def test_simulate_microlensing_model_parameters():
event = mock.MagicMock()
telescope = mock.MagicMock()
telescope.name = 'NDG'
telescope.lightcurve_flux = np.array([[0, 1], [2, 2]]).T
event.telescopes = []
event.telescopes.append(telescope)
model = microlsimulator.simulate_a_microlensing_model(event)
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
assert -300 < parameters[0]
assert parameters[0] < 300
assert 0 < parameters[1]
assert parameters[1] < 2
assert 1 < parameters[2]
assert parameters[2] < 500
model = microlsimulator.simulate_a_microlensing_model(event, 'FSPL')
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
assert parameters[1] < 0.1
assert parameters[3] < 0.05
model = microlsimulator.simulate_a_microlensing_model(event, 'DSPL')
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
assert parameters[2] < 100
def test_simulate_microlensing_model_parameters():
event = mock.MagicMock()
telescope = mock.MagicMock()
telescope.name = 'NDG'
telescope.lightcurve_flux = np.array([[0, 1], [2, 2]]).T
event.telescopes = []
event.telescopes.append(telescope)
model = microlsimulator.simulate_a_microlensing_model(event)
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
assert -300 < parameters[0]
assert parameters[0] < 300
assert 0 < parameters[1]
assert parameters[1] < 2
assert 1 < parameters[2]
assert parameters[2] < 300
model = microlsimulator.simulate_a_microlensing_model(event,'FSPL')
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
assert parameters[1] < 0.1
assert parameters[3] < 0.05
model = microlsimulator.simulate_a_microlensing_model(event, 'DSPL')
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
assert parameters[2] < 100
def test_simulate_lightcurve_flux():
event = mock.MagicMock()
telescope = mock.MagicMock()
telescope.name = 'NDG'
telescope.lightcurve_flux = np.array([[0, 51, 69], [2, 42, 28.65]])
event.telescopes = []
event.telescopes.append(telescope)
model = microlsimulator.simulate_a_microlensing_model(event)
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
fake_flux_parameters = microlsimulator.simulate_fluxes_parameters(
event.telescopes)
pyLIMA_parameters = model.compute_pyLIMA_parameters(parameters +
fake_flux_parameters)
microlsimulator.simulate_lightcurve_flux(model, pyLIMA_parameters, 'No')
assert np.all(telescope.lightcurve_flux[:, 1] != [51, 42])
assert np.all(telescope.lightcurve_flux[:, 2] != [69, 28.65])
microlsimulator.simulate_lightcurve_flux(model, pyLIMA_parameters, 'Yes')
assert np.all(telescope.lightcurve_flux[:, 1] != [51, 42])
assert np.all(telescope.lightcurve_flux[:, 2] != [69, 28.65])
def test_simulate_a_microlensing_model():
event = mock.MagicMock()
telescope = mock.MagicMock()
telescope.name = 'NDG'
telescope.lightcurve_flux = np.array([[0, 1], [2, 2]]).T
event.telescopes = []
event.telescopes.append(telescope)
model = microlsimulator.simulate_a_microlensing_model(event)
assert model.model_type == 'PSPL'
def test_simulate_a_microlensing_model():
event = mock.MagicMock()
telescope = mock.MagicMock()
telescope.name = 'NDG'
telescope.lightcurve_flux = np.array([[0, 1], [2, 2]]).T
event.telescopes = []
event.telescopes.append(telescope)
print event.telescopes[0].lightcurve_flux[:, 0]
model = microlsimulator.simulate_a_microlensing_model(event)
assert model.model_type == 'PSPL'
def test_simulate_lightcurve_flux():
event = mock.MagicMock()
telescope = mock.MagicMock()
telescope.name = 'NDG'
telescope.lightcurve_flux = np.array([[0, 51, 69], [2, 42, 28.65]])
event.telescopes = []
event.telescopes.append(telescope)
model = microlsimulator.simulate_a_microlensing_model(event)
parameters = microlsimulator.simulate_microlensing_model_parameters(model)
fake_flux_parameters = microlsimulator.simulate_fluxes_parameters(event.telescopes)
pyLIMA_parameters = model.compute_pyLIMA_parameters(parameters + fake_flux_parameters)
microlsimulator.simulate_lightcurve_flux(model, pyLIMA_parameters, 'No')
assert np.all(telescope.lightcurve_flux[:, 1] != [51, 42])
assert np.all(telescope.lightcurve_flux[:, 2] != [69, 28.65])
microlsimulator.simulate_lightcurve_flux(model, pyLIMA_parameters, 'Yes')
assert np.all(telescope.lightcurve_flux[:, 1] != [51, 42])
assert np.all(telescope.lightcurve_flux[:, 2] != [69, 28.65])
### If you want to simulate a event from space, you can use :
#my_space_telescope = microlsimulator.simulate_a_telescope('Gaia',my_own_creation, 2457585.5, 2457615.5,2, 'Space','G',
# uniform_sampling=True, spacecraft_name='Gaia')
# Note that the spacecraft name shoudl match JPL horizon ephemeris, see microlparallax. If you include this
# telescope in your analysis, you will need to give to the model parallax = ['Full,to_par] in order to have
# correct simulation.
#OK now we can choose the model we would like to simulate, here let's have a double source point lens one (DSPL). More details on models can be seen here [pyLIMA documentation](file/../../doc/build/html/pyLIMA.microlmodels.html)
#More details on parameters generation can be found here [pyLIMA documentation](file/../../doc/build/html/pyLIMA.microlsimulator.html)
### What model you want? Let's have DSPL!
my_own_model = microlsimulator.simulate_a_microlensing_model(
my_own_creation,
model='DSPL',
parallax=['None', 0.0],
xallarap=['None', 0.0],
orbital_motion=['None', 0.0],
source_spots='None')
# Find some model parameters. If you want specific parameters, you need to respet pyLIMA convention when you create your
# parameters. For the DSPL example, my_own_parameters = [to, uo, delta_to, delta_uo, tE].
my_own_parameters = microlsimulator.simulate_microlensing_model_parameters(
my_own_model)
# Which source magnitude? Which blending?
# Same here, you can create your own flux parameters with the convention
# [ [magnitude_source_i, blending ratio_i]] for i in telescopes. In our case it looks :
# [ [magnitude_source_survey, blending ratio_survey], [ magnitude_source_SAAO_I, blending ratio_SAAO_I],
# [magnitude_source_SAAO_V, blending ratio_SAAO_V]], i.e [[18.5,0.3],[19.5,1.2],[20.2,1.6]] (example).
while count < 2000:
#model_choice = np.random.randint(0,5)
model_choice = each
t0 = np.random.uniform(
np.percentile(wfirst3.lightcurve_flux[:, 0], 10),
np.percentile(wfirst3.lightcurve_flux[:, 0], 90))
tE = np.random.normal(30, 10)
rho = np.random.uniform(-4, -1.30)
rho = 10**rho
if model_choice == 0:
directory = 'PSPL'
u0 = np.random.uniform(-0.5, 0.5)
my_own_model = microlsimulator.simulate_a_microlensing_model(
my_own_creation, model='PSPL')
my_own_parameters = [t0, u0, tE]
if model_choice == 1:
directory = 'FSPL'
u0 = np.random.uniform(-rho, rho)
my_own_model = microlsimulator.simulate_a_microlensing_model(
my_own_creation, model='FSPL')
my_own_parameters = [t0, u0, tE, rho]
if model_choice == 2:
directory = 'DSPL'
u0 = np.random.uniform(-0.5, 0.5)
u02 = np.random.uniform(-u0, u0)
sign = np.random.choice([-1, 1])
delta_u0 = u0 - sign * u02
