autolens.Imaging#

class Imaging[source]#

Bases: AbstractDataset

A class containing an imaging dataset, including the image data, noise-map and a point spread function (PSF).

Parameters:

data (Array2D) – The array of the image data, for example in units of electrons per second.
noise_map (Optional[Array2D]) – An array describing the RMS standard deviation error in each pixel, for example in units of electrons per second.
psf (Optional[Kernel2D]) – An array describing the Point Spread Function kernel of the image which accounts for diffraction due to the telescope optics via 2D convolution.
settings (SettingsImaging) – Controls settings of how the dataset is set up (e.g. the types of grids used to perform calculations).
check_noise_map (bool) – If True, the noise-map is checked to ensure all values are above zero.

Methods

`apply_mask`	Apply a mask to the imaging dataset, whereby the mask is applied to the image data, noise-map and other quantities one-by-one.
`apply_settings`	Returns a new instance of the imaging with the input SettingsImaging applied to them.
`from_fits`	Load an imaging dataset from multiple .fits file.
`output_to_fits`	Output an imaging dataset to multiple .fits file.
`trimmed_after_convolution_from`	rtype: `AbstractDataset`

Attributes

`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.
`grid`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`grid_pixelization`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`mask`	rtype: `Union`[`Mask1D`, `Mask2D`]
`noise_covariance_matrix_inv`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`pixel_scales`
`shape_native`
`shape_slim`
`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(pixel_scales, data_path, noise_map_path, data_hdu=0, noise_map_hdu=0, psf_path=None, psf_hdu=0, noise_covariance_matrix=None)[source]#

Load an imaging dataset from multiple .fits file.

For each attribute of the imaging data (e.g. data, noise_map, pre_cti_data) the path to the .fits and the hdu containing the data can be specified.

The noise_map assumes the noise value in each data value are independent, where these values are the the RMS standard deviation error in each pixel.

A noise_covariance_matrix can be input instead, which represents the covariance between noise values in the data and can be used to fit the data accounting for correlations (the noise_map is the diagonal values of this matrix).

If the dataset has a mask associated with it (e.g. in a mask.fits file) the file must be loaded separately via the Mask2D object and applied to the imaging after loading via fits using the from_fits method.

Parameters:

pixel_scales (Union[Tuple[float], Tuple[float, float], float]) – The (y,x) arcsecond-to-pixel units conversion factor of every pixel. If this is input as a float, it is converted to a (float, float).
data_path (Union[Path, str]) – The path to the data .fits file containing the image data (e.g. ‘/path/to/image.fits’).
data_hdu (int) – The hdu the image data is contained in the .fits file specified by data_path.
psf_path (Union[Path, str, None]) – The path to the psf .fits file containing the psf (e.g. ‘/path/to/psf.fits’).
psf_hdu (int) – The hdu the psf is contained in the .fits file specified by psf_path.
noise_map_path (Union[Path, str]) – The path to the noise_map .fits file containing the noise_map (e.g. ‘/path/to/noise_map.fits’).
noise_map_hdu (int) – The hdu the noise map is contained in the .fits file specified by noise_map_path.
noise_covariance_matrix (Optional[ndarray]) – A noise-map covariance matrix representing the covariance between noise in every data value.

Return type:

Imaging

apply_mask(mask)[source]#

Apply a mask to the imaging dataset, whereby the mask is applied to the image data, noise-map and other quantities one-by-one.

The original unmasked imaging data is stored as the self.unmasked attribute. This is used to ensure that if the apply_mask function is called multiple times, every mask is always applied to the original unmasked imaging dataset.

Parameters:: mask (Mask2D) – The 2D mask that is applied to the image.
Return type:: Imaging

apply_settings(settings)[source]#

Returns a new instance of the imaging with the input SettingsImaging applied to them.

This can be used to update settings like the types of grids associated with the dataset that are used to perform calculations or putting a limit of the dataset’s signal-to-noise.

Parameters:: settings (SettingsImaging) – The settings for the imaging data that control things like the grids used for calculations.
Return type:: Imaging

output_to_fits(data_path, psf_path=None, noise_map_path=None, overwrite=False)[source]#

Output an imaging dataset to multiple .fits file.

For each attribute of the imaging data (e.g. data, noise_map) the path to the .fits can be specified, with hdu=0 assumed automatically.

If the data has been masked, the masked data is output to .fits files. A mask can be separately output to a file mask.fits via the Mask objects output_to_fits method.

Parameters:

data_path (Union[Path, str]) – The path to the data .fits file where the image data is output (e.g. ‘/path/to/data.fits’).
psf_path (Union[Path, str, None]) – The path to the psf .fits file where the psf is output (e.g. ‘/path/to/psf.fits’).
noise_map_path (Union[Path, str, None]) – The path to the noise_map .fits where the noise_map is output (e.g. ‘/path/to/noise_map.fits’).
overwrite (bool) – If True, the .fits files are overwritten if they already exist, if False they are not and an exception is raised.