def test_create_bad_model():
# Both tests are equivalent
event = _create_event()
# Using a context manager
with pytest.raises(microlmodels.ModelException) as model_exception:
microlmodels.create_model('BAD', event)
assert 'Unknown model "BAD"' in str(model_exception)
# Manually checking for an exception and error message
try:
microlmodels.create_model('BAD', event)
pytest.fail()
except microlmodels.ModelException as model_exception:
assert 'Unknown model "BAD"' in str(model_exception)
def simulate_a_microlensing_model(event,
model='PSPL',
args=(),
parallax=['None', 0.0],
xallarap=['None'],
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 microlmodel 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 test_define_pyLIMA_standard_parameters_with_xallarap():
event = _create_event()
Model = microlmodels.create_model('FSPL', event, xallarap=['Yes', 598.9])
assert Model.Jacobian_flag == 'No way'
assert Model.pyLIMA_standards_dictionnary['XiEN'] == 4
assert Model.pyLIMA_standards_dictionnary['XiEE'] == 5
def test_no_fancy_parameters_to_pyLIMA_standard_parameters():
event = _create_event()
Model = microlmodels.create_model('PSPL', event)
parameters = [42, 51]
fancy = Model.fancy_parameters_to_pyLIMA_standard_parameters(parameters)
assert parameters == fancy
def test_create_VariablePL_model():
event = _create_event()
varpl_model = microlmodels.create_model('VariablePL',
event,
model_arguments=[1])
assert isinstance(varpl_model, microlmodels.ModelVariablePL)
def test_align_the_data_to_the_reference_telescope():
fit = mock.MagicMock()
even = event.Event()
telescope_0 = mock.MagicMock()
telescope_0.name = 'Survey'
telescope_0.lightcurve_flux = np.random.random((100, 3))
telescope_0.lightcurve_magnitude = np.random.random((100, 3))
even.telescopes.append(telescope_0)
telescope_1 = mock.MagicMock()
telescope_1.name = 'Followup'
telescope_1.lightcurve_flux = np.random.random((100, 3)) * 2
telescope_1.lightcurve_magnitude = np.random.random((100, 3)) * 2
even.telescopes.append(telescope_1)
model = microlmodels.create_model('PSPL', even, blend_flux_ratio=True)
model.define_model_parameters()
fit.event = even
fit.model = model
expected_lightcurves = microltoolbox.align_the_data_to_the_reference_telescope(
fit, 0, [10, 0.1, 30, 10, 15, 1., 25])
assert np.allclose(expected_lightcurves[0],
even.telescopes[0].lightcurve_magnitude)
def test_define_parameters_boundaries():
event = _create_event()
Model = microlmodels.create_model('FSPL', event)
assert Model.parameters_boundaries == [(-300, 342), (0.0, 2.0), (1.0, 300),
(5 * 1e-5, 0.05)]
def test_complicated_mixing_fancy_parameters_to_pyLIMA_standard_parameters():
event = _create_event()
Model = microlmodels.create_model('FSPL', event)
Model.fancy_to_pyLIMA_dictionnary = {'tstar': 'tE', 'logrho': 'rho'}
Model.pyLIMA_to_fancy = {
'logrho': lambda parameters: np.log10(parameters.rho),
'tstar': lambda parameters: (parameters.logrho * parameters.tE)
}
Model.fancy_to_pyLIMA = {
'rho': lambda parameters: 10**parameters.logrho,
'tE': lambda parameters: (parameters.tstar / parameters.logrho)
}
Model.define_model_parameters()
tE = 35.6
uo = 0.1
logrho = -1.30102
Parameters = [0.28, uo, tE * logrho, logrho]
pyLIMA_parameters = Model.compute_pyLIMA_parameters(Parameters)
assert pyLIMA_parameters.to == 0.28
assert pyLIMA_parameters.uo == uo
assert pyLIMA_parameters.tE == tE
assert pyLIMA_parameters.tstar == np.log10(pyLIMA_parameters.rho) * tE
assert pyLIMA_parameters.logrho == logrho
assert np.allclose(pyLIMA_parameters.rho, 0.05, rtol=0.001, atol=0.001)
def test_create_RRLyraeFS_model():
event = _create_event()
rrfs_model = microlmodels.create_model('RRLyraeFS',
event,
model_arguments=[1])
assert isinstance(rrfs_model, microlmodels.ModelRRLyraeFS)
def test_define_parameters_boundaries():
event = _create_event()
Model = microlmodels.create_model('FSPL', event, blend_flux_ratio=False)
assert Model.parameters_boundaries == [(0, 42), (0.0, 1.0), (1.0, 500),
(5 * 10**-5, 0.05)]
def test_source_trajectory_with_parallax():
to = 0.28
tE = 35.6
uo = 0.1
piEN = 0.1
piEE = -0.97
Parameters = collections.namedtuple('parameters',
['to', 'uo', 'tE', 'piEN', 'piEE'])
parameters = Parameters(to, uo, tE, piEN, piEE)
telescope = mock.MagicMock()
telescope.lightcurve_flux = np.array([[0, 1, 1], [42, 6, 6]])
telescope.deltas_positions = np.array([[1, 2], [3, 4]])
event = _create_event()
event.telescopes[0] = telescope
Model = microlmodels.create_model('PSPL', event, parallax=['Annual', 0])
source_X, source_Y, dseparation = Model.source_trajectory(
telescope, to, uo, tE, parameters)
assert len(source_X) == len(source_Y) == 2
tau = (telescope.lightcurve_flux[:, 0] - to) / tE
piE = np.array([piEN, piEE])
delta_tau, delta_beta = microlparallax.compute_parallax_curvature(
piE, telescope.deltas_positions)
tau += delta_tau
beta = uo + delta_beta
assert np.allclose(source_X, tau) # rotation of alpha
assert np.allclose(source_Y, beta)
def main():
my_event = event.Event()
my_event.name = 'my_event'
#my_event.ra = 269.39166666666665
#my_event.dec = -29.22083333333333
data_K0 = np.loadtxt("./fits_files/lightcurve_gen_K0_text_2.txt")
data_V0 = np.loadtxt("./fits_files/lightcurve_gen_V0_text_2.txt")
telescope_K0 = telescopes.Telescope(name="LSST", camera_filter="K", light_curve_magnitude=data_K0)
telescope_V0 = telescopes.Telescope(name="LSST", camera_filter="V", light_curve_magnitude=data_V0)
my_event.telescopes.append(telescope_K0)
#my_event.telescopes.append(telescope_V0)
my_event.check_event()
model = microlmodels.create_model("PSPL", my_event)
my_event.fit(model, "LM")
my_event.fits[0].produce_outputs()
chi2_LM = my_event.fits[0].outputs.fit_parameters.chichi
print("Chi2_LM: {}".format(chi2_LM))
figure = my_event.fits[0].outputs.figure_lightcurve
#print "Other output:",figure.as_list()
plt.show()
def test_magnification_USBL_computation():
event = _create_event()
Model = microlmodels.create_model('USBL', event)
Parameters = collections.namedtuple(
'parameters', ['to', 'uo', 'tE', 'rho', 'logs', 'logq', 'alpha'])
tc = 0
uc = 0.70710678
tE = 1
rho = 0.0033
s = np.log10(1.0)
q = np.log10(0.02)
alpha = -np.pi / 4
parameters = Parameters(tc, uc, tE, rho, s, q, alpha)
tol = 0.001
reltol = 0.001
magnification = Model.model_magnification(event.telescopes[0], parameters)
assert np.allclose(magnification,
np.array([1.6311724868, 1.00005927]),
rtol=reltol,
atol=tol)
assert np.allclose(magnification,
np.array([1.6311724868, 1.00005927]),
rtol=reltol,
atol=tol)
def test_mlfit_PSPL_LM_without_guess():
current_event = _create_event()
model = model = microlmodels.create_model('PSPL',current_event)
fit = microlfits.MLFits(current_event)
fit.mlfit(model, 'LM')
assert fit.fit_covariance.shape == (3 + 2 * len(current_event.telescopes), 3 + 2 * len(current_event.telescopes))
assert len(fit.fit_results) == 3 + 2 * len(current_event.telescopes) + 1
def test_define_pyLIMA_standard_parameters_with_orbital_motion():
event = _create_event()
Model = microlmodels.create_model('FSPL',
event,
orbital_motion=['2D', 598.9])
assert Model.Jacobian_flag == 'No way'
assert Model.pyLIMA_standards_dictionnary['dsdt'] == 4
assert Model.pyLIMA_standards_dictionnary['dalphadt'] == 5
def test_define_parameters_model_dictionnary():
event = _create_event()
Model = microlmodels.create_model('FSPL', event, blend_flux_ratio=False)
Model.define_model_parameters()
assert list(Model.model_dictionnary.keys()) == [
'to', 'uo', 'tE', 'rho', 'fs_Test', 'fb_Test'
]
assert list(Model.model_dictionnary.values()) == [0, 1, 2, 3, 4, 5]
def test_define_parameters_model_dictionnary():
event = _create_event()
Model = microlmodels.create_model('FSPL', event)
Model.define_model_parameters()
assert Model.model_dictionnary.keys() == [
'to', 'uo', 'tE', 'rho', 'fs_Test', 'g_Test'
]
assert Model.model_dictionnary.values() == [0, 1, 2, 3, 4, 5]
def test_mlfit_FSPL_LM_with_guess():
current_event = _create_event()
model = microlmodels.create_model('FSPL',current_event)
model.parameters_boundaries = [[0, 100], [0, 1], [0, 300], [0, 1]]
fit = microlfits.MLFits(current_event)
fit.mlfit(model, 'LM')
assert fit.fit_covariance.shape == (4 + 2 * len(current_event.telescopes), 4 + 2 * len(current_event.telescopes))
assert len(fit.fit_results) == 4 + 2 * len(current_event.telescopes) + 1
def pyLIMAfit(filename):
ML_event = event.Event()
ML_event.name = str(filename).replace(".dat", "")
ML_event.ra = 269.39166666666665
ML_event.dec = -29.22083333333333
fileDirectoryR = lightcurve_path + 'Rfilter/' + str(filename)
fileDirectoryG = lightcurve_path + 'Gfilter/' + str(filename)
if ML_event.name.endswith('R'): #color == 'R':
data_1 = np.loadtxt(fileDirectoryR)
telescope_1 = telescopes.Telescope(name='LCOGT',
camera_filter='R',
light_curve_magnitude=data_1)
ML_event.telescopes.append(telescope_1)
if ML_event.name.endswith('G'): #color == 'G':
data_2 = np.loadtxt(fileDirectoryG)
telescope_2 = telescopes.Telescope(name='LCOGT',
camera_filter='G',
light_curve_magnitude=data_2)
ML_event.telescopes.append(telescope_2)
ML_event.find_survey('LCOGT')
ML_event.check_event()
PSPL_model = microlmodels.create_model('PSPL', ML_event)
ML_event.fit(PSPL_model, 'DE')
ML_event.fits[-1].produce_outputs()
try:
initial_parameters = [
getattr(ML_event.fits[-2].outputs.fit_parameters, key)
for key in ML_event.fits[-2].outputs.fit_parameters._fields[:4]
]
PSPL_model.parameters_guess = initial_parameters
ML_event.fit(PSPL_model, 'LM')
ML_event.fits[-1].produce_outputs()
except:
pass
output1 = plt.figure(1)
plt.savefig(lightcurve_path + 'pyLIMA_fits/' +
str(filename).replace(".dat", "") + '_pyLIMA_fit.png')
output2 = plt.figure(2)
plt.savefig(lightcurve_path + 'pyLIMA_fits/' +
str(filename).replace(".dat", "") + '_pyLIMA_parameters.png')
plt.close('all')
return 0
def test_magnification_PSPL_computation():
event = _create_event()
Model = microlmodels.create_model('PSPL', event)
Parameters = collections.namedtuple('parameters', ['to', 'uo', 'tE'])
to = 0.0
uo = 0.1
tE = 1.0
parameters = Parameters(to, uo, tE)
amplification = Model.model_magnification(event.telescopes[0], parameters)
assert np.allclose(amplification, np.array([10.03746101, 1.00]))
def test_magnification_FSPL_computation():
event = _create_event()
Model = microlmodels.create_model('FSPL', event)
Parameters = collections.namedtuple('parameters',
['to', 'uo', 'tE', 'rho'])
to = 0.0
uo = 0.1
tE = 1.0
rho = 0.05
parameters = Parameters(to, uo, tE, rho)
amplification = Model.model_magnification(event.telescopes[0], parameters)
assert np.allclose(amplification, np.array([10.34817883, 1.00000064]))
def test_default_microlensing_model_no_fluxes():
event = _create_event()
Model = microlmodels.create_model('PSPL', event)
Parameters = collections.namedtuple(
'parameters', ['to', 'uo', 'tE', 'fs_Test', 'fb_Test'])
to = 0.0
uo = 0.1
tE = 1.0
parameters = Parameters(to, uo, tE, 'ihih', None)
microlensing_model, fs, fb = Model.compute_the_microlensing_model(
event.telescopes[0], parameters)
assert np.allclose(microlensing_model, np.array([1, 6]))
def test_magnification_PSBL_computation():
event = _create_event()
Model = microlmodels.create_model('PSBL', event)
Parameters = collections.namedtuple(
'parameters', ['to', 'uo', 'tE', 'logs', 'logq', 'alpha'])
tc = 0
uc = 0.70710678
tE = 1.0
s = np.log10(1.0)
q = np.log10(0.02)
alpha = -np.pi / 4
parameters = Parameters(tc, uc, tE, s, q, alpha)
magnification = Model.model_magnification(event.telescopes[0], parameters)
assert np.allclose(magnification, np.array([1.63109244, 1.00000063]))
def test_magnification_DSPL_computation():
event = _create_event()
Model = microlmodels.create_model('DSPL', event)
Parameters = collections.namedtuple(
'parameters', ['to', 'uo', 'delta_to', 'delta_uo', 'tE', 'q_flux_I'])
to = 0.0
uo = 0.1
delta_to = 42.0
delta_uo = -0.05
tE = 5.0
q_flux_I = 0.1
parameters = Parameters(to, uo, delta_to, delta_uo, tE, q_flux_I)
amplification = Model.model_magnification(event.telescopes[0], parameters)
assert np.allclose(amplification, np.array([9.21590819, 2.72932227]))
def test_check_fit_source_flux():
current_event = _create_event()
model = microlmodels.create_model('FSPL', current_event)
model.define_model_parameters()
fit = microlfits.MLFits(current_event)
fit.model = model
fit.fit_results = [0.0, 0.0, 0.0, 0.0, 1.0, 0.0]
flag = fit.check_fit()
assert flag == 'Good Fit'
fit.fit_results = [0.0, 0.0, 0.0, 0.8, -1.0, 0.0]
flag = fit.check_fit()
assert flag == 'Bad Fit'
def test_PSPL_Jacobian():
event = _create_event()
Model = microlmodels.create_model('PSPL', event, blend_flux_ratio=False)
Parameters = collections.namedtuple(
'parameters', ['to', 'uo', 'tE', 'fs_Test', 'fb_Test'])
to = 0.0
uo = 0.1
tE = 1.0
fs = 10
g = 1.0
parameters = Parameters(to, uo, tE, fs, g)
Jacobian = Model.model_Jacobian(event.telescopes[0], parameters)
assert Jacobian.shape == (5, len(event.telescopes[0].lightcurve_flux))
def test_FSPL_Jacobian():
event = _create_event()
Model = microlmodels.create_model('FSPL', event)
Parameters = collections.namedtuple(
'parameters', ['to', 'uo', 'tE', 'rho', 'fs_Test', 'g_Test'])
to = 0.0
uo = 0.1
tE = 1.0
rho = 0.26
fs = 10
g = 1.0
parameters = Parameters(to, uo, tE, rho, fs, g)
Jacobian = Model.model_Jacobian(event.telescopes[0], parameters)
assert Jacobian.shape == (6, len(event.telescopes[0].lightcurve_flux))
def test_PSPL_computate_microlensing_model():
event = _create_event()
Model = microlmodels.create_model('PSPL', event, blend_flux_ratio=False)
Parameters = collections.namedtuple(
'parameters', ['to', 'uo', 'tE', 'fs_Test', 'fb_Test'])
to = 0.0
uo = 0.1
tE = 1.0
fs = 10
fb = 1
parameters = Parameters(to, uo, tE, fs, fb)
model, fs, fb = Model.compute_the_microlensing_model(
event.telescopes[0], parameters)
assert np.allclose(model,
np.array([fs * (10.03746101) + fb, fs * (1.00) + fb]))
def test_one_fancy_parameters_to_pyLIMA_standard_parameters():
event = _create_event()
Model = microlmodels.create_model('FSPL', event)
Model.fancy_to_pyLIMA_dictionnary = {'logrho': 'rho'}
Model.pyLIMA_to_fancy = {
'logrho': lambda parameters: np.log10(parameters.rho)
}
Model.fancy_to_pyLIMA = {'rho': lambda parameters: 10**parameters.logrho}
Model.define_model_parameters()
Parameters = [0.28, 0.1, 35.6, -1.30102]
pyLIMA_parameters = Model.compute_pyLIMA_parameters(Parameters)
assert pyLIMA_parameters.to == 0.28
assert pyLIMA_parameters.uo == 0.1
assert pyLIMA_parameters.tE == 35.6
assert pyLIMA_parameters.logrho == -1.30102
assert np.allclose(pyLIMA_parameters.rho, 0.05, rtol=0.001, atol=0.001)
def test_pyLIMA_standard_parameters_to_fancy_parameters():
event = _create_event()
Model = microlmodels.create_model('FSPL', event, source_spots='Yes')
Model.fancy_to_pyLIMA_dictionnary = {'logrho': 'rho'}
Model.pyLIMA_to_fancy = {
'logrho': lambda parameters: np.log10(parameters.rho)
}
Model.fancy_to_pyLIMA = {'rho': lambda parameters: 10**parameters.logrho}
Model.define_model_parameters()
# to, uo ,tE, log10(0.001), spot
Parameters = [42.0, 56.9, 2.89, -3.0, 0.0]
pyLIMA_parameters = Model.compute_pyLIMA_parameters(Parameters)
fancy_parameters = Model.pyLIMA_standard_parameters_to_fancy_parameters(
pyLIMA_parameters)
assert pyLIMA_parameters.rho == 0.001
assert fancy_parameters.logrho == -3.0
### Find the survey telescope :
your_event.find_survey("OGLE")
### Sanity check
your_event.check_event()
### set gamma for each telescopes :
your_event.telescopes[0].gamma = 0.5
your_event.telescopes[1].gamma = 0.5
### Let's go basic for FSPL :
model_1 = microlmodels.create_model("FSPL", your_event)
### Let's cheat and use the results from example_1 :
model_1.parameters_guess = [79.9, 0.008, 10.1, 0.023]
your_event.fit(model_1, "LM")
### Plot the results
your_event.fits[-1].produce_outputs()
print "Chi2_LM :", your_event.fits[-1].outputs.fit_parameters.chichi
print "Fit parameters : ", your_event.fits[-1].fit_results
plt.show()
your_event.telescopes.append(telescope_1)
your_event.telescopes.append(telescope_2)
### Find the survey telescope :
your_event.find_survey('OGLE')
### Sanity check
your_event.check_event()
### Construct the model you want to fit. Let's go basic with a PSPL, without second_order effects :
model_1 = microlmodels.create_model('PSPL', your_event)
### Let's try with the simplest Levenvberg_Marquardt algorithm :
your_event.fit(model_1,'LM')
### Let's see some plots.
your_event.fits[0].produce_outputs()
print 'Chi2_LM :',your_event.fits[0].outputs.fit_parameters.chichi
plt.show()
### Let's try with differential evolution algorithm. WAIT UNTIL THE FIGURE POP UP.
telescope.location = "Space"
current_event.telescopes.append(telescope)
count += 1
print "Start;", current_event.name
import pdb
pdb.set_trace()
current_event.find_survey("OGLE_I")
current_event.check_event()
print [i.name for i in current_event.telescopes]
# Model = microlmodels.MLModels(current_event, command_line.model,
# parallax=['None', 50.0])
Model = microlmodels.create_model(
"PSPL", current_event, parallax=["Annual", 2457512], orbital_motion=["None", 2457143]
)
# Model.USBL_windows = [2456850, 2457350]
# Model.parameters_guess = [2457228.6691167313, 0.5504378287491769, 119.57049960671444, 0.12837357112315345, 0.10480898512419343]
# 0.007759720542532279, 0.2082247217878811, -0.0894830786973533, -2.810468587634137,
# 0.2, -0.1
# ]
# Model.parameters_guess = [2457118.2589892996, 0.04693224313041394, 97.82343956853856, 0.008179766610627337, 0.19677292474954153,
# -0.027979987829886924, -2.7820828889654297, 0.10258095275259316, -0.060878335472263845]
# Model.parameters_guess = [2457124.8648720165, 0.19, 91.73208043047485, 0.0076081975458405365,
# 0.21238283769339375, -0.10463980545672134, -2.8938338520787865, 0.23125922595225648,
# -0.08570629277441434, -0.0003936674943793056, -0.0003000986621273718]
# Model.parameters_guess = [2457123.7393666035, 0.2059, 100.19567145132673,