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_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_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_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])
#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). 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)