autocti.Mask2D#

class autocti.Mask2D(mask: Union[ndarray, List], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float, float] = (0.0, 0.0), invert: bool = False, *args, **kwargs)[source]#

Bases: Mask2D

A 2D mask, used for masking values which are associated with a a uniform rectangular grid of pixels.

When applied to 2D data with the same shape, values in the mask corresponding to False entries are unmasked and therefore used in subsequent calculations. .

The ``Mask2D`, has in-built functionality which:

Maps data structures between two data representations: slim` (all unmasked False values in a 1D ndarray) and native (all unmasked values in a 2D or 3D ndarray).
Has a Geometry2D object (defined by its (y,x) pixel scales, (y,x) origin and sub_size) which defines how coordinates are converted from pixel units to scaled units.
Associates Cartesian Grid2D objects of (y,x) coordinates with the data structure (e.g. a (y,x) grid of all unmasked pixels) via the DeriveGrid2D object.
This includes sub-grids, which perform calculations higher resolutions which are then binned up.

A detailed description of the 2D mask API is provided below.

SLIM DATA REPRESENTATION (sub-size=1)

Below is a visual illustration of a Mask2D, where a total of 10 pixels are unmasked (values are False):

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 mask are also shown on the y and x axes).

<--- -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
 x x x x 0 1 x x x x +ve
 x x x 2 3 4 5 x x x  y
 x x x 6 7 8 9 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

The Mask2D’s 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:

mask[3] = the 4th unmasked pixel's value.
mask[6] = the 7th unmasked pixel's value.

A Cartesian grid of (y,x) coordinates, corresponding to all slim values (e.g. unmasked pixels) is given by mask.derive_grid.masked.slim.

NATIVE DATA REPRESENTATION (sub_size=1)

Masked data 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

The mask has the following indexes:

<--- -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
 x x x x 0 1 x x x x +ve
 x x x 2 3 4 5 x x x  y
 x x x 6 7 8 9 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

In the above array:

- mask[0,0] = True (it is masked)
- mask[0,0] = True (it is masked)
- mask[3,3] = True (it is masked)
- mask[3,3] = True (it is masked)
- mask[3,4] = False (not masked)
- mask[3,5] = False (not masked)
- mask[4,5] = False (not masked)

SLIM TO NATIVE MAPPING

The Mask2D 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, its slim and native data representations have 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 slim representation as follows:

Pixel 0 - (2x2):

       slim[0] = value of first sub-pixel in pixel 0.
 0 1   slim[1] = value of first sub-pixel in pixel 1.
 2 3   slim[2] = value of first sub-pixel in pixel 2.
       slim[3] = value of first sub-pixel in pixel 3.

Pixel 1 - (2x2):

       slim[4] = value of first sub-pixel in pixel 0.
 4 5   slim[5] = value of first sub-pixel in pixel 1.
 6 7   slim[6] = value of first sub-pixel in pixel 2.
       slim[7] = value of first sub-pixel in pixel 3.

For the native data representation we get the following mappings:

Pixel 0 - (2x2):

       native[8, 2] = value of first sub-pixel in pixel 0.
 0 1   native[8, 3] = value of first sub-pixel in pixel 1.
 2 3   native[9, 2] = value of first sub-pixel in pixel 2.
       native[9, 3] = value of first sub-pixel in pixel 3.

Pixel 1 - (2x2):

       native[10, 4] = value of first sub-pixel in pixel 0.
 4 5   native[10, 5] = value of first sub-pixel in pixel 1.
 6 7   native[11, 4] = value of first sub-pixel in pixel 2.
       native[11, 5] = value of first sub-pixel in pixel 3.

Other entries (all masked sub-pixels are zero):

       native[0, 0] = 0.0 (it is masked, thus zero)
       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:

        slim[0] = value of first sub-pixel in pixel 0.
        slim[1] = value of first sub-pixel in pixel 1.
        slim[2] = value of first sub-pixel in pixel 2.
0 1 2   slim[3] = value of first sub-pixel in pixel 3.
3 4 5   slim[4] = value of first sub-pixel in pixel 4.
6 7 8   slim[5] = value of first sub-pixel in pixel 5.
        slim[6] = value of first sub-pixel in pixel 6.
        slim[7] = value of first sub-pixel in pixel 7.
        slim[8] = value of first sub-pixel in pixel 8.

Parameters:

mask – The ndarray of shape [total_y_pixels, total_x_pixels] containing the bool’s representing the mask, where False signifies an entry is unmasked and used in calculations.
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.
origin – The (y,x) scaled units origin of the mask’s coordinate system.

Methods

`all`	Returns True if all elements evaluate to True.
`all_false`	Create a mask where all pixels are False and therefore unmasked.
`any`	Returns True if any of the elements of a evaluate to True.
`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.
`circular`	Returns a Mask2D (see Mask2D.__new__) where all False entries are within a circle of input radius.
`circular_annular`	Returns a Mask2D (see Mask2D.__new__) where all False entries are within an annulus of input inner radius and outer radius.
`circular_anti_annular`	Returns a Mask2D (see Mask2D.__new__) where all False entries are within an inner circle and second outer circle, forming an inverse annulus.
`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.
`elliptical`	Returns a Mask2D (see Mask2D.__new__) where all False entries are within an ellipse.
`elliptical_annular`	Returns a Mask2D (see Mask2D.__new__) where all False entries are within an elliptical annulus of input inner and outer scaled major-axis and centre.
`fill`	Fill the array with a scalar value.
`flatten`	Return a copy of the array collapsed into one dimension.
`from_cosmic_ray_map_buffed`	Returns the mask used for CTI Calibration, which is all False unless specific regions are input for masking.
`from_fits`	Loads the image from a .fits file.
`from_masked_regions`
`from_pixel_coordinates`	Returns a Mask2D (see Mask2D.__new__) where all False entries are defined from an input list of list of pixel coordinates.
`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)
`manual`	Create a Mask2D (see Mask2D.__new__) by inputting the array values in 2D, for example:
`mask_new_sub_size_from`	Returns the mask on the same scaled coordinate system but with a sub-grid of an inputsub_size.
`masked_fpr_and_eper_from`
`masked_parallel_eper_from`
`masked_parallel_fpr_from`
`masked_serial_eper_from`
`masked_serial_fpr_from`
`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.
`nonzero`	Return the indices of the elements that are non-zero.
`output_to_fits`	Write the 2D Mask to a .fits file.
`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.
`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.
`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_array_from`	Map a padded 1D array of values to its original 2D array, trimming all edge values.
`unmasked_blurred_array_from`	For a padded grid and psf, compute an unmasked blurred image from an unmasked unblurred image.
`var`	Returns the variance of the array elements, along given axis.
`view`	New view of array with the same data.

Attributes

`T`	The transposed array.
`base`	Base object if memory is from some other object.
`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`	A property that is only computed once per instance and then replaces itself with an ordinary attribute.
`derive_mask`
`dimensions`
`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`	Return the 2D geometry of the mask, representing its uniform rectangular grid of (y,x) coordinates defined by its `shape_native`.
`imag`	The imaginary part of the array.
`is_all_false`	Returns False if all pixels in a mask are False, else returns True.
`is_all_true`	Returns True if all pixels in a mask are True, else returns False.
`itemsize`	Length of one array element in bytes.
`mask_centre`
`native`	Returns the data structure in its native format which contains all unmaksed values to the native dimensions.
`nbytes`	Total bytes consumed by the elements of the array.
`ndim`	Number of array dimensions.
`pixel_scale`	For a mask with dimensions two or above check that are pixel scales are the same, and if so return this single value as a float.
`pixel_scales`
`pixels_in_mask`	The total number of unmasked pixels (values are False) in the mask.
`real`	The real part of the array.
`shape`	Tuple of array dimensions.
`shape_native`
`shape_native_masked_pixels`	The (y,x) shape corresponding to the extent of unmasked pixels that go vertically and horizontally across the mask.
`shape_slim`	The 1D shape of the mask, which is equivalent to the total number of unmasked pixels in the mask.
`size`	Number of elements in the array.
`strides`	Tuple of bytes to step in each dimension when traversing an array.
`sub_fraction`	The fraction of the area of a pixel every sub-pixel contains.
`sub_length`	The total number of sub-pixels in a give pixel,
`sub_pixels_in_mask`	The total number of unmasked sub-pixels (values are False) in the mask.
`sub_shape_native`
`sub_shape_slim`	The 1D shape of the mask's sub-grid, which is equivalent to the total number of unmasked pixels in the mask.
`zoom_centre`
`zoom_mask_unmasked`	The scaled-grid of (y,x) coordinates of every pixel.
`zoom_offset_pixels`
`zoom_offset_scaled`
`zoom_region`	The zoomed rectangular region corresponding to the square encompassing all unmasked values.
`zoom_shape_native`

classmethod manual(mask, pixel_scales, origin=(0.0, 0.0), invert=False)[source]#

Create a Mask2D (see Mask2D.__new__) by inputting the array values in 2D, for example:

mask=np.array([[False, False],: [True, False]])
mask=[[False, False],: [True, False]]

Parameters:

mask – The bool values of the mask input as an ndarray of shape [total_y_pixels, total_x_pixels ]or a list of lists.
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.
origin ((float, float)) – The (y,x) scaled units origin of the mask’s coordinate system.
invert – If True, the bool’s of the input mask are inverted, for example False’s become True and visa versa.

classmethod all_false(shape_native, pixel_scales, origin=(0.0, 0.0), invert=False)[source]#

Create a mask where all pixels are False and therefore unmasked.

Parameters:

mask – The bool values of the mask input as an ndarray of shape [total_y_pixels, total_x_pixels ]or a list of lists.
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.
origin ((float, float)) – The (y,x) scaled units origin of the mask’s coordinate system.
invert – If True, the bool’s of the input mask are inverted, for example False’s become True and visa versa.

classmethod from_cosmic_ray_map_buffed(cosmic_ray_map, settings=<autocti.mask.mask_2d.SettingsMask2D object>)[source]#

Returns the mask used for CTI Calibration, which is all False unless specific regions are input for masking.

Parameters:

cosmic_ray_map (array_2d.Array2D) – 2D arrays flagging where cosmic rays on the image.
cosmic_ray_parallel_buffer – The number of pixels from each ray pixels are masked in the parallel direction.
cosmic_ray_serial_buffer – The number of pixels from each ray pixels are masked in the serial direction.
cosmic_ray_diagonal_buffer – The number of pixels from each ray pixels are masked in the digonal up from the parallel + serial direction.

classmethod from_fits(file_path, pixel_scales, hdu=0, origin=(0.0, 0.0), resized_mask_shape=None)[source]#

Loads the image from a .fits file.

Parameters:

file_path – The full path of the fits file.
hdu – The HDU number in the fits file containing the image image.
(float (pixel_scales or) – The arc-second to pixel conversion factor of each pixel.
float) – The arc-second to pixel conversion factor of each pixel.