def simulate_a_microlensing_model(event,
model='PSPL',
args=(),
parallax=['None', 0.0],
xallarap=['None', 0.0],
orbital_motion=['None', 0.0],
source_spots='None'):
""" Simulate a a microlensing model.
:param object event: the microlensing event you look at. More details in event module
:param str model: the microlensing model you want. Default is 'PSPL'. More details in microlmodels module
:param array_like parallax : the parallax effect you want to add. Default is no parallax.
More details in microlmodels module
:param array_like xallarap : the xallarap effect you want to add. Default is no parallax.
More details in microlmodels module
:param str source_spots : If you want to add source spots. Default is no source_spots.
More details in microlmodels module
:return: a model object
:rtype: object
"""
fake_model = microlmodels.create_model(model, event, args, parallax,
xallarap, orbital_motion,
source_spots)
fake_model.define_model_parameters()
return fake_model
def simulate_a_microlensing_model(event, model='PSPL', parallax=['None', 0.0], xallarap=['None', 0.0],
orbital_motion=['None', 0.0], source_spots='None'):
""" Simulate a a microlensing model.
:param object event: the microlensing event you look at. More details in event module
:param str model: the microlensing model you want. Default is 'PSPL'. More details in microlmodels module
:param array_like parallax : the parallax effect you want to add. Default is no parallax.
More details in microlmodels module
:param array_like xallarap : the xallarap effect you want to add. Default is no parallax.
More details in microlmodels module
:param str source_spots : If you want to add source spots. Default is no source_spots.
More details in microlmodels module
:return: a model object
:rtype: object
"""
fake_model = microlmodels.create_model(model, event, parallax, xallarap,
orbital_motion, source_spots)
fake_model.define_model_parameters()
return fake_model
def initial_guess(self):
"""Try to estimate the microlensing parameters. Only use for PSPL and FSPL
models. More details on microlguess module.
:return guess_parameters: a list containing parameters guess related to the model.
:rtype: list
"""
if len(self.model.parameters_guess) == 0:
# Estimate the Paczynski parameters
if self.model.model_type == 'PSPL':
guess_paczynski_parameters, f_source = microlguess.initial_guess_PSPL(self.event)
if self.model.model_type == 'FSPL':
guess_paczynski_parameters, f_source = microlguess.initial_guess_FSPL(self.event)
if self.model.model_type == 'DSPL':
guess_paczynski_parameters, f_source = microlguess.initial_guess_DSPL(self.event)
# Estimate the telescopes fluxes (flux_source + g_blending) parameters, with a PSPL model
fake_model = microlmodels.create_model('PSPL', self.event)
fake_model.define_model_parameters()
telescopes_fluxes = self.find_fluxes(guess_paczynski_parameters, fake_model)
# The survey fluxes are already known from microlguess
telescopes_fluxes[0] = f_source
telescopes_fluxes[1] = 0.0
if self.model.parallax_model[0] != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [0.0, 0.0]
if self.model.xallarap_model[0] != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [0, 0]
if self.model.orbital_motion_model[0] != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [0, 0]
if self.model.source_spots_model != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [0]
else:
guess_paczynski_parameters = list(self.model.parameters_guess)
telescopes_fluxes = self.find_fluxes(guess_paczynski_parameters, self.model)
guess_paczynski_parameters += telescopes_fluxes
guess_parameters_pyLIMA_standards = collections.namedtuple('parameters',
self.model.pyLIMA_standards_dictionnary.keys())
for key_parameter in self.model.pyLIMA_standards_dictionnary.keys():
try:
setattr(guess_parameters_pyLIMA_standards, key_parameter,
guess_paczynski_parameters[self.model.pyLIMA_standards_dictionnary[key_parameter]])
except:
pass
fancy_parameters_guess = self.model.pyLIMA_standard_parameters_to_fancy_parameters(
guess_parameters_pyLIMA_standards)
model_guess_parameters = []
for key_parameter in self.model.model_dictionnary.keys():
model_guess_parameters.append(getattr(fancy_parameters_guess, key_parameter))
print sys._getframe().f_code.co_name, ' : Initial parameters guess SUCCESS'
return model_guess_parameters
def initial_guess(self):
"""Try to estimate the microlensing parameters. Only use for PSPL and FSPL
models. More details on microlguess module.
:return guess_parameters: a list containing parameters guess related to the model.
:rtype: list
"""
if len(self.model.parameters_guess) == 0:
# Estimate the Paczynski parameters
if self.model.model_type == 'PSPL':
guess_paczynski_parameters, f_source = microlguess.initial_guess_PSPL(
self.event)
if self.model.model_type == 'FSPL':
guess_paczynski_parameters, f_source = microlguess.initial_guess_FSPL(
self.event)
if self.model.model_type == 'DSPL':
guess_paczynski_parameters, f_source = microlguess.initial_guess_DSPL(
self.event)
# Estimate the telescopes fluxes (flux_source + g_blending) parameters, with a PSPL model
fake_model = microlmodels.create_model('PSPL', self.event)
fake_model.define_model_parameters()
telescopes_fluxes = self.find_fluxes(guess_paczynski_parameters,
fake_model)
# The survey fluxes are already known from microlguess
telescopes_fluxes[0] = f_source
telescopes_fluxes[1] = 0.0
if self.model.parallax_model[0] != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [
0.0, 0.0
]
if self.model.xallarap_model[0] != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [
0, 0
]
if self.model.orbital_motion_model[0] != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [
0, 0
]
if self.model.source_spots_model != 'None':
guess_paczynski_parameters = guess_paczynski_parameters + [0]
else:
guess_paczynski_parameters = list(self.model.parameters_guess)
telescopes_fluxes = self.find_fluxes(guess_paczynski_parameters,
self.model)
guess_paczynski_parameters += telescopes_fluxes
guess_parameters_pyLIMA_standards = collections.namedtuple(
'parameters', self.model.pyLIMA_standards_dictionnary.keys())
for key_parameter in self.model.pyLIMA_standards_dictionnary.keys():
try:
setattr(
guess_parameters_pyLIMA_standards, key_parameter,
guess_paczynski_parameters[
self.model.pyLIMA_standards_dictionnary[key_parameter]]
)
except:
pass
fancy_parameters_guess = self.model.pyLIMA_standard_parameters_to_fancy_parameters(
guess_parameters_pyLIMA_standards)
model_guess_parameters = []
for key_parameter in self.model.model_dictionnary.keys():
model_guess_parameters.append(
getattr(fancy_parameters_guess, key_parameter))
print sys._getframe(
).f_code.co_name, ' : Initial parameters guess SUCCESS'
return model_guess_parameters