autolens.Visibilities#
- class Visibilities[source]#
A collection of (real, imag) visibilities which are used to represent the data in an Interferometer dataset.
The (real, imag) visibilities are stored as a 1D complex NumPy array of shape [total_visibilities]. These can be mapped to a 2D real NumPy array of shape [total_visibilities, 2] and a Grid2DIrregular data structure which is used for plotting the visibilities in 2D in the complex plane.
Calculations should use the NumPy array structure wherever possible for efficient calculations.
The vectors input to this function can have any of the following forms (they will be converted to the 1D complex NumPy array structure and can be converted back using the object’s properties):
[1.0+1.0j, 2.0+2.0j] [[1.0, 1.0], [2.0, 2.0]]
- Parameters
visibilities – The (real, imag) visibilities values.
- __init__(*args, **kwargs)#
Methods
__init__(*args, **kwargs)all([axis, out, keepdims, where])Returns True if all elements evaluate to True.
any([axis, out, keepdims, where])Returns True if any of the elements of a evaluate to True.
argmax([axis, out, keepdims])Return indices of the maximum values along the given axis.
argmin([axis, out, keepdims])Return indices of the minimum values along the given axis.
argpartition(kth[, axis, kind, order])Returns the indices that would partition this array.
argsort([axis, kind, order])Returns the indices that would sort this array.
astype(dtype[, order, casting, subok, copy])Copy of the array, cast to a specified type.
byteswap([inplace])Swap the bytes of the array elements
choose(choices[, out, mode])Use an index array to construct a new array from a set of choices.
clip([min, max, out])Return an array whose values are limited to
[min, max].compress(condition[, axis, out])Return selected slices of this array along given axis.
conj()Complex-conjugate all elements.
conjugate()Return the complex conjugate, element-wise.
copy([order])Return a copy of the array.
cumprod([axis, dtype, out])Return the cumulative product of the elements along the given axis.
cumsum([axis, dtype, out])Return the cumulative sum of the elements along the given axis.
diagonal([offset, axis1, axis2])Return specified diagonals.
dotdump(file)Dump a pickle of the array to the specified file.
dumps()Returns the pickle of the array as a string.
fill(value)Fill the array with a scalar value.
flatten([order])Return a copy of the array collapsed into one dimension.
flip_hdu_for_ds9(values)from_fits(file_path, hdu)Create Visibilities (see AbstractVisibilities.__new__) by loading the(real, imag) values from a .fits file.
full(fill_value, shape_slim)Create Visibilities (see AbstractVisibilities.__new__) where all (real, imag) values are filled with an input fill value, analogous to the method numpy ndarray.full.
getfield(dtype[, offset])Returns a field of the given array as a certain type.
item(*args)Copy an element of an array to a standard Python scalar and return it.
itemset(*args)Insert scalar into an array (scalar is cast to array's dtype, if possible)
max([axis, out, keepdims, initial, where])Return the maximum along a given axis.
mean([axis, dtype, out, keepdims, where])Returns the average of the array elements along given axis.
min([axis, out, keepdims, initial, where])Return the minimum along a given axis.
newbyteorder([new_order])Return the array with the same data viewed with a different byte order.
nonzero()Return the indices of the elements that are non-zero.
ones(shape_slim)Create Visibilities (see AbstractVisibilities.__new__) where all (real, imag) values are filled with ones, analogous to the method np.ones().
output_to_fits(file_path[, overwrite])Output the visibilities to a .fits file.
partition(kth[, axis, kind, order])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([axis, dtype, out, keepdims, initial, ...])Return the product of the array elements over the given axis
ptp([axis, out, keepdims])Peak to peak (maximum - minimum) value along a given axis.
put(indices, values[, mode])Set
a.flat[n] = values[n]for all n in indices.ravel([order])Return a flattened array.
repeat(repeats[, axis])Repeat elements of an array.
reshape(shape[, order])Returns an array containing the same data with a new shape.
resize(new_shape[, refcheck])Change shape and size of array in-place.
round([decimals, out])Return a with each element rounded to the given number of decimals.
searchsorted(v[, side, sorter])Find indices where elements of v should be inserted in a to maintain order.
setfield(val, dtype[, offset])Put a value into a specified place in a field defined by a data-type.
setflags([write, align, uic])Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
sort([axis, kind, order])Sort an array in-place.
squeeze([axis])Remove axes of length one from a.
std([axis, dtype, out, ddof, keepdims, where])Returns the standard deviation of the array elements along given axis.
structure_2d_from(result)- rtype
Structure
structure_2d_list_from(result_list)- rtype
List[Structure]
sum([axis, dtype, out, keepdims, initial, where])Return the sum of the array elements over the given axis.
swapaxes(axis1, axis2)Return a view of the array with axis1 and axis2 interchanged.
take(indices[, axis, out, mode])Return an array formed from the elements of a at the given indices.
tobytes([order])Construct Python bytes containing the raw data bytes in the array.
tofile(fid[, sep, format])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([order])A compatibility alias for tobytes, with exactly the same behavior.
trace([offset, axis1, axis2, dtype, out])Return the sum along diagonals of the array.
transpose(*axes)Returns a view of the array with axes transposed.
trimmed_after_convolution_from(kernel_shape)- rtype
Structure
var([axis, dtype, out, ddof, keepdims, where])Returns the variance of the array elements, along given axis.
view([dtype][, type])New view of array with the same data.
zeros(shape_slim)Create Visibilities (see AbstractVisibilities.__new__) where all (real, imag) values are filled with zeros, analogous to the method np.zeros().
Attributes
TThe transposed array.
amplitudesA property that is only computed once per instance and then replaces itself with an ordinary attribute.
baseBase object if memory is from some other object.
ctypesAn object to simplify the interaction of the array with the ctypes module.
dataPython buffer object pointing to the start of the array's data.
derive_grid- rtype
DeriveGrid2D
derive_indexes- rtype
DeriveIndexes2D
derive_mask- rtype
DeriveMask2D
dtypeData-type of the array's elements.
flagsInformation about the memory layout of the array.
flatA 1-D iterator over the array.
geometryhdu_for_outputThe visibilities as an HDU object, which can be output to a .fits file.
imagThe imaginary part of the array.
in_arrayReturns the 1D complex NumPy array of values with shape [total_visibilities] as a NumPy float array of shape [total_visibilities, 2].
in_gridReturns the 1D complex NumPy array of values as an irregular grid.
itemsizeLength of one array element in bytes.
masknativeReturns the data structure in its native format which contains all unmaksed values to the native dimensions.
nbytesTotal bytes consumed by the elements of the array.
ndimNumber of array dimensions.
originphasesA property that is only computed once per instance and then replaces itself with an ordinary attribute.
pixel_areapixel_scale- rtype
pixel_scale_header- rtype
pixel_scalesrealThe real part of the array.
scaled_maximaThe maximum values of the visibilities if they are treated as a 2D grid in the complex plane.
scaled_minimaThe minimum values of the visibilities if they are treated as a 2D grid in the complex plane.
shapeTuple of array dimensions.
shape_nativeshape_slim- rtype
sizeNumber of elements in the array.
slimReturns the data structure in its slim format which flattens all unmasked values to a 1D array.
stridesTuple of bytes to step in each dimension when traversing an array.
sub_shape_nativesub_shape_slim- rtype
sub_size- rtype
total_areatotal_pixels- rtype
unmasked_grid- static __new__(cls, visibilities, *args, **kwargs)#
A collection of (real, imag) visibilities which are used to represent the data in an Interferometer dataset.
The (real, imag) visibilities are stored as a 1D complex NumPy array of shape [total_visibilities]. These can be mapped to a 2D real NumPy array of shape [total_visibilities, 2] and a Grid2DIrregular data structure which is used for plotting the visibilities in 2D in the complex plane.
Calculations should use the NumPy array structure wherever possible for efficient calculations.
The vectors input to this function can have any of the following forms (they will be converted to the 1D complex NumPy array structure and can be converted back using the object’s properties):
[1.0+1.0j, 2.0+2.0j] [[1.0, 1.0], [2.0, 2.0]]
- __init__(*args, **kwargs)#