def test_least_squares_lc_fit_community_params(self):
dinit = {
"system":
{
'semi_major_axis': {
'value': 11.0, # 12.62
'fixed': False,
'min': 7.0,
'max': 15.0
},
'mass_ratio': {
'value': 0.7, # 0.5
'fixed': False,
'min': 0.3,
'max': 2.0
},
'inclination': {
'value': 90.0,
'fixed': True
},
'eccentricity': {
'value': 0.0,
'fixed': True
},
'argument_of_periastron': {
'value': 0.0,
'fixed': True
},
'period': {
'value': 3.0,
'fixed': True
}
},
"primary": {
't_eff': {
'value': 5000.0,
'fixed': True
},
'surface_potential': {
'value': 5.0,
'fixed': True
},
'gravity_darkening': {
'value': 1.0,
'fixed': True
},
'albedo': {
'value': 1.0,
'fixed': True
}
},
"secondary": {
't_eff': {
'value': 7000.0,
'fixed': True
},
'surface_potential': {
'value': 5,
'fixed': True
},
'gravity_darkening': {
'value': 1.0,
'fixed': True
},
'albedo': {
'value': 1.0,
'fixed': True
}
}
}
lc_v = LCData(
x_data=self.phases['Generic.Bessell.V'],
y_data=self.flux['Generic.Bessell.V'],
x_unit=u.dimensionless_unscaled,
y_unit=u.dimensionless_unscaled
)
lc_b = LCData(
x_data=self.phases['Generic.Bessell.B'],
y_data=self.flux['Generic.Bessell.B'],
x_unit=u.dimensionless_unscaled,
y_unit=u.dimensionless_unscaled
)
self.model_generator.keep_out = True
with mock.patch("elisa.analytics.models.lc.synthetic_binary", self.model_generator.lc_generator):
lc_initial = BinaryInitialParameters(**dinit)
data = {'Generic.Bessell.V': lc_v, 'Generic.Bessell.B': lc_b}
task = LCBinaryAnalyticsTask(data=data, method='least_squares', expected_morphology='detached')
result = task.fit(x0=lc_initial)
self.assertTrue(1.0 > result["r_squared"]['value'] > 0.9)
def test_mcmc_lc_fit_std_params_detached(self):
dinit = {
"primary": {
"mass": {
'value': 1.8, # 2.0
'fixed': False,
'min': 1.5,
'max': 2.2
},
"t_eff": {
'value': 5000.0,
'fixed': True
},
"surface_potential": {
'value': 5.0,
'fixed': True
},
"gravity_darkening": {
'value': 1.0,
'fixed': True
},
"albedo": {
'value': 1.0,
'fixed': True
}
},
"secondary": {
"mass": {
'value': 1.0,
'fixed': True
},
"t_eff": {
'value': 6500.0, # 7000
'fixed': False,
'min': 5000.0,
'max': 10000.0
},
"surface_potential": {
'value': 5,
'fixed': True
},
"gravity_darkening": {
'value': 1.0,
'fixed': True
},
"albedo": {
'value': 1.0,
'fixed': True
}
},
"system": {
"inclination": {
'value': 90.0,
'fixed': True
},
"eccentricity": {
'value': 0.0,
'fixed': True
},
"argument_of_periastron": {
'value': 0.0,
'fixed': True
},
"period": {
'value': 3.0,
'fixed': True
}
}
}
lc_v = LCData(
x_data=self.phases['Generic.Bessell.V'],
y_data=self.flux['Generic.Bessell.V'],
x_unit=u.dimensionless_unscaled,
y_unit=u.dimensionless_unscaled
)
lc_b = LCData(
x_data=self.phases['Generic.Bessell.B'],
y_data=self.flux['Generic.Bessell.B'],
x_unit=u.dimensionless_unscaled,
y_unit=u.dimensionless_unscaled
)
self.model_generator.keep_out = True
with mock.patch("elisa.analytics.models.lc.synthetic_binary", self.model_generator.lc_generator):
lc_initial = BinaryInitialParameters(**dinit)
task = LCBinaryAnalyticsTask(data={'Generic.Bessell.V': lc_v, 'Generic.Bessell.B': lc_b},
expected_morphology="detached", method='mcmc')
task.fit(x0=lc_initial, nsteps=10, discretization=5.0)
def main():
lc = get_lc()
phases = {band: np.arange(-0.6, 0.62, 0.02) for band in lc}
n = len(lc["Generic.Bessell.V"])
sigma = 0.004
bias = {
passband: np.random.normal(0, sigma, n)
for passband, curve in lc.items()
}
lc = {passband: curve + bias[passband] for passband, curve in lc.items()}
lc_err = {
passband: sigma * np.ones(curve.shape)
for passband, curve in lc.items()
}
data = {
passband: LCData(
**{
"x_data": phases[passband],
"y_data": lc[passband],
"y_err": lc_err[passband],
"x_unit": units.dimensionless_unscaled,
"y_unit": units.dimensionless_unscaled,
"passband": passband
})
for passband in lc
}
result = {
"primary": {
"t_eff": {
"value": 8099.941757469695,
"fixed": False,
"unit": "K",
"min": 7800.0,
"max": 8800.0
},
"surface_potential": {
"value": 3.9079468419211425,
"fixed": False,
"unit": None,
"min": 3.0,
"max": 5.0
},
"albedo": {
"value": 0.6,
"fixed": True,
"unit": None
},
"gravity_darkening": {
"value": 0.32,
"fixed": True,
"unit": None
}
},
"secondary": {
"t_eff": {
"value": 5970.458040348652,
"fixed": False,
"unit": "K",
"min": 4000.0,
"max": 7000.0
},
"surface_potential": {
"value": 5.9166442699971284,
"fixed": False,
"unit": None,
"min": 5.0,
"max": 7.0
},
"albedo": {
"value": 0.6,
"fixed": True,
"unit": None
},
"gravity_darkening": {
"value": 0.32,
"fixed": True,
"unit": None
}
},
"system": {
"inclination": {
"value": 83.56583703194174,
"fixed": False,
"unit": "deg",
"min": 80.0,
"max": 90.0
},
"eccentricity": {
"value": 0.0,
"fixed": True,
"unit": None
},
"argument_of_periastron": {
"value": 0.0,
"fixed": True,
"unit": "deg"
},
"period": {
"value": 4.5,
"fixed": True,
"unit": "d"
},
"mass_ratio": {
"value": 0.5,
"fixed": True,
"unit": None
},
"semi_major_axis": {
"value": 16.61968275372717,
"constraint": "16.515 / sin(radians(system@inclination))",
"unit": "solRad"
}
},
"r_squared": {
"value": 0.9943566454789291,
"unit": None
}
}
task = LCBinaryAnalyticsTask(data=data,
method='least_squares',
expected_morphology="detached")
task.set_result(result)
task.plot.model()
def main():
lc = get_lc()
phases = {band: np.arange(-0.6, 0.62, 0.02) for band in lc}
n = len(lc["Generic.Bessell.V"])
sigma = 0.004
bias = {passband: np.random.normal(0, sigma, n) for passband, curve in lc.items()}
lc = {passband: curve + bias[passband] for passband, curve in lc.items()}
lc_err = {passband: sigma * np.ones(curve.shape) for passband, curve in lc.items()}
data = {passband: LCData(**{
"x_data": phases[passband],
"y_data": lc[passband],
"y_err": lc_err[passband],
"x_unit": units.dimensionless_unscaled,
"y_unit": units.dimensionless_unscaled,
"passband": passband
}) for passband in lc}
lc_initial = {
"system": {
"semi_major_axis": {
"value": 16.515,
"constraint": "16.515 / sin(radians(system@inclination))"
},
"inclination": {
"value": 85.0,
"fixed": False,
"min": 80,
"max": 90
},
"argument_of_periastron": {
"value": 0.0,
"fixed": True
},
"mass_ratio": {
"value": 0.5,
"fixed": True
},
"eccentricity": {
"value": 0.0,
"fixed": True
},
"period": {
"value": 4.5,
"fixed": True,
"unit": units.d
}
},
"primary": {
"t_eff": {
"value": 8307.0,
"fixed": False,
"min": 7800.0,
"max": 8800.0,
"unit": units.K
},
"surface_potential": {
"value": 3.0,
"fixed": False,
"min": 3,
"max": 5
},
"gravity_darkening": {
"value": 0.32,
"fixed": True
},
"albedo": {
"value": 0.6,
"fixed": True
},
},
"secondary": {
"t_eff": {
"value": 4000.0,
"fixed": False,
"min": 4000.0,
"max": 7000.0
},
"surface_potential": {
"value": 5.0,
"fixed": False,
"min": 5.0,
"max": 7.0
},
"gravity_darkening": {
"value": 0.32,
"fixed": True
},
"albedo": {
"value": 0.6,
"fixed": True
}
}
}
lc_initial = BinaryInitialParameters(**lc_initial)
task = LCBinaryAnalyticsTask(data=data, method='least_squares', expected_morphology="detached")
result = task.fit(x0=lc_initial)
print(json.dumps(result, indent=4))
def main():
lc = get_lc()
phases = {band: np.arange(-0.6, 0.62, 0.02) for band in lc}
n = len(lc["Generic.Bessell.V"])
sigma = 0.004
bias = {
passband: np.random.normal(0, sigma, n)
for passband, curve in lc.items()
}
lc = {passband: curve + bias[passband] for passband, curve in lc.items()}
lc_err = {
passband: sigma * np.ones(curve.shape)
for passband, curve in lc.items()
}
data = {
passband: LCData(
**{
"x_data": phases[passband],
"y_data": lc[passband],
"y_err": lc_err[passband],
"x_unit": au.dimensionless_unscaled,
"y_unit": au.dimensionless_unscaled,
"passband": passband
})
for passband in lc
}
result = {
"primary": {
"t_eff": {
"value": 8230.84100260351,
"confidence_interval": {
"min": 7926.453297478265,
"max": 8591.643787140914
},
"fixed": False,
"min": 7800.0,
"max": 8800.0,
"unit": "K"
},
"surface_potential": {
"value": 3.9337264746233775,
"confidence_interval": {
"min": 3.697563664749108,
"max": 4.199838559400843
},
"fixed": False,
"min": 3.0,
"max": 5.0,
"unit": None
},
"albedo": {
"value": 0.6,
"fixed": True,
"unit": None
},
"gravity_darkening": {
"value": 0.32,
"fixed": True,
"unit": None
}
},
"secondary": {
"t_eff": {
"value": 5983.733468261532,
"confidence_interval": {
"min": 5393.912953493938,
"max": 6474.502604079509
},
"fixed": False,
"min": 4000.0,
"max": 7000.0,
"unit": "K"
},
"surface_potential": {
"value": 6.112664406249967,
"confidence_interval": {
"min": 5.728751677639996,
"max": 6.477440272860319
},
"fixed": False,
"min": 5.0,
"max": 7.0,
"unit": None
},
"albedo": {
"value": 0.6,
"fixed": True,
"unit": None
},
"gravity_darkening": {
"value": 0.32,
"fixed": True,
"unit": None
}
},
"system": {
"inclination": {
"value": 85.33146246324937,
"confidence_interval": {
"min": 82.21719586922225,
"max": 88.54153499552233
},
"fixed": False,
"min": 80.0,
"max": 90.0,
"unit": "deg"
},
"eccentricity": {
"value": 0.0,
"fixed": True,
"unit": None
},
"argument_of_periastron": {
"value": 0.0,
"fixed": True,
"unit": "deg"
},
"period": {
"value": 4.5,
"fixed": True,
"unit": "d"
},
"mass_ratio": {
"value": 0.5,
"fixed": True,
"unit": None
},
"semi_major_axis": {
"value": 16.569975351859675,
"constraint": "16.515 / sin(radians(system@inclination))",
"unit": "solRad"
}
}
}
task = LCBinaryAnalyticsTask(data=data, method='mcmc')
task.set_result(result)
task.load_chain("mcmc_lc_fit")
# task.plot.model()
# task.plot.corner(truths=True)
# task.plot.traces()
# task.plot.autocorrelation()
task.result_summary()