def test_norm(self):
"""
Test that the special test case getImsimFluxNorm
returns the same value as calling calcFluxNorm actually
passing in the imsim Bandpass
"""
bp = Bandpass()
bp.imsimBandpass()
rng = np.random.RandomState(1123)
wavelen = np.arange(300.0, 2000.0, 0.17)
for ix in range(10):
flux = rng.random_sample(len(wavelen)) * 100.0
sed = Sed()
sed.setSED(wavelen=wavelen, flambda=flux)
magmatch = rng.random_sample() * 5.0 + 10.0
control = sed.calcFluxNorm(magmatch, bp)
test = getImsimFluxNorm(sed, magmatch)
# something about how interpolation is done in Sed means
# that the values don't come out exactly equal. They come
# out equal to 8 seignificant digits, though.
self.assertEqual(control, test)
def calcBasicColors(self, sedList, bandpassDict, makeCopy=False):
"""
This will calculate a set of colors from a list of SED objects when there is no need to redshift
the SEDs.
@param [in] sedList is the set of spectral objects from the models SEDs provided by loaders in
rgStar or rgGalaxy. NOTE: Since this uses photometryBase.manyMagCalc_list the SED objects
will be changed.
@param [in] bandpassDict is a BandpassDict class instance with the Bandpasses set to those
for the magnitudes given for the catalog object
@param [in] makeCopy indicates whether or not to operate on copies of the SED objects in sedList
since this method will change the wavelength grid.
@param [out] modelColors is the set of colors in the Bandpasses provided for the given sedList.
"""
modelColors = []
for specObj in sedList:
if makeCopy == True:
fileSED = Sed()
fileSED.setSED(wavelen=specObj.wavelen,
flambda=specObj.flambda)
sEDMags = bandpassDict.magListForSed(fileSED)
else:
sEDMags = bandpassDict.magListForSed(specObj)
colorInfo = []
for filtNum in range(0, len(bandpassDict) - 1):
colorInfo.append(sEDMags[filtNum] - sEDMags[filtNum + 1])
modelColors.append(colorInfo)
return modelColors
def test_norm(self):
"""
Test that the special test case getImsimFluxNorm
returns the same value as calling calcFluxNorm actually
passing in the imsim Bandpass
"""
bp = Bandpass()
bp.imsimBandpass()
rng = np.random.RandomState(1123)
wavelen = np.arange(300.0, 2000.0, 0.17)
for ix in range(10):
flux = rng.random_sample(len(wavelen))*100.0
sed = Sed()
sed.setSED(wavelen=wavelen, flambda=flux)
magmatch = rng.random_sample()*5.0 + 10.0
control = sed.calcFluxNorm(magmatch, bp)
test = getImsimFluxNorm(sed, magmatch)
# something about how interpolation is done in Sed means
# that the values don't come out exactly equal. They come
# out equal to 8 seignificant digits, though.
self.assertEqual(control, test)
def calcBasicColors(self, sedList, bandpassDict, makeCopy = False):
"""
This will calculate a set of colors from a list of SED objects when there is no need to redshift
the SEDs.
@param [in] sedList is the set of spectral objects from the models SEDs provided by loaders in
rgStar or rgGalaxy. NOTE: Since this uses photometryBase.manyMagCalc_list the SED objects
will be changed.
@param [in] bandpassDict is a BandpassDict class instance with the Bandpasses set to those
for the magnitudes given for the catalog object
@param [in] makeCopy indicates whether or not to operate on copies of the SED objects in sedList
since this method will change the wavelength grid.
@param [out] modelColors is the set of colors in the Bandpasses provided for the given sedList.
"""
modelColors = []
for specObj in sedList:
if makeCopy==True:
fileSED = Sed()
fileSED.setSED(wavelen = specObj.wavelen, flambda = specObj.flambda)
sEDMags = bandpassDict.magListForSed(fileSED)
else:
sEDMags = bandpassDict.magListForSed(specObj)
colorInfo = []
for filtNum in range(0, len(bandpassDict)-1):
colorInfo.append(sEDMags[filtNum] - sEDMags[filtNum+1])
modelColors.append(colorInfo)
return modelColors
def returnMags(self, bandpass=None):
"""
Convert the computed spectra to magnitudes using the supplied bandpasses,
or, if self.mags=True, just return the mags in the LSST filters
If mags=True when initialized, return mags returns an structured array with
dtype names u,g,r,i,z,y.
"""
if self.mags:
if bandpass:
warnings.warn('Ignoring set bandpasses and returning LSST ugrizy.')
mags = -2.5*np.log10(self.spec)+np.log10(3631.)
# Mask out high airmass
mags[self.mask] *= np.nan
# Convert to a structured array
mags = np.core.records.fromarrays(mags.transpose(),
names='u,g,r,i,z,y',
formats='float,'*6)
else:
mags = np.zeros(self.npts, dtype=float)-666
tempSed = Sed()
isThrough = np.where(bandpass.sb > 0)
minWave = bandpass.wavelen[isThrough].min()
maxWave = bandpass.wavelen[isThrough].max()
inBand = np.where((self.wave >= minWave) & (self.wave <= maxWave))
for i, ra in enumerate(self.ra):
# Check that there is flux in the band, otherwise calcMag fails
if np.max(self.spec[i, inBand]) > 0:
tempSed.setSED(self.wave, flambda=self.spec[i, :])
mags[i] = tempSed.calcMag(bandpass)
# Mask out high airmass
mags[self.mask] *= np.nan
return mags
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.wavelenMatch)
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.wavelenMatch)
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 get_mags(self,source, phase ):
sed = Sed()
print "==========================================="
print phase
print "==========================================="
if phase > -20 and phase < 50 :
sourceflux = source.flux(phase=phase, wave=self.rband.wavelen*10.)
sed.setSED(wavelen=self.rband.wavelen, flambda=sourceflux/10.)
else:
sed.setSED(wavelen=self.rband.wavelen, flambda=flambda)
#sed.redshiftSED(redshift=_z[i], dimming=True)
return [sed.calcMag(bandpass=self.uband),
sed.calcMag(bandpass=self.gband),
sed.calcMag(bandpass=self.rband),
sed.calcMag(bandpass=self.iband),
sed.calcMag(bandpass=self.zband)]
def returnMags(self, bandpasses=None):
"""
Convert the computed spectra to a magnitude using the supplied bandpass,
or, if self.mags=True, return the mags in the LSST filters
If mags=True when initialized, return mags returns an structured array with
dtype names u,g,r,i,z,y.
bandpasses: optional dictionary with bandpass name keys and bandpass object values.
"""
if self.azs is None:
raise ValueError(
'No coordinates set. Use setRaDecMjd, setRaDecAltAzMjd, or setParams methods before calling returnMags.'
)
if self.mags:
if bandpasses:
warnings.warn(
'Ignoring set bandpasses and returning LSST ugrizy.')
mags = -2.5 * np.log10(self.spec) + np.log10(3631.)
# Mask out high airmass
mags[self.mask] *= np.nan
mags = mags.swapaxes(0, 1)
magsBack = {}
for i, f in enumerate(self.filterNames):
magsBack[f] = mags[i]
else:
magsBack = {}
for key in bandpasses:
mags = np.zeros(self.npts, dtype=float) - 666
tempSed = Sed()
isThrough = np.where(bandpasses[key].sb > 0)
minWave = bandpasses[key].wavelen[isThrough].min()
maxWave = bandpasses[key].wavelen[isThrough].max()
inBand = np.where((self.wave >= minWave)
& (self.wave <= maxWave))
for i, ra in enumerate(self.ra):
# Check that there is flux in the band, otherwise calcMag fails
if np.max(self.spec[i, inBand]) > 0:
tempSed.setSED(self.wave, flambda=self.spec[i, :])
mags[i] = tempSed.calcMag(bandpasses[key])
# Mask out high airmass
mags[self.mask] *= np.nan
magsBack[key] = mags
return magsBack
def computeMags(self, bandpass=None):
"""After the spectra have been computed, optionally convert to mags"""
if self.mags:
mags = -2.5 * np.log10(self.spec) + np.log10(3631.)
else:
mags = np.zeros(self.npts, dtype=float) - 666
tempSed = Sed()
isThrough = np.where(bandpass.sb > 0)
minWave = bandpass.wavelen[isThrough].min()
maxWave = bandpass.wavelen[isThrough].max()
inBand = np.where((self.wave >= minWave) & (self.wave <= maxWave))
for i, ra in enumerate(self.ra):
if np.max(self.spec[i, inBand]) > 0:
tempSed.setSED(self.wave, flambda=self.spec[i, :])
# Need to try/except because the spectra might be zero in the filter
# XXX-upgrade this to check if it's zero
mags[i] = tempSed.calcMag(bandpass)
return mags
def computeMags(self, bandpass=None):
"""After the spectra have been computed, optionally convert to mags"""
if self.mags:
mags = -2.5*np.log10(self.spec)+np.log10(3631.)
else:
mags = np.zeros(self.npts, dtype=float)-666
tempSed = Sed()
isThrough = np.where(bandpass.sb > 0)
minWave = bandpass.wavelen[isThrough].min()
maxWave = bandpass.wavelen[isThrough].max()
inBand = np.where( (self.wave >= minWave) & (self.wave <= maxWave))
for i, ra in enumerate(self.ra):
if np.max(self.spec[i,inBand]) > 0:
tempSed.setSED(self.wave, flambda=self.spec[i,:])
# Need to try/except because the spectra might be zero in the filter
# XXX-upgrade this to check if it's zero
mags[i] = tempSed.calcMag(bandpass)
return mags
def returnMags(self, bandpasses=None):
"""
Convert the computed spectra to a magnitude using the supplied bandpass,
or, if self.mags=True, return the mags in the LSST filters
If mags=True when initialized, return mags returns an structured array with
dtype names u,g,r,i,z,y.
bandpasses: optional dictionary with bandpass name keys and bandpass object values.
"""
if self.azs is None:
raise ValueError('No coordinates set. Use setRaDecMjd, setRaDecAltAzMjd, or setParams methods before calling returnMags.')
if self.mags:
if bandpasses:
warnings.warn('Ignoring set bandpasses and returning LSST ugrizy.')
mags = -2.5*np.log10(self.spec)+np.log10(3631.)
# Mask out high airmass
mags[self.mask] *= np.nan
mags = mags.swapaxes(0, 1)
magsBack = {}
for i, f in enumerate(self.filterNames):
magsBack[f] = mags[i]
else:
magsBack = {}
for key in bandpasses:
mags = np.zeros(self.npts, dtype=float)-666
tempSed = Sed()
isThrough = np.where(bandpasses[key].sb > 0)
minWave = bandpasses[key].wavelen[isThrough].min()
maxWave = bandpasses[key].wavelen[isThrough].max()
inBand = np.where((self.wave >= minWave) & (self.wave <= maxWave))
for i, ra in enumerate(self.ra):
# Check that there is flux in the band, otherwise calcMag fails
if np.max(self.spec[i, inBand]) > 0:
tempSed.setSED(self.wave, flambda=self.spec[i, :])
mags[i] = tempSed.calcMag(bandpasses[key])
# Mask out high airmass
mags[self.mask] *= np.nan
magsBack[key] = mags
return magsBack
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['%.1f' % lower][:,0],
flambda = self.meanLookups['%.1f' % lower][:,1])
upperSed.setSED(self.meanLookups['%.1f' % upper][:,0],
flambda = self.meanLookups['%.1f' % lower][:,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 packageLowerAtm():
dataDir = getPackageDir('SIMS_SKYBRIGHTNESS_DATA')
outDir = os.path.join(dataDir, 'ESO_Spectra/LowerAtm')
# Read in all the spectra from ESO call and package into a single npz file
files = glob.glob('LowerAtm/skytable*.fits')
temp = pyfits.open(files[0])
wave = temp[1].data['lam'].copy()*1e3
airmasses = []
nightTimes = []
specs = []
for i,filename in enumerate(files):
fits = pyfits.open(filename)
if np.max(fits[1].data['flux']) > 0:
specs.append(fits[1].data['flux'].copy())
header = fits[0].header['comment']
for card in header:
if 'SKYMODEL.TARGET.AIRMASS' in card:
airmasses.append(float(card.split('=')[-1]))
elif 'SKYMODEL.TIME' in card:
nightTimes.append(float(card.split('=')[-1]))
airmasses = np.array(airmasses)
nigtTimes = np.array(nightTimes)
nrec = airmasses.size
nwave = wave.size
dtype = [('airmass', 'float'),
('nightTimes', 'float'),
('spectra', 'float', (nwave)), ('mags', 'float', (6))]
Spectra = np.zeros(nrec, dtype=dtype)
Spectra['airmass'] = airmasses
Spectra['nightTimes'] = nightTimes
Spectra['spectra'] = specs
hPlank = 6.626068e-27 # erg s
cLight = 2.99792458e10 # cm/s
# Convert spectra from ph/s/m2/micron/arcsec2 to erg/s/cm2/nm/arcsec2
Spectra['spectra'] = Spectra['spectra']/(100.**2)*hPlank*cLight/(wave*1e-7)/1e3
# Sort things since this might be helpful later
Spectra.sort(order=['airmass','nightTimes'])
# Load LSST filters
throughPath = os.path.join(getPackageDir('throughputs'),'baseline')
keys = ['u','g','r','i','z','y']
nfilt = len(keys)
filters = {}
for filtername in keys:
bp = np.loadtxt(os.path.join(throughPath, 'filter_'+filtername+'.dat'),
dtype=zip(['wave','trans'],[float]*2 ))
tempB = Bandpass()
tempB.setBandpass(bp['wave'],bp['trans'])
filters[filtername] = tempB
filterWave = np.array([filters[f].calcEffWavelen()[0] for f in keys ])
for i,spectrum in enumerate(Spectra['spectra']):
tempSed = Sed()
tempSed.setSED(wave,flambda=spectrum)
for j,filtName in enumerate(keys):
try:
Spectra['mags'][i][j] = tempSed.calcMag(filters[filtName])
except:
pass
np.savez(os.path.join(outDir,'Spectra.npz'), wave = wave, spec=Spectra, filterWave=filterWave)
def packageZodiacal():
dataDir = getPackageDir('SIMS_SKYBRIGHTNESS_DATA')
outDir = os.path.join(dataDir, 'ESO_Spectra/Zodiacal')
nside = 4
# Read in all the spectra from ESO call and package into a single npz file
files = glob.glob('Zodiacal/skytable*.fits')
temp = pyfits.open(files[0])
wave = temp[1].data['lam'].copy()*1e3
airmasses = []
eclLon = []
eclLat = []
specs = []
for i,filename in enumerate(files):
fits = pyfits.open(filename)
if np.max(fits[1].data['flux']) > 0:
specs.append(fits[1].data['flux'].copy())
header = fits[0].header['comment']
for card in header:
if 'SKYMODEL.TARGET.AIRMASS' in card:
airmasses.append(float(card.split('=')[-1]))
elif 'SKYMODEL.ECL.LON' in card:
eclLon.append(float(card.split('=')[-1]))
elif 'SKYMODEL.ECL.LAT' in card:
eclLat.append(float(card.split('=')[-1]))
airmasses = np.array(airmasses)
eclLon = np.array(eclLon)
eclLat = np.array(eclLat)
wrapA = np.where(eclLon < 0.)
eclLon[wrapA] = eclLon[wrapA]+360.
uAM = np.unique(airmasses)
nAM = uAM.size
nwave = wave.size
dtype = [('airmass', 'float'),
('hpid', 'int' ),
('spectra', 'float', (nwave)), ('mags', 'float', (6))]
npix = hp.nside2npix(nside)
Spectra = np.zeros(nAM*npix, dtype=dtype)
for i,am in enumerate(uAM):
Spectra['airmass'][i*npix:i*npix+npix] = am
Spectra['hpid'][i*npix:i*npix+npix] = np.arange(npix)
for am, lat, lon, spec in zip(airmasses,eclLat, eclLon, specs):
hpid = hp.ang2pix(nside, np.radians(lat+90.), np.radians(lon) )
good = np.where( (Spectra['airmass'] == am) & (Spectra['hpid'] == hpid))
Spectra['spectra'][good] = spec.copy()
hPlank = 6.626068e-27 # erg s
cLight = 2.99792458e10 # cm/s
# Convert spectra from ph/s/m2/micron/arcsec2 to erg/s/cm2/nm/arcsec2
Spectra['spectra'] = Spectra['spectra']/(100.**2)*hPlank*cLight/(wave*1e-7)/1e3
# Sort things since this might be helpful later
Spectra.sort(order=['airmass', 'hpid'])
# Load LSST filters
throughPath = os.path.join(getPackageDir('throughputs'),'baseline')
keys = ['u','g','r','i','z','y']
nfilt = len(keys)
filters = {}
for filtername in keys:
bp = np.loadtxt(os.path.join(throughPath, 'filter_'+filtername+'.dat'),
dtype=zip(['wave','trans'],[float]*2 ))
tempB = Bandpass()
tempB.setBandpass(bp['wave'],bp['trans'])
filters[filtername] = tempB
filterWave = np.array([filters[f].calcEffWavelen()[0] for f in keys ])
for i,spectrum in enumerate(Spectra['spectra']):
tempSed = Sed()
tempSed.setSED(wave,flambda=spectrum)
for j,filtName in enumerate(keys):
try:
Spectra['mags'][i][j] = tempSed.calcMag(filters[filtName])
except:
pass
#span this over multiple files to store in github
nbreak = 3
nrec = np.size(Spectra)
for i in np.arange(nbreak):
np.savez(os.path.join(outDir,'zodiacalSpectra_'+str(i)+'.npz'), wave = wave,
spec=Spectra[i*nrec/nbreak:(i+1)*nrec/nbreak], filterWave=filterWave)
def packageMoon():
dataDir = getPackageDir('SIMS_SKYBRIGHTNESS_DATA')
outDir = os.path.join(dataDir, 'ESO_Spectra/Moon')
# Read in all the spectra from ESO call and package into a single npz file
files = glob.glob('Moon/skytable*.fits')
temp = pyfits.open(files[0])
moonWave = temp[1].data['lam'].copy()*1e3
moonSpec = []
moonAM = [] # Actually target airmass
moonAlt=[] # altitude of the moon
moonSunSep=[] # moon Phase
moonTargetSep=[]
hpid=[]
for i,filename in enumerate(files):
fits = pyfits.open(filename)
try:
if np.max(fits[1].data['flux']) > 0:
moonSpec.append(fits[1].data['flux'].copy())
header = fits[0].header['comment']
for card in header:
if 'SKYMODEL.MOON.SUN.SEP' in card:
moonSunSep.append(float(card.split('=')[-1]))
elif 'SKYMODEL.TARGET.AIRMASS' in card:
#moonAM.append( 1./np.cos(np.radians(90.-float(card.split('=')[-1]))) )
moonAM.append( float(card.split('=')[-1]) )
elif 'SKYMODEL.MOON.TARGET.SEP' in card:
moonTargetSep.append(float(card.split('=')[-1]))
elif 'SKYMODEL.MOON.ALT' in card:
moonAlt.append(float(card.split('=')[-1]))
except:
print filename, ' Failed'
import healpy as hp
from lsst.sims.utils import haversine
nside = 4
lat, az = hp.pix2ang(nside, np.arange(hp.nside2npix(nside)))
alt = np.pi/2.-lat
airmass = 1./np.cos(np.pi/2.-alt)
# Only need low airmass and then 1/2 to sky
good = np.where( (az >= 0) & (az <= np.pi) & (airmass <=2.6) & (airmass >= 1.) )
airmass = airmass[good]
alt=alt[good]
az = az[good]
moonAM = np.array(moonAM)
moonAlt = np.array(moonAlt)
moonSunSep = np.array(moonSunSep)
moonTargetSep = np.array(moonTargetSep)
moonAzDiff = moonTargetSep*0
targetAlt = np.pi/2.-np.arccos(1./moonAM)
# Compute the azimuth difference given the moon-target-seperation
# Let's just do a stupid loop:
for i in np.arange(targetAlt.size):
possibleDistances = haversine(0., np.radians(moonAlt[i]), az, az*0+targetAlt[i])
diff = np.abs(possibleDistances - np.radians(moonTargetSep[i]))
good = np.where(diff == diff.min())
moonAzDiff[i] = az[good][0]
# ok, now I have an alt and az, I can convert that back to a healpix id.
hpid.append(hp.ang2pix(nside, np.pi/2.-targetAlt[i], moonAzDiff[i]))
nrec = moonAM.size
nwave = moonWave.size
dtype = [('hpid', 'int'),
('moonAltitude', 'float'),
('moonSunSep', 'float'),
('spectra', 'float', (nwave)), ('mags', 'float', (6))]
moonSpectra = np.zeros(nrec, dtype=dtype)
moonSpectra['hpid'] = hpid
moonSpectra['moonAltitude'] = moonAlt
moonSpectra['moonSunSep'] = moonSunSep
moonSpectra['spectra'] = moonSpec
hPlank = 6.626068e-27 # erg s
cLight = 2.99792458e10 # cm/s
# Convert spectra from ph/s/m2/micron/arcsec2 to erg/s/cm2/nm/arcsec2
moonSpectra['spectra'] = moonSpectra['spectra']/(100.**2)*hPlank*cLight/(moonWave*1e-7)/1e3
# Sort things since this might be helpful later
moonSpectra.sort(order=['moonSunSep','moonAltitude', 'hpid'])
# Crop off the incomplete ones
good =np.where((moonSpectra['moonAltitude'] >= 0) & (moonSpectra['moonAltitude'] < 89.) )
moonSpectra = moonSpectra[good]
# Load LSST filters
throughPath = os.path.join(getPackageDir('throughputs'),'baseline')
keys = ['u','g','r','i','z','y']
nfilt = len(keys)
filters = {}
for filtername in keys:
bp = np.loadtxt(os.path.join(throughPath, 'filter_'+filtername+'.dat'),
dtype=zip(['wave','trans'],[float]*2 ))
tempB = Bandpass()
tempB.setBandpass(bp['wave'],bp['trans'])
filters[filtername] = tempB
filterWave = np.array([filters[f].calcEffWavelen()[0] for f in keys ])
for i,spectrum in enumerate(moonSpectra['spectra']):
tempSed = Sed()
tempSed.setSED(moonWave,flambda=spectrum)
for j,filtName in enumerate(keys):
try:
moonSpectra['mags'][i][j] = tempSed.calcMag(filters[filtName])
except:
pass
nbreak=5
nrec = np.size(moonSpectra)
for i in np.arange(nbreak):
np.savez(os.path.join(outDir,'moonSpectra_'+str(i)+'.npz'), wave = moonWave, spec=moonSpectra[i*nrec/nbreak:(i+1)*nrec/nbreak], filterWave=filterWave)
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
throughPath = os.getenv('LSST_THROUGHPUTS_BASELINE')
keys = ['u', 'g', 'r', 'i', 'z', 'y']
nfilt = len(keys)
filters = {}
for filtername in keys:
bp = np.loadtxt(os.path.join(throughPath, 'filter_' + filtername + '.dat'),
dtype=zip(['wave', 'trans'], [float] * 2))
tempB = Bandpass()
tempB.setBandpass(bp['wave'], bp['trans'])
filters[filtername] = tempB
filterWave = np.array([filters[f].calcEffWavelen()[0] for f in keys])
for i, spectrum in enumerate(moonSpectra['spectra']):
tempSed = Sed()
tempSed.setSED(moonWave, flambda=spectrum)
for j, filtName in enumerate(keys):
try:
moonSpectra['mags'][i][j] = tempSed.calcMag(filters[filtName])
except:
pass
nbreak = 5
nrec = np.size(moonSpectra)
for i in np.arange(nbreak):
np.savez(os.path.join(outDir, 'moonSpectra_' + str(i) + '.npz'),
wave=moonWave,
spec=moonSpectra[i * nrec / nbreak:(i + 1) * nrec / nbreak],
filterWave=filterWave)
# Sort things since this might be helpful later
Spectra.sort(order=['airmass', 'nightTimes'])
# Load LSST filters
throughPath = os.getenv('LSST_THROUGHPUTS_BASELINE')
keys = ['u', 'g', 'r', 'i', 'z', 'y']
nfilt = len(keys)
filters = {}
for filtername in keys:
bp = np.loadtxt(os.path.join(throughPath, 'filter_' + filtername + '.dat'),
dtype=list(zip(['wave', 'trans'], [float] * 2)))
tempB = Bandpass()
tempB.setBandpass(bp['wave'], bp['trans'])
filters[filtername] = tempB
filterWave = np.array([filters[f].calcEffWavelen()[0] for f in keys])
for i, spectrum in enumerate(Spectra['spectra']):
tempSed = Sed()
tempSed.setSED(wave, flambda=spectrum)
for j, filtName in enumerate(keys):
try:
Spectra['mags'][i][j] = tempSed.calcMag(filters[filtName])
except:
pass
np.savez(os.path.join(outDir, 'Spectra.npz'),
wave=wave,
spec=Spectra,
filterWave=filterWave)
throughPath = os.getenv('LSST_THROUGHPUTS_BASELINE')
keys = ['u', 'g', 'r', 'i', 'z', 'y']
nfilt = len(keys)
filters = {}
for filtername in keys:
bp = np.loadtxt(os.path.join(throughPath, 'filter_'+filtername+'.dat'),
dtype=list(zip(['wave', 'trans'], [float]*2)))
tempB = Bandpass()
tempB.setBandpass(bp['wave'], bp['trans'])
filters[filtername] = tempB
filterWave = np.array([filters[f].calcEffWavelen()[0] for f in keys])
for i, spectrum in enumerate(moonSpectra['spectra']):
tempSed = Sed()
tempSed.setSED(moonWave, flambda=spectrum)
for j, filtName in enumerate(keys):
try:
moonSpectra['mags'][i][j] = tempSed.calcMag(filters[filtName])
except:
pass
nbreak = 5
nrec = np.size(moonSpectra)
for i in np.arange(nbreak):
np.savez(os.path.join(outDir, 'moonSpectra_'+str(i)+'.npz'), wave=moonWave,
spec=moonSpectra[i*nrec/nbreak:(i+1)*nrec/nbreak], filterWave=filterWave)
# Sort things since this might be helpful later
Spectra.sort(order=['airmass', 'nightTimes'])
# Load LSST filters
throughPath = os.getenv('LSST_THROUGHPUTS_BASELINE')
keys = ['u', 'g', 'r', 'i', 'z', 'y']
nfilt = len(keys)
filters = {}
for filtername in keys:
bp = np.loadtxt(os.path.join(throughPath, 'filter_'+filtername+'.dat'),
dtype=list(zip(['wave', 'trans'], [float]*2)))
tempB = Bandpass()
tempB.setBandpass(bp['wave'], bp['trans'])
filters[filtername] = tempB
filterWave = np.array([filters[f].calcEffWavelen()[0] for f in keys])
for i, spectrum in enumerate(Spectra['spectra']):
tempSed = Sed()
tempSed.setSED(wave, flambda=spectrum)
for j, filtName in enumerate(keys):
try:
Spectra['mags'][i][j] = tempSed.calcMag(filters[filtName])
except:
pass
np.savez(os.path.join(outDir, 'Spectra.npz'), wave=wave, spec=Spectra, filterWave=filterWave)
from builtins import zip
import numpy as np
import lsst.sims.photUtils.Sed as Sed
import os
dataDir = os.getenv('SIMS_SKYBRIGHTNESS_DATA_DIR')
data = np.genfromtxt(os.path.join(dataDir, 'solarSpec/solarSpec.dat'),
dtype=list(zip(['microns', 'Irr'], [float]*2)))
# data['Irr'] = data['Irr']*1 #convert W/m2/micron to erg/s/cm2/nm (HA, it's the same!)
sun = Sed()
sun.setSED(data['microns']*1e3, flambda=data['Irr'])
# Match the wavelenth spacing and range to the ESO spectra
airglowSpec = np.load(os.path.join(dataDir, 'ESO_Spectra/Airglow/airglowSpectra.npz'))
sun.resampleSED(wavelen_match=airglowSpec['wave'])
np.savez(os.path.join(dataDir, 'solarSpec/solarSpec.npz'), wave=sun.wavelen, spec=sun.flambda)
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['%.1f' % lower][:, 0],
flambda=self.meanLookups['%.1f' % lower][:, 1])
upperSed.setSED(self.meanLookups['%.1f' % upper][:, 0],
flambda=self.meanLookups['%.1f' % lower][:, 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
import numpy as np
import lsst.sims.photUtils.Sed as Sed
import os
dataDir = os.getenv('SIMS_SKYBRIGHTNESS_DATA_DIR')
data = np.genfromtxt(os.path.join(dataDir, 'solarSpec/solarSpec.dat'), dtype=zip(['microns','Irr'],[float]*2))
#data['Irr'] = data['Irr']*1 #convert W/m2/micron to erg/s/cm2/nm (HA, it's the same!)
sun = Sed()
sun.setSED(data['microns']*1e3, flambda=data['Irr'])
# Match the wavelenth spacing and range to the ESO spectra
airglowSpec = np.load(os.path.join(dataDir, 'ESO_Spectra/Airglow/airglowSpectra.npz'))
sun.resampleSED(wavelen_match=airglowSpec['wave'])
np.savez(os.path.join(dataDir,'solarSpec/solarSpec.npz'), wave=sun.wavelen, spec=sun.flambda)
