def test_initial_guess_DSPL():
event = _create_event()
guesses = microlguess.initial_guess_DSPL(event)
assert len(guesses) == 2
assert len(guesses[0]) == 6
def test_initial_guess_DSPL():
event = _create_event()
guesses = microlguess.initial_guess_DSPL(event)
assert len(guesses) == 2
assert len(guesses[0]) == 6
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
telescopes_fluxes = self.find_fluxes(guess_paczynski_parameters, self.model)
# The survey fluxes are already known from microlguess
telescopes_fluxes[0] = f_source
telescopes_fluxes[1] = 0.0
if 'piEN' in self.model.model_dictionnary.keys():
guess_paczynski_parameters = guess_paczynski_parameters + [0.0, 0.0]
if 'XiEN' in self.model.model_dictionnary.keys():
guess_paczynski_parameters = guess_paczynski_parameters + [0, 0]
if 'dsdt' in self.model.model_dictionnary.keys():
guess_paczynski_parameters = guess_paczynski_parameters + [0, 0]
if 'spot_size' in self.model.model_dictionnary.keys():
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)
if len(guess_paczynski_parameters) != len(list(self.model.model_dictionnary.keys())):
guess_paczynski_parameters += telescopes_fluxes
print(sys._getframe().f_code.co_name, ' : Initial parameters guess SUCCESS')
return guess_paczynski_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 list(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