def gpSimFull(carmaTerm, SNR, duration, N, nLC=1):
"""Simulate full CARMA time series.
Args:
carmaTerm (object): celerite GP term.
SNR (float): Signal to noise ratio defined as ratio between
CARMA amplitude and the mode of the errors (simulated using
log normal).
duration (float): The duration of the simulated time series in days.
N (int): The number of data points.
nLC (int, optional): Number of light curves to simulate. Defaults to 1.
Raises:
RuntimeError: If the input CARMA term/model is not stable, thus cannot be
solved by celerite.
Returns:
Arrays: t, y and yerr of the simulated light curves in numpy arrays.
Note that errors have been added to y.
"""
assert isinstance(
carmaTerm,
celerite.celerite.terms.Term), "carmaTerm must a celerite GP term"
if (not isinstance(carmaTerm,
DRW_term)) and (carmaTerm._arroots.real > 0).any():
raise RuntimeError(
"The covariance matrix of the provided CARMA term is not positive definite!"
)
t = np.linspace(0, duration, N)
noise = carmaTerm.get_rms_amp() / SNR
yerr = np.random.lognormal(0, 0.25, N) * noise
yerr = yerr[np.argsort(np.abs(yerr))]
# init GP and solve matrix
gp_sim = GP(carmaTerm)
gp_sim.compute(t)
# simulate, assign yerr based on y
t = np.repeat(t[None, :], nLC, axis=0)
y = gp_sim.sample(size=nLC)
# format yerr making it heteroscedastic
y_rank = y.argsort(axis=1).argsort(axis=1)
yerr = np.repeat(yerr[None, :], nLC, axis=0)
yerr = np.array(list(map(lambda x, y: x[y], yerr, y_rank)))
yerr_sign = np.random.binomial(1, 0.5, yerr.shape)
yerr_sign[yerr_sign < 1] = -1
yerr = yerr * yerr_sign
if nLC == 1:
return t[0], y[0] + yerr[0], yerr[0]
else:
return t, y + yerr, yerr
def gpSimFull(carmaTerm, SNR, duration, N, nLC=1, log_flux=True):
"""
Simulate CARMA time series using uniform sampling.
Args:
carmaTerm (object): An EzTao CARMA kernel.
SNR (float): Signal-to-noise defined as ratio between CARMA RMS amplitude and
the median of the measurement errors (simulated using log normal).
duration (float): The duration of the simulated time series (default in days).
N (int): The number of data points in the simulated time series.
nLC (int, optional): Number of time series to simulate. Defaults to 1.
log_flux (bool): Whether the flux/y values are in astronomical magnitude.
This argument affects how errors are assigned. Defaults to True.
Raises:
RuntimeError: If the input CARMA term/model is not stable, thus cannot be
solved by celerite.
Returns:
(array(float), array(float), array(float)): Time stamps (default in day), y
values and measurement errors of the simulated time series.
"""
assert isinstance(
carmaTerm,
celerite.celerite.terms.Term), "carmaTerm must a celerite GP term"
if (not isinstance(carmaTerm,
DRW_term)) and (carmaTerm._arroots.real > 0).any():
raise RuntimeError(
"The covariance matrix of the provided CARMA term is not positive definite!"
)
t = np.linspace(0, duration, N)
noise = carmaTerm.get_rms_amp() / SNR
yerr = np.random.lognormal(0, 0.25, N) * noise
yerr = yerr[np.argsort(np.abs(yerr))] # small->large
# init GP and solve matrix
gp_sim = GP(carmaTerm)
gp_sim.compute(t)
# simulate, assign yerr based on y
t = np.repeat(t[None, :], nLC, axis=0)
y = gp_sim.sample(size=nLC)
# format yerr making it heteroscedastic
yerr = np.repeat(yerr[None, :], nLC, axis=0)
# if in mag, large value with large error; in flux, the opposite
if log_flux:
# ascending sort
y_rank = y.argsort(axis=1).argsort(axis=1)
yerr = np.array(list(map(lambda x, y: x[y], yerr, y_rank)))
else:
# descending sort
y_rank = (-y).argsort(axis=1).argsort(axis=1)
yerr = np.array(list(map(lambda x, y: x[y], yerr, y_rank)))
if nLC == 1:
return t[0], y[0], yerr[0]
else:
return t, y, yerr