autolens.Kernel2D#

class Kernel2D[source]#

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)[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*(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)[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).

  • 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)[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).

  • 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)[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).

  • 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)[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.

  • 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)[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.

Return type

Kernel2D

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