Simulations

pyLIMA.simulations.simulator.moon_illumination(sun, moon)

Compute the Moon illuminations

Parameters:

  • sun (array, SkyCoord of the Sun)

  • moono (array, SkyCoord of the Moon)

Returns:

illumniation

Return type:

array, the Moon illumination

pyLIMA.simulations.simulator.simulate_a_microlensing_event(name='Microlensing pyLIMA simulation', ra=270, dec=-30)

Function to find initial DSPL guess

Parameters:

  • name (str, event name)

  • ra (float, the event right ascension)

  • dec (float, the event dec)

Returns:

fake_event

Return type:

object, an event object

pyLIMA.simulations.simulator.simulate_a_telescope(name, time_start=2460000, time_end=2460500, sampling=0.25, uniform_sampling=False, timestamps=[], location='Earth', spacecraft_name=None, spacecraft_positions={'astrometry': [], 'photometry': []}, camera_filter='I', altitude=0, longitude=0, latitude=0, bad_weather_percentage=0.0, minimum_alt=20, moon_windows_avoidance=20, maximum_moon_illumination=100.0, photometry=True, astrometry=True, pixel_scale=100, ra=270, dec=-30)

Simulate a telescope. Can mimic real observations (Moon and Sun avoidance, bad weather etc…), having uniform sampling or custom timerange.

Parameters:

  • name (str, event name)

  • time_start (float, the JD time start of observations)

  • time_end (float, the JD time end of observations)

  • sampling (float, the sampling rate (in days))

  • uniform_sampling (bool, turn on/off any observational constraints)

  • timestamps (array, an array of time)

  • location (str, Earth or Space)

  • spacecraft_name (str, the name of the satellite)

  • spacecraft_positions (dict, give the JPL Horizons positions)

  • camera_filter (str, the filter of observations)

  • altitude (float, the telescope altitude in m)

  • longitude (float, the telescope longitude)

  • latitude (float, the telescope latitde)

  • bad_weather_percentage (float, fraction of nights lost due to bad weather)

  • minimum_alt (float, minimum altitude of observations in degrees)

  • moon_windows_avoidance (float, minimum distance to the Moon in degrees)

  • maximum_moon_illumination (float, maximum allowed Moon brightness)

  • photometry (bool, simulate photometric observations)

  • astrometry (bool, simulate astrometric observations)

  • pixel_scale (float, the pixel scale of the camera in mas/pix)

  • ra (float, right ascension of the target in degrees)

  • dec (float, declination of the target in degrees)

Returns:

telescope

Return type:

object, a telescope object

pyLIMA.simulations.simulator.simulate_astrometry(model, pyLIMA_parameters, add_noise=True)

Simulate the astrometric signal in the telescopes according to the model and parameters

Parameters:

  • model (object, a microlensing model object)

  • pyLIMA_parameters (dict, a pyLIMA_parameters object)

  • add_noise (bool, adding Poisson noise or not)

pyLIMA.simulations.simulator.simulate_fluxes_parameters(list_of_telescopes, source_magnitude=[10, 20], blend_magnitude=[10, 20])

Compute the source and blend fluxes for a list of telescopes

Parameters:

  • list_of_telescopes (list, a list of telescope objects)

  • source_magnitude (list, [mag_min,max_max] range of the source magnitudes)

  • blend_magnitude (list, [mag_min,max_max] range of the blend magnitudes)

Returns:

fake_fluxes_telescopes

Return type:

list, a list of 2*Ntelescopes fluxes

pyLIMA.simulations.simulator.simulate_lightcurve(model, pyLIMA_parameters, add_noise=True, efficiency=None)

Simulate the fluxes in the telescopes according to the model and parameters

Parameters:

  • model (object, a microlensing model object)

  • pyLIMA_parameters (dict, a pyLIMA_parameters object)

  • add_noise (bool, adding Poisson noise or not)

pyLIMA.simulations.simulator.simulate_microlensing_model_parameters(model)

Given a microlensing model, compute a random parameters (uniform distribution in the bounds)

Parameters:

model (object, a microlensing model)

Returns:

fake_parameters

Return type:

list, a list of simulated parameters

pyLIMA.simulations.simulator.time_simulation(time_start, time_end, sampling, bad_weather_percentage)

Simulate the timestamps

Parameters:

  • time_start (float, the JD time start of observations)

  • time_end (float, the JD time end of observations)

  • bad_weather_percentage (float, fraction of nights lost due to bad weather)

Returns:

time_of_observations

Return type:

array, an array of time