def widelane_solve(dd, station_data, sta1, sta2, prn1, prn2, ticks):
lambda_w = lambda_ws[prn1[0]]
vr1s1s = []
vr1s2s = []
vr2s1s = []
vr2s2s = []
for tick in ticks:
vr1s1s.append(tec.melbourne_wubbena(station_data[sta1][prn1][tick])[0])
vr1s2s.append(tec.melbourne_wubbena(station_data[sta1][prn2][tick])[0])
vr2s1s.append(tec.melbourne_wubbena(station_data[sta2][prn1][tick])[0])
vr2s2s.append(tec.melbourne_wubbena(station_data[sta2][prn2][tick])[0])
vr1s1 = numpy.mean(vr1s1s)
vr1s2 = numpy.mean(vr1s2s)
vr2s1 = numpy.mean(vr2s1s)
vr2s2 = numpy.mean(vr2s2s)
return dd_solve(dd, vr1s1, vr1s2, vr2s1, vr2s2, lambda_w)
def test_n1(N_w, station_data, sta, prn, ticks):
# given N_w = N_1 - N_2
# find "optimal" N1
lambda_1 = lambda_1s[prn[0]]
lambda_2 = lambda_2s[prn[0]]
lambda_n = lambda_ns[prn[0]]
lambda_w = lambda_ws[prn[0]]
# N_w = -N_w
b_w = numpy.array([
tec.melbourne_wubbena(station_data[sta][prn][tick])[0]
for tick in ticks
]) - lambda_w * N_w
freqs = tec.F_lookup[prn[0]]
# TODO why are f1 and f2 reversed from what I expect?
delay_factor_w = freqs[0] * freqs[1] / (freqs[1]**2 - freqs[0]**2)
B_c = numpy.array(
[estimate_Bc(station_data[sta][prn][tick])[0] for tick in ticks])
gf_bias = bias(tec.geometry_free)
B_i = numpy.array(
[gf_bias(station_data[sta][prn][tick])[0] for tick in ticks])
divisor = delay_factor_w * (lambda_2 - lambda_1) - lambda_n
rhs = b_w - B_c - delay_factor_w * (
B_i + lambda_2 * N_w) + lambda_n * N_w * lambda_w / lambda_2
res = rhs / divisor
n1 = numpy.mean(res)
return int(n1)
def test_n1_(N_w, station_data, sta, prn, ticks):
# given N_w = N_1 - N_2
# test out a N1/N2 combinations
# get things that don't change base on N_1:
# using N_w get b_w, delay_factor_w, B_c, B_i, wavelengths
# for each N_1 candidate:
# using N_w, N_1_candidate, B_c, estimate b_c, use that to get
# a b_I estimate plug in to get ERR_1
# using b_c, b_w, N_1_candidate, delay_factor_w, estimate b_I
# and use that to get ERR_1
lambda_1 = lambda_1s[prn[0]]
lambda_2 = lambda_2s[prn[0]]
lambda_n = lambda_ns[prn[0]]
lambda_w = lambda_ws[prn[0]]
b_w = numpy.mean([
tec.melbourne_wubbena(station_data[sta][prn][tick]) for tick in ticks
]) - lambda_w * N_w
freqs = tec.F_lookup[prn[0]]
# TODO why are f1 and f2 reversed from what I expect?
delay_factor_w = freqs[0]*freqs[1]/(freqs[1]**2 - freqs[0]**2)
# TODO don't average out... look at results for each tick?
B_c = numpy.mean([
estimate_Bc(station_data[sta][prn][tick])[0] for tick in ticks
])
gf_bias = bias(tec.geometry_free)
B_i = numpy.mean([
gf_bias(station_data[sta][prn][tick]) for tick in ticks
])
N_1_best = None
err_best = 10000
for N_1_cand in range(-400, 400):
N_2_cand = N_1_cand - N_w
# TODO ???
N_1_cand, N_2_cand = N_2_cand, N_1_cand
b_c_meas = B_c - lambda_n * (N_1_cand + N_w * lambda_w / lambda_2)
b_i_meas = B_i - lambda_1 * N_1_cand + lambda_2 * N_2_cand
b_i_est = (b_w - b_c_meas) / delay_factor_w
b_c_est = b_w - delay_factor_w * b_i_meas
err1 = (b_i_est - b_i_meas)**2
err2 = (b_c_est - b_c_meas)**2
if err1 + err2 < err_best:
err_best = err1 + err2
N_1_best = N_1_cand
return N_1_best, err_best
def widelane_solve(dd, station_data, sta1, sta2, prn1, prn2, tick):
vr1s1, lambda_w = tec.melbourne_wubbena(station_data[sta1][prn1][tick])
vr1s2, _ = tec.melbourne_wubbena(station_data[sta1][prn2][tick])
vr2s1, _ = tec.melbourne_wubbena(station_data[sta2][prn1][tick])
vr2s2, _ = tec.melbourne_wubbena(station_data[sta2][prn2][tick])
return dd_solve(dd, vr1s1, vr1s2, vr2s1, vr2s2, lambda_w)