Python numpy.rad2deg方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def test_look_at_updates_for_children():
    dist = 2.
    cam = StereoCameraGroup(distance=dist)
    point = np.array([0, 0, 0, 1]).reshape(-1, 1)
    point[2] = -1 #np.random.randint(1, 6)

    angle = np.arctan(point[2]/(cam.distance/2))[0]
    cam.right.rotation.y = -np.rad2deg(angle)
    cam.left.rotation.y = np.rad2deg(angle)
    point_view_mat_left = np.dot(cam.left.view_matrix, point)
    point_view_mat_right = np.dot(cam.right.view_matrix, point)
    assert (point_view_mat_left == point_view_mat_right).all()

    cam2 = StereoCameraGroup(distance=dist)
    cam2.look_at(*point[:3])
    point_view_mat_left2 = np.dot(cam2.left.view_matrix, point)
    point_view_mat_right2 = np.dot(cam2.right.view_matrix, point)
    assert (point_view_mat_left == point_view_mat_left2).all() and (point_view_mat_right == point_view_mat_right2).all() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def sample_data_3d(shape):
    """Returns `lons`, `lats`, and fake `data`

    adapted from:
    http://scitools.org.uk/cartopy/docs/v0.15/examples/axes_grid_basic.html
    """

    nlons, nlats = shape
    lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)
    lons = np.linspace(0, 2 * np.pi, nlons)
    lons, lats = np.meshgrid(lons, lats)
    wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)
    mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)

    lats = np.rad2deg(lats)
    lons = np.rad2deg(lons)
    data = wave + mean

    return lons, lats, data


# get data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def main_sawyer():
    import intera_interface
    from visual_mpc.envs.robot_envs.sawyer.sawyer_impedance import SawyerImpedanceController
    
    controller = SawyerImpedanceController('sawyer', True, gripper_attached='none')       # doesn't initial gripper object even if gripper is attached

    def print_eep(value):
        if not value:
            return
        xyz, quat = controller.get_xyz_quat()
        yaw, roll, pitch = [np.rad2deg(x) for x in controller.quat_2_euler(quat)]
        logging.getLogger('robot_logger').info("XYZ IS: {}, ROTATION IS: yaw={} roll={} pitch={}".format(xyz, yaw, roll, pitch))
    
    navigator = intera_interface.Navigator()
    navigator.register_callback(print_eep, 'right_button_show')
    rospy.spin() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def __call__(self, x, pos=None):
        vmin, vmax = self.axis.get_view_interval()
        d = np.rad2deg(abs(vmax - vmin))
        digits = max(-int(np.log10(d) - 1.5), 0)

        if rcParams['text.usetex'] and not rcParams['text.latex.unicode']:
            format_str = r"${value:0.{digits:d}f}^\circ$"
            return format_str.format(value=np.rad2deg(x), digits=digits)
        else:
            # we use unicode, rather than mathtext with \circ, so
            # that it will work correctly with any arbitrary font
            # (assuming it has a degree sign), whereas $5\circ$
            # will only work correctly with one of the supported
            # math fonts (Computer Modern and STIX)
            format_str = "{value:0.{digits:d}f}\N{DEGREE SIGN}"
            return format_str.format(value=np.rad2deg(x), digits=digits) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def get_affine_components(matrix):
    """ get the main components of Affine transform

    :param ndarray matrix: affine transformation matrix for 2d
    :return dict: dictionary of float values

    >>> mtx = np.array([[ -0.95,   0.1,  65.], [  0.1,   0.95, -60.], [  0.,   0.,   1.]])
    >>> import  pandas as pd
    >>> aff = pd.Series(get_affine_components(mtx)).sort_index()
    >>> aff  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    rotation                           173.9...
    scale                    (0.95..., 0.95...)
    shear                              -3.14...
    translation                   (65.0, -60.0)
    dtype: object
    """
    aff = AffineTransform(matrix)
    norm_rotation = norm_angle(np.rad2deg(aff.rotation), deg=True)
    comp = {
        'rotation': float(norm_rotation),
        'translation': tuple(aff.translation.tolist()),
        'scale': aff.scale,
        'shear': aff.shear,
    }
    return comp 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def orientArrow(self):
        phi = num.nanmedian(self.model.scene.phi)
        theta = num.nanmedian(self.model.scene.theta)

        angle = 180. - num.rad2deg(phi)

        theta_f = theta / (num.pi/2)

        tipAngle = 30. + theta_f * 20.
        tailLen = 15 + theta_f * 15.

        self.arrow.setStyle(
            angle=0.,
            tipAngle=tipAngle,
            tailLen=tailLen,
            tailWidth=6,
            headLen=25)
        self.arrow.setRotation(angle)

        rect_label = self.label.boundingRect()
        rect_arr = self.arrow.boundingRect()

        self.label.setPos(-rect_label.width()/2., rect_label.height()*1.33) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def heliocentric_latitude(jme):
    b0 = 0.0
    b1 = 0.0
    for row in range(HELIO_LAT_TABLE.shape[1]):
        b0 += (HELIO_LAT_TABLE[0, row, 0]
               * np.cos(HELIO_LAT_TABLE[0, row, 1]
                        + HELIO_LAT_TABLE[0, row, 2] * jme)
               )
        b1 += (HELIO_LAT_TABLE[1, row, 0]
               * np.cos(HELIO_LAT_TABLE[1, row, 1]
                        + HELIO_LAT_TABLE[1, row, 2] * jme)
               )

    b_rad = (b0 + b1 * jme)/10**8
    b = np.rad2deg(b_rad)
    return b 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def _draw_gaze_vector(self, face: Face) -> None:
        length = self.config.demo.gaze_visualization_length
        if self.config.mode == GazeEstimationMethod.MPIIGaze.name:
            for key in [FacePartsName.REYE, FacePartsName.LEYE]:
                eye = getattr(face, key.name.lower())
                self.visualizer.draw_3d_line(
                    eye.center, eye.center + length * eye.gaze_vector)
                pitch, yaw = np.rad2deg(eye.vector_to_angle(eye.gaze_vector))
                logger.info(
                    f'[{key.name.lower()}] pitch: {pitch:.2f}, yaw: {yaw:.2f}')
        elif self.config.mode == GazeEstimationMethod.MPIIFaceGaze.name:
            self.visualizer.draw_3d_line(
                face.center, face.center + length * face.gaze_vector)
            pitch, yaw = np.rad2deg(face.vector_to_angle(face.gaze_vector))
            logger.info(f'[face] pitch: {pitch:.2f}, yaw: {yaw:.2f}')
        else:
            raise ValueError 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def _embedding_delay_select(metric_values, algorithm="first local minimum"):

    if algorithm == "first local minimum":
        optimal = signal_findpeaks(-1 * metric_values, relative_height_min=0.1, relative_max=True)["Peaks"][0]
    elif algorithm == "first 1/e crossing":
        metric_values = metric_values - 1 / np.exp(1)
        optimal = signal_zerocrossings(metric_values)[0]
    elif algorithm == "first zero crossing":
        optimal = signal_zerocrossings(metric_values)[0]
    elif algorithm == "closest to 40% of the slope":
        slope = np.diff(metric_values) * len(metric_values)
        slope_in_deg = np.rad2deg(np.arctan(slope))
        optimal = np.where(slope_in_deg == find_closest(40, slope_in_deg))[0][0]
    return optimal 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def triangle_angles(self):
        ''' Calculates minimum angle of the mesh triangles
        
        Returns
        --------
        min_triangle_angles: ElementData
            ElementData field with the angles for each triangle, in degrees
        '''
        tr_indexes = self.elm.triangles
        node_tr = self.nodes[self.elm[tr_indexes, :3]]
        a = np.linalg.norm(node_tr[:, 1] - node_tr[:, 0], axis=1)
        b = np.linalg.norm(node_tr[:, 2] - node_tr[:, 0], axis=1)
        c = np.linalg.norm(node_tr[:, 2] - node_tr[:, 1], axis=1)
        cos_angles = np.vstack([
            (a**2 + b**2 - c**2) / (2*a*b),
            (a**2 + c**2 - b**2) / (2*a*c),
            (b**2 + c**2 - a**2) / (2*b*c),
        ]).T
        angles = np.rad2deg(np.arccos(cos_angles))

        angle_field  = ElementData(
            np.nan*np.zeros((self.elm.nr, 3)),
            'min_triangle_angles'
        )
        angle_field[tr_indexes] = angles
        return angle_field 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def get_cluster_data(self, nr_motions=50):
        """
        get a certain subset data for clustering visualization and scoring
        :param nr_anims:
        :return: pre-processed data of shape (nr_views, nr_anims, 30, 64)
        """
        motion_items = self.motion_names
        if nr_motions < len(self.motion_names):
            idxes = np.linspace(0, len(self.motion_names) - 1, nr_motions, dtype=int)
            motion_items = [self.motion_names[i] for i in idxes]
        motion_names = np.array([item[0] for item in motion_items])

        all_data = []
        for mot in motion_items:
            char_data = []
            for char in self.character_names:
                item = self.build_item(mot, char)
                view_data = []
                for view in self.view_angles:
                    data = self.preprocessing(item, view, None)
                    view_data.append(data)
                char_data.append(torch.stack(view_data, dim=0))
            all_data.append(torch.stack(char_data, dim=0))
        all_data = torch.stack(all_data, dim=0)

        ret = (all_data, motion_names, self.character_names, np.rad2deg(self.view_angles))
        return ret 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def great_circle_distance(lat1, lon1, lat2, lon2, r=None):
    r"""Calculate the distance between two geographical positions

    This is the 'As-the-crow-flies' distance between two points, specified by
    their latitude and longitude. This uses the so-called haversine formula,
    defined by:

    .. math::
        d = 2 \arcsin \left( \sqrt{
            \sin \left(\frac{\varphi_2-\varphi_1}{2} \right)^2
            + \cos(\varphi_1) \cdot \cos(\varphi_1)
            \cdot \sin \left(\frac{\lambda_2-\lambda_1}{2} \right)^2} \right)

    Args:
        lat1: Latitude of position 1.
        lon1: Longitude of position 1.
        lat2: Latitude of position 2.
        lon2: Longitude of position 2.
        r (float): The radius (common for both points).

    Returns:
        If the optional argument *r* is given, the distance in m is returned.
        Otherwise the angular distance in degrees is returned.

    .. Taken from https://stackoverflow.com/a/29546836/9144990
    """
    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])

    dlon = lon2 - lon1
    dlat = lat2 - lat1

    a = np.sin(dlat / 2.0) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(
        dlon / 2.0) ** 2

    c = 2 * np.arcsin(np.sqrt(a))

    if r is None:
        return np.rad2deg(c)
    else:
        return r * c 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def geocentric2geodetic(latitude):
    """Translate geocentric to geodetic latitudes.

    Parameters:
        latitude (ndarray): latitude values in degree.

    Returns:
        (ndarray): geodetic latitudes.
    """

    return np.rad2deg(np.arctan(1.0067395 * np.tan(np.deg2rad(latitude)))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def degree_formatter(x, pos):
    """Create degree ticklabels for radian data."""
    return '{:.0f}\N{DEGREE SIGN}'.format(np.rad2deg(x))


# Read wind data. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import rad2deg [as 別名]
def rad2deg(x, out=None, where=None, **kwargs):
    """
    Convert angles from radians to degrees.

    Parameters
    ----------
    x : array_like
        Angle in radians.
    out : Tensor, None, or tuple of Tensor and None, optional
        A location into which the result is stored. If provided, it must have
        a shape that the inputs broadcast to. If not provided or `None`,
        a freshly-allocated tensor is returned. A tuple (possible only as a
        keyword argument) must have length equal to the number of outputs.
    where : array_like, optional
        Values of True indicate to calculate the ufunc at that position, values
        of False indicate to leave the value in the output alone.
    **kwargs

    Returns
    -------
    y : Tensor
        The corresponding angle in degrees.

    See Also
    --------
    deg2rad : Convert angles from degrees to radians.

    Notes
    -----
    rad2deg(x) is ``180 * x / pi``.

    Examples
    --------
    >>> import mars.tensor as mt

    >>> mt.rad2deg(mt.pi/2).execute()
    90.0
    """
    op = TensorRad2deg(**kwargs)
    return op(x, out=out, where=where)