def predictDiskMass(modelfunc,params,sf,cdist,fehdist,fehmin,fehmax,feh,
data,grmin,grmax,agemin,agemax,normalize='Z',
imfmodel='lognormalChabrier2001',
justnumber=False):
n= compareDataModel.comparernumberPlate(modelfunc,params,sf,
cdist,fehdist,data,
'all',
fehmin=fehmin,
fehmax=fehmax,
feh=feh,
noplot=True,
nodata=True)
if not normalize.lower() == 'z':
#Normalization of model
norm= integrate.dblquad(lambda r,f: r**2*numpy.sin(f)*modelfunc(r*numpy.sin(f),
r*numpy.cos(f),
params),
0.,numpy.pi,lambda x:0., lambda x: 200.)[0]\
*2.*numpy.pi
else:
norm= 1.
pred= numpy.sum(n)*7.*(numpy.pi/180.)**2.*(20.2-14.5)/1000#\
# *0.2*numpy.log(10.) #these are some factors left out of compareDataModel
if justnumber:
return len(data)*norm/pred
#print pred, len(data), 1.-params[2]
frac= fracMassGRRange(grmin,grmax,agemin,agemax,feh,imfmodel)
avgmass= averageMassGRRange(grmin,grmax,agemin,agemax,feh)
return len(data)*norm/pred/frac*avgmass
def predictBellHalo():
params= None
data= read_gdwarfs(ebv=True,sn=True)
plates= numpy.array(list(set(list(data.plate))),dtype='int') #Only load plates that we use
sf= segueSelect(plates=plates,sample='G',type_bright='tanhrcut',
type_faint='tanhrcut',sn=True)
cdist= _const_colordist
fehdist= haloMDF
n,d,x= compareDataModel.comparernumberPlate(bellfunc,params,sf,cdist,fehdist,data,'all',fehmin=-1.8,fehmax=-0.8,feh=-2.,noplot=True)
#Normalization of bell
bell= 10**8./(integrate.dblquad(lambda r,f: numpy.sin(f)*r**2*bellfunc(r*numpy.sin(f),r*numpy.cos(f),None),
0.,numpy.pi,lambda x:1., lambda x: 40.)[0]*2.*numpy.pi)
return numpy.sum(n)*7.*(numpy.pi/180.)**2.*(20.2-14.5)/1000*bell*0.2*numpy.log(10.) #these are some factors left out of compareDataModel
