Coordinates

class mpol.coordinates.GridCoords(cell_size: float, npix: int)

The GridCoords object uses desired image dimensions (via the cell_size and npix arguments) to define a corresponding Fourier plane grid.

Parameters:

• cell_size (float) – width of a single square pixel in [arcsec]

• npix (int) – number of pixels in the width of the image

Notes

The Fourier grid is defined over the domain \([-u,+u]\), \([-v,+v]\), even though for real images, technically we could use an RFFT grid from \([0,+u]\) to \([-v,+v]\). The reason we opt for a full FFT grid in this instance is implementation simplicity.

Images (and their corresponding Fourier transform quantities) are represented as two-dimensional arrays packed as [y, x] and [v, u]. This means that an image with dimensions (npix, npix) will also have a corresponding FFT Fourier grid with shape (npix, npix). The native GridCoords representation assumes the Fourier grid (and thus image) are laid out as one might normally expect an image (i.e., no np.fft.fftshift has been applied).

After the object is initialized, any of the instance variables listed below can be accessed. For example,

>>> myCoords = GridCoords(cell_size=0.005, npix=512)
>>> myCoords.img_ext

dl

image-plane cell spacing in RA direction (assumed to be positive) [radians]

Type:

float

dm

image-plane cell spacing in DEC direction [radians]

Type:

float

img_ext

The length-4 list of (left, right, bottom, top) expected by routines like matplotlib.pyplot.imshow in the extent parameter assuming origin='lower'. Units of [arcsec]

Type:

list

packed_x_centers_2D

2D array of l increasing, with fftshifted applied [arcseconds]. Useful for directly evaluating some function to create a packed cube.

Type:

numpy.ndarray

packed_y_centers_2D

2D array of m increasing, with fftshifted applied [arcseconds]. Useful for directly evaluating some function to create a packed cube.

Type:

numpy.ndarray

sky_x_centers_2D

2D array of l arranged for evaluating a sky image [arcseconds]. l coordinate increases to the left (as on sky).

Type:

numpy.ndarray

sky_y_centers_2D

2D array of m arranged for evaluating a sky image [arcseconds].

Type:

numpy.ndarray

du

Fourier-plane cell spacing in East-West direction [\(\lambda\)]

Type:

float

dv

Fourier-plane cell spacing in North-South direction [\(\lambda\)]

Type:

float

u_centers

1D array of cell centers in East-West direction [\(\lambda\)].

Type:

numpy.ndarray

v_centers

1D array of cell centers in North-West direction [\(\lambda\)].

Type:

numpy.ndarray

u_edges

1D array of cell edges in East-West direction [\(\lambda\)].

Type:

numpy.ndarray

v_edges

1D array of cell edges in North-South direction [\(\lambda\)].

Type:

numpy.ndarray

u_bin_min

minimum u edge [\(\lambda\)]

Type:

float

u_bin_max

maximum u edge [\(\lambda\)]

Type:

float

v_bin_min

minimum v edge [\(\lambda\)]

Type:

float

v_bin_max

maximum v edge [\(\lambda\)]

Type:

float

max_grid

maximum spatial frequency enclosed by Fourier grid [\(\lambda\)]

Type:

float

vis_ext

length-4 list of (left, right, bottom, top) expected by routines like matplotlib.pyplot.imshow in the extent parameter assuming origin='lower'. Units of [\(\lambda\)]

Type:

list

vis_ext_Mlam

like vis_ext, but in units of [\(\mathrm{M}\lambda\)].

Type:

list

check_data_fit(uu: torch.Tensor | npt.NDArray[np.floating[Any]], vv: torch.Tensor | npt.NDArray[np.floating[Any]]) → bool

Test whether loose data visibilities fit within the Fourier grid defined by cell_size and npix.

Parameters:

• uu (torch.Tensor) – u spatial frequency coordinates. Units of [\(\lambda\)]

• vv (torch.Tensor) – v spatial frequency coordinates. Units of [\(\lambda\)]

Returns:

True if all visibilities fit within the Fourier grid defined by [u_bin_min, u_bin_max, v_bin_min, v_bin_max]. Otherwise a mpol.exceptions.CellSizeError is raised on the first violated boundary.

Return type:

bool