Python numpy.radians方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def orthogonalization_matrix(lengths, angles):
    """Return orthogonalization matrix for crystallographic cell coordinates.
    Angles are expected in degrees.
    The de-orthogonalization matrix is the inverse.
    >>> O = orthogonalization_matrix((10., 10., 10.), (90., 90., 90.))
    >>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10)
    True
    >>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7])
    >>> numpy.allclose(numpy.sum(O), 43.063229)
    True
    """
    a, b, c = lengths
    angles = numpy.radians(angles)
    sina, sinb, _ = numpy.sin(angles)
    cosa, cosb, cosg = numpy.cos(angles)
    co = (cosa * cosb - cosg) / (sina * sinb)
    return numpy.array((
        ( a*sinb*math.sqrt(1.0-co*co),  0.0,    0.0, 0.0),
        (-a*sinb*co,                    b*sina, 0.0, 0.0),
        ( a*cosb,                       b*cosa, c,   0.0),
        ( 0.0,                          0.0,    0.0, 1.0)),
        dtype=numpy.float64) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def calculate_dayside_reconnection(inst):
    """ Calculate the dayside reconnection rate (Milan et al. 2014)

    Parameters
    -----------
    inst : pysat.Instrument
        Instrument with OMNI HRO data, requires BYZ_GSM and clock_angle

    Notes
    --------
    recon_day = 3.8 Re (Vx / 4e5 m/s)^1/3 Vx B_yz (sin(theta/2))^9/2
    """
    rearth = 6371008.8
    sin_htheta = np.power(np.sin(np.radians(0.5 * inst['clock_angle'])), 4.5)
    byz = inst['BYZ_GSM'] * 1.0e-9
    vx = inst['flow_speed'] * 1000.0

    recon_day = 3.8 * rearth * vx * byz * sin_htheta * np.power((vx / 4.0e5),
                                                                1.0/3.0)
    inst['recon_day'] = pds.Series(recon_day, index=inst.data.index)
    return 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def coords_to_vec(lon, lat):
    """ Converts longitute and latitude coordinates to a unit 3-vector

    return array(3,n) with v_x[i],v_y[i],v_z[i] = directional cosines
    """
    phi = np.radians(lon)
    theta = (np.pi / 2) - np.radians(lat)
    sin_t = np.sin(theta)
    cos_t = np.cos(theta)

    xVals = sin_t * np.cos(phi)
    yVals = sin_t * np.sin(phi)
    zVals = cos_t

    # Stack them into the output array
    out = np.vstack((xVals, yVals, zVals)).swapaxes(0, 1)
    return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def rotipp(acceleration_x, time_step_x, acceleration_y, time_step_y, periods,
        percentile, damping=0.05, units="cm/s/s", method="Nigam-Jennings"):
    """
    Returns the rotationally independent spectrum RotIpp as defined by
    Boore (2010)
    """
    if np.fabs(time_step_x - time_step_y) > 1E-10:
        raise ValueError("Record pair must have the same time-step!")
    acceleration_x, acceleration_y = equalise_series(acceleration_x,
                                                     acceleration_y)
    target, rota, rotv, rotd, angles = rotdpp(acceleration_x, time_step_x,
                                              acceleration_y, time_step_y,
                                              periods, percentile, damping,
                                              units, method)
    locn, penalty = _get_gmrotd_penalty(
        np.hstack([target["PGA"],target["Pseudo-Acceleration"]]),
        rota)
    target_theta = np.radians(angles[locn])
    arotpp = acceleration_x * np.cos(target_theta) +\
        acceleration_y * np.sin(target_theta)
    spec = get_response_spectrum(arotpp, time_step_x, periods, damping, units,
        method)[0]
    spec["GMRot{:2.0f}".format(percentile)] = target
    return spec 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def _latlon_to_cart(lat, lon, R = typhon.constants.earth_radius):
    """
    Simple conversion of latitude and longitude to Cartesian coordinates.
    Approximates the Earth as sphere with radius :code:`R` and computes
    cartesian x, y, z coordinates with the center of the Earth as origin.

    Args:
        lat: Array of latitude coordinates.
        lon: Array of longitude coordinates.
        R: The radius to assume.
    Returns:
        Tuple :code:`(x, y, z)` of arrays :code:`x, y, z` containing the
        resulting x-, y- and z-coordinates.
    """
    lat = np.radians(lat)
    lon = np.radians(lon)
    x = R * np.cos(lat) * np.cos(lon)
    y = R * np.cos(lat) * np.sin(lon)
    z = R * np.sin(lat)
    return x, y, z 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def HaversineDistance(lon1,lat1,lon2,lat2):
    """
    Function to calculate the great circle distance between two points
    using the Haversine formula
    """
    R = 6371. #Mean radius of the Earth

    # convert decimal degrees to radians
    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])

    # haversine formula
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = np.sin(dlat/2.)**2. + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.)**2.
    c = 2.*np.arcsin(np.sqrt(a))
    distance = R * c

    return distance 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def orthogonalization_matrix(lengths, angles):
  """Return orthogonalization matrix for crystallographic cell coordinates.

  Angles are expected in degrees.

  The de-orthogonalization matrix is the inverse.

  >>> O = orthogonalization_matrix([10, 10, 10], [90, 90, 90])
  >>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10)
  True
  >>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7])
  >>> numpy.allclose(numpy.sum(O), 43.063229)
  True

  """
  a, b, c = lengths
  angles = numpy.radians(angles)
  sina, sinb, _ = numpy.sin(angles)
  cosa, cosb, cosg = numpy.cos(angles)
  co = (cosa * cosb - cosg) / (sina * sinb)
  return numpy.array([
    [a * sinb * math.sqrt(1.0 - co * co), 0.0, 0.0, 0.0],
    [-a * sinb * co, b * sina, 0.0, 0.0],
    [a * cosb, b * cosa, c, 0.0],
    [0.0, 0.0, 0.0, 1.0]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def test_true_to_eccentric(self):
        # Data from NASA-TR-R-158
        data = [
            # ecc, E (deg), ta(deg)
            (0.0, 0.0, 0.0),
            (0.05, 10.52321, 11.05994),
            (0.10, 54.67466, 59.49810),
            (0.35, 142.27123, 153.32411),
            (0.61, 161.87359, 171.02189)
        ]
        for row in data:
            ecc, expected_E, ta = row

            E = angles.ta_to_E(radians(ta), ecc)

            self.assertAlmostEqual(degrees(E), expected_E, places=4) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def test_mean_to_true(self):
        # Data from Schlesinger & Udick, 1912
        data = [
            # ecc, M (deg), ta (deg)
            (0.0, 0.0, 0.0),
            (0.05, 10.0, 11.06),
            (0.06, 30.0, 33.67),
            (0.04, 120.0, 123.87),
            (0.14, 65.0, 80.50),
            (0.19, 21.0, 30.94),
            (0.35, 65.0, 105.71),
            (0.48, 180.0, 180.0),
            (0.75, 125.0, 167.57)
        ]
        for row in data:
            ecc, M, expected_ta = row

            ta = angles.M_to_ta(radians(M), ecc)

            self.assertAlmostEqual(degrees(ta), expected_ta, places=2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def test_true_to_mean(self):
        # Data from Schlesinger & Udick, 1912
        data = [
            # ecc, M (deg), ta (deg)
            (0.0, 0.0, 0.0),
            (0.05, 10.0, 11.06),
            (0.06, 30.0, 33.67),
            (0.04, 120.0, 123.87),
            (0.14, 65.0, 80.50),
            (0.19, 21.0, 30.94),
            (0.35, 65.0, 105.71),
            (0.48, 180.0, 180.0),
            (0.75, 125.0, 167.57)
        ]
        for row in data:
            ecc, expected_M, ta = row

            M = angles.ta_to_M(radians(ta), ecc)

            self.assertAlmostEqual(degrees(M), expected_M, places=1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def orthogonalization_matrix(lengths, angles):
    """Return orthogonalization matrix for crystallographic cell coordinates.

    Angles are expected in degrees.

    The de-orthogonalization matrix is the inverse.

    >>> O = orthogonalization_matrix([10, 10, 10], [90, 90, 90])
    >>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10)
    True
    >>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7])
    >>> numpy.allclose(numpy.sum(O), 43.063229)
    True

    """
    a, b, c = lengths
    angles = numpy.radians(angles)
    sina, sinb, _ = numpy.sin(angles)
    cosa, cosb, cosg = numpy.cos(angles)
    co = (cosa * cosb - cosg) / (sina * sinb)
    return numpy.array([
        [ a*sinb*math.sqrt(1.0-co*co),  0.0,    0.0, 0.0],
        [-a*sinb*co,                    b*sina, 0.0, 0.0],
        [ a*cosb,                       b*cosa, c,   0.0],
        [ 0.0,                          0.0,    0.0, 1.0]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def get_displacement_km(n1, x1, y1, n2, x2, y2):
    ''' Find displacement in kilometers using Haversine
        http://www.movable-type.co.uk/scripts/latlong.html
    Parameters
    ----------
        n1 : First Nansat object
        x1 : 1D vector - X coordinates of keypoints on image 1
        y1 : 1D vector - Y coordinates of keypoints on image 1
        n2 : Second Nansat object
        x1 : 1D vector - X coordinates of keypoints on image 2
        y1 : 1D vector - Y coordinates of keypoints on image 2
    Returns
    -------
        h : 1D vector - total displacement, km
    '''
    lon1, lat1 = n1.transform_points(x1, y1)
    lon2, lat2 = n2.transform_points(x2, y2)

    lt1, ln1, lt2, ln2 = map(np.radians, (lat1, lon1, lat2, lon2))
    dlat = lt2 - lt1
    dlon = ln2 - ln1
    d = (np.sin(dlat * 0.5) ** 2 +
         np.cos(lt1) * np.cos(lt2) * np.sin(dlon * 0.5) ** 2)
    return 2 * AVG_EARTH_RADIUS * np.arcsin(np.sqrt(d)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def __init__(self, rotation: Vector, translation: Vector, angle_unit: str, notation: str='XYZ'):
        self.rotation    = rotation
        self.translation = translation

        self.angle_unit = angle_unit
        if self.angle_unit == 'degrees':
            self.rotation = Vector(*[radians(alpha) for alpha in rotation])

        self.R_x = None
        self.R_y = None
        self.R_z = None
        self.T   = None

        self.matrix = None
        self.notation = notation

        self._update_matrix() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def lb2pix(nside, l, b, nest=True):
    """
    Converts Galactic (l, b) to HEALPix pixel index.

    Args:
        nside (:obj:`int`): The HEALPix :obj:`nside` parameter.
        l (:obj:`float`, or array of :obj:`float`): Galactic longitude, in degrees.
        b (:obj:`float`, or array of :obj:`float`): Galactic latitude, in degrees.
        nest (Optional[:obj:`bool`]): If :obj:`True` (the default), nested pixel ordering
            will be used. If :obj:`False`, ring ordering will be used.

    Returns:
        The HEALPix pixel index or indices. Has the same shape as the input :obj:`l`
        and :obj:`b`.
    """

    theta = np.radians(90. - b)
    phi = np.radians(l)

    if not hasattr(l, '__len__'):
        if (b < -90.) or (b > 90.):
            return -1

        pix_idx = hp.pixelfunc.ang2pix(nside, theta, phi, nest=nest)

        return pix_idx

    idx = (b >= -90.) & (b <= 90.)

    pix_idx = np.empty(l.shape, dtype='i8')
    pix_idx[idx] = hp.pixelfunc.ang2pix(nside, theta[idx], phi[idx], nest=nest)
    pix_idx[~idx] = -1

    return pix_idx 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import radians [as 別名]
def _coords2vec(self, coords):
        """
        Converts from sky coordinates to unit vectors. Before conversion to unit
        vectors, the coordiantes are transformed to the coordinate system used
        internally by the :obj:`UnstructuredDustMap`, which can be set during
        initialization of the class.

        Args:
            coords (:obj:`astropy.coordinates.SkyCoord`): Input coordinates to
                convert to unit vectors.

        Returns:
            Cartesian unit vectors corresponding to the input coordinates, after
            transforming to the coordinate system used internally by the
            :obj:`UnstructuredDustMap`.
        """

        # c = coords.transform_to(self._frame)
        # vec = np.empty((c.shape[0], 2), dtype='f8')
        # vec[:,0] = coordinates.Longitude(coords.l, wrap_angle=360.*units.deg).deg[:]
        # vec[:,1] = coords.b.deg[:]
        # return np.radians(vec)

        c = coords.transform_to(self._frame).represent_as('cartesian')
        vec_norm = np.sqrt(c.x**2 + c.y**2 + c.z**2)

        vec = np.empty((c.shape[0], 3), dtype=c.x.dtype)
        vec[:,0] = (c.x / vec_norm).value[:]
        vec[:,1] = (c.y / vec_norm).value[:]
        vec[:,2] = (c.z / vec_norm).value[:]

        return vec