autocti.Array2D

class autocti.Array2D(values: Union[ndarray, List], mask: Mask2D, header: Optional[Header] = None, store_native: bool = False, skip_mask: bool = False, *args, **kwargs)

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) and native (all unmasked values in a 2D ndarray).

• 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 corresponding Mask2D. 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 its slim data representation is an ndarray of shape [total_unmasked_pixels].

For the Mask2D above the slim 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 by array_2d.derive_grid.masked.slim.

NATIVE DATA REPRESENTATION (sub_size=1)

The Array2D above, but represented as an an ndarray 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 the slim and native data representations.

For the example mask above, the 1D ndarray given by mask.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: an ndarray of shape [total_unmasked_pixels*sub_size**2].

• native representation: an ndarray 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 the array_2d’s slim 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.

Parameters:

• values – The values of the array, which can be input in the slim or native format.

• mask – The 2D mask associated with the array, defining the pixels each array value in its slim representation is paired with.

• store_native – 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 the Array2D.

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	Returns True if all elements evaluate to True.
any	Returns True if any of the elements of a evaluate to True.
apply_mask
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.
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.
from_fits	Returns an Array2D by loading the array values from a .fits file.
from_yx_and_values	Returns an Array2D by by inputting the y and x pixel values where the array is filled and the values that fill it.
full	Returns an Array2D where all values are filled with an input fill value, analogous to np.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_mask	Returns an Array2D from an array via inputs in its slim or native data representation.
nonzero	Return the indices of the elements that are non-zero.
ones	Returns an Array2D where all values are filled with ones, analogous to np.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.
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
structure_2d_list_from
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	Returns an Array2D where all values are filled with zeros, analogous to np.zeros().
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
binned_across_rows
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
derive_indexes
derive_mask
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
imag	The imaginary part of the array.
in_counts
in_counts_per_second
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.
origin
original_orientation
pixel_area
pixel_scale
pixel_scales
readout_offsets
real	The real part of the array.
shape	Tuple of array dimensions.
shape_native
shape_slim
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
sub_shape_slim
sub_size
total_area
total_pixels
unmasked_grid

classmethod no_mask(values: Union[ndarray, List], pixel_scales: Union[Tuple[float], Tuple[float, float], float], shape_native: Optional[Tuple[int, int]] = None, sub_size: int = 1, origin: Tuple[float, float] = (0.0, 0.0), header: Optional[Header] = None) → Array2D

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. The shape_native must therefore also be input into this method. The mask is setup as a unmasked Mask2D of shape_native.

For a full description of Array2D objects, including a description of the slim and native attribute used by the API, see the Array2D class API documentation.

Parameters:

• values – 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 – 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 – The 2D shape of the array in its native format, and its 2D mask (only required if input shape is in slim format).

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

• origin – 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,
)

classmethod full(fill_value: float, shape_native: Tuple[int, int], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float, float] = (0.0, 0.0), header: Optional[Header] = None) → Array2D

Returns an Array2D where all values are filled with an input fill value, analogous to np.full().

For a full description of Array2D objects, including a description of the slim and native attribute used by the API, see the Array2D 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 of shape_native.

Parameters:

• fill_value – The value all array elements are filled with.

• shape_native – The 2D shape of the array in its native format, and its 2D mask.

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

• origin – 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
)

classmethod ones(shape_native: Tuple[int, int], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float, float] = (0.0, 0.0), header: Optional[Header] = None) → Array2D

Returns an Array2D where all values are filled with ones, analogous to np.ones().

For a full description of Array2D objects, including a description of the slim and native attribute used by the API, see the Array2D 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 of shape_native.

Parameters:

• shape_native – The 2D shape of the array in its native format, and its 2D mask.

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

• origin – 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
)

classmethod zeros(shape_native: Tuple[int, int], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float, float] = (0.0, 0.0), header: Optional[Header] = None) → Array2D

Returns an Array2D where all values are filled with zeros, analogous to np.zeros().

For a full description of Array2D objects, including a description of the slim and native attribute used by the API, see the Array2D 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 of shape_native.

Parameters:

• shape_native – The 2D shape of the array in its native format, and its 2D mask.

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

• origin – 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
)

classmethod from_fits(file_path: str, pixel_scales: Union[Tuple[float], Tuple[float, float], float], hdu: int = 0, sub_size: int = 1, origin: Tuple[float, float] = (0.0, 0.0)) → Array2D

Returns an Array2D by loading the array values from a .fits file.

For a full description of Array2D objects, including a description of the slim and native attribute used by the API, see the Array2D class API documentation.

Parameters:

• file_path – The path the file is loaded from, including the filename and the .fits extension, e.g. ‘/path/to/filename.fits’

• pixel_scales – 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 – The Header-Data Unit of the .fits file the array data is loaded from.

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

• origin – 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
)

classmethod from_yx_and_values(y: Union[ndarray, List], x: Union[ndarray, List], values: Union[ndarray, List], shape_native: Tuple[int, int], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, header: Optional[Header] = None) → Array2D

Returns an Array2D by 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 the slim and native attribute used by the API, see the Array2D class API documentation.

Parameters:

• y – The y pixel indexes where value are input, with shape [total_unmasked_pixels*sub_size].

• x – 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 – The 2D shape of the array in its native format, and its 2D mask.

• pixel_scales – 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 – 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,
)