def calcMagNorm(self,
objectMags,
sedObj,
bandpassDict,
mag_error=None,
redshift=None,
filtRange=None):
"""
This will find the magNorm value that gives the closest match to the magnitudes of the object
using the matched SED. Uses scipy.optimize.leastsq to find the values of fluxNorm that minimizes
the function: ((flux_obs - (fluxNorm*flux_model))/flux_error)**2.
@param [in] objectMags are the magnitude values for the object with extinction matching that of
the SED object. In the normal case using the selectSED routines above it will be dereddened mags.
@param [in] sedObj is an Sed class instance that is set with the wavelength and flux of the
matched SED
@param [in] bandpassDict is a BandpassDict class instance with the Bandpasses set to those
for the magnitudes given for the catalog object
@param [in] mag_error are provided error values for magnitudes in objectMags. If none provided
then this defaults to 1.0. This should be an array of the same length as objectMags.
@param [in] redshift is the redshift of the object if the magnitude is observed
@param [in] filtRange is a selected range of filters specified by their indices in the bandpassList
to match up against. Used when missing data in some magnitude bands.
@param [out] bestMagNorm is the magnitude normalization for the given magnitudes and SED
"""
import scipy.optimize as opt
sedTest = Sed()
sedTest.setSED(sedObj.wavelen, flambda=sedObj.flambda)
if redshift is not None:
sedTest.redshiftSED(redshift)
imSimBand = Bandpass()
imSimBand.imsimBandpass()
zp = -2.5 * np.log10(3631) #Note using default AB zeropoint
flux_obs = np.power(10, (objectMags + zp) / (-2.5))
sedTest.resampleSED(wavelen_match=bandpassDict.values()[0].wavelen)
sedTest.flambdaTofnu()
flux_model = sedTest.manyFluxCalc(bandpassDict.phiArray,
bandpassDict.wavelenStep)
if filtRange is not None:
flux_obs = flux_obs[filtRange]
flux_model = flux_model[filtRange]
if mag_error is None:
flux_error = np.ones(len(flux_obs))
else:
flux_error = np.abs(flux_obs * (np.log(10) / (-2.5)) * mag_error)
bestFluxNorm = opt.leastsq(
lambda x: ((flux_obs - (x * flux_model)) / flux_error), 1.0)[0][0]
sedTest.multiplyFluxNorm(bestFluxNorm)
bestMagNorm = sedTest.calcMag(imSimBand)
return bestMagNorm
def calcMagNorm(self, objectMags, sedObj, bandpassDict, mag_error = None,
redshift = None, filtRange = None):
"""
This will find the magNorm value that gives the closest match to the magnitudes of the object
using the matched SED. Uses scipy.optimize.leastsq to find the values of fluxNorm that minimizes
the function: ((flux_obs - (fluxNorm*flux_model))/flux_error)**2.
@param [in] objectMags are the magnitude values for the object with extinction matching that of
the SED object. In the normal case using the selectSED routines above it will be dereddened mags.
@param [in] sedObj is an Sed class instance that is set with the wavelength and flux of the
matched SED
@param [in] bandpassDict is a BandpassDict class instance with the Bandpasses set to those
for the magnitudes given for the catalog object
@param [in] mag_error are provided error values for magnitudes in objectMags. If none provided
then this defaults to 1.0. This should be an array of the same length as objectMags.
@param [in] redshift is the redshift of the object if the magnitude is observed
@param [in] filtRange is a selected range of filters specified by their indices in the bandpassList
to match up against. Used when missing data in some magnitude bands.
@param [out] bestMagNorm is the magnitude normalization for the given magnitudes and SED
"""
import scipy.optimize as opt
sedTest = Sed()
sedTest.setSED(sedObj.wavelen, flambda = sedObj.flambda)
if redshift is not None:
sedTest.redshiftSED(redshift)
imSimBand = Bandpass()
imSimBand.imsimBandpass()
zp = -2.5*np.log10(3631) #Note using default AB zeropoint
flux_obs = np.power(10,(objectMags + zp)/(-2.5))
sedTest.resampleSED(wavelen_match=bandpassDict.values()[0].wavelen)
sedTest.flambdaTofnu()
flux_model = sedTest.manyFluxCalc(bandpassDict.phiArray, bandpassDict.wavelenStep)
if filtRange is not None:
flux_obs = flux_obs[filtRange]
flux_model = flux_model[filtRange]
if mag_error is None:
flux_error = np.ones(len(flux_obs))
else:
flux_error = np.abs(flux_obs*(np.log(10)/(-2.5))*mag_error)
bestFluxNorm = opt.leastsq(lambda x: ((flux_obs - (x*flux_model))/flux_error), 1.0)[0][0]
sedTest.multiplyFluxNorm(bestFluxNorm)
bestMagNorm = sedTest.calcMag(imSimBand)
return bestMagNorm
def testCalcMagNorm(self):
"""Tests the calculation of magnitude normalization for an SED with the given magnitudes
in the given bandpasses."""
testUtils = matchBase()
bandpassDir = os.path.join(lsst.utils.getPackageDir('throughputs'),
'sdss')
testPhot = BandpassDict.loadTotalBandpassesFromFiles(
self.filterList, bandpassDir=bandpassDir, bandpassRoot='sdss_')
unChangedSED = Sed()
unChangedSED.readSED_flambda(
str(self.galDir + os.listdir(self.galDir)[0]))
imSimBand = Bandpass()
imSimBand.imsimBandpass()
testSED = Sed()
testSED.setSED(unChangedSED.wavelen, flambda=unChangedSED.flambda)
magNorm = 20.0
redVal = 0.1
testSED.redshiftSED(redVal)
fluxNorm = testSED.calcFluxNorm(magNorm, imSimBand)
testSED.multiplyFluxNorm(fluxNorm)
sedMags = testPhot.magListForSed(testSED)
stepSize = 0.001
testMagNorm = testUtils.calcMagNorm(sedMags,
unChangedSED,
testPhot,
redshift=redVal)
# Test adding in mag_errors. If an array of np.ones is passed in we should get same result
testMagNormWithErr = testUtils.calcMagNorm(sedMags,
unChangedSED,
testPhot,
mag_error=np.ones(
len(sedMags)),
redshift=redVal)
# Also need to add in test for filtRange
sedMagsIncomp = sedMags
sedMagsIncomp[1] = None
filtRangeTest = [0, 2, 3, 4]
testMagNormFiltRange = testUtils.calcMagNorm(sedMagsIncomp,
unChangedSED,
testPhot,
redshift=redVal,
filtRange=filtRangeTest)
self.assertAlmostEqual(magNorm, testMagNorm, delta=stepSize)
self.assertAlmostEqual(magNorm, testMagNormWithErr, delta=stepSize)
self.assertAlmostEqual(magNorm, testMagNormFiltRange, delta=stepSize)
def testCalcMagNorm(self):
"""Tests the calculation of magnitude normalization for an SED with the given magnitudes
in the given bandpasses."""
testUtils = matchBase()
bandpassDir = os.path.join(lsst.utils.getPackageDir('throughputs'), 'sdss')
testPhot = BandpassDict.loadTotalBandpassesFromFiles(self.filterList,
bandpassDir = bandpassDir,
bandpassRoot = 'sdss_')
unChangedSED = Sed()
unChangedSED.readSED_flambda(str(self.galDir + os.listdir(self.galDir)[0]))
imSimBand = Bandpass()
imSimBand.imsimBandpass()
testSED = Sed()
testSED.setSED(unChangedSED.wavelen, flambda = unChangedSED.flambda)
magNorm = 20.0
redVal = 0.1
testSED.redshiftSED(redVal)
fluxNorm = testSED.calcFluxNorm(magNorm, imSimBand)
testSED.multiplyFluxNorm(fluxNorm)
sedMags = testPhot.magListForSed(testSED)
stepSize = 0.001
testMagNorm = testUtils.calcMagNorm(sedMags, unChangedSED, testPhot, redshift = redVal)
# Test adding in mag_errors. If an array of np.ones is passed in we should get same result
testMagNormWithErr = testUtils.calcMagNorm(sedMags, unChangedSED, testPhot,
mag_error = np.ones(len(sedMags)), redshift = redVal)
# Also need to add in test for filtRange
sedMagsIncomp = sedMags
sedMagsIncomp[1] = None
filtRangeTest = [0, 2, 3, 4]
testMagNormFiltRange = testUtils.calcMagNorm(sedMagsIncomp, unChangedSED, testPhot,
redshift = redVal, filtRange = filtRangeTest)
self.assertAlmostEqual(magNorm, testMagNorm, delta = stepSize)
self.assertAlmostEqual(magNorm, testMagNormWithErr, delta = stepSize)
self.assertAlmostEqual(magNorm, testMagNormFiltRange, delta = stepSize)
def matchToObserved(self, sedList, catMags, catRedshifts, catRA = None, catDec = None,
mag_error = None, bandpassDict = None, dzAcc = 2, reddening = True,
extCoeffs = (4.239, 3.303, 2.285, 1.698, 1.263)):
"""
This will find the closest match to the magnitudes of a galaxy catalog if those magnitudes are in
the observed frame and can correct for reddening from within the milky way as well if needed.
In order to make things faster it first calculates colors for all model SEDs at redshifts between
the minimum and maximum redshifts of the catalog objects provided with a grid spacing in redshift
defined by the parameter dzAcc. Objects without magnitudes in at least two adjacent bandpasses will
return as none and print out a message.
@param [in] sedList is the set of spectral objects from the models SEDs provided by loadBC03
or other custom loader routine.
@param [in] catMags is an array of the magnitudes of catalog objects to be matched with a model SED.
It should be organized so that there is one object's magnitudes along each row.
@param [in] catRedshifts is an array of the redshifts of each catalog object.
@param [in] catRA is an array of the RA positions for each catalog object.
@param [in] catDec is an array of the Dec position for each catalog object.
@param [in] mag_error are provided error values for magnitudes in objectMags. If none provided
then this defaults to 1.0. This should be an array of the same size as catMags.
@param [in] bandpassDict is a BandpassDict with which to calculate magnitudes.
If left equal to None it will by default load the SDSS [u,g,r,i,z] bandpasses and therefore agree with
default extCoeffs.
@param [in] dzAcc is the number of decimal places you want to use when building the redshift grid.
For example, dzAcc = 2 will create a grid between the minimum and maximum redshifts with colors
calculated at every 0.01 change in redshift.
@param [in] reddening is a boolean that determines whether to correct catalog magnitudes for
dust in the milky way. By default, it is True.
If true, this uses calculateEBV from EBV.py to find an EBV value for the object's
ra and dec coordinates and then uses the coefficients provided by extCoeffs which should come
from Schlafly and Finkbeiner (2011) for the correct filters and in the same order as provided
in bandpassDict.
If false, this means it will not run the dereddening procedure.
@param [in] extCoeffs are the Schlafly and Finkbeiner (2011) (ApJ, 737, 103) coefficients for the
given filters from bandpassDict and need to be in the same order as bandpassDict. The default given
are the SDSS [u,g,r,i,z] values.
@param [out] sedMatches is a list with the name of a model SED that matches most closely to each
object in the catalog.
@param [out] magNormMatches are the magnitude normalizations for the given magnitudes and
matched SED.
@param [out] matchErrors contains the Mean Squared Error between the colors of each object and
the colors of the matched SED.
"""
#Set up photometry to calculate model Mags
if bandpassDict is None:
galPhot = BandpassDict.loadTotalBandpassesFromFiles(['u','g','r','i','z'],
bandpassDir = os.path.join(lsst.utils.getPackageDir('throughputs'),'sdss'),
bandpassRoot = 'sdss_')
else:
galPhot = bandpassDict
#Calculate ebv from ra, dec coordinates if needed
if reddening == True:
#Check that catRA and catDec are included
if catRA is None or catDec is None:
raise RuntimeError("Reddening is True, but catRA and catDec are not included.")
calcEBV = ebv()
raDec = np.array((catRA,catDec))
#If only matching one object need to reshape for calculateEbv
if len(raDec.shape) == 1:
raDec = raDec.reshape((2,1))
ebvVals = calcEBV.calculateEbv(equatorialCoordinates = raDec)
objMags = self.deReddenMags(ebvVals, catMags, extCoeffs)
else:
objMags = catMags
minRedshift = np.round(np.min(catRedshifts), dzAcc)
maxRedshift = np.round(np.max(catRedshifts), dzAcc)
dz = np.power(10., (-1*dzAcc))
redshiftRange = np.round(np.arange(minRedshift - dz, maxRedshift + (2*dz), dz), dzAcc)
numRedshifted = 0
sedMatches = [None] * len(catRedshifts)
magNormMatches = [None] * len(catRedshifts)
matchErrors = [None] * len(catRedshifts)
redshiftIndex = np.argsort(catRedshifts)
numOn = 0
notMatched = 0
lastRedshift = -100
print 'Starting Matching. Arranged by redshift value.'
for redshift in redshiftRange:
if numRedshifted % 10 == 0:
print '%i out of %i redshifts gone through' % (numRedshifted, len(redshiftRange))
numRedshifted += 1
colorSet = []
for galSpec in sedList:
sedColors = []
fileSED = Sed()
fileSED.setSED(wavelen = galSpec.wavelen, flambda = galSpec.flambda)
fileSED.redshiftSED(redshift)
sedColors = self.calcBasicColors([fileSED], galPhot, makeCopy = True)
colorSet.append(sedColors)
colorSet = np.transpose(colorSet)
for currentIndex in redshiftIndex[numOn:]:
matchMags = objMags[currentIndex]
if lastRedshift < np.round(catRedshifts[currentIndex],dzAcc) <= redshift:
colorRange = np.arange(0, len(galPhot)-1)
matchColors = []
for colorNum in colorRange:
matchColors.append(matchMags[colorNum] - matchMags[colorNum+1])
#This is done to handle objects with incomplete magnitude data
filtNums = np.arange(0, len(galPhot))
if np.isnan(np.amin(matchColors))==True:
colorRange = np.where(np.isnan(matchColors)==False)[0]
filtNums = np.unique([colorRange, colorRange+1]) #Pick right filters in calcMagNorm
if len(colorRange) == 0:
print 'Could not match object #%i. No magnitudes for two adjacent bandpasses.' \
% (currentIndex)
notMatched += 1
#Don't need to assign 'None' here in result array, b/c 'None' is default value
else:
distanceArray = [np.zeros(len(sedList))]
for colorNum in colorRange:
distanceArray += np.power((colorSet[colorNum] - matchColors[colorNum]),2)
matchedSEDNum = np.nanargmin(distanceArray)
sedMatches[currentIndex] = sedList[matchedSEDNum].name
magNormVal = self.calcMagNorm(np.array(matchMags), sedList[matchedSEDNum],
galPhot, mag_error = mag_error,
redshift = catRedshifts[currentIndex],
filtRange = filtNums)
magNormMatches[currentIndex] = magNormVal
matchErrors[currentIndex] = (distanceArray[0,matchedSEDNum]/len(colorRange))
numOn += 1
else:
break
lastRedshift = redshift
print 'Done Matching. Matched %i of %i catalog objects to SEDs' % (len(catMags)-notMatched,
len(catMags))
if notMatched > 0:
print '%i objects did not get matched.' % (notMatched)
return sedMatches, magNormMatches, matchErrors
def applyIGM(self, redshift, sedobj):
"""
Apply IGM extinction to already redshifted sed with redshift
between zMin and zMax defined by range of lookup tables
@param [in] redshift is the redshift of the incoming SED object
@param [in] sedobj is the SED object to which IGM extinction will be applied. This object
will be modified as a result of this.
"""
if self.IGMisInitialized == False:
self.initializeIGM()
#First make sure redshift is in range of lookup tables.
if (redshift < self.zMin) or (redshift > self.zMax):
warnings.warn(
str("IGM Lookup tables only applicable for " + str(self.zMin) +
" < z < " + str(self.zMax) + ". No action taken"))
return
#Now read in closest two lookup tables for given redshift
lowerSed = Sed()
upperSed = Sed()
for lower, upper in zip(self.zRange[:-1], self.zRange[1:]):
if lower <= redshift <= upper:
lowerSed.setSED(self.meanLookups[str(lower)][:, 0],
flambda=self.meanLookups[str(lower)][:, 1])
upperSed.setSED(self.meanLookups[str(upper)][:, 0],
flambda=self.meanLookups[str(upper)][:, 1])
break
#Redshift lookup tables to redshift of source, i.e. if source redshift is 1.78 shift lookup
#table for 1.7 and lookup table for 1.8 to up and down to 1.78, respectively
zLowerShift = ((1.0 + redshift) / (1.0 + lower)) - 1.0
zUpperShift = ((1.0 + redshift) / (1.0 + upper)) - 1.0
lowerSed.redshiftSED(zLowerShift)
upperSed.redshiftSED(zUpperShift)
#Resample lower and upper transmission data onto same wavelength grid.
minWavelen = 300. #All lookup tables are usable above 300nm
maxWavelen = np.amin([lowerSed.wavelen[-1], upperSed.wavelen[-1]
]) - 0.01
lowerSed.resampleSED(wavelen_min=minWavelen,
wavelen_max=maxWavelen,
wavelen_step=0.01)
upperSed.resampleSED(wavelen_match=lowerSed.wavelen)
#Now insert this into a transmission array of 1.0 beyond the limits of current application
#So that we can get an sed back that extends to the longest wavelengths of the incoming sed
finalWavelen = np.arange(300., sedobj.wavelen[-1] + 0.01, 0.01)
finalFlambdaExtended = np.ones(len(finalWavelen))
#Weighted Average of Transmission from each lookup table to get final transmission
#table at desired redshift
dzGrid = self.zDelta #Step in redshift between transmission lookup table files
finalSed = Sed()
finalFlambda = (lowerSed.flambda * (1.0 -
((redshift - lower) / dzGrid)) +
upperSed.flambda * (1.0 -
((upper - redshift) / dzGrid)))
finalFlambdaExtended[0:len(finalFlambda)] = finalFlambda
finalSed.setSED(wavelen=finalWavelen, flambda=finalFlambdaExtended)
#Resample incoming sed to new grid so that we don't get warnings from multiplySED
#about matching wavelength grids
sedobj.resampleSED(wavelen_match=finalSed.wavelen)
#Now multiply transmission curve by input SED to get final result and make it the new flambda
#data in the original sed which also is now on a new grid starting at 300 nm
test = sedobj.multiplySED(finalSed)
sedobj.flambda = test.flambda
def testMatchToObserved(self):
"""Test that Galaxy SEDs with extinction or redshift are matched correctly"""
rng = np.random.RandomState(42)
galPhot = BandpassDict.loadTotalBandpassesFromFiles()
imSimBand = Bandpass()
imSimBand.imsimBandpass()
testMatching = selectGalaxySED(galDir = self.testSpecDir)
testSEDList = testMatching.loadBC03()
testSEDNames = []
testRA = []
testDec = []
testRedshifts = []
testMagNormList = []
magNormStep = 1
extCoeffs = [1.8140, 1.4166, 0.9947, 0.7370, 0.5790, 0.4761]
testMags = []
testMagsRedshift = []
testMagsExt = []
for testSED in testSEDList:
# As a check make sure that it matches when no extinction and no redshift are present
getSEDMags = Sed()
testSEDNames.append(testSED.name)
getSEDMags.setSED(wavelen = testSED.wavelen, flambda = testSED.flambda)
testMags.append(galPhot.magListForSed(getSEDMags))
# Check Extinction corrections
sedRA = rng.uniform(10, 170)
sedDec = rng.uniform(10, 80)
testRA.append(sedRA)
testDec.append(sedDec)
raDec = np.array((sedRA, sedDec)).reshape((2, 1))
ebvVal = ebv().calculateEbv(equatorialCoordinates = raDec)
extVal = ebvVal*extCoeffs
testMagsExt.append(galPhot.magListForSed(getSEDMags) + extVal)
# Setup magnitudes for testing matching to redshifted values
getRedshiftMags = Sed()
testZ = np.round(rng.uniform(1.1, 1.3), 3)
testRedshifts.append(testZ)
testMagNorm = np.round(rng.uniform(20.0, 22.0), magNormStep)
testMagNormList.append(testMagNorm)
getRedshiftMags.setSED(wavelen = testSED.wavelen, flambda = testSED.flambda)
getRedshiftMags.redshiftSED(testZ)
fluxNorm = getRedshiftMags.calcFluxNorm(testMagNorm, imSimBand)
getRedshiftMags.multiplyFluxNorm(fluxNorm)
testMagsRedshift.append(galPhot.magListForSed(getRedshiftMags))
# Will also test in passing of non-default bandpass
testNoExtNoRedshift = testMatching.matchToObserved(testSEDList, testMags, np.zeros(8),
reddening = False,
bandpassDict = galPhot)
testMatchingEbvVals = testMatching.matchToObserved(testSEDList, testMagsExt, np.zeros(8),
catRA = testRA, catDec = testDec,
reddening = True, extCoeffs = extCoeffs,
bandpassDict = galPhot)
# Substitute in nan values to simulate incomplete data and make sure magnorm works too.
testMagsRedshift[0][1] = np.nan
testMagsRedshift[0][3] = np.nan
testMagsRedshift[0][4] = np.nan
testMagsRedshift[1][1] = np.nan
testMatchingRedshift = testMatching.matchToObserved(testSEDList, testMagsRedshift, testRedshifts,
dzAcc = 3, reddening = False,
bandpassDict = galPhot)
self.assertEqual(testSEDNames, testNoExtNoRedshift[0])
self.assertEqual(testSEDNames, testMatchingEbvVals[0])
self.assertEqual(None, testMatchingRedshift[0][0])
self.assertEqual(testSEDNames[1:], testMatchingRedshift[0][1:])
self.assertEqual(None, testMatchingRedshift[1][0])
np.testing.assert_almost_equal(testMagNormList[1:], testMatchingRedshift[1][1:],
decimal = magNormStep)
# Test Match Errors
errMag = testMagsRedshift[2]
errRedshift = testRedshifts[2]
errMags = np.array((errMag, errMag, errMag, errMag))
errRedshifts = np.array((errRedshift, errRedshift, errRedshift, errRedshift))
errMags[1, 1] += 1. # Total MSE will be 2/(5 colors) = 0.4
errMags[2, 0:2] = np.nan
errMags[2, 3] += 1. # Total MSE will be 2/(3 colors) = 0.667
errMags[3, :] = None
errSED = testSEDList[2]
testMatchingResultsErrors = testMatching.matchToObserved([errSED], errMags, errRedshifts,
reddening = False,
bandpassDict = galPhot,
dzAcc = 3)
np.testing.assert_almost_equal(np.array((0.0, 0.4, 2./3.)), testMatchingResultsErrors[2][0:3],
decimal = 2) # Give a little more leeway due to redshifting effects
self.assertEqual(None, testMatchingResultsErrors[2][3])
def testAlternateBandpassesGalaxies(self):
"""
the same as testAlternateBandpassesStars, but for galaxies
"""
obs_metadata_pointed = ObservationMetaData(mjd=50000.0,
boundType='circle',
pointingRA=0.0, pointingDec=0.0,
boundLength=10.0)
dtype = np.dtype([('galid', np.int),
('ra', np.float),
('dec', np.float),
('uTotal', np.float),
('gTotal', np.float),
('rTotal', np.float),
('iTotal', np.float),
('zTotal', np.float),
('uBulge', np.float),
('gBulge', np.float),
('rBulge', np.float),
('iBulge', np.float),
('zBulge', np.float),
('uDisk', np.float),
('gDisk', np.float),
('rDisk', np.float),
('iDisk', np.float),
('zDisk', np.float),
('uAgn', np.float),
('gAgn', np.float),
('rAgn', np.float),
('iAgn', np.float),
('zAgn', np.float),
('bulgeName', str, 200),
('bulgeNorm', np.float),
('bulgeAv', np.float),
('diskName', str, 200),
('diskNorm', np.float),
('diskAv', np.float),
('agnName', str, 200),
('agnNorm', np.float),
('redshift', np.float)])
test_cat = cartoonGalaxies(self.galaxy, obs_metadata=obs_metadata_pointed)
with lsst.utils.tests.getTempFilePath('.txt') as catName:
test_cat.write_catalog(catName)
catData = np.genfromtxt(catName, dtype=dtype, delimiter=', ')
self.assertGreater(len(catData), 0)
cartoonDir = getPackageDir('sims_photUtils')
cartoonDir = os.path.join(cartoonDir, 'tests', 'cartoonSedTestData')
sedDir = getPackageDir('sims_sed_library')
testBandpasses = {}
keys = ['u', 'g', 'r', 'i', 'z']
for kk in keys:
testBandpasses[kk] = Bandpass()
testBandpasses[kk].readThroughput(os.path.join(cartoonDir, "test_bandpass_%s.dat" % kk))
imsimBand = Bandpass()
imsimBand.imsimBandpass()
specMap = defaultSpecMap
ct = 0
for line in catData:
bulgeMagList = []
diskMagList = []
agnMagList = []
if line['bulgeName'] == 'None':
for bp in keys:
np.testing.assert_equal(line['%sBulge' % bp], np.NaN)
bulgeMagList.append(np.NaN)
else:
ct += 1
dummySed = Sed()
dummySed.readSED_flambda(os.path.join(sedDir, specMap[line['bulgeName']]))
fnorm = dummySed.calcFluxNorm(line['bulgeNorm'], imsimBand)
dummySed.multiplyFluxNorm(fnorm)
a_int, b_int = dummySed.setupCCM_ab()
dummySed.addDust(a_int, b_int, A_v=line['bulgeAv'])
dummySed.redshiftSED(line['redshift'], dimming=True)
dummySed.resampleSED(wavelen_match=testBandpasses['u'].wavelen)
for bpName in keys:
mag = dummySed.calcMag(testBandpasses[bpName])
self.assertAlmostEqual(mag, line['%sBulge' % bpName], 10)
bulgeMagList.append(mag)
if line['diskName'] == 'None':
for bp in keys:
np.assert_equal(line['%sDisk' % bp], np.NaN)
diskMagList.append(np.NaN)
else:
ct += 1
dummySed = Sed()
dummySed.readSED_flambda(os.path.join(sedDir, specMap[line['diskName']]))
fnorm = dummySed.calcFluxNorm(line['diskNorm'], imsimBand)
dummySed.multiplyFluxNorm(fnorm)
a_int, b_int = dummySed.setupCCM_ab()
dummySed.addDust(a_int, b_int, A_v=line['diskAv'])
dummySed.redshiftSED(line['redshift'], dimming=True)
dummySed.resampleSED(wavelen_match=testBandpasses['u'].wavelen)
for bpName in keys:
mag = dummySed.calcMag(testBandpasses[bpName])
self.assertAlmostEqual(mag, line['%sDisk' % bpName], 10)
diskMagList.append(mag)
if line['agnName'] == 'None':
for bp in keys:
np.testing.assert_true(line['%sAgn' % bp], np.NaN)
agnMagList.append(np.NaN)
else:
ct += 1
dummySed = Sed()
dummySed.readSED_flambda(os.path.join(sedDir, specMap[line['agnName']]))
fnorm = dummySed.calcFluxNorm(line['agnNorm'], imsimBand)
dummySed.multiplyFluxNorm(fnorm)
dummySed.redshiftSED(line['redshift'], dimming=True)
dummySed.resampleSED(wavelen_match=testBandpasses['u'].wavelen)
for bpName in keys:
mag = dummySed.calcMag(testBandpasses[bpName])
self.assertAlmostEqual(mag, line['%sAgn' % bpName], 10)
agnMagList.append(mag)
totalMags = PhotometryGalaxies().sum_magnitudes(bulge=np.array(bulgeMagList),
disk=np.array(diskMagList),
agn=np.array(agnMagList))
for testMag, bpName in zip(totalMags, keys):
if np.isnan(line['%sTotal' % bpName]):
np.testing.assert_equal(testMag, np.NaN)
else:
self.assertAlmostEqual(testMag, line['%sTotal' % bpName], 10)
self.assertGreater(ct, 0)
def testAlternateBandpassesGalaxies(self):
"""
the same as testAlternateBandpassesStars, but for galaxies
"""
obs_metadata_pointed = ObservationMetaData(mjd=50000.0,
boundType='circle',
pointingRA=0.0,
pointingDec=0.0,
boundLength=10.0)
dtype = np.dtype([('galid', np.int), ('ra', np.float),
('dec', np.float), ('uTotal', np.float),
('gTotal', np.float), ('rTotal', np.float),
('iTotal', np.float), ('zTotal', np.float),
('uBulge', np.float), ('gBulge', np.float),
('rBulge', np.float), ('iBulge', np.float),
('zBulge', np.float), ('uDisk', np.float),
('gDisk', np.float), ('rDisk', np.float),
('iDisk', np.float), ('zDisk', np.float),
('uAgn', np.float), ('gAgn', np.float),
('rAgn', np.float), ('iAgn', np.float),
('zAgn', np.float), ('bulgeName', str, 200),
('bulgeNorm', np.float), ('bulgeAv', np.float),
('diskName', str, 200), ('diskNorm', np.float),
('diskAv', np.float), ('agnName', str, 200),
('agnNorm', np.float), ('redshift', np.float)])
test_cat = cartoonGalaxies(self.galaxy,
obs_metadata=obs_metadata_pointed)
with lsst.utils.tests.getTempFilePath('.txt') as catName:
test_cat.write_catalog(catName)
catData = np.genfromtxt(catName, dtype=dtype, delimiter=', ')
self.assertGreater(len(catData), 0)
cartoonDir = getPackageDir('sims_photUtils')
cartoonDir = os.path.join(cartoonDir, 'tests', 'cartoonSedTestData')
sedDir = getPackageDir('sims_sed_library')
testBandpasses = {}
keys = ['u', 'g', 'r', 'i', 'z']
for kk in keys:
testBandpasses[kk] = Bandpass()
testBandpasses[kk].readThroughput(
os.path.join(cartoonDir, "test_bandpass_%s.dat" % kk))
imsimBand = Bandpass()
imsimBand.imsimBandpass()
specMap = defaultSpecMap
ct = 0
for line in catData:
bulgeMagList = []
diskMagList = []
agnMagList = []
if line['bulgeName'] == 'None':
for bp in keys:
np.testing.assert_equal(line['%sBulge' % bp], np.NaN)
bulgeMagList.append(np.NaN)
else:
ct += 1
dummySed = Sed()
dummySed.readSED_flambda(
os.path.join(sedDir, specMap[line['bulgeName']]))
fnorm = dummySed.calcFluxNorm(line['bulgeNorm'], imsimBand)
dummySed.multiplyFluxNorm(fnorm)
a_int, b_int = dummySed.setupCCM_ab()
dummySed.addDust(a_int, b_int, A_v=line['bulgeAv'])
dummySed.redshiftSED(line['redshift'], dimming=True)
dummySed.resampleSED(wavelen_match=testBandpasses['u'].wavelen)
for bpName in keys:
mag = dummySed.calcMag(testBandpasses[bpName])
self.assertAlmostEqual(mag, line['%sBulge' % bpName], 10)
bulgeMagList.append(mag)
if line['diskName'] == 'None':
for bp in keys:
np.assert_equal(line['%sDisk' % bp], np.NaN)
diskMagList.append(np.NaN)
else:
ct += 1
dummySed = Sed()
dummySed.readSED_flambda(
os.path.join(sedDir, specMap[line['diskName']]))
fnorm = dummySed.calcFluxNorm(line['diskNorm'], imsimBand)
dummySed.multiplyFluxNorm(fnorm)
a_int, b_int = dummySed.setupCCM_ab()
dummySed.addDust(a_int, b_int, A_v=line['diskAv'])
dummySed.redshiftSED(line['redshift'], dimming=True)
dummySed.resampleSED(wavelen_match=testBandpasses['u'].wavelen)
for bpName in keys:
mag = dummySed.calcMag(testBandpasses[bpName])
self.assertAlmostEqual(mag, line['%sDisk' % bpName], 10)
diskMagList.append(mag)
if line['agnName'] == 'None':
for bp in keys:
np.testing.assert_true(line['%sAgn' % bp], np.NaN)
agnMagList.append(np.NaN)
else:
ct += 1
dummySed = Sed()
dummySed.readSED_flambda(
os.path.join(sedDir, specMap[line['agnName']]))
fnorm = dummySed.calcFluxNorm(line['agnNorm'], imsimBand)
dummySed.multiplyFluxNorm(fnorm)
dummySed.redshiftSED(line['redshift'], dimming=True)
dummySed.resampleSED(wavelen_match=testBandpasses['u'].wavelen)
for bpName in keys:
mag = dummySed.calcMag(testBandpasses[bpName])
self.assertAlmostEqual(mag, line['%sAgn' % bpName], 10)
agnMagList.append(mag)
totalMags = PhotometryGalaxies().sum_magnitudes(
bulge=np.array(bulgeMagList),
disk=np.array(diskMagList),
agn=np.array(agnMagList))
for testMag, bpName in zip(totalMags, keys):
if np.isnan(line['%sTotal' % bpName]):
np.testing.assert_equal(testMag, np.NaN)
else:
self.assertAlmostEqual(testMag, line['%sTotal' % bpName],
10)
self.assertGreater(ct, 0)
def testMatchToObserved(self):
"""Test that Galaxy SEDs with extinction or redshift are matched correctly"""
np.random.seed(42)
galPhot = BandpassDict.loadTotalBandpassesFromFiles()
imSimBand = Bandpass()
imSimBand.imsimBandpass()
testMatching = selectGalaxySED(galDir = self.testSpecDir)
testSEDList = testMatching.loadBC03()
testSEDNames = []
testRA = []
testDec = []
testRedshifts = []
testMagNormList = []
magNormStep = 1
extCoeffs = [1.8140, 1.4166, 0.9947, 0.7370, 0.5790, 0.4761]
testMags = []
testMagsRedshift = []
testMagsExt = []
for testSED in testSEDList:
#As a check make sure that it matches when no extinction and no redshift are present
getSEDMags = Sed()
testSEDNames.append(testSED.name)
getSEDMags.setSED(wavelen = testSED.wavelen, flambda = testSED.flambda)
testMags.append(galPhot.magListForSed(getSEDMags))
#Check Extinction corrections
sedRA = np.random.uniform(10,170)
sedDec = np.random.uniform(10,80)
testRA.append(sedRA)
testDec.append(sedDec)
raDec = np.array((sedRA, sedDec)).reshape((2,1))
ebvVal = ebv().calculateEbv(equatorialCoordinates = raDec)
extVal = ebvVal*extCoeffs
testMagsExt.append(galPhot.magListForSed(getSEDMags) + extVal)
#Setup magnitudes for testing matching to redshifted values
getRedshiftMags = Sed()
testZ = np.round(np.random.uniform(1.1,1.3),3)
testRedshifts.append(testZ)
testMagNorm = np.round(np.random.uniform(20.0,22.0),magNormStep)
testMagNormList.append(testMagNorm)
getRedshiftMags.setSED(wavelen = testSED.wavelen, flambda = testSED.flambda)
getRedshiftMags.redshiftSED(testZ)
fluxNorm = getRedshiftMags.calcFluxNorm(testMagNorm, imSimBand)
getRedshiftMags.multiplyFluxNorm(fluxNorm)
testMagsRedshift.append(galPhot.magListForSed(getRedshiftMags))
#Will also test in passing of non-default bandpass
testNoExtNoRedshift = testMatching.matchToObserved(testSEDList, testMags, np.zeros(20),
reddening = False,
bandpassDict = galPhot)
testMatchingEbvVals = testMatching.matchToObserved(testSEDList, testMagsExt, np.zeros(20),
catRA = testRA, catDec = testDec,
reddening = True, extCoeffs = extCoeffs,
bandpassDict = galPhot)
#Substitute in nan values to simulate incomplete data and make sure magnorm works too.
testMagsRedshift[0][1] = np.nan
testMagsRedshift[0][3] = np.nan
testMagsRedshift[0][4] = np.nan
testMagsRedshift[1][1] = np.nan
testMatchingRedshift = testMatching.matchToObserved(testSEDList, testMagsRedshift, testRedshifts,
dzAcc = 3, reddening = False,
bandpassDict = galPhot)
self.assertEqual(testSEDNames, testNoExtNoRedshift[0])
self.assertEqual(testSEDNames, testMatchingEbvVals[0])
self.assertEqual(None, testMatchingRedshift[0][0])
self.assertEqual(testSEDNames[1:], testMatchingRedshift[0][1:])
self.assertEqual(None, testMatchingRedshift[1][0])
np.testing.assert_almost_equal(testMagNormList[1:], testMatchingRedshift[1][1:],
decimal = magNormStep)
#Test Match Errors
errMag = testMagsRedshift[2]
errRedshift = testRedshifts[2]
errMags = np.array((errMag, errMag, errMag, errMag))
errRedshifts = np.array((errRedshift, errRedshift, errRedshift, errRedshift))
errMags[1,1] += 1. #Total MSE will be 2/(5 colors) = 0.4
errMags[2, 0:2] = np.nan
errMags[2, 3] += 1. #Total MSE will be 2/(3 colors) = 0.667
errMags[3, :] = None
errSED = testSEDList[2]
testMatchingResultsErrors = testMatching.matchToObserved([errSED], errMags, errRedshifts,
reddening = False,
bandpassDict = galPhot,
dzAcc = 3)
np.testing.assert_almost_equal(np.array((0.0, 0.4, 2./3.)), testMatchingResultsErrors[2][0:3],
decimal = 2) #Give a little more leeway due to redshifting effects
self.assertEqual(None, testMatchingResultsErrors[2][3])
def applyIGM(self, redshift, sedobj):
"""
Apply IGM extinction to already redshifted sed with redshift
between zMin and zMax defined by range of lookup tables
@param [in] redshift is the redshift of the incoming SED object
@param [in] sedobj is the SED object to which IGM extinction will be applied. This object
will be modified as a result of this.
"""
if self.IGMisInitialized == False:
self.initializeIGM()
# First make sure redshift is in range of lookup tables.
if (redshift < self.zMin) or (redshift > self.zMax):
warnings.warn(
str(
"IGM Lookup tables only applicable for "
+ str(self.zMin)
+ " < z < "
+ str(self.zMax)
+ ". No action taken"
)
)
return
# Now read in closest two lookup tables for given redshift
lowerSed = Sed()
upperSed = Sed()
for lower, upper in zip(self.zRange[:-1], self.zRange[1:]):
if lower <= redshift <= upper:
lowerSed.setSED(self.meanLookups[str(lower)][:, 0], flambda=self.meanLookups[str(lower)][:, 1])
upperSed.setSED(self.meanLookups[str(upper)][:, 0], flambda=self.meanLookups[str(upper)][:, 1])
break
# Redshift lookup tables to redshift of source, i.e. if source redshift is 1.78 shift lookup
# table for 1.7 and lookup table for 1.8 to up and down to 1.78, respectively
zLowerShift = ((1.0 + redshift) / (1.0 + lower)) - 1.0
zUpperShift = ((1.0 + redshift) / (1.0 + upper)) - 1.0
lowerSed.redshiftSED(zLowerShift)
upperSed.redshiftSED(zUpperShift)
# Resample lower and upper transmission data onto same wavelength grid.
minWavelen = 300.0 # All lookup tables are usable above 300nm
maxWavelen = np.amin([lowerSed.wavelen[-1], upperSed.wavelen[-1]]) - 0.01
lowerSed.resampleSED(wavelen_min=minWavelen, wavelen_max=maxWavelen, wavelen_step=0.01)
upperSed.resampleSED(wavelen_match=lowerSed.wavelen)
# Now insert this into a transmission array of 1.0 beyond the limits of current application
# So that we can get an sed back that extends to the longest wavelengths of the incoming sed
finalWavelen = np.arange(300.0, sedobj.wavelen[-1] + 0.01, 0.01)
finalFlambdaExtended = np.ones(len(finalWavelen))
# Weighted Average of Transmission from each lookup table to get final transmission
# table at desired redshift
dzGrid = self.zDelta # Step in redshift between transmission lookup table files
finalSed = Sed()
finalFlambda = lowerSed.flambda * (1.0 - ((redshift - lower) / dzGrid)) + upperSed.flambda * (
1.0 - ((upper - redshift) / dzGrid)
)
finalFlambdaExtended[0 : len(finalFlambda)] = finalFlambda
finalSed.setSED(wavelen=finalWavelen, flambda=finalFlambdaExtended)
# Resample incoming sed to new grid so that we don't get warnings from multiplySED
# about matching wavelength grids
sedobj.resampleSED(wavelen_match=finalSed.wavelen)
# Now multiply transmission curve by input SED to get final result and make it the new flambda
# data in the original sed which also is now on a new grid starting at 300 nm
test = sedobj.multiplySED(finalSed)
sedobj.flambda = test.flambda
def matchToObserved(self,
sedList,
catMags,
catRedshifts,
catRA=None,
catDec=None,
mag_error=None,
bandpassDict=None,
dzAcc=2,
reddening=True,
extCoeffs=(4.239, 3.303, 2.285, 1.698, 1.263)):
"""
This will find the closest match to the magnitudes of a galaxy catalog if those magnitudes are in
the observed frame and can correct for reddening from within the milky way as well if needed.
In order to make things faster it first calculates colors for all model SEDs at redshifts between
the minimum and maximum redshifts of the catalog objects provided with a grid spacing in redshift
defined by the parameter dzAcc. Objects without magnitudes in at least two adjacent bandpasses will
return as none and print out a message.
@param [in] sedList is the set of spectral objects from the models SEDs provided by loadBC03
or other custom loader routine.
@param [in] catMags is an array of the magnitudes of catalog objects to be matched with a model SED.
It should be organized so that there is one object's magnitudes along each row.
@param [in] catRedshifts is an array of the redshifts of each catalog object.
@param [in] catRA is an array of the RA positions for each catalog object.
@param [in] catDec is an array of the Dec position for each catalog object.
@param [in] mag_error are provided error values for magnitudes in objectMags. If none provided
then this defaults to 1.0. This should be an array of the same size as catMags.
@param [in] bandpassDict is a BandpassDict with which to calculate magnitudes.
If left equal to None it will by default load the SDSS [u,g,r,i,z] bandpasses and therefore agree with
default extCoeffs.
@param [in] dzAcc is the number of decimal places you want to use when building the redshift grid.
For example, dzAcc = 2 will create a grid between the minimum and maximum redshifts with colors
calculated at every 0.01 change in redshift.
@param [in] reddening is a boolean that determines whether to correct catalog magnitudes for
dust in the milky way. By default, it is True.
If true, this uses calculateEBV from EBV.py to find an EBV value for the object's
ra and dec coordinates and then uses the coefficients provided by extCoeffs which should come
from Schlafly and Finkbeiner (2011) for the correct filters and in the same order as provided
in bandpassDict.
If false, this means it will not run the dereddening procedure.
@param [in] extCoeffs are the Schlafly and Finkbeiner (2011) (ApJ, 737, 103) coefficients for the
given filters from bandpassDict and need to be in the same order as bandpassDict. The default given
are the SDSS [u,g,r,i,z] values.
@param [out] sedMatches is a list with the name of a model SED that matches most closely to each
object in the catalog.
@param [out] magNormMatches are the magnitude normalizations for the given magnitudes and
matched SED.
@param [out] matchErrors contains the Mean Squared Error between the colors of each object and
the colors of the matched SED.
"""
#Set up photometry to calculate model Mags
if bandpassDict is None:
galPhot = BandpassDict.loadTotalBandpassesFromFiles(
['u', 'g', 'r', 'i', 'z'],
bandpassDir=os.path.join(
lsst.utils.getPackageDir('throughputs'), 'sdss'),
bandpassRoot='sdss_')
else:
galPhot = bandpassDict
#Calculate ebv from ra, dec coordinates if needed
if reddening == True:
#Check that catRA and catDec are included
if catRA is None or catDec is None:
raise RuntimeError(
"Reddening is True, but catRA and catDec are not included."
)
calcEBV = ebv()
raDec = np.array((catRA, catDec))
#If only matching one object need to reshape for calculateEbv
if len(raDec.shape) == 1:
raDec = raDec.reshape((2, 1))
ebvVals = calcEBV.calculateEbv(equatorialCoordinates=raDec)
objMags = self.deReddenMags(ebvVals, catMags, extCoeffs)
else:
objMags = catMags
minRedshift = np.round(np.min(catRedshifts), dzAcc)
maxRedshift = np.round(np.max(catRedshifts), dzAcc)
dz = np.power(10., (-1 * dzAcc))
redshiftRange = np.round(
np.arange(minRedshift - dz, maxRedshift + (2 * dz), dz), dzAcc)
numRedshifted = 0
sedMatches = [None] * len(catRedshifts)
magNormMatches = [None] * len(catRedshifts)
matchErrors = [None] * len(catRedshifts)
redshiftIndex = np.argsort(catRedshifts)
numOn = 0
notMatched = 0
lastRedshift = -100
print 'Starting Matching. Arranged by redshift value.'
for redshift in redshiftRange:
if numRedshifted % 10 == 0:
print '%i out of %i redshifts gone through' % (
numRedshifted, len(redshiftRange))
numRedshifted += 1
colorSet = []
for galSpec in sedList:
sedColors = []
fileSED = Sed()
fileSED.setSED(wavelen=galSpec.wavelen,
flambda=galSpec.flambda)
fileSED.redshiftSED(redshift)
sedColors = self.calcBasicColors([fileSED],
galPhot,
makeCopy=True)
colorSet.append(sedColors)
colorSet = np.transpose(colorSet)
for currentIndex in redshiftIndex[numOn:]:
matchMags = objMags[currentIndex]
if lastRedshift < np.round(catRedshifts[currentIndex],
dzAcc) <= redshift:
colorRange = np.arange(0, len(galPhot) - 1)
matchColors = []
for colorNum in colorRange:
matchColors.append(matchMags[colorNum] -
matchMags[colorNum + 1])
#This is done to handle objects with incomplete magnitude data
filtNums = np.arange(0, len(galPhot))
if np.isnan(np.amin(matchColors)) == True:
colorRange = np.where(
np.isnan(matchColors) == False)[0]
filtNums = np.unique([
colorRange, colorRange + 1
]) #Pick right filters in calcMagNorm
if len(colorRange) == 0:
print 'Could not match object #%i. No magnitudes for two adjacent bandpasses.' \
% (currentIndex)
notMatched += 1
#Don't need to assign 'None' here in result array, b/c 'None' is default value
else:
distanceArray = [np.zeros(len(sedList))]
for colorNum in colorRange:
distanceArray += np.power(
(colorSet[colorNum] - matchColors[colorNum]),
2)
matchedSEDNum = np.nanargmin(distanceArray)
sedMatches[currentIndex] = sedList[matchedSEDNum].name
magNormVal = self.calcMagNorm(
np.array(matchMags),
sedList[matchedSEDNum],
galPhot,
mag_error=mag_error,
redshift=catRedshifts[currentIndex],
filtRange=filtNums)
magNormMatches[currentIndex] = magNormVal
matchErrors[currentIndex] = (
distanceArray[0, matchedSEDNum] / len(colorRange))
numOn += 1
else:
break
lastRedshift = redshift
print 'Done Matching. Matched %i of %i catalog objects to SEDs' % (
len(catMags) - notMatched, len(catMags))
if notMatched > 0:
print '%i objects did not get matched.' % (notMatched)
return sedMatches, magNormMatches, matchErrors
