autocti.Array1D#

class autocti.Array1D(values: Union[ndarray, List], mask: Mask1D, header: Optional[Header] = None, store_native: bool = False, *args, **kwargs)[source]#

Bases: Structure

Methods

`all`	Returns True if all elements evaluate to True.
`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.
`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.
`fill`	Fill the array with a scalar value.
`flatten`	Return a copy of the array collapsed into one dimension.
`from_fits`	Create an Array1D (see Array1D.__new__) by loading the array values from a .fits file.
`full`	Create an Array1D (see Array1D.__new__) where all values are filled with an input fill value, analogous to the method 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`	Create a Array1D (see Array1D.__new__) by inputting the array values in 1D
`nonzero`	Return the indices of the elements that are non-zero.
`ones`	Create an Array1D (see Array1D.__new__) where all values are filled with ones, analogous to the method np.ones().
`output_to_fits`	Output the array 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.
`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`
`var`	Returns the variance of the array elements, along given axis.
`view`	New view of array with the same data.
`zeros`	Create an Array1D (see Array1D.__new__) where all values are filled with zeros, analogous to the method np.zeros().

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`
`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`
`grid_radial`
`imag`	The imaginary part of the array.
`itemsize`	Length of one array element in bytes.
`native`	Return an Array1D where the data is stored in its native representation, which is an ndarray of shape [total_pixels * sub_size].
`nbytes`	Total bytes consumed by the elements of the array.
`ndim`	Number of array dimensions.
`origin`
`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 Array1D where the data is stored its slim representation, which is an ndarray of shape [total_unmasked_pixels * sub_size].
`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, Tuple[float], List[float]], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float] = (0.0,), header: Optional[Header] = None) → Array1D[source]#

Create a Array1D (see Array1D.__new__) by inputting the array values in 1D

Parameters:

values – The values of the array input as an ndarray of shape [total_unmasked_pixels*sub_size] or a list.
pixel_scales – The scaled units to pixel units conversion factor of the array data coordinates (e.g. the x-axis).
sub_size – The size of each unmasked pixels sub-grid.
origin – The origin of the 1D array’s mask.

Examples

import autoarray as aa

# Make Array1D from input np.ndarray.

array_1d = aa.Array1D.no_mask(values=np.array([1.0, 2.0, 3.0, 4.0]), pixel_scales=1.0)

# Make Array2D from input list.

array_1d = aa.Array1D.no_mask(values=[1.0, 2.0, 3.0, 4.0], pixel_scales=1.0)

# Print array's slim (masked 1D data representation) and
# native (masked 1D data representation)

print(array_1d.slim)
print(array_1d.native)

classmethod full(fill_value: float, shape_native: Union[int, Tuple[int]], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float] = (0.0,), header: Optional[Header] = None) → Array1D[source]#

Create an Array1D (see Array1D.__new__) where all values are filled with an input fill value, analogous to the method np.full().

From 1D input the method cannot determine the 1D shape of the array and its mask, thus the shape_native must be input into this method. The mask is setup as a unmasked Mask1D of size shape_native.

Parameters:

fill_value – The value all array elements are filled with.
shape_native (Tuple[int]) – The 1D shape of the mask the array is paired with.
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,) structure.
sub_size – The size (sub_size) of each unmasked pixels sub-array.
origin ((float,)) – The (x) scaled units origin of the mask’s coordinate system.

classmethod zeros(shape_native: Union[int, Tuple[int]], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float] = (0.0,), header: Optional[Header] = None) → Array1D[source]#

Create an Array1D (see Array1D.__new__) where all values are filled with zeros, analogous to the method np.zeros().

From 1D input the method cannot determine the 1D shape of the array and its mask, thus the shape_native must be input into this method. The mask is setup as a unmasked Mask1D of size shape_native.

Parameters:

shape_native (Tuple[int]) – The 1D shape of the mask the array is paired with.
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,) structure.
sub_size – The size (sub_size) of each unmasked pixels sub-array.
origin ((float,)) – The (x) scaled units origin of the mask’s coordinate system.

classmethod ones(shape_native: Union[int, Tuple[int]], pixel_scales: Union[Tuple[float], Tuple[float, float], float], sub_size: int = 1, origin: Tuple[float] = (0.0,), header: Optional[Header] = None) → Array1D[source]#

Create an Array1D (see Array1D.__new__) where all values are filled with ones, analogous to the method np.ones().

From 1D input the method cannot determine the 1D shape of the array and its mask, thus the shape_native must be input into this method. The mask is setup as a unmasked Mask1D of size shape_native.

Parameters:

shape_native (Tuple[int]) – The 1D shape of the mask the array is paired with.
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,) structure.
sub_size – The size (sub_size) of each unmasked pixels sub-array.
origin ((float,)) – The (x) scaled units origin of the mask’s coordinate system.

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] = (0.0, 0.0)) → Array1D[source]#

Create an Array1D (see Array1D.__new__) by loading the array values from a .fits file.

Parameters:

file_path – The path the file is loaded from, including the filename and the .fits extension, e.g. ‘/path/to/filename.fits’
hdu – The Header-Data Unit of the .fits file the array data is loaded from.
pixel_scales – The (x,) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float,) structure.
sub_size – The sub-size of each unmasked pixels sub-array.
origin – The (x,) scaled units origin of the coordinate system.

property slim: Array1D#

Return an Array1D where the data is stored its slim representation, which is an ndarray of shape [total_unmasked_pixels * sub_size].

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 Array1D.

property native: Array1D#

Return an Array1D where the data is stored in its native representation, which is an ndarray of shape [total_pixels * sub_size].

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 Array1D.

output_to_fits(file_path: str, overwrite: bool = False)[source]#

Output the array to a .fits file.

Parameters:

file_path – The output path of the file, including the filename and the .fits extension e.g. ‘/path/to/filename.fits’
overwrite – If a file already exists at the path, if overwrite=True it is overwritten else an error is raised.