default=False,
help="If set, fit for a dispersion scale length offsett")
#Isochrone IMF
parser.add_option("--fitdm",action="store_true", dest="fitdm",
default=False,
help="If set, fit for a distance modulus offset")
parser.add_option("--fitah",action="store_true", dest="fitah",
default=False,
help="If set, fit for an extinction-correction offset")
parser.add_option("--fitfeh",action="store_true", dest="fitfeh",
default=False,
help="If set, fit for a [Fe/H] offset (with indivfeh)")
#Add dwarf part?
parser.add_option("--dwarf",action="store_true",
dest="dwarf",
default=False,
help="setting this adds dwarf contamination")
parser.add_option("--nsamples",dest='nsamples',default=10,type='int',
help="Number ofsamples to plot")
#Output file
parser.add_option("-o",dest='plotfilename',default=None,
help="Name of the file for the plot")
return parser
if __name__ == '__main__':
numpy.random.seed(1) #We need to seed to get, e.g., the same permutation when downsampling
parser= get_options()
(options,args)= parser.parse_args()
plot_rotcurve_samples(options,args)
action="store_true",
dest="fitfeh",
default=False,
help="If set, fit for a [Fe/H] offset (with indivfeh)")
#Add dwarf part?
parser.add_option("--dwarf",
action="store_true",
dest="dwarf",
default=False,
help="setting this adds dwarf contamination")
parser.add_option("--nsamples",
dest='nsamples',
default=10,
type='int',
help="Number ofsamples to plot")
#Output file
parser.add_option("-o",
dest='plotfilename',
default=None,
help="Name of the file for the plot")
return parser
if __name__ == '__main__':
numpy.random.seed(
1
) #We need to seed to get, e.g., the same permutation when downsampling
parser = get_options()
(options, args) = parser.parse_args()
plot_rotcurve_samples(options, args)
def logl(init=None,data=None,options=None):
if options is None:
parser= get_options()
options, args= parser.parse_args([])
if data is None:
#Read data
data= readVclosData(lmin=25.,
bmax=2.,
ak=True,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
correctak=options.correctak,
jkmax=1.1)
#HACK
indx= (data['J0MAG']-data['K0MAG'] < 0.5)
data['J0MAG'][indx]= 0.5+data['K0MAG'][indx]
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile= open(options.isofile,'rb')
iso= pickle.load(isofile)
if options.indivfeh:
zs= pickle.load(isofile)
elif options.varfeh:
locl= pickle.load(isofile)
isofile.close()
else:
if options.indivfeh:
#Load all isochrones
iso= []
zs= numpy.arange(0.0005,0.03005,0.0005)
for ii in range(len(zs)):
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs= list(set(data['LOCATION']))
iso= []
for ii in range(len(locs)):
indx= (data['LOCATION'] == locs[ii])
locl= numpy.mean(data['GLON'][indx]*_DEGTORAD)
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso= isomodel.isomodel(Z=0.019)
if options.dwarf:
iso= [iso,
isomodel.isomodel(Z=0.019,
dwarf=True)]
else:
iso= [iso]
if not options.isofile is None:
isofile= open(options.isofile,'wb')
pickle.dump(iso,isofile)
if options.indivfeh:
pickle.dump(zs,isofile)
elif options.varfeh:
pickle.dump(locl,isofile)
isofile.close()
df= None
#Pre-calculate distance prior
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,
_BINTEGRATENBINS)
dm= _dm(ds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin((thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpiso[ii,:]= iso[0](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
if options.dwarf:
logpisodwarf= numpy.zeros((len(data),_BINTEGRATENBINS))
dwarfds= numpy.linspace(_BINTEGRATEDMIN_DWARF,_BINTEGRATEDMAX_DWARF,
_BINTEGRATENBINS)
dm= _dm(dwarfds)
for ii in range(len(data)):
mh= data['H0MAG'][ii]-dm
logpisodwarf[ii,:]= iso[1](numpy.zeros(_BINTEGRATENBINS)
+(data['J0MAG']-data['K0MAG'])[ii],mh)
else:
logpisodwarf= None
if isinstance(init,str): #FILE
#Load initial parameters from file
savefile= open(init,'rb')
params= pickle.load(savefile)
savefile.close()
else: #Array
params= init
#Prep data
l= data['GLON']*_DEGTORAD
b= data['GLAT']*_DEGTORAD
sinl= numpy.sin(l)
cosl= numpy.cos(l)
sinb= numpy.sin(b)
cosb= numpy.cos(b)
jk= data['J0MAG']-data['K0MAG']
try:
jk[(jk < 0.5)]= 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL
except TypeError:
pass #HACK
h= data['H0MAG']
options.multi= 1
out= -mloglike(params,data['VHELIO'],
l,
b,
jk,
h,
df,options,
sinl,
cosl,
cosb,
sinb,
logpiso,
logpisodwarf,True,None,iso,data['FEH']) #None iso for now
return out
