lsst.ip.diffim.computeSpatiallySampledMetrics Namespace Reference

Classes
class	SpatiallySampledMetricsConnections

Variables
	science : `lsst.afw.image.ExposureF`
	template : `lsst.afw.image.ExposureF`
	difference : `lsst.afw.image.ExposureF`
	diaSources : `lsst.afw.table.SourceCatalog`
	psfMatchingKernel : `~lsst.afw.math.LinearCombinationKernel`
	results : `lsst.pipe.base.Struct`
	src : `lsst.afw.table.SourceRecord`
	metricsMaskPlanes : `list` of `str`

Variable Documentation

diaSources

lsst.ip.diffim.computeSpatiallySampledMetrics.diaSources : `lsst.afw.table.SourceCatalog`

Definition at line 231 of file computeSpatiallySampledMetrics.py.

difference

lsst.ip.diffim.computeSpatiallySampledMetrics.difference : `lsst.afw.image.ExposureF`

Definition at line 229 of file computeSpatiallySampledMetrics.py.

metricsMaskPlanes

lsst.ip.diffim.computeSpatiallySampledMetrics.metricsMaskPlanes : `list` of `str`

Definition at line 278 of file computeSpatiallySampledMetrics.py.

psfMatchingKernel

lsst.ip.diffim.computeSpatiallySampledMetrics.psfMatchingKernel : `~lsst.afw.math.LinearCombinationKernel`

Definition at line 233 of file computeSpatiallySampledMetrics.py.

results

lsst.ip.diffim.computeSpatiallySampledMetrics.results : `lsst.pipe.base.Struct`

Definition at line 238 of file computeSpatiallySampledMetrics.py.

science

lsst.ip.diffim.computeSpatiallySampledMetrics.science : `lsst.afw.image.ExposureF`

metricsMaskPlanes = lsst.pex.config.ListField(
    dtype=str,
    doc="List of mask planes to include in metrics",
    default=('BAD', 'CLIPPED', 'CR', 'DETECTED', 'DETECTED_NEGATIVE', 'EDGE',
             'INEXACT_PSF', 'INJECTED', 'INJECTED_TEMPLATE', 'INTRP', 'NOT_DEBLENDED',
             'NO_DATA', 'REJECTED', 'SAT', 'SAT_TEMPLATE', 'SENSOR_EDGE', 'STREAK', 'SUSPECT',
             'UNMASKEDNAN',
             ),
)
metricSources = pexConfig.ConfigurableField(
    target=SkyObjectsTask,
    doc="Generate QA metric sources",
)

def setDefaults(self):
    self.metricSources.avoidMask = ["NO_DATA", "EDGE"]


class SpatiallySampledMetricsTask(lsst.pipe.base.PipelineTask):

ConfigClass = SpatiallySampledMetricsConfig
_DefaultName = "spatiallySampledMetrics"

def __init__(self, **kwargs):
    super().__init__(**kwargs)

    self.makeSubtask("metricSources")
    self.schema = afwTable.SourceTable.makeMinimalSchema()
    self.schema.addField(
        "x", "F",
        "X location of the metric evaluation.",
        units="pixel")
    self.schema.addField(
        "y", "F",
        "Y location of the metric evaluation.",
        units="pixel")
    self.metricSources.skySourceKey = self.schema.addField("sky_source", type="Flag",
                                                           doc="Metric evaluation objects.")
    self.schema.addField(
        "source_density", "F",
        "Density of diaSources at location.",
        units="count/degree^2")
    self.schema.addField(
        "dipole_density", "F",
        "Density of dipoles at location.",
        units="count/degree^2")
    self.schema.addField(
        "dipole_direction", "F",
        "Mean dipole orientation.",
        units="radian")
    self.schema.addField(
        "dipole_separation", "F",
        "Mean dipole separation.",
        units="pixel")
    self.schema.addField(
        "template_value", "F",
        "Median of template at location.",
        units="nJy")
    self.schema.addField(
        "template_variance", "F",
        "Median of template variance at location.",
        units="nJy^2")
    self.schema.addField(
        "science_value", "F",
        "Median of science at location.",
        units="nJy")
    self.schema.addField(
        "science_variance", "F",
        "Median of science variance at location.",
        units="nJy^2")
    self.schema.addField(
        "diffim_value", "F",
        "Median of diffim at location.",
        units="nJy")
    self.schema.addField(
        "diffim_variance", "F",
        "Median of diffim variance at location.",
        units="nJy^2")
    self.schema.addField(
        "diffim_chi2PerPix", "F",
        "Robust normalized noise of diffim at location:"
        " (1.4826*MAD(image))^2 / median(variance), evaluated on background"
        " pixels (DETECTED, DETECTED_NEGATIVE, BAD, SAT, EDGE, NO_DATA excluded)."
        " Expected ~1.0 for a well-decorrelated diffim; values >>1 indicate"
        " residual structure, <<1 indicates over-estimated variance.")
    self.schema.addField(
        "science_psfSize", "F",
        "Width of the science image PSF at location.",
        units="pixel")
    self.schema.addField(
        "template_psfSize", "F",
        "Width of the template image PSF at location.",
        units="pixel")
    for maskPlane in self.config.metricsMaskPlanes:
        self.schema.addField(
            "%s_mask_fraction"%maskPlane.lower(), "F",
            "Fraction of pixels with %s mask"%maskPlane
        )
    self.schema.addField(
        "psfMatchingKernel_sum", "F",
        "PSF matching kernel sum at location.")
    self.schema.addField(
        "psfMatchingKernel_dx", "F",
        "PSF matching kernel centroid offset in x at location.",
        units="pixel")
    self.schema.addField(
        "psfMatchingKernel_dy", "F",
        "PSF matching kernel centroid offset in y at location.",
        units="pixel")
    self.schema.addField(
        "psfMatchingKernel_length", "F",
        "PSF matching kernel centroid offset module.",
        units="arcsecond")
    self.schema.addField(
        "psfMatchingKernel_position_angle", "F",
        "PSF matching kernel centroid offset position angle.",
        units="radian")
    self.schema.addField(
        "psfMatchingKernel_direction", "F",
        "PSF matching kernel centroid offset direction in detector plane.",
        units="radian")
    self.schema.addField(
        "psfMatchingKernel_residualNorm", "F",
        "Shape-only PSF match residual at location:"
        "L2 norm of (K-convolved template PSF - science PSF),"
        " relative to the science PSF L2 norm. Larger values indicate worse"
        " PSF matching. Assumes the kernel was solved to convolve the template.")

@timeMethod
def run(self, science, template, difference, diaSources, psfMatchingKernel):

Definition at line 224 of file computeSpatiallySampledMetrics.py.

src

lsst.ip.diffim.computeSpatiallySampledMetrics.src : `lsst.afw.table.SourceRecord`

idFactory = lsst.meas.base.IdGenerator().make_table_id_factory()

spatiallySampledMetrics = afwTable.SourceCatalog(self.schema)
spatiallySampledMetrics.getTable().setIdFactory(idFactory)

self.metricSources.run(mask=science.mask, seed=difference.info.id, catalog=spatiallySampledMetrics)

metricsMaskPlanes = []
for maskPlane in self.config.metricsMaskPlanes:
    try:
        metricsMaskPlanes.append(maskPlane)
    except InvalidParameterError:
        self.log.info("Unable to calculate metrics for mask plane %s: not in image"%maskPlane)

for src in spatiallySampledMetrics:
    self._evaluateLocalMetric(src, science, template, difference, diaSources,
                              metricsMaskPlanes=metricsMaskPlanes,
                              psfMatchingKernel=psfMatchingKernel)
spatiallySampledMetrics = spatiallySampledMetrics.copy(deep=True).asAstropy()
return pipeBase.Struct(spatiallySampledMetrics=spatiallySampledMetrics)

def _evaluateLocalMetric(self, src, science, template, difference, diaSources,
                     metricsMaskPlanes, psfMatchingKernel):

Definition at line 270 of file computeSpatiallySampledMetrics.py.

template

lsst.ip.diffim.computeSpatiallySampledMetrics.template : `lsst.afw.image.ExposureF`

Definition at line 226 of file computeSpatiallySampledMetrics.py.