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_fluxes_parameters():
telescope = mock.MagicMock()
telescopes = [telescope]
fake_flux_parameters = microlsimulator.simulate_fluxes_parameters(telescopes)
assert len(fake_flux_parameters) == 2
assert fake_flux_parameters[0] > 10 ** ((27.4 - 22) / 2.5)
assert fake_flux_parameters[0] < 10 ** ((27.4 - 14) / 2.5)
assert fake_flux_parameters[1] < 1
assert fake_flux_parameters[1] > 0
def test_simulate_fluxes_parameters():
telescope = mock.MagicMock()
telescopes = [telescope]
fake_flux_parameters = microlsimulator.simulate_fluxes_parameters(telescopes)
assert len(fake_flux_parameters) == 2
assert fake_flux_parameters[0] > 10 ** ((27.4 - 22) / 2.5)
assert fake_flux_parameters[0] < 10 ** ((27.4 - 14) / 2.5)
assert fake_flux_parameters[1] < 1
assert fake_flux_parameters[1] > 0
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])
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).
my_own_flux_parameters = microlsimulator.simulate_fluxes_parameters(
my_own_creation.telescopes)
my_own_parameters += my_own_flux_parameters
#Now we need to transform these parameters into a parameter class object (this is a "technical" part but the interested reader can found the function here [pyLIMA #documentation](file/../../doc/build/html/pyLIMA.microlmodels.html))
# Transform into pyLIMA standards
pyLIMA_parameters = my_own_model.compute_pyLIMA_parameters(my_own_parameters)
#Ok now we have the model we want to simulate, we then need to updates our telescopes observations!
# update the telescopes lightcurve in your event :
microlsimulator.simulate_lightcurve_flux(my_own_model,
pyLIMA_parameters,
red_noise_apply='Yes')
#### Plot it!
