doa.ndim != 2:
        raise ValueError("DoA array should be 1D or 2D.")

    doa = np.array(doa)

    if doa.ndim == 0 or doa.ndim == 1:
        return np.array([np.cos(doa), np.sin(doa)])

    elif doa.ndim == 2 and doa.shape[0] == 1:
        return np.array([np.cos(doa[0]), np.sin(doa[0])])

    elif doa.ndim == 2 and doa.shape[0] == 2:
        s = np.sin(doa[1])
        return np.array([s * np.cos(doa[0]), s * np.sin(doa[0]), np.cos(doa[1])]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def load_RSM(filename):
    om, tt, psd = xu.io.getxrdml_map(filename)
    om = np.deg2rad(om)
    tt = np.deg2rad(tt)
    wavelength = 1.54056

    q_y = (1 / wavelength) * (np.cos(tt) - np.cos(2 * om - tt))
    q_x = (1 / wavelength) * (np.sin(tt) - np.sin(2 * om - tt))

    xi = np.linspace(np.min(q_x), np.max(q_x), 100)
    yi = np.linspace(np.min(q_y), np.max(q_y), 100)
    psd[psd < 1] = 1
    data_grid = griddata(
        (q_x, q_y), psd, (xi[None, :], yi[:, None]), fill_value=1, method="cubic"
    )
    nx, ny = data_grid.shape

    range_values = [np.min(q_x), np.max(q_x), np.min(q_y), np.max(q_y)]
    output_data = (
        Panel(np.log(data_grid).reshape(nx, ny, 1), minor_axis=["RSM"])
        .transpose(2, 0, 1)
        .to_frame()
    )

    return range_values, output_data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def rotation_matrix_3D(u, th):
    """
    rotation_matrix_3D(u, t) yields a 3D numpy matrix that rotates any vector about the axis u
    t radians counter-clockwise.
    """
    # normalize the axis:
    u = normalize(u)
    # We use the Euler-Rodrigues formula;
    # see https://en.wikipedia.org/wiki/Euler-Rodrigues_formula
    a = math.cos(0.5 * th)
    s = math.sin(0.5 * th)
    (b, c, d) = -s * u
    (a2, b2, c2, d2) = (a*a, b*b, c*c, d*d)
    (bc, ad, ac, ab, bd, cd) = (b*c, a*d, a*c, a*b, b*d, c*d)
    return np.array([[a2 + b2 - c2 - d2, 2*(bc + ad),         2*(bd - ac)],
                     [2*(bc - ad),       a2 + c2 - b2 - d2,   2*(cd + ab)],
                     [2*(bd + ac),       2*(cd - ab),         a2 + d2 - b2 - c2]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def get_rotation_matrix(rotationVector, angle):
    """
    Calculate the rotation (3X3) matrix about an axis (rotationVector)
    by a rotation angle.

    :Parameters:
        #. rotationVector (list, tuple, numpy.ndarray): Rotation axis
           coordinates.
        #. angle (float): Rotation angle in rad.

    :Returns:
        #. rotationMatrix (numpy.ndarray): Computed (3X3) rotation matrix
    """
    angle = float(angle)
    axis = rotationVector/np.sqrt(np.dot(rotationVector , rotationVector))
    a = np.cos(angle/2)
    b,c,d = -axis*np.sin(angle/2.)
    return np.array( [ [a*a+b*b-c*c-d*d, 2*(b*c-a*d), 2*(b*d+a*c)],
                       [2*(b*c+a*d), a*a+c*c-b*b-d*d, 2*(c*d-a*b)],
                       [2*(b*d-a*c), 2*(c*d+a*b), a*a+d*d-b*b-c*c] ] , dtype = FLOAT_TYPE) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def get_loc_axis(self, node, delta_theta, perturb=None):
    """Based on the node orientation returns X, and Y axis. Used to sample the
    map in egocentric coordinate frame.
    """
    if type(node) == tuple:
      node = np.array([node])
    if perturb is None:
      perturb = np.zeros((node.shape[0], 4))
    xyt = self.to_actual_xyt_vec(node)
    x = xyt[:,[0]] + perturb[:,[0]]
    y = xyt[:,[1]] + perturb[:,[1]]
    t = xyt[:,[2]] + perturb[:,[2]]
    theta = t*delta_theta
    loc = np.concatenate((x,y), axis=1)
    x_axis = np.concatenate((np.cos(theta), np.sin(theta)), axis=1)
    y_axis = np.concatenate((np.cos(theta+np.pi/2.), np.sin(theta+np.pi/2.)),
                            axis=1)
    # Flip the sampled map where need be.
    y_axis[np.where(perturb[:,3] > 0)[0], :] *= -1.
    return loc, x_axis, y_axis, theta 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def sample(self):
    """Samples new points around some existing point.

    Removes the sampling base point and also stores the new jksampled points if
    they are far enough from all existing points.
    """
    active_point = self._active_list.pop()
    for _ in xrange(self._max_sample_size):
      # Generate random points near the current active_point between the radius
      random_radius = np.random.uniform(self._min_radius, 2 * self._min_radius)
      random_angle = np.random.uniform(0, 2 * math.pi)

      # The sampled 2D points near the active point
      sample = random_radius * np.array(
          [np.cos(random_angle), np.sin(random_angle)]) + active_point

      if not self._is_in_grid(sample):
        continue

      if self._is_close_to_existing_points(sample):
        continue

      self._active_list.append(sample)
      self._grid[self._point_to_index_1d(sample)] = sample 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def alive_bonus(self, z, pitch):
		if self.frame%30==0 and self.frame>100 and self.on_ground_frame_counter==0:
			target_xyz  = np.array(self.body_xyz)
			robot_speed = np.array(self.robot_body.speed())
			angle = self.np_random.uniform(low=-3.14, high=3.14)
			from_dist   = 4.0
			attack_speed   = self.np_random.uniform(low=20.0, high=30.0)  # speed 20..30 (* mass in cube.urdf = impulse)
			time_to_travel = from_dist / attack_speed
			target_xyz += robot_speed*time_to_travel  # predict future position at the moment the cube hits the robot
			position = [target_xyz[0] + from_dist*np.cos(angle),
				target_xyz[1] + from_dist*np.sin(angle),
				target_xyz[2] + 1.0]
			attack_speed_vector = target_xyz - np.array(position)
			attack_speed_vector *= attack_speed / np.linalg.norm(attack_speed_vector)
			attack_speed_vector += self.np_random.uniform(low=-1.0, high=+1.0, size=(3,))
			self.aggressive_cube.reset_position(position)
			self.aggressive_cube.reset_velocity(linearVelocity=attack_speed_vector)
		if z < 0.8:
			self.on_ground_frame_counter += 1
		elif self.on_ground_frame_counter > 0:
			self.on_ground_frame_counter -= 1
		# End episode if the robot can't get up in 170 frames, to save computation and decorrelate observations.
		self.frame += 1
		return self.potential_leak() if self.on_ground_frame_counter<170 else -1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def calc_state(self):
		theta, self.theta_dot = self.central_joint.current_relative_position()
		self.gamma, self.gamma_dot = self.elbow_joint.current_relative_position()
		target_x, _ = self.jdict["target_x"].current_position()
		target_y, _ = self.jdict["target_y"].current_position()
		self.to_target_vec = np.array(self.fingertip.pose().xyz()) - np.array(self.target.pose().xyz())
		return np.array([
			target_x,
			target_y,
			self.to_target_vec[0],
			self.to_target_vec[1],
			np.cos(theta),
			np.sin(theta),
			self.theta_dot,
			self.gamma,
			self.gamma_dot,
		]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def calc_state(self):
		self.theta, theta_dot = self.j1.current_position()
		x, vx = self.slider.current_position()
		assert( np.isfinite(x) )

		if not np.isfinite(x):
			print("x is inf")
			x = 0

		if not np.isfinite(vx):
			print("vx is inf")
			vx = 0

		if not np.isfinite(self.theta):
			print("theta is inf")
			self.theta = 0

		if not np.isfinite(theta_dot):
			print("theta_dot is inf")
			theta_dot = 0

		return np.array([
			x, vx,
			np.cos(self.theta), np.sin(self.theta), theta_dot
			]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def random_quat(rand=None):
    """Return uniform random unit quaternion.
    rand: array like or None
        Three independent random variables that are uniformly distributed
        between 0 and 1.
    >>> q = random_quat()
    >>> np.allclose(1.0, vector_norm(q))
    True
    >>> q = random_quat(np.random.random(3))
    >>> q.shape
    (4,)
    """
    if rand is None:
        rand = np.random.rand(3)
    else:
        assert len(rand) == 3
    r1 = np.sqrt(1.0 - rand[0])
    r2 = np.sqrt(rand[0])
    pi2 = math.pi * 2.0
    t1 = pi2 * rand[1]
    t2 = pi2 * rand[2]
    return np.array(
        (np.sin(t1) * r1, np.cos(t1) * r1, np.sin(t2) * r2, np.cos(t2) * r2),
        dtype=np.float32,
    ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def subspace_disagreement_measure(self, Ps, Pt, Pst):
        """
        Get the best value for the number of subspaces
        For more details, read section 3.4 of the paper.
        **Parameters**
          Ps: Source subspace
          Pt: Target subspace
          Pst: Source + Target subspace
        """

        def compute_angles(A, B):
            _, S, _ = np.linalg.svd(np.dot(A.T, B))
            S[np.where(np.isclose(S, 1, atol=self.eps) == True)[0]] = 1
            return np.arccos(S)

        max_d = min(Ps.shape[1], Pt.shape[1], Pst.shape[1])
        alpha_d = compute_angles(Ps, Pst)
        beta_d = compute_angles(Pt, Pst)
        d = 0.5 * (np.sin(alpha_d) + np.sin(beta_d))
        return np.argmax(d) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def cosine(M):
    """Gernerate a halfcosine window of given length

    Uses :code:`scipy.signal.cosine` by default. However since this window
    function has only recently been merged into mainline SciPy, a fallback
    calculation is in place.

    Parameters
    ----------
    M : int
        Length of the window.

    Returns
    -------
    data : array_like
        The window function

    """
    try:
        import scipy.signal
        return scipy.signal.cosine(M)
    except AttributeError:
        return numpy.sin(numpy.pi / M * (numpy.arange(0, M) + .5)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def test_analytical():
    """
    Run the test model though a year with analytical solution values to
    ensure reservoir just contains sufficient volume.
    """

    S = 100.0  # supply amplitude
    D = S  # demand
    w = 2*np.pi/365  # frequency (annual)
    V0 = S/w  # initial reservoir level

    model = make_simple_model(S, D, w, V0)

    T = np.arange(1, 365)
    V_anal = S*(np.sin(w*T)/w+T) - D*T + V0
    V_model = np.empty(T.shape)

    for i, t in enumerate(T):
        model.step()
        V_model[i] = model.nodes['reservoir'].volume[0]

    # Relative error from initial volume
    error = np.abs(V_model - V_anal) / V0
    assert np.all(error < 1e-4) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def mindmag(self, s):
        """Calculates the minimum value of dMag for projected separation
        
        Args:
            s (float):
                Projected separations (AU)
        
        Returns:
            mindmag (float):
                Minimum planet delta magnitude
        """
        if s == 0.0:
            mindmag = self.cdmin1
        elif s < self.rmin*np.sin(self.bstar):
            mindmag = self.cdmin1-2.5*np.log10(self.Phi(np.arcsin(s/self.rmin)))
        elif s < self.rmax*np.sin(self.bstar):
            mindmag = self.cdmin2+5.0*np.log10(s)
        elif s <= self.rmax:
            mindmag = self.cdmin3-2.5*np.log10(self.Phi(np.arcsin(s/self.rmax)))
        else:
            mindmag = np.inf
        
        return mindmag 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import sin [as 別名]
def Jac(self, b):
        """Calculates determinant of the Jacobian transformation matrix to get
        the joint probability density of dMag and s
        
        Args:
            b (ndarray):
                Phase angles
                
        Returns:
            f (ndarray):
                Determinant of Jacobian transformation matrix
        
        """
        
        f = -2.5/(self.Phi(b)*np.log(10.0))*self.dPhi(b)*np.sin(b) - 5./np.log(10.0)*np.cos(b)
        
        return f