def testSignalToNoise(self):
"""
Test that calcSNR_m5 and calcSNR_sed give similar results
"""
defaults = LSSTdefaults()
photParams = PhotometricParameters()
totalDict, hardwareDict = BandpassDict.loadBandpassesFromFiles()
skySED = Sed()
skySED.readSED_flambda(
os.path.join(lsst.utils.getPackageDir('throughputs'), 'baseline',
'darksky.dat'))
m5 = []
for filt in totalDict:
m5.append(
calcM5(skySED,
totalDict[filt],
hardwareDict[filt],
photParams,
seeing=defaults.seeing(filt)))
sedDir = lsst.utils.getPackageDir('sims_sed_library')
sedDir = os.path.join(sedDir, 'starSED', 'kurucz')
fileNameList = os.listdir(sedDir)
numpy.random.seed(42)
offset = numpy.random.random_sample(len(fileNameList)) * 2.0
for ix, name in enumerate(fileNameList):
if ix > 100:
break
spectrum = Sed()
spectrum.readSED_flambda(os.path.join(sedDir, name))
ff = spectrum.calcFluxNorm(m5[2] - offset[ix],
totalDict.values()[2])
spectrum.multiplyFluxNorm(ff)
magList = []
controlList = []
magList = []
for filt in totalDict:
controlList.append(
calcSNR_sed(spectrum, totalDict[filt], skySED,
hardwareDict[filt], photParams,
defaults.seeing(filt)))
magList.append(spectrum.calcMag(totalDict[filt]))
testList, gammaList = calcSNR_m5(numpy.array(magList),
numpy.array(totalDict.values()),
numpy.array(m5), photParams)
for tt, cc in zip(controlList, testList):
msg = '%e != %e ' % (tt, cc)
self.assertTrue(numpy.abs(tt / cc - 1.0) < 0.001, msg=msg)
def testSystematicUncertainty(self):
"""
Test that systematic uncertainty is added correctly.
"""
sigmaSys = 0.002
m5 = [23.5, 24.3, 22.1, 20.0, 19.5, 21.7]
photParams = PhotometricParameters(sigmaSys=sigmaSys)
bandpassDict = BandpassDict.loadTotalBandpassesFromFiles()
obs_metadata = ObservationMetaData(unrefractedRA=23.0,
unrefractedDec=45.0,
m5=m5,
bandpassName=self.bandpasses)
magnitudes = bandpassDict.magListForSed(self.starSED)
skySeds = []
for i in range(len(self.bandpasses)):
skyDummy = Sed()
skyDummy.readSED_flambda(
os.path.join(lsst.utils.getPackageDir('throughputs'),
'baseline', 'darksky.dat'))
normalizedSkyDummy = setM5(obs_metadata.m5[self.bandpasses[i]],
skyDummy,
self.totalBandpasses[i],
self.hardwareBandpasses[i],
seeing=LSSTdefaults().seeing(
self.bandpasses[i]),
photParams=PhotometricParameters())
skySeds.append(normalizedSkyDummy)
for i in range(len(self.bandpasses)):
snr = calcSNR_sed(self.starSED,
self.totalBandpasses[i],
skySeds[i],
self.hardwareBandpasses[i],
seeing=LSSTdefaults().seeing(self.bandpasses[i]),
photParams=PhotometricParameters())
testSNR, gamma = calcSNR_m5(
numpy.array([magnitudes[i]]), [self.totalBandpasses[i]],
numpy.array([m5[i]]),
photParams=PhotometricParameters(sigmaSys=0.0))
self.assertAlmostEqual(snr, testSNR[0], 10, msg = 'failed on calcSNR_m5 test %e != %e ' \
% (snr, testSNR[0]))
control = numpy.sqrt(
numpy.power(magErrorFromSNR(testSNR), 2) +
numpy.power(sigmaSys, 2))
def testSystematicUncertainty(self):
"""
Test that systematic uncertainty is added correctly.
"""
sigmaSys = 0.002
m5 = [23.5, 24.3, 22.1, 20.0, 19.5, 21.7]
photParams = PhotometricParameters(sigmaSys=sigmaSys)
bandpassDict = BandpassDict.loadTotalBandpassesFromFiles()
obs_metadata = ObservationMetaData(unrefractedRA=23.0, unrefractedDec=45.0, m5=m5, bandpassName=self.bandpasses)
magnitudes = bandpassDict.magListForSed(self.starSED)
skySeds = []
for i in range(len(self.bandpasses)):
skyDummy = Sed()
skyDummy.readSED_flambda(os.path.join(lsst.utils.getPackageDir("throughputs"), "baseline", "darksky.dat"))
normalizedSkyDummy = setM5(
obs_metadata.m5[self.bandpasses[i]],
skyDummy,
self.totalBandpasses[i],
self.hardwareBandpasses[i],
seeing=LSSTdefaults().seeing(self.bandpasses[i]),
photParams=PhotometricParameters(),
)
skySeds.append(normalizedSkyDummy)
for i in range(len(self.bandpasses)):
snr = calcSNR_sed(
self.starSED,
self.totalBandpasses[i],
skySeds[i],
self.hardwareBandpasses[i],
seeing=LSSTdefaults().seeing(self.bandpasses[i]),
photParams=PhotometricParameters(),
)
testSNR, gamma = calcSNR_m5(
numpy.array([magnitudes[i]]),
[self.totalBandpasses[i]],
numpy.array([m5[i]]),
photParams=PhotometricParameters(sigmaSys=0.0),
)
self.assertAlmostEqual(snr, testSNR[0], 10, msg="failed on calcSNR_m5 test %e != %e " % (snr, testSNR[0]))
control = numpy.sqrt(numpy.power(magErrorFromSNR(testSNR), 2) + numpy.power(sigmaSys, 2))
def testSignalToNoise(self):
"""
Test that calcSNR_m5 and calcSNR_sed give similar results
"""
defaults = LSSTdefaults()
photParams = PhotometricParameters()
totalDict, hardwareDict = BandpassDict.loadBandpassesFromFiles()
skySED = Sed()
skySED.readSED_flambda(os.path.join(lsst.utils.getPackageDir("throughputs"), "baseline", "darksky.dat"))
m5 = []
for filt in totalDict:
m5.append(calcM5(skySED, totalDict[filt], hardwareDict[filt], photParams, seeing=defaults.seeing(filt)))
sedDir = lsst.utils.getPackageDir("sims_sed_library")
sedDir = os.path.join(sedDir, "starSED", "kurucz")
fileNameList = os.listdir(sedDir)
numpy.random.seed(42)
offset = numpy.random.random_sample(len(fileNameList)) * 2.0
for ix, name in enumerate(fileNameList):
if ix > 100:
break
spectrum = Sed()
spectrum.readSED_flambda(os.path.join(sedDir, name))
ff = spectrum.calcFluxNorm(m5[2] - offset[ix], totalDict.values()[2])
spectrum.multiplyFluxNorm(ff)
magList = []
controlList = []
magList = []
for filt in totalDict:
controlList.append(
calcSNR_sed(
spectrum, totalDict[filt], skySED, hardwareDict[filt], photParams, defaults.seeing(filt)
)
)
magList.append(spectrum.calcMag(totalDict[filt]))
testList, gammaList = calcSNR_m5(
numpy.array(magList), numpy.array(totalDict.values()), numpy.array(m5), photParams
)
for tt, cc in zip(controlList, testList):
msg = "%e != %e " % (tt, cc)
self.assertTrue(numpy.abs(tt / cc - 1.0) < 0.001, msg=msg)
