def getFakeSources(butler,
dataId,
tol=1.0,
extraCols=('zeropoint', 'visit', 'ccd'),
includeMissing=False,
footprints=False,
radecMatch=None,
multiband=False,
reffMatch=False,
pix=0.168,
minRad=None,
raCol='RA',
decCol='Dec'):
"""
Get list of sources which agree in pixel position with fake ones with tol.
This returns a sourceCatalog of all the matched fake objects,
note, there will be duplicates in this list, since I haven't
checked deblend.nchild, and I'm only doing a tolerance match,
which could include extra sources
The outputs can include extraCols as long as they are one of:
zeropoint, visit, ccd, thetaNorth, pixelScale
If includeMissing is true, then the pipeline looks at the fake sources
added in the header and includes an entry in the table for sources without
any measurements, specifically the 'id' column will be 0
radecMatch is the fakes table. if it's not None(default), then do an ra/dec
match with the input catalog instead of looking in the header for where the
sources where added
"""
coaddData = "deepCoadd_calexp"
coaddMeta = "deepCoadd_calexp_md"
availExtras = {
'zeropoint': {
'type': float,
'doc': 'zeropoint'
},
'visit': {
'type': int,
'doc': 'visit id'
},
'ccd': {
'type': int,
'doc': 'ccd id'
},
'thetaNorth': {
'type': lsst.afw.geom.Angle,
'doc': 'angle to north'
},
'pixelScale': {
'type': float,
'doc': 'pixelscale in arcsec/pixel'
}
}
if not np.in1d(extraCols, list(availExtras.keys())).all():
print("extraCols must be in ", availExtras)
try:
if 'filter' not in dataId:
sources = butler.get('src',
dataId,
flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS,
immediate=True)
cal = butler.get('calexp', dataId, immediate=True)
cal_md = butler.get('calexp_md', dataId, immediate=True)
else:
meas = butler.get('deepCoadd_meas',
dataId,
flags=NO_FOOTPRINT,
immediate=True)
force = butler.get('deepCoadd_forced_src',
dataId,
flags=NO_FOOTPRINT,
immediate=True)
sources = combineWithForce(meas, force)
cal = butler.get(coaddData, dataId, immediate=True)
cal_md = butler.get(coaddMeta, dataId, immediate=True)
except RuntimeError:
print("skipping", dataId)
return None
if ('pixelScale' in extraCols) or ('thetaNorth' in extraCols):
wcs = cal.getWcs()
availExtras['pixelScale']['value'] = wcs.getPixelScale().asArcseconds()
# The 8 lines of code below find the angle to north, first the mid pixel of the calexp is found,
# then the pixel to sky matrix at this point, the coordinate this gives can then be used to find the
# linearized sky to pixel matrix which can then be used to find the angle.
xMid = cal.getWidth() // 2
yMid = cal.getHeight() // 2
midPoint = lsst.afw.geom.Point2D(xMid, yMid)
midCoord = wcs.pixelToSky(midPoint)
northSkyToPixelMatrix = wcs.linearizeSkyToPixel(
midCoord, lsst.afw.geom.degrees)
northSkyToPixelMatrix = northSkyToPixelMatrix.getLinear()
availExtras['thetaNorth']['value'] = (np.arctan2(
*tuple(northSkyToPixelMatrix(lsst.afw.geom.Point2D(1.0, 0.0))))
) * lsst.afw.geom.radians
if 'visit' in extraCols:
availExtras['visit']['value'] = dataId['visit']
if 'ccd' in extraCols:
availExtras['ccd']['value'] = dataId['ccd']
if 'zeropoint' in extraCols:
zeropoint = 2.5 * np.log10(cal_md.getScalar('FLUXMAG0'))
availExtras['zeropoint']['value'] = zeropoint
if radecMatch is None:
fakeXY, srcIndex = getFakeMatchesHeader(cal_md, sources, tol=tol)
else:
if minRad is not None:
print("# The min matching radius is %4.1f pixel" % minRad)
bbox = lsst.afw.geom.Box2D(cal.getBBox(lsst.afw.image.PARENT))
fakeXY, srcIndex, srcClose = getFakeMatchesRaDec(sources,
radecMatch,
bbox,
cal.getWcs(),
tol=tol,
reffMatch=reffMatch,
pix=pix,
minRad=minRad,
raCol=raCol,
decCol=decCol)
mapper = SchemaMapper(sources.schema)
mapper.addMinimalSchema(sources.schema)
newSchema = mapper.getOutputSchema()
newSchema.addField('fakeId',
type=np.int32,
doc='id of fake source matched to position')
newSchema.addField('nMatched',
type=np.int32,
doc='Number of matched objects')
newSchema.addField('nPrimary',
type=np.int32,
doc='Number of unique matched objects')
newSchema.addField('nNoChild',
type=np.int32,
doc='Number of matched objects with nchild==0')
newSchema.addField('rMatched',
type=float,
doc='Radius used form atching obects, in pixel')
newSchema.addField('fakeOffX',
type=float,
doc='offset from input fake position in X (pixels)')
newSchema.addField('fakeOffY',
type=float,
doc='offset from input fake position in Y (pixels)')
newSchema.addField('fakeOffR',
type=float,
doc='offset from input fake position in radius')
newSchema.addField('fakeClosest',
type="Flag",
doc='Is this match the closest one?')
for extraName in set(extraCols).intersection(availExtras):
newSchema.addField(extraName,
type=availExtras[extraName]['type'],
doc=availExtras[extraName]['doc'])
srcList = SourceCatalog(newSchema)
srcList.reserve(
sum([len(s) for s in srcIndex.values()]) +
(0 if not includeMissing else list(srcIndex.values()).count([])))
centroidKey = sources.getCentroidKey()
isPrimary = sources.schema.find('detect_isPrimary').getKey()
nChild = sources.schema.find('force_deblend_nChild').getKey()
for ident, sindlist in srcIndex.items():
rMatched = fakeXY[ident][2]
if minRad is not None:
if rMatched < minRad:
rMatched = minRad
nMatched = len(sindlist)
nPrimary = np.sum(
[sources[int(obj)].get(isPrimary) for obj in sindlist])
nNoChild = np.sum([(sources[int(obj)].get(nChild) == 0)
for obj in sindlist])
if includeMissing and (nMatched == 0):
newRec = srcList.addNew()
newRec.set('fakeId', ident)
newRec.set('id', 0)
newRec.set('nMatched', 0)
newRec.set('rMatched', rMatched)
for ss in sindlist:
newRec = srcList.addNew()
newRec.assign(sources[int(ss)], mapper)
newRec.set('fakeId', ident)
newRec.set('nMatched', nMatched)
newRec.set('nPrimary', nPrimary)
newRec.set('nNoChild', nNoChild)
newRec.set('rMatched', rMatched)
offsetX = (sources[int(ss)].get(centroidKey).getX() -
fakeXY[ident][0])
newRec.set('fakeOffX', offsetX)
offsetY = (sources[int(ss)].get(centroidKey).getY() -
fakeXY[ident][1])
newRec.set('fakeOffY', offsetY)
newRec.set('fakeOffR', np.sqrt(offsetX**2.0 + offsetY**2.0))
if radecMatch:
if int(ss) == int(srcClose[ident]):
newRec.set('fakeClosest', True)
else:
newRec.set('fakeClosest', False)
if includeMissing:
srcList = srcList.copy(deep=True)
for extraName in set(extraCols).intersection(availExtras):
tempCol = srcList.get(extraName)
tempCol.fill(availExtras[extraName]['value'])
return srcList
def getFakeSources(butler, dataId, tol=1.0, extraCols=('zeropoint', 'visit', 'ccd'),
includeMissing=False, footprints=False, radecMatch=None):
"""Get list of sources which agree in pixel position with fake ones with tol
this returns a sourceCatalog of all the matched fake objects,
note, there will be duplicates in this list, since I haven't checked deblend.nchild,
and I'm only doing a tolerance match, which could include extra sources
the outputs can include extraCols as long as they are one of:
zeropoint, visit, ccd, thetaNorth, pixelScale
if includeMissing is true, then the pipeline looks at the fake sources
added in the header and includes an entry in the table for sources without
any measurements, specifically the 'id' column will be 0
radecMatch is the fakes table. if it's not None(default), then do an ra/dec
match with the input catalog instead of looking in the header for where the
sources where added
"""
availExtras = {'zeropoint':{'type':float, 'doc':'zeropoint'},
'visit':{'type':int, 'doc':'visit id'},
'ccd':{'type':int, 'doc':'ccd id'},
'thetaNorth':{'type':lsst.afw.geom.Angle, 'doc':'angle to north'},
'pixelScale':{'type':float, 'doc':'pixelscale in arcsec/pixel'}}
if not np.in1d(extraCols, availExtras.keys()).all():
print "extraCols must be in ",availExtras
try:
if not 'filter' in dataId:
sources = butler.get('src', dataId,
flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS,
immediate=True)
cal = butler.get('calexp', dataId, immediate=True)
cal_md = butler.get('calexp_md', dataId, immediate=True)
else:
sources = butler.get('deepCoadd_src', dataId,
flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS,
immediate=True)
cal = butler.get('deepCoadd', dataId, immediate=True)
cal_md = butler.get('deepCoadd_md', dataId, immediate=True)
except (lsst.pex.exceptions.LsstException, RuntimeError) as e:
print "skipping", dataId
return None
if ('pixelScale' in extraCols) or ('thetaNorth' in extraCols):
wcs = cal.getWcs()
availExtras['pixelScale']['value'] = wcs.pixelScale().asArcseconds()
availExtras['thetaNorth']['value'] = lsst.afw.geom.Angle(
np.arctan2(*tuple(wcs.getLinearTransform().invert()
(lsst.afw.geom.Point2D(1.0,0.0)))))
if 'visit' in extraCols:
availExtras['visit']['value'] = dataId['visit']
if 'ccd' in extraCols:
availExtras['ccd']['value'] = dataId['ccd']
if 'zeropoint' in extraCols:
availExtras['zeropoint']['value'] = 2.5*np.log10(cal_md.get('FLUXMAG0'))
if radecMatch is None:
fakeXY, srcIndex = getFakeMatchesHeader(cal_md, sources, tol=tol)
else:
fakeXY, srcIndex = getFakeMatchesRaDec(sources, radecMatch,
lsst.afw.geom.Box2D(cal.getBBox(lsst.afw.image.PARENT)),
cal.getWcs(),
tol=tol)
mapper = SchemaMapper(sources.schema)
mapper.addMinimalSchema(sources.schema)
newSchema = mapper.getOutputSchema()
newSchema.addField('fakeId', type=int, doc='id of fake source matched to position')
newSchema.addField('fakeOffset', type=lsst.afw.geom.Point2D,
doc='offset from input fake position (pixels)')
for extraName in set(extraCols).intersection(availExtras):
newSchema.addField(extraName, type=availExtras[extraName]['type'],
doc=availExtras[extraName]['doc'])
srcList = SourceCatalog(newSchema)
srcList.reserve(sum([len(s) for s in srcIndex.values()]) +
(0 if not includeMissing else srcIndex.values().count([])))
centroidKey = sources.schema.find('centroid.sdss').getKey()
for ident, sindlist in srcIndex.items():
if includeMissing and (len(sindlist)==0):
newRec = srcList.addNew()
newRec.set('fakeId', ident)
newRec.set('id', 0)
for ss in sindlist:
newRec = srcList.addNew()
newRec.assign(sources[ss], mapper)
newRec.set('fakeId', ident)
newRec.set('fakeOffset',
lsst.afw.geom.Point2D(sources[ss].get(centroidKey).getX() -
fakeXY[ident][0],
sources[ss].get(centroidKey).getY() -
fakeXY[ident][1]))
if includeMissing:
srcList = srcList.copy(deep=True)
for extraName in set(extraCols).intersection(availExtras):
tempCol = srcList.get(extraName)
tempCol.fill(availExtras[extraName]['value'])
return srcList
def getFakeSources(
butler,
dataId,
tol=1.0,
extraCols=("zeropoint", "visit", "ccd"),
includeMissing=False,
footprints=False,
radecMatch=None,
multiband=False,
reffMatch=False,
pix=0.168,
minRad=None,
raCol="RA",
decCol="Dec",
):
"""
Get list of sources which agree in pixel position with fake ones with tol.
This returns a sourceCatalog of all the matched fake objects,
note, there will be duplicates in this list, since I haven't
checked deblend.nchild, and I'm only doing a tolerance match,
which could include extra sources
The outputs can include extraCols as long as they are one of:
zeropoint, visit, ccd, thetaNorth, pixelScale
If includeMissing is true, then the pipeline looks at the fake sources
added in the header and includes an entry in the table for sources without
any measurements, specifically the 'id' column will be 0
radecMatch is the fakes table. if it's not None(default), then do an ra/dec
match with the input catalog instead of looking in the header for where the
sources where added
"""
pipeVersion = dafPersist.eupsVersions.EupsVersions().versions["hscPipe"]
if StrictVersion(pipeVersion) >= StrictVersion("3.9.0"):
coaddData = "deepCoadd_calexp"
coaddMeta = "deepCoadd_calexp_md"
else:
coaddData = "deepCoadd"
coaddMeta = "deepCoadd_md"
availExtras = {
"zeropoint": {"type": float, "doc": "zeropoint"},
"visit": {"type": int, "doc": "visit id"},
"ccd": {"type": int, "doc": "ccd id"},
"thetaNorth": {"type": lsst.afw.geom.Angle, "doc": "angle to north"},
"pixelScale": {"type": float, "doc": "pixelscale in arcsec/pixel"},
}
if not np.in1d(extraCols, availExtras.keys()).all():
print "extraCols must be in ", availExtras
try:
if "filter" not in dataId:
sources = butler.get("src", dataId, flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS, immediate=True)
cal = butler.get("calexp", dataId, immediate=True)
cal_md = butler.get("calexp_md", dataId, immediate=True)
else:
meas = butler.get("deepCoadd_meas", dataId, flags=NO_FOOTPRINT, immediate=True)
force = butler.get("deepCoadd_forced_src", dataId, flags=NO_FOOTPRINT, immediate=True)
sources = combineWithForce(meas, force)
cal = butler.get(coaddData, dataId, immediate=True)
cal_md = butler.get(coaddMeta, dataId, immediate=True)
except (lsst.pex.exceptions.LsstException, RuntimeError):
print "skipping", dataId
return None
if ("pixelScale" in extraCols) or ("thetaNorth" in extraCols):
wcs = cal.getWcs()
availExtras["pixelScale"]["value"] = wcs.pixelScale().asArcseconds()
availExtras["thetaNorth"]["value"] = lsst.afw.geom.Angle(
np.arctan2(*tuple(wcs.getLinearTransform().invert()(lsst.afw.geom.Point2D(1.0, 0.0))))
)
if "visit" in extraCols:
availExtras["visit"]["value"] = dataId["visit"]
if "ccd" in extraCols:
availExtras["ccd"]["value"] = dataId["ccd"]
if "zeropoint" in extraCols:
zeropoint = 2.5 * np.log10(cal_md.get("FLUXMAG0"))
availExtras["zeropoint"]["value"] = zeropoint
if radecMatch is None:
fakeXY, srcIndex = getFakeMatchesHeader(cal_md, sources, tol=tol)
else:
if minRad is not None:
print "# The min matching radius is %4.1f pixel" % minRad
bbox = lsst.afw.geom.Box2D(cal.getBBox(lsst.afw.image.PARENT))
fakeXY, srcIndex, srcClose = getFakeMatchesRaDec(
sources,
radecMatch,
bbox,
cal.getWcs(),
tol=tol,
reffMatch=reffMatch,
pix=pix,
minRad=minRad,
raCol=raCol,
decCol=decCol,
)
mapper = SchemaMapper(sources.schema)
mapper.addMinimalSchema(sources.schema)
newSchema = mapper.getOutputSchema()
newSchema.addField("fakeId", type=int, doc="id of fake source matched to position")
newSchema.addField("nMatched", type=int, doc="Number of matched objects")
newSchema.addField("nPrimary", type=int, doc="Number of unique matched objects")
newSchema.addField("nNoChild", type=int, doc="Number of matched objects with nchild==0")
newSchema.addField("rMatched", type=float, doc="Radius used form atching obects, in pixel")
newSchema.addField("fakeOffX", type=float, doc="offset from input fake position in X (pixels)")
newSchema.addField("fakeOffY", type=float, doc="offset from input fake position in Y (pixels)")
newSchema.addField("fakeOffR", type=float, doc="offset from input fake position in radius")
newSchema.addField("fakeClosest", type="Flag", doc="Is this match the closest one?")
for extraName in set(extraCols).intersection(availExtras):
newSchema.addField(extraName, type=availExtras[extraName]["type"], doc=availExtras[extraName]["doc"])
srcList = SourceCatalog(newSchema)
srcList.reserve(
sum([len(s) for s in srcIndex.values()]) + (0 if not includeMissing else srcIndex.values().count([]))
)
centroidKey = sources.schema.find("centroid.sdss").getKey()
isPrimary = sources.schema.find("detect.is-primary").getKey()
nChild = sources.schema.find("force.deblend.nchild").getKey()
for ident, sindlist in srcIndex.items():
rMatched = fakeXY[ident][2]
if minRad is not None:
if rMatched < minRad:
rMatched = minRad
nMatched = len(sindlist)
nPrimary = np.sum([sources[obj].get(isPrimary) for obj in sindlist])
nNoChild = np.sum([(sources[obj].get(nChild) == 0) for obj in sindlist])
if includeMissing and (nMatched == 0):
newRec = srcList.addNew()
newRec.set("fakeId", ident)
newRec.set("id", 0)
newRec.set("nMatched", 0)
newRec.set("rMatched", rMatched)
for ss in sindlist:
newRec = srcList.addNew()
newRec.assign(sources[ss], mapper)
newRec.set("fakeId", ident)
newRec.set("nMatched", nMatched)
newRec.set("nPrimary", nPrimary)
newRec.set("nNoChild", nNoChild)
newRec.set("rMatched", rMatched)
offsetX = sources[ss].get(centroidKey).getX() - fakeXY[ident][0]
newRec.set("fakeOffX", offsetX)
offsetY = sources[ss].get(centroidKey).getY() - fakeXY[ident][1]
newRec.set("fakeOffY", offsetY)
newRec.set("fakeOffR", np.sqrt(offsetX ** 2.0 + offsetY ** 2.0))
if radecMatch:
if ss == srcClose[ident]:
newRec.set("fakeClosest", True)
else:
newRec.set("fakeClosest", False)
if includeMissing:
srcList = srcList.copy(deep=True)
for extraName in set(extraCols).intersection(availExtras):
tempCol = srcList.get(extraName)
tempCol.fill(availExtras[extraName]["value"])
return srcList
def getFakeSources(butler,
dataId,
tol=1.0,
extraCols=('zeropoint', 'visit', 'ccd'),
includeMissing=False,
footprints=False,
radecMatch=None):
"""Get list of sources which agree in pixel position with fake ones with tol
this returns a sourceCatalog of all the matched fake objects,
note, there will be duplicates in this list, since I haven't checked deblend.nchild,
and I'm only doing a tolerance match, which could include extra sources
the outputs can include extraCols as long as they are one of:
zeropoint, visit, ccd, thetaNorth, pixelScale
if includeMissing is true, then the pipeline looks at the fake sources
added in the header and includes an entry in the table for sources without
any measurements, specifically the 'id' column will be 0
radecMatch is the fakes table. if it's not None(default), then do an ra/dec
match with the input catalog instead of looking in the header for where the
sources where added
"""
availExtras = {
'zeropoint': {
'type': float,
'doc': 'zeropoint'
},
'visit': {
'type': int,
'doc': 'visit id'
},
'ccd': {
'type': int,
'doc': 'ccd id'
},
'thetaNorth': {
'type': lsst.afw.geom.Angle,
'doc': 'angle to north'
},
'pixelScale': {
'type': float,
'doc': 'pixelscale in arcsec/pixel'
}
}
if not np.in1d(extraCols, availExtras.keys()).all():
print "extraCols must be in ", availExtras
try:
if not 'filter' in dataId:
sources = butler.get('src',
dataId,
flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS,
immediate=True)
cal = butler.get('calexp', dataId, immediate=True)
cal_md = butler.get('calexp_md', dataId, immediate=True)
else:
sources = butler.get('deepCoadd_src',
dataId,
flags=lsst.afw.table.SOURCE_IO_NO_FOOTPRINTS,
immediate=True)
cal = butler.get('deepCoadd', dataId, immediate=True)
cal_md = butler.get('deepCoadd_md', dataId, immediate=True)
except (lsst.pex.exceptions.LsstException, RuntimeError) as e:
print "skipping", dataId
return None
if ('pixelScale' in extraCols) or ('thetaNorth' in extraCols):
wcs = cal.getWcs()
availExtras['pixelScale']['value'] = wcs.pixelScale().asArcseconds()
availExtras['thetaNorth']['value'] = lsst.afw.geom.Angle(
np.arctan2(*tuple(wcs.getLinearTransform().invert()(
lsst.afw.geom.Point2D(1.0, 0.0)))))
if 'visit' in extraCols:
availExtras['visit']['value'] = dataId['visit']
if 'ccd' in extraCols:
availExtras['ccd']['value'] = dataId['ccd']
if 'zeropoint' in extraCols:
availExtras['zeropoint']['value'] = 2.5 * np.log10(
cal_md.get('FLUXMAG0'))
if radecMatch is None:
fakeXY, srcIndex = getFakeMatchesHeader(cal_md, sources, tol=tol)
else:
fakeXY, srcIndex = getFakeMatchesRaDec(
sources,
radecMatch,
lsst.afw.geom.Box2D(cal.getBBox(lsst.afw.image.PARENT)),
cal.getWcs(),
tol=tol)
mapper = SchemaMapper(sources.schema)
mapper.addMinimalSchema(sources.schema)
newSchema = mapper.getOutputSchema()
newSchema.addField('fakeId',
type=int,
doc='id of fake source matched to position')
newSchema.addField('fakeOffset',
type=lsst.afw.geom.Point2D,
doc='offset from input fake position (pixels)')
for extraName in set(extraCols).intersection(availExtras):
newSchema.addField(extraName,
type=availExtras[extraName]['type'],
doc=availExtras[extraName]['doc'])
srcList = SourceCatalog(newSchema)
srcList.reserve(
sum([len(s) for s in srcIndex.values()]) +
(0 if not includeMissing else srcIndex.values().count([])))
centroidKey = sources.schema.find('centroid.sdss').getKey()
for ident, sindlist in srcIndex.items():
if includeMissing and (len(sindlist) == 0):
newRec = srcList.addNew()
newRec.set('fakeId', ident)
newRec.set('id', 0)
for ss in sindlist:
newRec = srcList.addNew()
newRec.assign(sources[ss], mapper)
newRec.set('fakeId', ident)
newRec.set(
'fakeOffset',
lsst.afw.geom.Point2D(
sources[ss].get(centroidKey).getX() - fakeXY[ident][0],
sources[ss].get(centroidKey).getY() - fakeXY[ident][1]))
if includeMissing:
srcList = srcList.copy(deep=True)
for extraName in set(extraCols).intersection(availExtras):
tempCol = srcList.get(extraName)
tempCol.fill(availExtras[extraName]['value'])
return srcList
