Classes
class	MatchFakesConnections

Variables
	fakeCats : `pandas.DataFrame`

	skyMap : `lsst.skymap.SkyMap`

	diffIm : `lsst.afw.image.Exposure`

	associatedDiaSources : `pandas.DataFrame`

	result : `lsst.pipe.base.Struct`

	fakeCat : `pandas.DataFrame`

	combinedFakeCat : `pandas.DataFrame`

	exposure : `lsst.afw.image.exposure.exposure.ExposureF`

	movingFakeCat : `pandas.DataFrame`

	image : `lsst.afw.image.exposure.exposure.ExposureF`

	ras : `numpy.ndarray`, (N,)

	decs : `numpy.ndarray`, (N,)

	vectors : `numpy.ndarray`, (N, 3)

	ccdVisitFakeMagnitudes : `pandas.DataFrame`

	band : `str`

Variable Documentation

◆ associatedDiaSources

lsst.pipe.tasks.matchFakes.associatedDiaSources : `pandas.DataFrame`

Definition at line 136 of file matchFakes.py.

◆ band

lsst.pipe.tasks.matchFakes.band : `str`

Definition at line 405 of file matchFakes.py.

◆ ccdVisitFakeMagnitudes

lsst.pipe.tasks.matchFakes.ccdVisitFakeMagnitudes : `pandas.DataFrame`

Definition at line 403 of file matchFakes.py.

◆ combinedFakeCat

lsst.pipe.tasks.matchFakes.combinedFakeCat : `pandas.DataFrame`

Definition at line 210 of file matchFakes.py.

◆ decs

lsst.pipe.tasks.matchFakes.decs : `numpy.ndarray`, (N,)

Definition at line 316 of file matchFakes.py.

◆ diffIm

lsst.pipe.tasks.matchFakes.diffIm : `lsst.afw.image.Exposure`

Definition at line 134 of file matchFakes.py.

◆ exposure

lsst.pipe.tasks.matchFakes.exposure : `lsst.afw.image.exposure.exposure.ExposureF`

Definition at line 236 of file matchFakes.py.

◆ fakeCat

lsst.pipe.tasks.matchFakes.fakeCat : `pandas.DataFrame`

fakeCat = self.composeFakeCat(fakeCats, skyMap)

if self.config.doMatchVisit:
    fakeCat = self.getVisitMatchedFakeCat(fakeCat, diffIm)

return self._processFakes(fakeCat, diffIm, associatedDiaSources)

def _processFakes(self, fakeCat, diffIm, associatedDiaSources):

if len(fakeCats) == 1:
    return fakeCats[0].get()
outputCat = []
for fakeCatRef in fakeCats:
    cat = fakeCatRef.get()
    tractId = fakeCatRef.dataId["tract"]
    # Make sure all data is within the inner part of the tract.
    outputCat.append(cat[
        skyMap.findTractIdArray(cat[self.config.ra_col],
                                cat[self.config.dec_col],
                                degrees=False)
        == tractId])

return pd.concat(outputCat)

def getVisitMatchedFakeCat(self, fakeCat, exposure):

selected = exposure.getInfo().getVisitInfo().getId() == fakeCat["visit"]

return fakeCat[selected]

def _addPixCoords(self, fakeCat, image):

wcs = image.getWcs()
ras = fakeCat[self.config.ra_col].values
decs = fakeCat[self.config.dec_col].values
xs, ys = wcs.skyToPixelArray(ras, decs)
fakeCat["x"] = xs
fakeCat["y"] = ys

return fakeCat

def _trimFakeCat(self, fakeCat, image):

vectors = np.empty((len(ras), 3))

vectors[:, 2] = np.sin(decs)
vectors[:, 0] = np.cos(decs) * np.cos(ras)
vectors[:, 1] = np.cos(decs) * np.sin(ras)

return vectors


class MatchVariableFakesConnections(MatchFakesConnections):
ccdVisitFakeMagnitudes = connTypes.Input(
doc="Catalog of fakes with magnitudes scattered for this ccdVisit.",
name="{fakesType}ccdVisitFakeMagnitudes",
storageClass="DataFrame",
dimensions=("instrument", "visit", "detector"),
)


class MatchVariableFakesConfig(MatchFakesConfig,
                       pipelineConnections=MatchVariableFakesConnections):

pass


class MatchVariableFakesTask(MatchFakesTask):

_DefaultName = "matchVariableFakes"
ConfigClass = MatchVariableFakesConfig

def runQuantum(self, butlerQC, inputRefs, outputRefs):
    inputs = butlerQC.get(inputRefs)
    inputs["band"] = butlerQC.quantum.dataId["band"]

    outputs = self.run(**inputs)
    butlerQC.put(outputs, outputRefs)

def run(self, fakeCats, ccdVisitFakeMagnitudes, skyMap, diffIm, associatedDiaSources, band):

fakeCat = self.composeFakeCat(fakeCats, skyMap)
self.computeExpectedDiffMag(fakeCat, ccdVisitFakeMagnitudes, band)
return self._processFakes(fakeCat, diffIm, associatedDiaSources)

def computeExpectedDiffMag(self, fakeCat, ccdVisitFakeMagnitudes, band):

Definition at line 159 of file matchFakes.py.

◆ fakeCats

lsst.pipe.tasks.matchFakes.fakeCats : `pandas.DataFrame`

matchDistanceArcseconds = pexConfig.RangeField(
    doc="Distance in arcseconds to match within.",
    dtype=float,
    default=0.5,
    min=0,
    max=10,
)

doMatchVisit = pexConfig.Field(
    dtype=bool,
    default=False,
    doc="Match visit to trim the fakeCat"
)

trimBuffer = pexConfig.Field(
    doc="Size of the pixel buffer surrounding the image. Only those fake sources with a centroid"
    "falling within the image+buffer region will be considered matches.",
    dtype=int,
    default=100,
)


class MatchFakesTask(PipelineTask):

_DefaultName = "matchFakes"
ConfigClass = MatchFakesConfig

def run(self, fakeCats, skyMap, diffIm, associatedDiaSources):

trimmedFakes = self._trimFakeCat(fakeCat, diffIm)
nPossibleFakes = len(trimmedFakes)

fakeVects = self._getVectors(trimmedFakes[self.config.ra_col],
                             trimmedFakes[self.config.dec_col])
diaSrcVects = self._getVectors(
    np.radians(associatedDiaSources.loc[:, "ra"]),
    np.radians(associatedDiaSources.loc[:, "dec"]))

diaSrcTree = cKDTree(diaSrcVects)
dist, idxs = diaSrcTree.query(
    fakeVects,
    distance_upper_bound=np.radians(self.config.matchDistanceArcseconds / 3600))
nFakesFound = np.isfinite(dist).sum()

self.log.info("Found %d out of %d possible.", nFakesFound, nPossibleFakes)
diaSrcIds = associatedDiaSources.iloc[np.where(np.isfinite(dist), idxs, 0)]["diaSourceId"].to_numpy()
matchedFakes = trimmedFakes.assign(diaSourceId=np.where(np.isfinite(dist), diaSrcIds, 0))

return Struct(
    matchedDiaSources=matchedFakes.merge(
        associatedDiaSources.reset_index(drop=True), on="diaSourceId", how="left")
)

def composeFakeCat(self, fakeCats, skyMap):

Definition at line 130 of file matchFakes.py.

◆ image

lsst.pipe.tasks.matchFakes.image : `lsst.afw.image.exposure.exposure.ExposureF`

Definition at line 256 of file matchFakes.py.

◆ movingFakeCat

lsst.pipe.tasks.matchFakes.movingFakeCat : `pandas.DataFrame`

Definition at line 241 of file matchFakes.py.

◆ ras

lsst.pipe.tasks.matchFakes.ras : `numpy.ndarray`, (N,)

# fakeCat must be processed with _addPixCoords before trimming
if ('x' not in fakeCat.columns) or ('y' not in fakeCat.columns):
    fakeCat = self._addPixCoords(fakeCat, image)

# Prefilter in ra/dec to avoid cases where the wcs incorrectly maps
# input fakes which are really off the chip onto it.
ras = fakeCat[self.config.ra_col].values * u.rad
decs = fakeCat[self.config.dec_col].values * u.rad

isContainedRaDec = image.containsSkyCoords(ras, decs, padding=0)

# now use the exact pixel BBox to filter to only fakes that were inserted
xs = fakeCat["x"].values
ys = fakeCat["y"].values

bbox = Box2D(image.getBBox())
isContainedXy = xs >= bbox.minX
isContainedXy &= xs <= bbox.maxX
isContainedXy &= ys >= bbox.minY
isContainedXy &= ys <= bbox.maxY

return fakeCat[isContainedRaDec & isContainedXy]

def _getVectors(self, ras, decs):

Definition at line 314 of file matchFakes.py.

◆ result

lsst.pipe.tasks.matchFakes.result : `lsst.pipe.base.Struct`

Definition at line 141 of file matchFakes.py.

◆ skyMap

lsst.pipe.tasks.matchFakes.skyMap : `lsst.skymap.SkyMap`

Definition at line 132 of file matchFakes.py.

◆ vectors

lsst.pipe.tasks.matchFakes.vectors : `numpy.ndarray`, (N, 3)

Definition at line 321 of file matchFakes.py.

Classes

Variables