autolens.Grid2D#
- class Grid2D[source]#
Bases:
Structure
A grid of 2D (y,x) coordinates, which are paired to a uniform 2D mask of pixels and sub-pixels. Each entry on the grid corresponds to the (y,x) coordinates at the centre of a sub-pixel of an unmasked pixel.
A Grid2D is ordered such that pixels begin from the top-row (e.g. index [0, 0]) of the corresponding mask and go right and down. The positive y-axis is upwards and positive x-axis to the right.
The grid can be stored in two formats:
slimmed: all masked entries are removed so the ndarray is shape [total_unmasked_coordinates*sub_size**2, 2]
native: it retains the original shape of the grid so the ndarray is shape [total_y_coordinates*sub_size, total_x_coordinates*sub_size, 2].
Case 1 (sub-size=1, slim)
The Grid2D is an ndarray of shape [total_unmasked_coordinates, 2], therefore when slim the shape of the grid is 2, not 1.
The first element of the ndarray corresponds to the pixel index and second element the y or x coordinate value.
For example:
grid[3,0] = the 4th unmasked pixel’s y-coordinate.
grid[6,1] = the 7th unmasked pixel’s x-coordinate.
Below is a visual illustration of a grid, where a total of 10 pixels are unmasked and are included in the grid.
x x x x x x x x x x x x x x x x x x x x This is an example mask.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 grid) x x x O O O O x x x O = `False` (Pixel is not masked and included in the grid) 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 will come out like this (and the direction of scaled coordinates is highlighted around the mask.
pixel_scales = 1.0" <--- -ve x +ve --> y x x x x x x x x x x x ^ grid[0] = [ 1.5, -0.5] x x x x x x x x x x I grid[1] = [ 1.5, 0.5] x x x x x x x x x x I grid[2] = [ 0.5, -1.5] x x x x 0 1 x x x x +ve grid[3] = [ 0.5, -0.5] x x x 2 3 4 5 x x x y grid[4] = [ 0.5, 0.5] x x x 6 7 8 9 x x x -ve grid[5] = [ 0.5, 1.5] x x x x x x x x x x I grid[6] = [-0.5, -1.5] x x x x x x x x x x I grid[7] = [-0.5, -0.5] x x x x x x x x x x \/ grid[8] = [-0.5, 0.5] x x x x x x x x x x grid[9] = [-0.5, 1.5]
Case 2 (sub-size>1, slim)
If the mask’s sub_size is > 1, the grid is defined as a sub-grid where each entry corresponds to the (y,x) coordinates at the centre of each sub-pixel of an unmasked pixel. The Grid2D is therefore stored as an ndarray of shape [total_unmasked_coordinates*sub_size**2, 2]
The sub-grid 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 sub-grid is an ndarray of shape [total_unmasked_coordinates*(sub_grid_shape)**2, 2].
For example:
grid[9, 1] - using a 2x2 sub-grid, gives the 3rd unmasked pixel’s 2nd sub-pixel x-coordinate.
grid[9, 1] - using a 3x3 sub-grid, gives the 2nd unmasked pixel’s 1st sub-pixel x-coordinate.
grid[27, 0] - using a 3x3 sub-grid, gives the 4th unmasked pixel’s 1st sub-pixel y-coordinate.
Below is a visual illustration of a sub grid. Indexing of each sub-pixel goes from the top-left corner. In contrast to the grid 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 mask.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 x x x O O 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
Our grid with a sub-size looks like it did before:
pixel_scales = 1.0" <--- -ve x +ve --> x x x x x x x x x x ^ x x x x x x x x x x I x x x x x x x x x x I y x x x x x x x x x x x +ve grid[0] = [0.5, -1.5] x x x x 0 1 x x x x y grid[1] = [0.5, -0.5] x x x x x x x x x x -ve x x x x x x x x x x I x x x x x x x x x x I x x x x x x x x x x \/ x x x x x x x x x x
However, if the sub-size is 2, we go to each unmasked pixel and allocate sub-pixel coordinates for it. For example, for pixel 0, if sub_size=2, we use a 2x2 sub-grid:
Pixel 0 - (2x2): y x grid[0] = [0.66, -1.66] I0I1I grid[1] = [0.66, -1.33] I2I3I grid[2] = [0.33, -1.66] grid[3] = [0.33, -1.33]
If we used a sub_size of 3, for the pixel we we would create a 3x3 sub-grid:
y x grid[0] = [0.75, -0.75] grid[1] = [0.75, -0.5] grid[2] = [0.75, -0.25] I0I1I2I grid[3] = [0.5, -0.75] I3I4I5I grid[4] = [0.5, -0.5] I6I7I8I grid[5] = [0.5, -0.25] grid[6] = [0.25, -0.75] grid[7] = [0.25, -0.5] grid[8] = [0.25, -0.25]
Case 3 (sub_size=1, native)
The Grid2D has the same properties as Case 1, but is stored as an an ndarray of shape [total_y_coordinates, total_x_coordinates, 2]. Therefore when native the shape of the grid is 3, not 2.
All masked entries on the grid has (y,x) values of (0.0, 0.0).
For the following example 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 mask.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 grid) x x x O O O O x x x O = `False` (Pixel is not masked and included in the grid) 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
In the above grid:
grid[0,0,0] = 0.0 (it is masked, thus zero)
grid[0,0,1] = 0.0 (it is masked, thus zero)
grid[3,3,0] = 0.0 (it is masked, thus zero)
grid[3,3,1] = 0.0 (it is masked, thus zero)
grid[3,4,0] = 1.5
grid[3,4,1] = -0.5
Case 4 (sub_size>1 native)
The properties of this grid can be derived by combining Case’s 2 and 3 above, whereby the grid is stored as an ndarray of shape [total_y_coordinates*sub_size, total_x_coordinates*sub_size, 2].
All sub-pixels in masked pixels have values (0.0, 0.0).
Grid2D Mapping:
Every set of (y,x) coordinates in a pixel of the sub-grid maps to an unmasked pixel in the mask. For a uniform grid, every (y,x) coordinate directly corresponds to the location of its paired unmasked pixel.
It is not a requirement that grid is uniform and that their coordinates align with the mask. The input grid could be an irregular set of (y,x) coordinates where the indexing signifies that the (y,x) coordinate originates or is paired with the mask’s pixels but has had its value change by some aspect of the calculation.
This is important for the child project PyAutoLens, where grids in the image-plane are ray-traced and deflected to perform lensing calculations. The grid indexing is used to map pixels between the image-plane and source-plane.
- Parameters:
values (
Union
[ndarray
,List
]) – The (y,x) coordinates of the grid.mask (
Mask2D
) – The 2D mask associated with the grid, defining the pixels each grid coordinate is paired with and originates from.store_native (
bool
) – If True, the ndarray is stored in its native format [total_y_pixels, total_x_pixels, 2]. This avoids mapping large data arrays to and from the slim / native formats, which can be a computational bottleneck.
Methods
all
astype
- rtype:
AbstractNDArray
Setup a blurring-grid from a mask, where a blurring grid consists of all pixels that are masked (and therefore have their values set to (0.0, 0.0)), but are close enough to the unmasked pixels that their values will be convolved into the unmasked those pixels.
Returns the blurring grid from a grid, via an input 2D kernel shape.
Create a Grid2D (see Grid2D.__new__) from an input bounding box with coordinates [y_min, y_max, x_min, x_max], where the shape_native is used to compute the (y,x) grid values within this bounding box.
copy
Returns the distance of every coordinate on the grid from an input (y,x) coordinate.
The extent of the grid in scaled units returned as a list [x_min, x_max, y_min, y_max], where all values are buffed such that their extent is further than the grid's extent..
flip_hdu_for_ds9
Create a Grid2D (see Grid2D.__new__) by inputting the extent of the (y,x) grid coordinates as an input (x0, x1, y0, y1) tuple.
Create a Grid2D (see Grid2D.__new__) from a mask, where only unmasked pixels are included in the grid (if the grid is represented in its native 2D masked values are (0.0, 0.0)).
Create a Grid2D (see Grid2D.__new__) from a mask, where only unmasked pixels are included in the grid (if the grid is represented in its native 2D masked values are (0.0, 0.0)).
Create a Grid2D (see Grid2D.__new__) by inputting the grid coordinates as 1D y and x values.
Create a Grid2D (see Grid2D.__new__) by inputting the grid coordinates as 2D y and x values.
Determine a projected radial grid of points from a 2D region of coordinates defined by an extent [xmin, xmax, ymin, ymax] and with a (y,x) centre.
The function grid_scaled_2d_slim_radial_projected_from() determines a projected radial grid of points from a 2D region of coordinates defined by an extent [xmin, xmax, ymin, ymax] and with a (y,x) centre.
Returns a new Grid2D from this grid, where the (y,x) coordinates of this grid have a grid of (y,x) values, termed the deflection grid, subtracted from them to determine the new grid of (y,x) values.
Remove all coordinates from this Grid2D which are within a certain distance of an input list of coordinates.
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
Create a Grid2D (see Grid2D.__new__) by inputting the grid coordinates in 1D or 2D, automatically determining whether to use the 'manual_slim' or 'manual_native' methods.
output_to_fits
Output the grid to a .fits file.
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.
Relocate the coordinates of a grid to the border of this grid if they are outside the border, where the border is defined as all pixels at the edge of the grid's mask (see mask._border_1d_indexes).
Relocate the coordinates of a pixelization grid to the border of this grid.
reshape
- rtype:
AbstractNDArray
sqrt
- rtype:
AbstractNDArray
Returns the squared distance of every coordinate on the grid from an input coordinate.
Convert a result from an ndarray to an aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows:
Convert a result from a list of ndarrays to a list of aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows:
sum
trimmed_after_convolution_from
- rtype:
Structure
Create a Grid2D (see Grid2D.__new__) as a uniform grid of (y,x) values given an input shape_native and pixel_scales of the grid:
Create a ArrayIrregular object from a 1D NumPy array of values of shape [total_coordinates].
with_new_array
Copy this object but give it a new array.
Attributes
array
Return a Grid2D of the binned-up grid in its 1D representation, which is stored with shape [total_unmasked_pixels, 2].
derive_grid
- rtype:
DeriveGrid2D
derive_indexes
- rtype:
DeriveIndexes2D
derive_mask
- rtype:
DeriveMask2D
dtype
Return the grid as an ndarray of shape [total_unmasked_pixels, 2] with flipped values such that coordinates are given as (x,y) values.
geometry
hdu_for_output
imag
- rtype:
AbstractNDArray
Return the grid as an ndarray where all (y,x) values are converted to Radians.
Return a Grid2D where the data is stored in its native representation, which has shape [sub_size*total_y_pixels, sub_size*total_x_pixels, 2].
ndim
origin
pixel_area
pixel_scale
- rtype:
pixel_scale_header
- rtype:
pixel_scales
real
- rtype:
AbstractNDArray
The (y,x) maximum values of the grid in scaled units, buffed such that their extent is further than the grid's extent.
The (y,x) minimum values of the grid in scaled units, buffed such that their extent is further than the grid's extent.
shape
shape_native
The (y,x) interior 2D shape of the grid in scaled units, computed from the minimum and maximum y and x values of the grid.
shape_slim
- rtype:
size
Return a Grid2D where the data is stored its slim representation, which is an ndarray of shape [total_unmasked_pixels * sub_size**2, 2].
sub_border_grid
A property that is only computed once per instance and then replaces itself with an ordinary attribute.
sub_shape_native
sub_shape_slim
- rtype:
sub_size
- rtype:
total_area
total_pixels
- rtype:
unmasked_grid
- classmethod no_mask(values, pixel_scales, shape_native=None, sub_size=1, origin=(0.0, 0.0))[source]#
Create a Grid2D (see Grid2D.__new__) by inputting the grid coordinates in 1D or 2D, automatically determining whether to use the ‘manual_slim’ or ‘manual_native’ methods.
From 1D input the method cannot determine the 2D shape of the grid and its mask, thus the shape_native must be input into this method. The mask is setup as a unmasked Mask2D of shape_native.
The 2D shape of the grid and its mask are determined from the input grid and the mask is setup as an unmasked Mask2D of shape_native.
- Parameters:
values (
Union
[ndarray
,List
]) – The (y,x) coordinates of the grid input as an ndarray of shape [total_unmasked_pixells*(sub_size**2), 2] or a list of lists.shape_native (
Optional
[Tuple
[int
,int
]]) – The 2D shape of the mask the grid 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 (
int
) – The size (sub_size x sub_size) of each unmasked pixels sub-grid.origin (
Tuple
[float
,float
]) – The origin of the grid’s mask.
- Return type:
- classmethod from_yx_1d(y, x, shape_native, pixel_scales, sub_size=1, origin=(0.0, 0.0))[source]#
Create a Grid2D (see Grid2D.__new__) by inputting the grid coordinates as 1D y and x values.
From 1D input the method cannot determine the 2D shape of the grid 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:
list (x or) – The y coordinates of the grid input as an ndarray of shape [total_coordinates] or list.
list – The x coordinates of the grid input as an ndarray of shape [total_coordinates] or list.
shape_native (
Tuple
[int
,int
]) – The 2D shape of the mask the grid 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 (
int
) – The size (sub_size x sub_size) of each unmasked pixels sub-grid.origin (
Tuple
[float
,float
]) – The origin of the grid’s mask.
Examples
import autoarray as aa # Make Grid2D from input np.ndarray. grid_2d = aa.Grid2D.from_yx_1d( y=np.array([1.0, 3.0, 5.0, 7.0]), x=np.array([2.0, 4.0, 6.0, 8.0]), shape_native=(2, 2), pixel_scales=1.0, ) # Make Grid2D from input list. grid_2d = aa.Grid2D.from_yx_1d( y=[1.0, 3.0, 5.0, 7.0], x=[2.0, 4.0, 6.0, 8.0], shape_native=(2, 2), pixel_scales=1.0, ) # Print grid's slim (masked 1D data representation) and # native (masked 2D data representation) print(grid_2d.slim) print(grid_2d.native)
- Return type:
- classmethod from_yx_2d(y, x, pixel_scales, sub_size=1, origin=(0.0, 0.0))[source]#
Create a Grid2D (see Grid2D.__new__) by inputting the grid coordinates as 2D y and x values.
The 2D shape of the grid and its mask are determined from the input grid and the mask is setup as an unmasked Mask2D of shape_native.
- Parameters:
list (x or) – The y coordinates of the grid input as an ndarray of shape [total_coordinates] or list.
list – The x coordinates of the grid input as an ndarray of shape [total_coordinates] or list.
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 (
int
) – The size (sub_size x sub_size) of each unmasked pixels sub-grid.origin (
Tuple
[float
,float
]) – The origin of the grid’s mask.
Examples
import autoarray as aa # Make Grid2D from input list(s). grid_2d = aa.Grid2D.from_yx_2d( y=[[1.0], [3.0]], x=[[2.0], [4.0]], pixel_scales=1.0 )
- Return type:
- classmethod from_extent(extent, shape_native, sub_size=1)[source]#
Create a Grid2D (see Grid2D.__new__) by inputting the extent of the (y,x) grid coordinates as an input (x0, x1, y0, y1) tuple.
The (y,x) shape_native in pixels is also input which determines the resolution of the Grid2D.
(The PyAutoArray API typically uses a (y,x) notation, however extent variables begin with x currently. This will be updated in a future release):
extent = (x0, x1, y0, y1) = (2.0, 4.0, -2.0, 6.0) shape_native = (y,x) = (10, 20)
- Parameters:
extent (
Tuple
[float
,float
,float
,float
]) – The (x0, x1, y0, y1) extent of the grid in scaled coordinates over which the grid is created.shape_native (
Tuple
[int
,int
]) – The 2D shape of the grid that is created within this extent.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).
sub_size (
int
) – The size (sub_size x sub_size) of each unmasked pixels sub-grid.origin – The origin of the grid’s mask.
- Return type:
- classmethod uniform(shape_native, pixel_scales, sub_size=1, origin=(0.0, 0.0))[source]#
Create a Grid2D (see Grid2D.__new__) as a uniform grid of (y,x) values given an input shape_native and pixel_scales of the grid:
- Parameters:
shape_native (
Tuple
[int
,int
]) – The 2D shape of the uniform grid and the mask that it is paired with.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) tuple.sub_size (
int
) – The size (sub_size x sub_size) of each unmasked pixels sub-grid.origin (
Tuple
[float
,float
]) – The origin of the grid’s mask.
- Return type:
- classmethod bounding_box(bounding_box, shape_native, sub_size=1, buffer_around_corners=False)[source]#
Create a Grid2D (see Grid2D.__new__) from an input bounding box with coordinates [y_min, y_max, x_min, x_max], where the shape_native is used to compute the (y,x) grid values within this bounding box.
If buffer_around_corners=True, the grid’s (y,x) values fully align with the input bounding box values. This means the mask’s edge pixels extend beyond the bounding box by pixel_scale/2.0. If buffer_around_corners=False, the grid (y,x) coordinates are defined within the bounding box such that the mask’s edge pixels align with the bouning box.
- Parameters:
shape_native (
Tuple
[int
,int
]) – The 2D shape of the uniform grid and the mask that it is paired with.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).
sub_size (
int
) – The size (sub_size x sub_size) of each unmasked pixels sub-grid.origin – The origin of the grid’s mask.
buffer_around_corners (
bool
) – Whether the grid is buffered such that the (y,x) values in the centre of its masks’ edge pixels align with the input bounding box values.
- Return type:
- classmethod from_mask(mask)[source]#
Create a Grid2D (see Grid2D.__new__) from a mask, where only unmasked pixels are included in the grid (if the grid is represented in its native 2D masked values are (0.0, 0.0)).
The mask’s pixel_scales, sub_size and origin properties are used to compute the grid (y,x) coordinates.
- classmethod from_fits(file_path, pixel_scales, sub_size=1, origin=(0.0, 0.0))[source]#
Create a Grid2D (see Grid2D.__new__) from a mask, where only unmasked pixels are included in the grid (if the grid is represented in its native 2D masked values are (0.0, 0.0)).
The mask’s pixel_scales, sub_size and origin properties are used to compute the grid (y,x) coordinates.
- Parameters:
mask – The mask whose masked pixels are used to setup the sub-pixel grid.
- Return type:
- classmethod blurring_grid_from(mask, kernel_shape_native)[source]#
Setup a blurring-grid from a mask, where a blurring grid consists of all pixels that are masked (and therefore have their values set to (0.0, 0.0)), but are close enough to the unmasked pixels that their values will be convolved into the unmasked those pixels. This when computing images from light profile objects.
The mask’s pixel_scales, sub_size and origin properties are used to compute the blurring grid’s (y,x) coordinates.
For example, if our mask is as follows:
x x x x x x x x x xI x x x x x x x x x xI This is an imaging.Mask2D, where x x x x x x x x x xI x x x x x x x x x xI x = `True` (Pixel is masked and excluded from lens) x x x O O O x x x xI O = `False` (Pixel is not masked and included in lens) x x x O O O x x x xI x x x O O O x x x xI x x x x x x x x x xI x x x x x x x x x xI x x x x x x x x x xI
For a PSF of shape (3,3), the following blurring mask is computed (noting that only pixels that are direct neighbors of the unmasked pixels above will blur light into an unmasked pixel)
x x x x x x x x xI This is an example grid.Mask2D, where x x x x x x x x xI x x O O O O O x xI x = `True` (Pixel is masked and excluded from lens) x x O x x x O x xI O = `False` (Pixel is not masked and included in lens) x x O x x x O x xI x x O x x x O x xI x x O O O O O x xI x x x x x x x x xI x x x x x x x x xI
Thus, the blurring grid coordinates and indexes will be as follows
pixel_scales = 1.0" positive negative y x y x x x x x x x x x xI I blurring_grid[0] = [2.0, -2.0] blurring_grid[9] = [-1.0, -2.0] x x x x x x x x xI I blurring_grid[1] = [2.0, -1.0] blurring_grid[10] = [-1.0, 2.0] x xI0 I1 I2 I3 I4 x xI pos blurring_grid[2] = [2.0, 0.0] blurring_grid[11] = [-2.0, -2.0] x xI5 x x x I6 x xI y blurring_grid[3] = [2.0, 1.0] blurring_grid[12] = [-2.0, -1.0] x xI7 x x x I8 x xI I blurring_grid[4] = [2.0, 2.0] blurring_grid[13] = [-2.0, 0.0] x xI9 x x x I10 x xI neg blurring_grid[5] = [1.0, -2.0] blurring_grid[14] = [-2.0, 1.0] x xI11I12I13I14I15 x xI I blurring_grid[6] = [1.0, 2.0] blurring_grid[15] = [-2.0, 2.0] x x x x x x x x xI I blurring_grid[7] = [0.0, -2.0] x x x x x x x x xI I blurring_grid[8] = [0.0, 2.0]
For a PSF of shape (5,5), the following blurring mask is computed (noting that pixels are 2 pixels from a direct unmasked pixels now blur light into an unmasked pixel)
x x x x x x x x xI This is an example grid.Mask2D, where xIoIoIoIoIoIoIo xI xIoIoIoIoIoIoIo xI x = `True` (Pixel is masked and excluded from lens) xIoIo x x xIoIo xI O = `False` (Pixel is not masked and included in lens) xIoIo x x xIoIo xI xIoIo x x xIoIo xI xIoIoIoIoIoIoIo xI xIoIoIoIoIoIoIo xI x x x x x x x x xI
- property slim: Grid2D#
Return a Grid2D where the data is stored its slim representation, which is an ndarray of shape [total_unmasked_pixels * sub_size**2, 2].
If it is already stored in its slim representation it is returned as it is. If not, it is mapped from native to slim and returned as a new Grid2D.
- Return type:
- property native: Grid2D#
Return a Grid2D where the data is stored in its native representation, which has shape [sub_size*total_y_pixels, sub_size*total_x_pixels, 2].
If it is already stored in its native representation it is return as it is. If not, it is mapped from slim to native and returned as a new Grid2D.
This method is used in the child Grid2D classes to create their native properties.
- Return type:
- property binned: Grid2D#
Return a Grid2D of the binned-up grid in its 1D representation, which is stored with shape [total_unmasked_pixels, 2].
The binning up process converts a grid from (y,x) values where each value is a coordinate on the sub-grid to (y,x) values where each coordinate is at the centre of its mask (e.g. a grid with a sub_size of 1). This is performed by taking the mean of all (y,x) values in each sub pixel.
If the grid is stored in 1D it is return as is. If it is stored in 2D, it must first be mapped from 2D to 1D.
- Return type:
- property flipped: Grid2D#
Return the grid as an ndarray of shape [total_unmasked_pixels, 2] with flipped values such that coordinates are given as (x,y) values.
This is used to interface with Python libraries that require the grid in (x,y) format.
- Return type:
- property in_radians: Grid2D#
Return the grid as an ndarray where all (y,x) values are converted to Radians.
This grid is used by the interferometer module.
- Return type:
- grid_2d_via_deflection_grid_from(deflection_grid)[source]#
Returns a new Grid2D from this grid, where the (y,x) coordinates of this grid have a grid of (y,x) values, termed the deflection grid, subtracted from them to determine the new grid of (y,x) values.
This is used by PyAutoLens to perform grid ray-tracing.
- blurring_grid_via_kernel_shape_from(kernel_shape_native)[source]#
Returns the blurring grid from a grid, via an input 2D kernel shape.
For a full description of blurring grids, checkout blurring_grid_from.
- Parameters:
- kernel_shape_native
The 2D shape of the kernel which convolves signal from masked pixels to unmasked pixels.
- :rtype: :py:class:`~autoarray.structures.grids.uniform_2d.Grid2D`
- grid_with_coordinates_within_distance_removed_from(coordinates, distance)[source]#
Remove all coordinates from this Grid2D which are within a certain distance of an input list of coordinates.
For example, if the grid has the coordinate (0.0, 0.0) and coordinates=[(0.0, 0.0)], distance=0.1 is input into this function, a new Grid2D will be created which removes the coordinate (0.0, 0.0).
- structure_2d_from(result)[source]#
Convert a result from an ndarray to an aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows:
1D np.ndarray -> aa.Array2D
2D np.ndarray -> aa.Grid2D
This function is used by the grid_2d_to_structure decorator to convert the output result of a function to an autoarray structure when a Grid2D instance is passed to the decorated function.
- structure_2d_list_from(result_list)[source]#
Convert a result from a list of ndarrays to a list of aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows:
[1D np.ndarray] -> [aa.Array2D]
[2D np.ndarray] -> [aa.Grid2D]
This function is used by the grid_like_list_to_structure-list decorator to convert the output result of a function to a list of autoarray structure when a Grid2D instance is passed to the decorated function.
- values_from(array_slim)[source]#
Create a ArrayIrregular object from a 1D NumPy array of values of shape [total_coordinates]. The ArrayIrregular are structured following this Grid2DIrregular instance.
- Return type:
- squared_distances_to_coordinate_from(coordinate=(0.0, 0.0))[source]#
Returns the squared distance of every coordinate on the grid from an input coordinate.
- distances_to_coordinate_from(coordinate=(0.0, 0.0))[source]#
Returns the distance of every coordinate on the grid from an input (y,x) coordinate.
- grid_2d_radial_projected_shape_slim_from(centre=(0.0, 0.0))[source]#
The function grid_scaled_2d_slim_radial_projected_from() determines a projected radial grid of points from a 2D region of coordinates defined by an extent [xmin, xmax, ymin, ymax] and with a (y,x) centre.
To do this, the function first performs these 3 steps:
Given the region defined by the extent [xmin, xmax, ymin, ymax], the algorithm finds the longest 1D distance of the 4 paths from the (y,x) centre to the edge of the region (e.g. following the positive / negative y and x axes).
Use the pixel-scale corresponding to the direction chosen (e.g. if the positive x-axis was the longest, the pixel_scale in the x dimension is used).
Determine the number of pixels between the centre and the edge of the region using the longest path between the two chosen above.
A schematic is shown below:
------------------- | | |<- - - - ->x | x = centre | | <-> = longest radial path from centre to extent edge | | -------------------
Using the centre x above, this function finds the longest radial path to the edge of the extent window.
This function returns the integer number of pixels given by this radial grid, which is then used to create the radial grid.
- Parameters:
extent – The extent of the grid the radii grid is computed using, with format [xmin, xmax, ymin, ymax]
centre ((float, flloat)) – The (y,x) central coordinate which the radial grid is traced outwards from.
pixel_scales – The (y,x) scaled units to pixel units conversion factor of the 2D mask array.
sub_size – The size of the sub-grid that each pixel of the 2D mask array is divided into.
- Returns:
The 1D integer shape of a radial set of points sampling the longest distance from the centre to the edge of the extent in along the positive x-axis.
- Return type:
- grid_2d_radial_projected_from(centre=(0.0, 0.0), angle=0.0, shape_slim=0)[source]#
Determine a projected radial grid of points from a 2D region of coordinates defined by an extent [xmin, xmax, ymin, ymax] and with a (y,x) centre.
This functions operates as follows:
Given the region defined by the extent [xmin, xmax, ymin, ymax], the algorithm finds the longest 1D distance of the 4 paths from the (y,x) centre to the edge of the region e.g. following the positive / negative y and x axes.
Use the pixel-scale corresponding to the direction chosen e.g. if the positive x-axis was the longest, the pixel_scale in the x dimension is used.
Determine the number of pixels between the centre and the edge of the region using the longest path between the two chosen above.
Create a (y,x) grid of radial points where all points are at the centre’s y value = 0.0 and the x values iterate from the centre in increasing steps of the pixel-scale.
Rotate these radial coordinates by the input angle clockwise.
A schematic is shown below:
------------------- | | |<- - - - ->x | x = centre | | <-> = longest radial path from centre to extent edge | | -------------------
- Parameters:
- Returns:
A radial set of points sampling the longest distance from the centre to the edge of the extent in along the positive x-axis.
- Return type:
- property shape_native_scaled_interior: Tuple[float, float]#
The (y,x) interior 2D shape of the grid in scaled units, computed from the minimum and maximum y and x values of the grid.
This differs from the shape_native_scaled because the edges of the shape are at the maxima and minima of the grid’s (y,x) values, whereas the shape_native_scaled uses the uniform geometry of the grid and its
pixel_scales
, which means it has a buffer at each edge of half apixel_scale
.
- property scaled_minima: Tuple#
The (y,x) minimum values of the grid in scaled units, buffed such that their extent is further than the grid’s extent.
- Return type:
- property scaled_maxima: Tuple#
The (y,x) maximum values of the grid in scaled units, buffed such that their extent is further than the grid’s extent.
- Return type:
- extent_with_buffer_from(buffer=1e-08)[source]#
The extent of the grid in scaled units returned as a list [x_min, x_max, y_min, y_max], where all values are buffed such that their extent is further than the grid’s extent..
This follows the format of the extent input parameter in the matplotlib method imshow (and other methods) and is used for visualization in the plot module.
- padded_grid_from(kernel_shape_native)[source]#
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.
To ensure this signal is included the padded grid is used, which is ‘buffed’ such that it includes all pixels whose signal will be convolved into the unmasked pixels given the 2D kernel shape.
- relocated_grid_from(grid)[source]#
Relocate the coordinates of a grid to the border of this grid if they are outside the border, where the border is defined as all pixels at the edge of the grid’s mask (see mask._border_1d_indexes).
This is performed as follows:
1: Use the mean value of the grid’s y and x coordinates to determine the origin of the grid. 2: Compute the radial distance of every grid coordinate from the origin. 3: For every coordinate, find its nearest pixel in the border. 4: Determine if it is outside the border, by comparing its radial distance from the origin to its paired border pixel’s radial distance. 5: If its radial distance is larger, use the ratio of radial distances to move the coordinate to the border (if its inside the border, do nothing).
The method can be used on uniform or irregular grids, however for irregular grids the border of the ‘image-plane’ mask is used to define border pixels.
- relocated_mesh_grid_from(mesh_grid)[source]#
Relocate the coordinates of a pixelization grid to the border of this grid. See the method
relocated_grid_from()
for a full description of how this grid relocation works.- Parameters:
grid – The pixelization grid whose pixels are relocated to the border edge if outside it.
- Return type: