redback.simulate_transients.SimulateGenericTransient

class redback.simulate_transients.SimulateGenericTransient(model, parameters, times, model_kwargs, data_points, seed=1234, multiwavelength_transient=False, noise_term=0.2, noise_type='gaussianmodel', extra_scatter=0.0)[source]

Bases: object

__init__(model, parameters, times, model_kwargs, data_points, seed=1234, multiwavelength_transient=False, noise_term=0.2, noise_type='gaussianmodel', extra_scatter=0.0)[source]

A generic interface to simulating transients

Parameters:

  • model – String corresponding to redback model

  • parameters – Dictionary of parameters describing a single transient

  • times – Time values that the model is evaluated from

  • model_kwargs – Additional keyword arguments, must include all the keyword arguments required by the model. Refer to the model documentation for details

  • data_points – Number of data points to randomly sample. This will randomly sample data_points in time and in bands or frequency.

  • seed – random seed for reproducibility

  • multiwavelength_transient – Boolean. If True, the model is assumed to be a transient which has multiple bands/frequency and the data points are sampled in bands/frequency as well, rather than just corresponding to one wavelength/filter. This also allows the same time value to be sampled multiple times.

  • noise_type – String. Type of noise to add to the model. Default is ‘gaussianmodel’ where sigma is noise_term * model. Another option is ‘gaussian’ i.e., a simple Gaussian noise with sigma = noise_term.

  • noise_term – Float. Factor which is multiplied by the model flux/magnitude to give the sigma or is sigma itself for ‘gaussian’ noise.

  • extra_scatter – Float. Sigma of normal added to output for additional scatter.

__call__(**kwargs)

Call self as a function.

Methods

__init__(model, parameters, times, ...[, ...])

A generic interface to simulating transients

save_transient(name)

Save the transient observations to a csv file.
save_transient(name)[source]

Save the transient observations to a csv file. This will save the full observational dataframe including non-detections etc. This will save the data to a folder called ‘simulated’ with the name of the transient and a csv file of the injection parameters

Parameters:

name – name to save transient.