def Realize(self, ras, decs, filtstr=None, doAddErr = False, doDith = False, version="opsim3_61"):
'''
Realize the time sampling of a set of pointings based on a database of survey pointings
Inputs:
ras -- list of RA values in degrees for sampling the lightcurves
decs -- list of Declination values in degrees for sampling the lightcurves
doAddErr -- boolean, True to simulate errors on measurement based on 5 sigma limiting magnitude,
False to return interpolated lightcurve uncorrected for errors
doDith -- select sampling from the dithered version of the OpSim pointings
version -- select the version of the Operations Simulator to use.
Currently there are four options: Cronos92 (old), OpSim5_72, OpSim1_29, OpSim3_61 (default)
Return:
LightCurve object containing TimeSeries resampled based on the chosen operation simulator run
'''
#numpy array of ra positions
ras = num.asarray(ras)
#numpy array of dec positions
decs = num.asarray(decs)
assert len(ras) == len(decs), "ra and dec arrays must be the same length"
db = DB()
mutils = MagUtils()
lc = []
#Loop over positions for each TimeSeries
for ra,dec in zip(ras,decs):
tsi = []
#Loop over TimeSeries array
for ts in self.tss:
isTimeSeries = isinstance(ts, TimeSeriesMag)
tmpmag = []
#Filter string
if isTimeSeries:
fs = ts.getFilter().lower()
if filtstr != None:
assert filtstr == fs, "Filter specified in constructor does not match that from the TimeSeries object"
else:
fs = filtstr
if fs not in self.gamma:
#We only know sloan filters, so if something different assume r
print "Don't know parameters for filter",fs,"\n Assuming r..."
fs = 'r'
#Get time sampling and 5 sigma limiting magnitude information from the database
(time, m5) = db.getTimeMagSQL(ra, dec, fs, doDith, version)
#If no data in database return a None object
if len(time) == 0:
tsi.append(TimeSeriesMag(None, None, None, None, fs, calcspline = False, ra = ra, dec = dec))
continue
#Interpolatd flux values based on time sampling from database
if isTimeSeries:
fluxinterp = mutils.toFluxArr(ts.evaluate(time), fs)
else:
fluxinterp = mutils.toFluxArr(ts.evaluate(time, filt=fs), fs)
#Calculate total photometric error from interpolated magnitudes and 5 sigma limiting magnitues.
#Systematic error is assumed to be 0.01 magnitudes
m1flux = mutils.toFluxArr(m5, fs)/5.
sysmag = (mutils.toMagArr(fluxinterp, fs) - 0.01)
sysfluxerr = mutils.toFluxArr(sysmag, fs) - fluxinterp
sigs = []
magerrfp = []
magerrfm = []
tmpflux = []
if doAddErr:
#Add random error to interpolated magnitudes
tmpflux = fluxinterp + m1flux*num.random.normal(0,1,len(m1flux))
tmpflux = tmpflux + sysfluxerr*num.random.normal(0,1,len(sysfluxerr))
tmpmag = mutils.toMagArr(tmpflux, fs)
sigs = tmpflux/m1flux
else:
tmpflux = fluxinterp
tmpmag = mutils.toMagArr(tmpflux, fs)
sigs = tmpflux/m1flux
x = 10.0**(0.4*(tmpmag-m5))
magerr = num.sqrt((0.04 - self.gamma[fs])*x + self.gamma[fs]*x**2)
magerr = num.sqrt(magerr**2 + 0.01**2)
magerrfp = tmpmag - mutils.toMagArr(tmpflux + m1flux, fs)
magerrfm = mutils.toMagArr(tmpflux - m1flux, fs) - tmpmag
#at S/N > 10 the errors from flux differ from those calculated from the m5 by less
#than the systematic error of 0.01 mag
magerrfp = num.where(sigs > 10, magerr, magerrfp)
magerrfm = num.where(sigs > 10, magerr, magerrfm)
#if the detection is < 1 sigma indicate this by setting error to -9999
magerrfp = num.where(sigs < 1, -9999, magerrfp)
magerrfm = num.where(sigs < 1, -9999, magerrfm)
#Append resampled TimeSeries to output list of TimeSeries
tsi.append(TimeSeriesMag(time, tmpmag, magerrfp, magerrfm, fs, calcspline = False, ra = ra, dec = dec, m5 = m5))
#Append LightCurve for each ra/dec location
lc.append(LightCurve(tsi, self.isperiodic))
db.close()
return lc
