LSST Applications 27.0.0,g0265f82a02+469cd937ee,g02d81e74bb+21ad69e7e1,g1470d8bcf6+cbe83ee85a,g2079a07aa2+e67c6346a6,g212a7c68fe+04a9158687,g2305ad1205+94392ce272,g295015adf3+81dd352a9d,g2bbee38e9b+469cd937ee,g337abbeb29+469cd937ee,g3939d97d7f+72a9f7b576,g487adcacf7+71499e7cba,g50ff169b8f+5929b3527e,g52b1c1532d+a6fc98d2e7,g591dd9f2cf+df404f777f,g5a732f18d5+be83d3ecdb,g64a986408d+21ad69e7e1,g858d7b2824+21ad69e7e1,g8a8a8dda67+a6fc98d2e7,g99cad8db69+f62e5b0af5,g9ddcbc5298+d4bad12328,ga1e77700b3+9c366c4306,ga8c6da7877+71e4819109,gb0e22166c9+25ba2f69a1,gb6a65358fc+469cd937ee,gbb8dafda3b+69d3c0e320,gc07e1c2157+a98bf949bb,gc120e1dc64+615ec43309,gc28159a63d+469cd937ee,gcf0d15dbbd+72a9f7b576,gdaeeff99f8+a38ce5ea23,ge6526c86ff+3a7c1ac5f1,ge79ae78c31+469cd937ee,gee10cc3b42+a6fc98d2e7,gf1cff7945b+21ad69e7e1,gfbcc870c63+9a11dc8c8f
LSST Data Management Base Package
|
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.