autolens.SimulatorImaging

class autolens.SimulatorImaging(exposure_time: float, background_sky_level: float = 0.0, psf: autoarray.structures.kernel_2d.Kernel2D = None, normalize_psf: bool = True, read_noise: float = None, add_poisson_noise: bool = True, noise_if_add_noise_false: float = 0.1, noise_seed: int = -1)
__init__(exposure_time: float, background_sky_level: float = 0.0, psf: autoarray.structures.kernel_2d.Kernel2D = None, normalize_psf: bool = True, read_noise: float = None, add_poisson_noise: bool = True, noise_if_add_noise_false: float = 0.1, noise_seed: int = -1)

A class representing a Imaging observation, using the shape of the image, the pixel scale, psf, exposure time, etc.

Parameters:
  • psf (Kernel2D) – An arrays describing the PSF kernel of the image.
  • exposure_time – The exposure time of the simulated imaging.
  • background_sky_level – The level of the background sky of the simulated imaging.
  • normalize_psf (bool) – If True, the PSF kernel is normalized so all values sum to 1.0.
  • read_noise – The level of read-noise added to the simulated imaging by drawing from a Gaussian distribution with sigma equal to the value read_noise.
  • add_poisson_noise (bool) – Whether Poisson noise corresponding to photon count statistics on the imaging observation is added.
  • noise_if_add_noise_false – If noise is not added to the simulated dataset a noise_map must still be returned. This value gives the value of noise assigned to every pixel in the noise-map.
  • noise_seed (int) – The random seed used to add random noise, where -1 corresponds to a random seed every run.

Methods

__init__(exposure_time, …) A class representing a Imaging observation, using the shape of the image, the pixel scale, psf, exposure time, etc.
via_deflections_and_galaxies_from(…[, name])
via_galaxies_from(galaxies, grid[, name]) Simulate imaging data for this data, as follows:
via_image_from(image, name) Returns a realistic simulated image by applying effects to a plain simulated image.
via_tracer_from(tracer, grid[, name]) Returns a realistic simulated image by applying effects to a plain simulated image.
via_tracer_from(tracer, grid, name=None)

Returns a realistic simulated image by applying effects to a plain simulated image.

Parameters:
  • name –
  • image (np.ndarray) – The image before simulating (e.g. the lens and source galaxies before optics blurring and Imaging read-out).
  • pixel_scales (float) – The scale of each pixel in arc seconds
  • exposure_time_map (np.ndarray) – An arrays representing the effective exposure time of each pixel.
  • psf (PSF) – An arrays describing the PSF the simulated image is blurred with.
  • background_sky_map (np.ndarray) – The value of background sky in every image pixel (electrons per second).
  • add_poisson_noise (Bool) – If True poisson noise_maps is simulated and added to the image, based on the total counts in each image pixel
  • noise_seed (int) – A seed for random noise_maps generation
via_galaxies_from(galaxies, grid, name=None)

Simulate imaging data for this data, as follows:

  1. Setup the image-plane grid of the Imaging arrays, which defines the coordinates used for the ray-tracing.
  2. Use this grid and the lens and source galaxies to setup a tracer, which generates the image of the simulated imaging data.
  3. Simulate the imaging data, using a special image which ensures edge-effects don’t degrade simulator of the telescope optics (e.g. the PSF convolution).
  4. Plot the image using Matplotlib, if the plot_imaging bool is True.
  5. Output the dataset to .fits format if a dataset_path and data_name are specified. Otherwise, return the simulated imaging data instance.