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) 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 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`	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.
`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`	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`	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`	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`
`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_sizetotal_y_pixels, sub_sizetotal_x_pixels].
`native_skip_mask`	Return a Array2D where the data is stored in its native representation, which is an `ndarray` of shape [sub_sizetotal_y_pixels, sub_sizetotal_x_pixels].
`nbytes`	Total bytes consumed by the elements of the array.
`ndim`	Number of array dimensions.
`origin`	rtype `Tuple`[`int`, `...`]
`original_orientation`	rtype `Union`[`ndarray`, `Array2D`]
`pixel_area`
`pixel_scale`	rtype `float`
`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, 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. 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 (Union[ndarray, List]) – 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 its native format, and its 2D mask (only required if input shape is in slim 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: Array2D

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 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 (float) – The value all array elements are filled with.
shape_native (Tuple[int, int]) – The 2D shape of the array in its native 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: Array2D

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 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.

Parameters

shape_native (Tuple[int, int]) – The 2D shape of the array in its native 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: Array2D

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 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.

Parameters

shape_native (Tuple[int, int]) – The 2D shape of the array in its native 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: Array2D

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

Parameters

file_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]) – 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: Array2D

classmethod from_yx_and_values(y, x, values, shape_native, pixel_scales, sub_size=1, header=None)[source]#

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 (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 its native 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: Array2D