autolens.Kernel2D#

class Kernel2D[source]#

Bases: AbstractArray2D

An array of values, which are paired to a uniform 2D mask of pixels. Each entry on the array corresponds to a value at the centre of a pixel in an unmasked pixel. See the Array2D class for a full description of how Arrays work.

The Kernel2D class is an Array2D but with additioonal methods that allow it to be convolved with data.

Parameters:

values – The values of the array.
mask – The 2D mask associated with the array, defining the pixels each array value is paired with and originates from.
normalize (bool) – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

Methods

`all`
`apply_mask`
`astype`
`convolved_array_from`	Convolve an array with this Kernel2D
`convolved_array_with_mask_from`	Convolve an array with this Kernel2D
`copy`
`extent_of_zoomed_array`	For an extracted zoomed array computed from the method zoomed_around_mask compute its extent in scaled coordinates.
`flip_hdu_for_ds9`
`from_as_gaussian_via_alma_fits_header_parameters`
`from_fits`	Loads the Kernel2D from a .fits file.
`from_gaussian`	Setup the Kernel2D as a 2D symmetric elliptical Gaussian profile, according to the equation:
`from_primary_hdu`	Returns an `Kernel2D` by from a PrimaryHDU object which has been loaded via astropy.fits
`full`	Create a Kernel2D (see Kernel2D.__new__) where all values are filled with an input fill value, analogous to the method numpy ndarray.full.
`instance_flatten`	Flatten an instance of an autoarray class into a tuple of its attributes (i.e.. a pytree).
`instance_unflatten`	Unflatten a tuple of attributes (i.e. a pytree) into an instance of an autoarray class.
`invert`
`max`
`min`
`no_blur`	Setup the Kernel2D as a kernel which does not convolve any signal, which is simply an array of shape (1, 1) with value 1.
`no_mask`	Create a Kernel2D (see Kernel2D.__new__) by inputting the kernel values in 1D or 2D, automatically determining whether to use the 'manual_slim' or 'manual_native' methods.
`ones`	Create an Kernel2D (see Kernel2D.__new__) where all values are filled with ones, analogous to the method numpy ndarray.ones.
`output_to_fits`	Output the array to a .fits file.
`padded_before_convolution_from`	When the edge pixels of a mask are unmasked and a convolution is to occur, the signal of edge pixels will be 'missing' if the grid is used to evaluate the signal via an analytic function.
`rescaled_with_odd_dimensions_from`	If the PSF kernel has one or two even-sized dimensions, return a PSF object where the kernel has odd-sized dimensions (odd-sized dimensions are required by a Convolver).
`reshape`
`resized_from`	Resize the array around its centre to a new input shape.
`sqrt`
`sum`
`trimmed_after_convolution_from`	When the edge pixels of a mask are unmasked and a convolution is to occur, the signal of edge pixels will be 'missing' if the grid is used to evaluate the signal via an analytic function.
`with_new_array`	Copy this object but give it a new array.
`zeros`	Create an Kernel2D (see Kernel2D.__new__) where all values are filled with zeros, analogous to the method numpy ndarray.ones.
`zoomed_around_mask`	Extract the 2D region of an array corresponding to the rectangle encompassing all unmasked values.

Attributes

`array`
`binned_across_columns`	Bins the 2D array up to a 1D array, where each value is the mean of all unmasked values in each column.
`binned_across_rows`	Bins the 2D array up to a 1D array, where each value is the mean of all unmasked values in each row.
`derive_grid`
`derive_indexes`
`derive_mask`
`dtype`
`geometry`
`hdu_for_output`	The array as an HDU object, which can be output to a .fits file.
`imag`
`in_counts`
`in_counts_per_second`
`native`	Return a Array2D where the data is stored in its native representation, which is an `ndarray` of shape [total_y_pixels, total_x_pixels].
`native_skip_mask`	Return a Array2D where the data is stored in its native representation, which is an `ndarray` of shape [total_y_pixels, total_x_pixels].
`ndim`
`normalized`	Normalize the Kernel2D such that its data_vector values sum to unity.
`origin`
`original_orientation`
`pixel_area`
`pixel_scale`
`pixel_scale_header`
`pixel_scales`
`readout_offsets`
`real`
`shape`
`shape_native`
`shape_slim`
`size`
`slim`	Return an Array2D where the data is stored its slim representation, which is an `ndarray` of shape [total_unmasked_pixels].
`store_native`
`total_area`
`total_pixels`
`unmasked_grid`
`values`

classmethod no_mask(values, pixel_scales, shape_native=None, origin=(0.0, 0.0), normalize=False)[source]#

Create a Kernel2D (see Kernel2D.__new__) by inputting the kernel values in 1D or 2D, automatically determining whether to use the ‘manual_slim’ or ‘manual_native’ methods.

See the manual_slim and manual_native methods for examples. :type values: Union[ndarray, List] :param values: The values of the array input as an ndarray of shape [total_unmasked_pixels] or a list of

lists.

Parameters:

shape_native (Tuple[int, int]) – The 2D shape of the mask the array is paired with.
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).
origin (Tuple[float, float]) – The (y,x) scaled units origin of the mask’s coordinate system.
normalize (bool) – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

classmethod full(fill_value, shape_native, pixel_scales, origin=(0.0, 0.0), normalize=False)[source]#

Create a Kernel2D (see Kernel2D.__new__) where all values are filled with an input fill value, analogous to the method numpy ndarray.full.

From 1D input the method cannot determine the 2D shape of the array and its mask, thus the shape_native must be input into this method. The mask is setup as a unmasked Mask2D of shape_native.

Parameters:

fill_value (float) – The value all array elements are filled with.
shape_native (Tuple[int, int]) – The 2D shape of the mask the array is paired with.
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).
origin (Tuple[float, float]) – The (y,x) scaled units origin of the mask’s coordinate system.
normalize (bool) – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

Return type:

Kernel2D

classmethod ones(shape_native, pixel_scales, origin=(0.0, 0.0), normalize=False)[source]#

Create an Kernel2D (see Kernel2D.__new__) where all values are filled with ones, analogous to the method numpy ndarray.ones.

From 1D input the method cannot determine the 2D shape of the array and its mask, thus the shape_native must be input into this method. The mask is setup as a unmasked Mask2D of shape_native.

Parameters:

shape_native (Tuple[int, int]) – The 2D shape of the mask the array is paired with.
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).
origin (Tuple[float, float]) – The (y,x) scaled units origin of the mask’s coordinate system.
normalize (bool) – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

classmethod zeros(shape_native, pixel_scales, origin=(0.0, 0.0), normalize=False)[source]#

Create an Kernel2D (see Kernel2D.__new__) where all values are filled with zeros, analogous to the method numpy ndarray.ones.

From 1D input the method cannot determine the 2D shape of the array and its mask, thus the shape_native must be input into this method. The mask is setup as a unmasked Mask2D of shape_native.

Parameters:

shape_native (Tuple[int, int]) – The 2D shape of the mask the array is paired with.
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).
origin (Tuple[float, float]) – The (y,x) scaled units origin of the mask’s coordinate system.
normalize (bool) – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

Return type:

Kernel2D

classmethod no_blur(pixel_scales)[source]#

Setup the Kernel2D as a kernel which does not convolve any signal, which is simply an array of shape (1, 1) with value 1.

Parameters:: pixel_scales – 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).

classmethod from_gaussian(shape_native, pixel_scales, sigma, centre=(0.0, 0.0), axis_ratio=1.0, angle=0.0, normalize=False)[source]#

Setup the Kernel2D as a 2D symmetric elliptical Gaussian profile, according to the equation:

(1.0 / (sigma * sqrt(2.0*pi))) * exp(-0.5 * (r/sigma)**2)

Parameters:

shape_native (Tuple[int, int]) – The 2D shape of the mask the array is paired with.
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).
sigma (float) – The value of sigma in the equation, describing the size and full-width half maximum of the Gaussian.
centre (Tuple[float, float]) – The (y,x) central coordinates of the Gaussian.
axis_ratio (float) – The axis-ratio of the elliptical Gaussian.
angle (float) – The rotational angle of the Gaussian’s ellipse defined counter clockwise from the positive x-axis.
normalize (bool) – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

Return type:

Kernel2D

classmethod from_fits(file_path, hdu, pixel_scales, origin=(0.0, 0.0), normalize=False)[source]#

Loads the Kernel2D from a .fits file.

Parameters:

file_path (Union[Path, str]) – The path the file is loaded from, including the filename and the .fits extension, e.g. ‘/path/to/filename.fits’
hdu (int) – The Header-Data Unit of the .fits file the array data is loaded from.
pixel_scales – 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).
origin – The (y,x) scaled units origin of the mask’s coordinate system.
normalize (bool) – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

Return type:

Kernel2D

classmethod from_primary_hdu(primary_hdu, origin=(0.0, 0.0))[source]#

Returns an Kernel2D by from a PrimaryHDU object which has been loaded via astropy.fits

This assumes that the header of the PrimaryHDU contains an entry named PIXSCALE which gives the pixel-scale of the array.

For a full description of Kernel2D objects, including a description of the slim and native attribute used by the API, see the Kernel2D class API documentation.

Parameters:

primary_hdu (PrimaryHDU) – The PrimaryHDU object which has already been loaded from a .fits file via astropy.fits and contains the array data and the pixel-scale in the header with an entry named PIXSCALE.
origin (Tuple[float, float]) – The (y,x) scaled units origin of the coordinate system.

Return type:

Kernel2D

Examples

from astropy.io import fits
import autoarray as aa

primary_hdu = fits.open("path/to/file.fits")

array_2d = aa.Kernel2D.from_primary_hdu(
    primary_hdu=primary_hdu,
)

rescaled_with_odd_dimensions_from(rescale_factor, normalize=False)[source]#

If the PSF kernel has one or two even-sized dimensions, return a PSF object where the kernel has odd-sized dimensions (odd-sized dimensions are required by a Convolver).

The PSF can be scaled to larger / smaller sizes than the input size, if the rescale factor uses values that deviate furher from 1.0.

Kernels are rescald using the scikit-image routine rescale, which performs rescaling via an interpolation routine. This may lead to loss of accuracy in the PSF kernel and it is advised that users, where possible, create their PSF on an odd-sized array using their data reduction pipelines that remove this approximation.

Parameters:

rescale_factor (float) – The factor by which the kernel is rescaled. If this has a value of 1.0, the kernel is rescaled to the closest odd-sized dimensions (e.g. 20 -> 19). Higher / lower values scale to higher / lower dimensions.
normalize (bool) – Whether the PSF should be normalized after being rescaled.

Return type:

Kernel2D

property normalized: Kernel2D#: Normalize the Kernel2D such that its data_vector values sum to unity.

convolved_array_from(array)[source]#

Convolve an array with this Kernel2D

Parameters:: image – An array representing the image the Kernel2D is convolved with.
Returns:: An array representing the image after convolution.
Return type:: convolved_image
Raises:: KernelException if either Kernel2D psf dimension is odd –

convolved_array_with_mask_from(array, mask)[source]#

Convolve an array with this Kernel2D

Parameters:: image – An array representing the image the Kernel2D is convolved with.
Returns:: An array representing the image after convolution.
Return type:: convolved_image
Raises:: KernelException if either Kernel2D psf dimension is odd –