lsst.scarlet.lite.measure Namespace Reference

Functions
float	calculate_snr (Image images, Image variance, np.ndarray psfs, tuple[int, int] center)

Function Documentation

calculate_snr()

float lsst.scarlet.lite.measure.calculate_snr	(	Image	images,
		Image	variance,
		np.ndarray	psfs,
		tuple[int, int]	center )

Calculate the signal to noise for a point source

This is done by weighting the image with the PSF in each band
and dividing by the PSF weighted variance.

Parameters
----------
images:
    The 3D (bands, y, x) image containing the data.
variance:
    The variance of `images`.
psfs:
    The PSF in each band.
center:
    The center of the signal.

Returns
-------
snr:
    The signal to noise of the source.

Definition at line 30 of file measure.py.

) -> float:
    """Calculate the signal to noise for a point source
 
    This is done by weighting the image with the PSF in each band
    and dividing by the PSF weighted variance.
 
    Parameters
    ----------
    images:
        The 3D (bands, y, x) image containing the data.
    variance:
        The variance of `images`.
    psfs:
        The PSF in each band.
    center:
        The center of the signal.
 
    Returns
    -------
    snr:
        The signal to noise of the source.
    """
    py = psfs.shape[1] // 2
    px = psfs.shape[2] // 2
    bbox = Box(psfs[0].shape, origin=(-py + center[0], -px + center[1]))
    overlap = images.bbox & bbox
    noise = variance[overlap].data
    img = images[overlap].data
    _psfs = Image(psfs, bands=images.bands, yx0=cast(tuple[int, int], bbox.origin))[overlap].data
    numerator = img * _psfs
    denominator = (_psfs * noise) * _psfs
    return np.sum(numerator) / np.sqrt(np.sum(denominator))