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lsst.pipe.tasks gfb5511b3f7+6fc9c088ec
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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` | |
| lsst.pipe.tasks.matchFakes.associatedDiaSources : `pandas.DataFrame` |
Definition at line 136 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.band : `str` |
Definition at line 405 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.ccdVisitFakeMagnitudes : `pandas.DataFrame` |
Definition at line 403 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.combinedFakeCat : `pandas.DataFrame` |
Definition at line 210 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.decs : `numpy.ndarray`, (N,) |
Definition at line 316 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.diffIm : `lsst.afw.image.Exposure` |
Definition at line 134 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.exposure : `lsst.afw.image.exposure.exposure.ExposureF` |
Definition at line 236 of file matchFakes.py.
| 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.
| 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.
| lsst.pipe.tasks.matchFakes.image : `lsst.afw.image.exposure.exposure.ExposureF` |
Definition at line 256 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.movingFakeCat : `pandas.DataFrame` |
Definition at line 241 of file matchFakes.py.
| 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.
| lsst.pipe.tasks.matchFakes.result : `lsst.pipe.base.Struct` |
Definition at line 141 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.skyMap : `lsst.skymap.SkyMap` |
Definition at line 132 of file matchFakes.py.
| lsst.pipe.tasks.matchFakes.vectors : `numpy.ndarray`, (N, 3) |
Definition at line 321 of file matchFakes.py.