autolens.Kernel2D

class Kernel2D

Bases: AbstractArray2D

An array of values, which are paired to a uniform 2D mask of pixels and sub-pixels. Each entry on the array corresponds to a value at the centre of a sub-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 – If True, the Kernel2D’s array values are normalized such that they sum to 1.0.

Methods

all	Returns True if all elements evaluate to True.
any	Returns True if any of the elements of a evaluate to True.
apply_mask	rtype Array2D
argmax	Return indices of the maximum values along the given axis.
argmin	Return indices of the minimum values along the given axis.
argpartition	Returns the indices that would partition this array.
argsort	Returns the indices that would sort this array.
astype	Copy of the array, cast to a specified type.
byteswap	Swap the bytes of the array elements
choose	Use an index array to construct a new array from a set of choices.
clip	Return an array whose values are limited to [min, max].
compress	Return selected slices of this array along given axis.
conj	Complex-conjugate all elements.
conjugate	Return the complex conjugate, element-wise.
convolved_array_from	Convolve an array with this Kernel2D
convolved_array_with_mask_from	Convolve an array with this Kernel2D
copy	Return a copy of the array.
cumprod	Return the cumulative product of the elements along the given axis.
cumsum	Return the cumulative sum of the elements along the given axis.
diagonal	Return specified diagonals.
dot
dump	Dump a pickle of the array to the specified file.
dumps	Returns the pickle of the array as a string.
extent_of_zoomed_array	For an extracted zoomed array computed from the method zoomed_around_mask compute its extent in scaled coordinates.
fill	Fill the array with a scalar value.
flatten	Return a copy of the array collapsed into one dimension.
flip_hdu_for_ds9
from_as_gaussian_via_alma_fits_header_parameters	rtype Kernel2D
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.
getfield	Returns a field of the given array as a certain type.
item	Copy an element of an array to a standard Python scalar and return it.
itemset	Insert scalar into an array (scalar is cast to array's dtype, if possible)
max	Return the maximum along a given axis.
mean	Returns the average of the array elements along given axis.
min	Return the minimum along a given axis.
newbyteorder	Return the array with the same data viewed with a different byte order.
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.
nonzero	Return the indices of the elements that are non-zero.
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.
partition	Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array.
prod	Return the product of the array elements over the given axis
ptp	Peak to peak (maximum - minimum) value along a given axis.
put	Set a.flat[n] = values[n] for all n in indices.
ravel	Return a flattened array.
repeat	Repeat elements of an array.
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	Returns an array containing the same data with a new shape.
resize	Change shape and size of array in-place.
resized_from	Resize the array around its centre to a new input shape.
round	Return a with each element rounded to the given number of decimals.
searchsorted	Find indices where elements of v should be inserted in a to maintain order.
setfield	Put a value into a specified place in a field defined by a data-type.
setflags	Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
sort	Sort an array in-place.
squeeze	Remove axes of length one from a.
std	Returns the standard deviation of the array elements along given axis.
structure_2d_from	rtype Structure
structure_2d_list_from	rtype List[Structure]
sum	Return the sum of the array elements over the given axis.
swapaxes	Return a view of the array with axis1 and axis2 interchanged.
take	Return an array formed from the elements of a at the given indices.
tobytes	Construct Python bytes containing the raw data bytes in the array.
tofile	Write array to a file as text or binary (default).
tolist	Return the array as an a.ndim-levels deep nested list of Python scalars.
tostring	A compatibility alias for tobytes, with exactly the same behavior.
trace	Return the sum along diagonals of the array.
transpose	Returns a view of the array with axes transposed.
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.
var	Returns the variance of the array elements, along given axis.
view	New view of array with the same data.
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

T	The transposed array.
base	Base object if memory is from some other object.
binned	Convenience method to access the binned-up array in its 1D representation, which is a Grid2D stored as an ndarray of shape [total_unmasked_pixels, 2].
binned_across_columns	rtype Array1D
binned_across_rows	rtype Array1D
ctypes	An object to simplify the interaction of the array with the ctypes module.
data	Python buffer object pointing to the start of the array's data.
derive_grid	rtype DeriveGrid2D
derive_indexes	rtype DeriveIndexes2D
derive_mask	rtype DeriveMask2D
dtype	Data-type of the array's elements.
flags	Information about the memory layout of the array.
flat	A 1-D iterator over the array.
geometry
hdu_for_output	The array as an HDU object, which can be output to a .fits file.
imag	The imaginary part of the array.
in_counts	rtype Array2D
in_counts_per_second	rtype Array2D
itemsize	Length of one array element in bytes.
native	Return a Array2D where the data is stored in its native representation, which is an ndarray of shape [sub_size*total_y_pixels, sub_size*total_x_pixels].
native_skip_mask	Return a Array2D where the data is stored in its native representation, which is an ndarray of shape [sub_size*total_y_pixels, sub_size*total_x_pixels].
nbytes	Total bytes consumed by the elements of the array.
ndim	Number of array dimensions.
normalized	Normalize the Kernel2D such that its data_vector values sum to unity.
origin	rtype Tuple[int, ...]
original_orientation	rtype Union[ndarray, Array2D]
pixel_area
pixel_scale	rtype float
pixel_scale_header	rtype Dict
pixel_scales	rtype Tuple[float, ...]
readout_offsets	rtype Tuple[int, int]
real	The real part of the array.
shape	Tuple of array dimensions.
shape_native	rtype Tuple[int, ...]
shape_slim	rtype int
size	Number of elements in the array.
slim	Return an Array2D where the data is stored its slim representation, which is an ndarray of shape [total_unmasked_pixels * sub_size**2].
store_native
strides	Tuple of bytes to step in each dimension when traversing an array.
sub_shape_native	rtype Tuple[int, ...]
sub_shape_slim	rtype int
sub_size	rtype int
total_area
total_pixels	rtype int
unmasked_grid	rtype Union[Grid1D, Grid2D]

classmethod no_mask(values, pixel_scales, shape_native=None, origin=(0.0, 0.0), normalize=False)

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*(sub_size**2)] or a list of

lists.

Parameters

• shape_native (Optional[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).

• sub_size – The size (sub_size x sub_size) of each unmasked pixels sub-array.

• 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)

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).

• sub_size – The size (sub_size x sub_size) of each unmasked pixels sub-array.

• 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)

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).

• sub_size – The size (sub_size x sub_size) of each unmasked pixels sub-array.

• 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)

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).

• sub_size – The size (sub_size x sub_size) of each unmasked pixels sub-array.

• 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)

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)

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)

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))

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.

• sub_size – The size (sub_size x sub_size) of each unmasked pixels sub-array.

• origin (Tuple[float, float]) – The (y,x) scaled units origin of the coordinate system.

Examples

from astropy.io import fits
import autoarray as aa

# Make Kernel2D with sub_size 1.

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

array_2d = aa.Kernel2D.from_primary_hdu(
    primary_hdu=primary_hdu,
    sub_size=1
)

import autoarray as aa

# Make Kernel2D with sub_size 2.
# (It is uncommon that a sub-gridded array would be loaded from
# a .fits, but the API support its).

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

array_2d = aa.Kernel2D.from_primary_hdu(
    primary_hdu=primary_hdu,
    sub_size=2
)

Return type

Kernel2D

rescaled_with_odd_dimensions_from(rescale_factor, normalize=False)

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.

Return type

Kernel2D

convolved_array_from(array)

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)

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 –