def get_position():
# type: () -> PositionType
px, py, pz = fit_position_xvalidation(state_time,
state_position,
state_velocity,
max_degree=6)
return PositionType(ARPPoly=XYZPolyType(X=px, Y=py, Z=pz))
def get_position(): # type: () -> PositionType
T = h5_dict['State Vectors Times'] # in seconds relative to ref time
T += ref_time_offset
Pos = h5_dict['ECEF Satellite Position']
Vel = h5_dict['ECEF Satellite Velocity']
P_x, P_y, P_z = fit_position_xvalidation(T, Pos, Vel, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def get_position() -> PositionType:
gp = hf['/science/LSAR/SLC/metadata/orbit']
ref_time = _get_ref_time(gp['time'].attrs['units'])
T = gp['time'][:] + get_seconds(ref_time, collect_start, precision='ns')
Pos = gp['position'][:]
Vel = gp['velocity'][:]
P_x, P_y, P_z = fit_position_xvalidation(T, Pos, Vel, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def _get_position(self):
"""
Gets the Position.
Returns
-------
PositionType
"""
start_time = self._get_start_time()
# get radar position state information
state_vectors = self._findall('./sourceAttributes'
'/orbitAndAttitude'
'/orbitInformation'
'/stateVector')
# convert to relevant numpy arrays for polynomial fitting
T = numpy.array([
get_seconds(parse_timestring(state_vec.find('timeStamp').text),
start_time,
precision='us') for state_vec in state_vectors
],
dtype=numpy.float64)
Pos = numpy.hstack((numpy.array([
float(state_vec.find('xPosition').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('yPosition').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('zPosition').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:,
numpy.newaxis]))
Vel = numpy.hstack((numpy.array([
float(state_vec.find('xVelocity').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('yVelocity').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:, numpy.newaxis],
numpy.array([
float(state_vec.find('zVelocity').text)
for state_vec in state_vectors
],
dtype=numpy.float64)[:,
numpy.newaxis]))
P_x, P_y, P_z = fit_position_xvalidation(T, Pos, Vel, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def get_position():
# type: () -> PositionType
# fetch the state information
times_str = hf['state_vector_time_utc'][:, 0]
times = numpy.zeros((times_str.shape[0], ), dtype='float64')
positions = numpy.zeros((times.size, 3), dtype='float64')
velocities = numpy.zeros((times.size, 3), dtype='float64')
for i, entry in enumerate(times_str):
times[i] = get_seconds(_parse_time(entry), start_time, precision='us')
positions[:, 0], positions[:, 1], positions[:, 2] = hf['posX'][:], hf['posY'][:], hf['posZ'][:]
velocities[:, 0], velocities[:, 1], velocities[:, 2] = hf['velX'][:], hf['velY'][:], hf['velZ'][:]
# fir the the position polynomial using cross validation
P_x, P_y, P_z = fit_position_xvalidation(times, positions, velocities, max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
