lsst.meas.algorithms.skyObjects Namespace Reference

Classes
class	SkyObjectsConfig
class	SkyObjectsTask

Functions
	generateSkyObjects (mask, seed, config)

Function Documentation

generateSkyObjects()

lsst.meas.algorithms.skyObjects.generateSkyObjects	(		mask,
			seed,
			config )

Generate a list of Footprints of sky objects

Sky objects don't overlap with other objects. This is determined
through the provided `mask` (in which objects are typically flagged
as `DETECTED`).

Sky objects are positioned using a quasi-random Halton sequence number
generator. This is a deterministic sequence that mimics a random trial and
error approach whilst acting to minimize clustering of points for a given
field of view. Up to `nTrialSources` points are generated, returning the
first `nSources` that do not overlap with the mask.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Input mask plane, which identifies pixels to avoid for the sky
    objects.
seed : `int`
    Random number generator seed.
config : `SkyObjectsConfig`
    Configuration for finding sky objects.

Returns
-------
skyFootprints : `list` of `lsst.afw.detection.Footprint`
    Footprints of sky objects. Each will have a peak at the center
    of the sky object.

Definition at line 71 of file skyObjects.py.

def generateSkyObjects(mask, seed, config):
    """Generate a list of Footprints of sky objects
 
    Sky objects don't overlap with other objects. This is determined
    through the provided `mask` (in which objects are typically flagged
    as `DETECTED`).
 
    Sky objects are positioned using a quasi-random Halton sequence number
    generator. This is a deterministic sequence that mimics a random trial and
    error approach whilst acting to minimize clustering of points for a given
    field of view. Up to `nTrialSources` points are generated, returning the
    first `nSources` that do not overlap with the mask.
 
    Parameters
    ----------
    mask : `lsst.afw.image.Mask`
        Input mask plane, which identifies pixels to avoid for the sky
        objects.
    seed : `int`
        Random number generator seed.
    config : `SkyObjectsConfig`
        Configuration for finding sky objects.
 
    Returns
    -------
    skyFootprints : `list` of `lsst.afw.detection.Footprint`
        Footprints of sky objects. Each will have a peak at the center
        of the sky object.
    """
    if config.nSources <= 0:
        return []
 
    skySourceRadius = config.sourceRadius
    nSkySources = config.nSources
    nTrialSkySources = config.nTrialSources
    if nTrialSkySources is None:
        nTrialSkySources = config.nTrialSourcesMultiplier*nSkySources
 
    box = mask.getBBox()
    box.grow(-(int(skySourceRadius) + 1))  # Avoid objects partially off the image
    xMin, yMin = box.getMin()
    xMax, yMax = box.getMax()
 
    avoid = lsst.afw.geom.SpanSet.fromMask(mask, mask.getPlaneBitMask(config.avoidMask))
    if config.growMask > 0:
        avoid = avoid.dilated(config.growMask)
 
    sampler = qmc.Halton(d=2, seed=seed).random(nTrialSkySources)
    sample = qmc.scale(sampler, [xMin, yMin], [xMax, yMax])
 
    skyFootprints = []
    for x, y in zip(sample[:, 0].astype(int), sample[:, 1].astype(int)):
        if len(skyFootprints) == nSkySources:
            break
 
        spans = lsst.afw.geom.SpanSet.fromShape(int(skySourceRadius), offset=(x, y))
        if spans.overlaps(avoid):
            continue
 
        fp = lsst.afw.detection.Footprint(spans, mask.getBBox())
        fp.addPeak(x, y, 0)
        skyFootprints.append(fp)
 
        # Add doubled-in-size sky object spanSet to the avoid mask.
        avoid = avoid.union(spans.dilated(int(skySourceRadius)))
 
    return skyFootprints