autolens.Imaging

class autolens.Imaging(image: autoarray.structures.arrays.two_d.array_2d.Array2D, noise_map: autoarray.structures.arrays.two_d.array_2d.Array2D, psf: autoarray.structures.kernel_2d.Kernel2D = None, settings=<autoarray.dataset.imaging.SettingsImaging object>, name: str = None, pad_for_convolver=False)
__init__(image: autoarray.structures.arrays.two_d.array_2d.Array2D, noise_map: autoarray.structures.arrays.two_d.array_2d.Array2D, psf: autoarray.structures.kernel_2d.Kernel2D = None, settings=<autoarray.dataset.imaging.SettingsImaging object>, name: str = None, pad_for_convolver=False)

A class containing the data, noise-map and point spread function of a 2D imaging dataset.

Parameters:
  • image – The array of the image data, in units of electrons per second.
  • noise_map (Array2D) – An array describing the RMS standard deviation error in each pixel in units of electrons per second.
  • psf – An array describing the Point Spread Function kernel of the image.
  • mask (Mask2D) – The 2D mask that is applied to the image.

Methods

__init__(image, noise_map, psf[, settings, …]) A class containing the data, noise-map and point spread function of a 2D imaging dataset.
apply_mask(mask)
apply_settings(settings)
from_fits(image_path, pixel_scales, …[, …]) Factory for loading the imaging data_type from .fits files, as well as computing properties like the noise-map, exposure-time map, etc.
load(filename) Load the dataset at the specified filename
output_to_fits(image_path[, psf_path, …])
signal_to_noise_limited_from(…[, mask])
trimmed_after_convolution_from(kernel_shape)

Attributes

absolute_signal_to_noise_map The estimated absolute_signal-to-noise_maps mappers of the image.
absolute_signal_to_noise_max The maximum value of absolute signal-to-noise_map in an image pixel in the image’s signal-to-noise_maps mappers.
blurring_grid A property that is only computed once per instance and then replaces itself with an ordinary attribute.
convolver A property that is only computed once per instance and then replaces itself with an ordinary attribute.
image
inverse_noise_map
mask
name
pixel_scales
potential_chi_squared_map The potential chi-squared-map of the imaging data_type.
potential_chi_squared_max The maximum value of the potential chi-squared-map.
psf
shape_native
signal_to_noise_map The estimated signal-to-noise_maps mappers of the image.
signal_to_noise_max The maximum value of signal-to-noise_maps in an image pixel in the image’s signal-to-noise_maps mappers.
w_tilde A property that is only computed once per instance and then replaces itself with an ordinary attribute.
classmethod from_fits(image_path, pixel_scales, noise_map_path, image_hdu=0, noise_map_hdu=0, psf_path=None, psf_hdu=0, name=None)

Factory for loading the imaging data_type from .fits files, as well as computing properties like the noise-map, exposure-time map, etc. from the imaging-data.

This factory also includes a number of routines for converting the imaging-data from unit_label not supported by PyAutoLens (e.g. adus, electrons) to electrons per second.

Parameters:
  • noise_map_non_constant
  • name
  • image_path (str) – The path to the image .fits file containing the image (e.g. ‘/path/to/image.fits’)
  • pixel_scales – The size of each pixel in scaled units.
  • image_hdu – The hdu the image is contained in the .fits file specified by image_path.
  • psf_path (str) – The path to the psf .fits file containing the psf (e.g. ‘/path/to/psf.fits’)
  • psf_hdu – The hdu the psf is contained in the .fits file specified by psf_path.
  • noise_map_path (str) – The path to the noise_map .fits file containing the noise_map (e.g. ‘/path/to/noise_map.fits’)
  • noise_map_hdu – The hdu the noise_map is contained in the .fits file specified by noise_map_path.