lsst.pipe.tasks.maskStreaks Namespace Reference

Classes
class	Line
class	LineCollection
class	LineProfile
class	MaskStreaksConfig
class	MaskStreaksTask

Functions
def	setDetectionMask (maskedImage, forceSlowBin=False, binning=None, detectedPlane="DETECTED", badMaskPlanes=("NO_DATA", "INTRP", "BAD", "SAT", "EDGE"), detectionThreshold=5)

Function Documentation

setDetectionMask()

def lsst.pipe.tasks.maskStreaks.setDetectionMask	(		maskedImage,
			forceSlowBin = False,
			binning = None,
			detectedPlane = "DETECTED",
			badMaskPlanes = ("NO_DATA", "INTRP", "BAD", "SAT", "EDGE"),
			detectionThreshold = 5
	)

Make detection mask and set the mask plane

Creat a binary image from a masked image by setting all data with signal-to-
noise below some threshold to zero, and all data above the threshold to one.
If the binning parameter has been set, this procedure will be preceded by a
weighted binning of the data in order to smooth the result, after which the
result is scaled back to the original dimensions. Set the detection mask
plane with this binary image.

Parameters
----------
maskedImage : `lsst.afw.image.maskedImage`
    Image to be (optionally) binned and converted
forceSlowBin : bool (optional)
    Force usage of slower binning method to check that the two methods
    give the same result.
binning : int (optional)
    Number of pixels by which to bin image
detectedPlane : str (optional)
    Name of mask with pixels that were detected above threshold in image
badMaskPlanes : set (optional)
    Names of masks with pixels that are rejected
detectionThreshold : float (optional)
    Boundary in signal-to-noise between non-detections and detections for
    making a binary image from the original input image

Definition at line 39 of file maskStreaks.py.

                     badMaskPlanes=("NO_DATA", "INTRP", "BAD", "SAT", "EDGE"), detectionThreshold=5):
    """Make detection mask and set the mask plane
 
    Creat a binary image from a masked image by setting all data with signal-to-
    noise below some threshold to zero, and all data above the threshold to one.
    If the binning parameter has been set, this procedure will be preceded by a
    weighted binning of the data in order to smooth the result, after which the
    result is scaled back to the original dimensions. Set the detection mask
    plane with this binary image.
 
    Parameters
    ----------
    maskedImage : `lsst.afw.image.maskedImage`
        Image to be (optionally) binned and converted
    forceSlowBin : bool (optional)
        Force usage of slower binning method to check that the two methods
        give the same result.
    binning : int (optional)
        Number of pixels by which to bin image
    detectedPlane : str (optional)
        Name of mask with pixels that were detected above threshold in image
    badMaskPlanes : set (optional)
        Names of masks with pixels that are rejected
    detectionThreshold : float (optional)
        Boundary in signal-to-noise between non-detections and detections for
        making a binary image from the original input image
    """
    data = maskedImage.image.array
    weights = 1 / maskedImage.variance.array
    mask = maskedImage.getMask()
 
    detectionMask = ((mask.array & mask.getPlaneBitMask(detectedPlane)))
    badPixelMask = mask.getPlaneBitMask(badMaskPlanes)
    badMask = (mask.array & badPixelMask) > 0
    fitMask = detectionMask.astype(bool) & ~badMask
 
    fitData = np.copy(data)
    fitData[~fitMask] = 0
    fitWeights = np.copy(weights)
    fitWeights[~fitMask] = 0
 
    if binning:
        # Do weighted binning:
        ymax, xmax = fitData.shape
        if (ymax % binning == 0) and (xmax % binning == 0) and (not forceSlowBin):
            # Faster binning method
            binNumeratorReshape = (fitData * fitWeights).reshape(ymax // binning, binning,
                                                                 xmax // binning, binning)
            binDenominatorReshape = fitWeights.reshape(binNumeratorReshape.shape)
            binnedNumerator = binNumeratorReshape.sum(axis=3).sum(axis=1)
            binnedDenominator = binDenominatorReshape.sum(axis=3).sum(axis=1)
        else:
            # Slower binning method when (image shape mod binsize) != 0
            warnings.warn('Using slow binning method--consider choosing a binsize that evenly divides '
                          f'into the image size, so that {ymax} mod binning == 0 '
                          f'and {xmax} mod binning == 0')
            xarray = np.arange(xmax)
            yarray = np.arange(ymax)
            xmesh, ymesh = np.meshgrid(xarray, yarray)
            xbins = np.arange(0, xmax + binning, binning)
            ybins = np.arange(0, ymax + binning, binning)
            numerator = fitWeights * fitData
            binnedNumerator, *_ = scipy.stats.binned_statistic_2d(ymesh.ravel(), xmesh.ravel(),
                                                                  numerator.ravel(), statistic='sum',
                                                                  bins=(ybins, xbins))
            binnedDenominator, *_ = scipy.stats.binned_statistic_2d(ymesh.ravel(), xmesh.ravel(),
                                                                    fitWeights.ravel(), statistic='sum',
                                                                    bins=(ybins, xbins))
        binnedData = np.zeros(binnedNumerator.shape)
        ind = binnedDenominator != 0
        np.divide(binnedNumerator, binnedDenominator, out=binnedData, where=ind)
        binnedWeight = binnedDenominator
        binMask = (binnedData * binnedWeight**0.5) > detectionThreshold
        tmpOutputMask = binMask.repeat(binning, axis=0)[:ymax]
        outputMask = tmpOutputMask.repeat(binning, axis=1)[:, :xmax]
    else:
        outputMask = (fitData * fitWeights**0.5) > detectionThreshold
 
    # Clear existing Detected Plane:
    maskedImage.mask.array &= ~maskedImage.mask.getPlaneBitMask(detectedPlane)
 
    # Set Detected Plane with the binary detection mask:
    maskedImage.mask.array[outputMask] |= maskedImage.mask.getPlaneBitMask(detectedPlane)
 
 
@dataclass