autolens.Array2D#
- class Array2D[source]#
Bases:
AbstractArray2D
A uniform 2D array of values, which are paired with a 2D mask of pixels which may be split into sub-pixels.
The
Array2D`, like all data structures (e.g. ``Grid2D
,VectorYX2D
) has in-built functionality which:Applies a 2D mask (a
Mask2D
object) to the da_ta structure’s values.Maps the data structure between two data representations: slim` (all unmasked values in a 1D
ndarray
) andnative
(all unmasked values in a 2Dndarray
).Associates Cartesian
Grid2D
objects of (y,x) coordinates with the data structure (e.g. a (y,x) grid of all unmasked pixels).Associates sub-grids with the data structure, which perform calculations higher resolutions which are then binned up.
Each entry of an
Array2D
corresponds to a value at the centre of a sub-pixel in its correspondingMask2D
. It is ordered such that pixels begin from the top-row of the corresponding mask and go right and down. The positive y-axis is upwards and positive x-axis to the right.A detailed description of the data structure API is provided below.
SLIM DATA REPRESENTATION (sub-size=1)
Below is a visual illustration of an
Array2D
’s 2D mask, where a total of 10 pixels are unmasked and are included in the array.x x x x x x x x x x x x x x x x x x x x This is an example ``Mask2D``, where: x x x x x x x x x x x x x x O O x x x x x = `True` (Pixel is masked and excluded from the array) x x x O O O O x x x O = `False` (Pixel is not masked and included in the array) x x x O O O O x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
The mask pixel index’s are as follows (the positive / negative direction of the
Grid2D
objects associated with the array are also shown on the y and x axes).<--- -ve x +ve --> x x x x x x x x x x ^ array_2d[0] = 10 x x x x x x x x x x I array_2d[1] = 20 x x x x x x x x x x I array_2d[2] = 30 x x x x 0 1 x x x x +ve array_2d[3] = 40 x x x 2 3 4 5 x x x y array_2d[4] = 50 x x x 6 7 8 9 x x x -ve array_2d[5] = 60 x x x x x x x x x x I array_2d[6] = 70 x x x x x x x x x x I array_2d[7] = 80 x x x x x x x x x x \/ array_2d[8] = 90 x x x x x x x x x x array_2d[9] = 100
The
Array2D
in itsslim
data representation is anndarray
of shape [total_unmasked_pixels].For the
Mask2D
above theslim
representation therefore contains 10 entries and two examples of these entries are:array[3] = the 4th unmasked pixel's value, given by value 40 above. array[6] = the 7th unmasked pixel's value, given by value 80 above.
A Cartesian grid of (y,x) coordinates, corresponding to all
slim
values (e.g. unmasked pixels) is given byarray_2d.derive_grid.masked.slim
.NATIVE DATA REPRESENTATION (sub_size=1)
The
Array2D
above, but represented as an anndarray
of shape [total_y_values, total_x_values], where all masked entries have values of 0.0.For the following mask:
x x x x x x x x x x x x x x x x x x x x This is an example ``Mask2D``, where: x x x x x x x x x x x x x x O O x x x x x = `True` (Pixel is masked and excluded from the array) x x x O O O O x x x O = `False` (Pixel is not masked and included in the array) x x x O O O O x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
Where the array has the following indexes (left figure) and values (right):
<--- -ve x +ve --> x x x x x x x x x x ^ array_2d[0] = 10 x x x x x x x x x x I array_2d[1] = 20 x x x x x x x x x x I array_2d[2] = 30 x x x x 0 1 x x x x +ve array_2d[3] = 40 x x x 2 3 4 5 x x x y array_2d[4] = 50 x x x 6 7 8 9 x x x -ve array_2d[5] = 60 x x x x x x x x x x I array_2d[6] = 70 x x x x x x x x x x I array_2d[7] = 80 x x x x x x x x x x \/ array_2d[8] = 90 x x x x x x x x x x array_2d[9] = 100
In the above array:
- array[0,0] = 0.0 (it is masked, thus zero) - array[0,0] = 0.0 (it is masked, thus zero) - array[3,3] = 0.0 (it is masked, thus zero) - array[3,3] = 0.0 (it is masked, thus zero) - array[3,4] = 10 - array[3,5] = 20 - array[4,5] = 50
SLIM TO NATIVE MAPPING
The
Array2D
has functionality which maps data between theslim
andnative
data representations.For the example mask above, the 1D
ndarray
given bymask.derive_indexes.slim_to_native
is:slim_to_native[0] = [3,4] slim_to_native[1] = [3,5] slim_to_native[2] = [4,3] slim_to_native[3] = [4,4] slim_to_native[4] = [4,5] slim_to_native[5] = [4,6] slim_to_native[6] = [5,3] slim_to_native[7] = [5,4] slim_to_native[8] = [5,5] slim_to_native[9] = [5,6]
SUB GRIDDING
If the
Mask2D
sub_size
is > 1, the array has entries corresponding to the values at the centre of every sub-pixel of each unmasked pixel.The sub-array indexes are ordered such that pixels begin from the first (top-left) sub-pixel in the first unmasked pixel. Indexes then go over the sub-pixels in each unmasked pixel, for every unmasked pixel.
Therefore, the shapes of the sub-array are as follows:
slim
representation: anndarray
of shape [total_unmasked_pixels*sub_size**2].native
representation: anndarray
of shape [total_y_values*sub_size, total_x_values*sub_size].
Below is a visual illustration of a sub array. Indexing of each sub-pixel goes from the top-left corner. In contrast to the array above, our illustration below restricts the mask to just 2 pixels, to keep the illustration brief.
x x x x x x x x x x x x x x x x x x x x This is an example ``Mask2D``, where: x x x x x x x x x x x x x x x x x x x x x = `True` (Pixel is masked and excluded from lens) x 0 0 x x x x x x x O = `False` (Pixel is not masked and included in lens) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
If
sub_size=2
, each unmasked pixel has 4 (2x2) sub-pixel values. For the example above, pixels 0 and 1 each have 4 values which map to thearray_2d
’sslim
representation as follows:Pixel 0 - (2x2): array_2d.slim[0] = value of first sub-pixel in pixel 0. 0 1 array_2d.slim[1] = value of first sub-pixel in pixel 1. 2 3 array_2d.slim[2] = value of first sub-pixel in pixel 2. array_2d.slim[3] = value of first sub-pixel in pixel 3. Pixel 1 - (2x2): array_2d.slim[4] = value of first sub-pixel in pixel 0. 4 5 array_2d.slim[5] = value of first sub-pixel in pixel 1. 6 7 array_2d.slim[6] = value of first sub-pixel in pixel 2. array_2d.slim[7] = value of first sub-pixel in pixel 3.
For the
native
data representation we get the following mappings:Pixel 0 - (2x2): array_2d.native[8, 2] = value of first sub-pixel in pixel 0. 0 1 array_2d.native[8, 3] = value of first sub-pixel in pixel 1. 2 3 array_2d.native[9, 2] = value of first sub-pixel in pixel 2. array_2d.native[9, 3] = value of first sub-pixel in pixel 3. Pixel 1 - (2x2): array_2d.native[10, 4] = value of first sub-pixel in pixel 0. 4 5 array_2d.native[10, 5] = value of first sub-pixel in pixel 1. 6 7 array_2d.native[11, 4] = value of first sub-pixel in pixel 2. array_2d.native[11, 5] = value of first sub-pixel in pixel 3. Other entries (all masked sub-pixels are zero): array_2d.native[0, 0] = 0.0 (it is masked, thus zero) array_2d.native[15, 12] = 0.0 (it is masked, thus zero)
If we used a sub_size of 3, for pixel 0 we we would create a 3x3 sub-array:
array_2d.slim[0] = value of first sub-pixel in pixel 0. array_2d.slim[1] = value of first sub-pixel in pixel 1. array_2d.slim[2] = value of first sub-pixel in pixel 2. 0 1 2 array_2d.slim[3] = value of first sub-pixel in pixel 3. 3 4 5 array_2d.slim[4] = value of first sub-pixel in pixel 4. 6 7 8 array_2d.slim[5] = value of first sub-pixel in pixel 5. array_2d.slim[6] = value of first sub-pixel in pixel 6. array_2d.slim[7] = value of first sub-pixel in pixel 7. array_2d.slim[8] = value of first sub-pixel in pixel 8.
In PyAutoCTI all Array2D objects are used in their native representation without sub-gridding. Significant memory can be saved by only store this format, thus the native_binned_only config override can force this behaviour. It is recommended users do not use this option to avoid unexpected behaviour.
- Parameters:
values (
Union
[ndarray
,List
,AbstractArray2D
]) – The values of the array, which can be input in theslim
ornative
format.mask (
Mask2D
) – The 2D mask associated with the array, defining the pixels each array value in itsslim
representation is paired with.store_native (
bool
) – If True, the ndarray is stored in its native format [total_y_pixels, total_x_pixels]. This avoids mapping large data arrays to and from the slim / native formats, which can be a computational bottleneck.
Examples
This example uses the
Array2D.no_mask
method to create theArray2D
.Different methods using different inputs are available and documented throughout this webpage.
import autoarray as aa # Make Array2D from input np.ndarray with sub_size 1. array_2d = aa.Array2D.no_mask( values=np.array([1.0, 2.0, 3.0, 4.0]), shape_native=(2, 2), pixel_scales=1.0, sub_size=1 ) # Make Array2D from input list with different shape_native and sub_size 1. array_2d = aa.Array2D.no_mask( values=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape_native=(2, 3), pixel_scales=1.0, sub_size=1 )
import autoarray as aa # Make Array2D with sub_size 2. array_2d = aa.Array2D.no_mask( values=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], shape_native=(2, 1), pixel_scales=1.0, sub_size=2, ) # Apply 2D mask to Array2D with sub_size 2, where the # True value masks entries (5.0, 6.0, 7.0, 8.0). mask = aa.Mask2D( mask=[[False], [True]], pixel_scales=2.0, sub_size=2 ) array_2d = array_2d.apply_mask(mask=mask) # Print certain array attributes. print(array_2d.slim) # masked 1D data representation on sub-grid. print(array_2d.native) # masked 2D data representation on sub-grid. print(array_2d.slim.binned) # masked 1D data representation binned up from sub-grid. print(array_2d.native.binned) # masked 2D data representation binned up from sub-grid. # Output array to .fits file. array_2d.output_to_fits(file_path="/path/for/output")
Methods
all
apply_mask
- rtype:
astype
- rtype:
AbstractNDArray
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
Returns an
Array2D
by loading the array values from a .fits file.Returns an
Array2D
by from a PrimaryHDU object which has been loaded via astropy.fitsReturns an
Array2D
by inputting the y and x pixel values where the array is filled and the values that fill it.Returns an
Array2D
where all values are filled with an input fill value, analogous tonp.full()
.instance_flatten
Flatten an instance of an autoarray class into a tuple of its attributes (i.e.
instance_unflatten
Unflatten a tuple of attributes (i.e.
invert
max
min
Returns an
Array2D
from an array via inputs in its slim or native data representation.Returns an
Array2D
where all values are filled with ones, analogous tonp.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.
reshape
- rtype:
AbstractNDArray
resized_from
Resize the array around its centre to a new input shape.
sqrt
- rtype:
AbstractNDArray
structure_2d_from
- rtype:
Structure
structure_2d_list_from
- rtype:
List
[Structure
]
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.
Returns an
Array2D
where all values are filled with zeros, analogous tonp.zeros()
.zoomed_around_mask
Extract the 2D region of an array corresponding to the rectangle encompassing all unmasked values.
Attributes
array
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
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
- rtype:
DeriveGrid2D
derive_indexes
- rtype:
DeriveIndexes2D
derive_mask
- rtype:
DeriveMask2D
dtype
geometry
hdu_for_output
The array as an HDU object, which can be output to a .fits file.
imag
- rtype:
AbstractNDArray
in_counts
- rtype:
in_counts_per_second
- rtype:
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].ndim
origin
original_orientation
pixel_area
pixel_scale
- rtype:
pixel_scale_header
- rtype:
pixel_scales
readout_offsets
real
- rtype:
AbstractNDArray
shape
shape_native
shape_slim
- rtype:
size
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
sub_shape_native
sub_shape_slim
- rtype:
sub_size
- rtype:
total_area
total_pixels
- rtype:
unmasked_grid
values
- classmethod no_mask(values, pixel_scales, shape_native=None, sub_size=1, origin=(0.0, 0.0), header=None)[source]#
Returns an
Array2D
from an array via inputs in its slim or native data representation.From a
slim
1D input the method cannot determine the 2D shape of the array and its mask. Theshape_native
must therefore also be input into this method. The mask is setup as a unmasked Mask2D ofshape_native
.For a full description of
Array2D
objects, including a description of theslim
andnative
attribute used by the API, see theArray2D class API documentation
.- Parameters:
values (
Union
[ndarray
,List
,AbstractArray2D
]) – The values of the array input with shape [total_unmasked_pixels*(sub_size**2)] or shape [total_y_pixels*sub_size, total_x_pixel*sub_size].pixel_scales (
Union
[Tuple
[float
],Tuple
[float
,float
],float
]) – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure.shape_native (
Optional
[Tuple
[int
,int
]]) – The 2D shape of the array in itsnative
format, and its 2D mask (only required if input shape is inslim
format).sub_size (
int
) – 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.
Examples
import autoarray as aa # Make Array2D from input list, native format with sub_size 1 # (This array has shape_native=(2,2)). array_2d = aa.Array2D.manual( array=np.array([[1.0, 2.0], [3.0, 4.0]]), pixel_scales=1.0. sub_size=1 )
import autoarray as aa # Make Array2D from input list, slim format with sub_size 2. array_2d = aa.Array2D.no_mask( values=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], shape_native=(2, 1), pixel_scales=1.0, sub_size=2, )
- Return type:
- classmethod full(fill_value, shape_native, pixel_scales, sub_size=1, origin=(0.0, 0.0), header=None)[source]#
Returns an
Array2D
where all values are filled with an input fill value, analogous tonp.full()
.For a full description of
Array2D
objects, including a description of theslim
andnative
attribute used by the API, see theArray2D class API documentation
.From this input the method cannot determine the 2D shape of the array and its mask. The
shape_native
must therefore also be input into this method. The mask is setup as a unmasked Mask2D ofshape_native
.- Parameters:
fill_value (
float
) – The value all array elements are filled with.shape_native (
Tuple
[int
,int
]) – The 2D shape of the array in itsnative
format, and its 2D mask.pixel_scales (
Union
[Tuple
[float
],Tuple
[float
,float
],float
]) – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure.sub_size (
int
) – 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.
Examples
import autoarray as aa # Make Array2D with sub_size 1. array_2d = aa.Array2D.full( fill_value=2.0, shape_native=(2, 2), pixel_scales=1.0, sub_size=1 )
import autoarray as aa # Make Array2D with sub_size 2. array_2d = aa.Array2D.full( fill_value=2.0, shape_native=(2, 2), pixel_scales=1.0, sub_size=2 )
- Return type:
- classmethod ones(shape_native, pixel_scales, sub_size=1, origin=(0.0, 0.0), header=None)[source]#
Returns an
Array2D
where all values are filled with ones, analogous tonp.ones()
.For a full description of
Array2D
objects, including a description of theslim
andnative
attribute used by the API, see theArray2D class API documentation
.From this input the method cannot determine the 2D shape of the array and its mask. The
shape_native
must therefore also be input into this method. The mask is setup as a unmasked Mask2D ofshape_native
.- Parameters:
shape_native (
Tuple
[int
,int
]) – The 2D shape of the array in itsnative
format, and its 2D mask.pixel_scales (
Union
[Tuple
[float
],Tuple
[float
,float
],float
]) – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure.sub_size (
int
) – 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.
Examples
import autoarray as aa # Make Array2D with sub_size 1. array_2d = aa.Array2D.ones( shape_native=(2, 2), pixel_scales=1.0, sub_size=1 )
import autoarray as aa # Make Array2D with sub_size 2. array_2d = aa.Array2D.ones( shape_native=(2, 2), pixel_scales=1.0, sub_size=2 )
- Return type:
- classmethod zeros(shape_native, pixel_scales, sub_size=1, origin=(0.0, 0.0), header=None)[source]#
Returns an
Array2D
where all values are filled with zeros, analogous tonp.zeros()
.For a full description of
Array2D
objects, including a description of theslim
andnative
attribute used by the API, see theArray2D class API documentation
.From this input the method cannot determine the 2D shape of the array and its mask. The
shape_native
must therefore also be input into this method. The mask is setup as a unmasked Mask2D ofshape_native
.- Parameters:
shape_native (
Tuple
[int
,int
]) – The 2D shape of the array in itsnative
format, and its 2D mask.pixel_scales (
Union
[Tuple
[float
],Tuple
[float
,float
],float
]) – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure.sub_size (
int
) – 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.
Examples
import autoarray as aa # Make Array2D with sub_size 1. array_2d = aa.Array2D.zeros( shape_native=(2, 2), pixel_scales=1.0, sub_size=1 )
import autoarray as aa # Make Array2D with sub_size 2. array_2d = aa.Array2D.zeros( shape_native=(2, 2), pixel_scales=1.0, sub_size=2 )
- Return type:
- classmethod from_fits(file_path, pixel_scales, hdu=0, sub_size=1, origin=(0.0, 0.0))[source]#
Returns an
Array2D
by loading the array values from a .fits file.For a full description of
Array2D
objects, including a description of theslim
andnative
attribute used by the API, see theArray2D class API documentation
.- 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’pixel_scales (
Union
[Tuple
[float
],Tuple
[float
,float
],float
,None
]) – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure.hdu (
int
) – The Header-Data Unit of the .fits file the array data is loaded from.sub_size (
int
) – 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
import autoarray as aa # Make Array2D with sub_size 1. array_2d = aa.Array2D.from_fits( file_path="path/to/file.fits", hdu=0, pixel_scales=1.0, sub_size=1 )
import autoarray as aa # Make Array2D with sub_size 2. # (It is uncommon that a sub-gridded array would be loaded from # a .fits, but the API support its). array_2d = aa.Array2D.from_fits( file_path="path/to/file.fits", hdu=0, pixel_scales=1.0, sub_size=2 )
- Return type:
- classmethod from_primary_hdu(primary_hdu, sub_size=1, origin=(0.0, 0.0))[source]#
Returns an
Array2D
by from a PrimaryHDU object which has been loaded via astropy.fitsThis 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
Array2D
objects, including a description of theslim
andnative
attribute used by the API, see theArray2D 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 (
int
) – 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 Array2D with sub_size 1. primary_hdu = fits.open("path/to/file.fits") array_2d = aa.Array2D.from_primary_hdu( primary_hdu=primary_hdu, sub_size=1 )
import autoarray as aa # Make Array2D 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.Array2D.from_primary_hdu( primary_hdu=primary_hdu, sub_size=2 )
- Return type:
- classmethod from_yx_and_values(y, x, values, shape_native, pixel_scales, sub_size=1, header=None)[source]#
Returns an
Array2D
by inputting the y and x pixel values where the array is filled and the values that fill it.For a full description of
Array2D
objects, including a description of theslim
andnative
attribute used by the API, see theArray2D class API documentation
.- Parameters:
y (
Union
[ndarray
,List
]) – The y pixel indexes where value are input, with shape [total_unmasked_pixels*sub_size].x (
Union
[ndarray
,List
]) – The x pixel indexes where value are input, with shape [total_unmasked_pixels*sub_size].list (values or) – The values which are used to fill in the array, with shape [total_unmasked_pixels*sub_size].
shape_native (
Tuple
[int
,int
]) – The 2D shape of the array in itsnative
format, and its 2D mask.pixel_scales (
Union
[Tuple
[float
],Tuple
[float
,float
],float
]) – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure.sub_size (
int
) – The size (sub_size x sub_size) of each unmasked pixels sub-grid.origin – The origin of the grid’s mask.
Examples
import autoarray as aa # Make Array2D with sub_size 1. array_2d = aa.Array2D.from_yx_and_values( y=np.array([0.5, 0.5, -0.5, -0.5]), x=np.array([-0.5, 0.5, -0.5, 0.5]), values=np.array([1.0, 2.0, 3.0, 4.0]), shape_native=(2, 2), pixel_scales=1.0, sub_size=1, )
import autoarray as aa # Make Array2D with sub_size 2. array_2d = aa.Array2D.from_yx_and_values( y=np.array([1.0, 1.0. 0.5, 0.5, -0.5, -0.5, -1.0, -1.0]), x=np.array([-0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5]), values=np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]), shape_native=(2, 1), pixel_scales=1.0, sub_size=2, )
- Return type: