def estimate(stage, dset):
"""Filter residuals
Args:
prev_stage (String): Name of previous stage.
stage (String): Name of current stage.
"""
partial_vectors = estimation.partial_vectors(dset, "estimate_method")
max_iterations = config.tech.estimate_max_iterations.int
for iter_num in itertools.count(start=1):
log.info(f"Estimating parameters for iteration {iter_num}")
estimation.call(
"estimate_method",
dset=dset,
partial_vectors=partial_vectors,
obs_noise=None # TODO: Add something here
)
rms = dset.rms("residual")
log.info(f"{dset.num_obs} observations, postfit residual = {rms:.4f}")
dset.write_as(stage=stage,
label=iter_num - 1,
sat_name=dset.vars["sat_name"])
# TODO:
# Do some iteration with removal of outliers?
break
if iter_num >= max_iterations:
break
estimation.solve_neq(dset)
dset.write()
def estimate(stage, dset):
"""Filter residuals
Args:
rundate (Datetime): The model run date.
session (String): Name of session.
prev_stage (String): Name of previous stage.
stage (String): Name of current stage.
"""
max_iterations = config.tech.estimate_max_iterations.int
delay_unit = "meter"
for iter_num in itertools.count(start=1):
partial_vectors = estimation.partial_vectors(dset, "estimate_method")
obs_noise = dset.observed_delay_ferr**2 + np.nan_to_num(
dset.iono_delay_ferr)**2 + 0.01**2
log.info(
f"Estimating parameters for iteration {iter_num} using Kalman Filter and continuous piecewise linear functions"
)
estimation.call("estimate_method",
dset=dset,
partial_vectors=partial_vectors,
obs_noise=obs_noise)
rms = dset.rms("residual")
log.info(
f"{dset.num_obs} observations, rms of postfit residuals = {rms:.4f} {delay_unit}"
)
dset.write_as(stage=stage, label=iter_num - 1)
if iter_num >= max_iterations:
break
# Detect and remove outliers
num_obs_before = dset.num_obs
independent = config.tech.estimate_obs_rejectors_independent.bool
dset = estimation.apply_observation_rejectors("estimate_obs_rejectors",
dset, independent)
log.blank()
if dset.num_obs == num_obs_before or dset.num_obs == 0:
break
log.blank()
if dset.num_obs > 0:
estimation.solve_neq(dset)
dset.write()
def estimate(stage, dset):
"""Filter residuals
Args:
rundate (Datetime): The model run date.
session (String): Name of session.
prev_stage (String): Name of previous stage.
stage (String): Name of current stage.
"""
max_iterations = config.tech.estimate_max_iterations.int
for iter_num in itertools.count(start=1):
partial_vectors = estimation.partial_vectors(dset, "estimate_method")
log.info(f"Estimating parameters for iteration {iter_num}")
estimation.call(
"estimate_method",
dset=dset,
partial_vectors=partial_vectors,
obs_noise=dset.observed_delay_ferr ** 2 + 0.01 ** 2,
)
rms = dset.rms("residual")
log.info(f"{dset.num_obs} observations, postfit residual = {rms:.4f}")
dset.write_as(stage=stage, label=iter_num - 1)
if iter_num >= max_iterations:
break
# Detect and remove outliers
num_obs_before = dset.num_obs
independent = config.tech.estimate_obs_rejectors_independent.bool
dset = estimation.apply_observation_rejectors("estimate_obs_rejectors", dset, independent)
log.blank()
if dset.num_obs == num_obs_before:
break
estimation.solve_neq(dset)
dset.write()