def fit_spectra(data, t0, sig, N=10000):
"""Fit spectra runs global mcmc on one data set
Parameters:
-----------
data: list
List containing a spectral object, SN template flux at the same
wavelengths as the spectral object, and a galaxy template flux at the
same wavelengths of the spectral object:w
Output
------
n1: float
Best normalization for the SN template
n2: float
Best normalization for the galaxy template
likelihood: float
Likelihood for best fit combination of SN and galaxy template
"""
global_mcmc.mcmc(likelihood, prior, t0, N, sig, data, "sn_spectral_fitting.txt")
# read in results from mcmc
p1, p2, l = np.loadtxt("sn_spectral_fitting.txt", unpack=True)
i = np.array(l).argmax()
n1 = p1[i]
n2 = p2[i]
return n1, n2, l[i]
fout=open('final_params.dat', 'w')
sig=np.array([0.1,0.1])*7
t0=np.array([0.5,0.5])
spec=loadtext(sys.argv[1])
spec.wavelength = spec.wavelength / (1+float(sys.argv[2]))
N=int(sys.argv[3])
for snt in snlist:
for galt in gallist:
sntmpl=loadtext2(snt)
galtmpl=loadsdss(pyfits.open(galt))
isnflux=interpo(spec.wavelength, sntmpl.wavelength, sntmpl.flux)
igalflux=interpo(spec.wavelength, galtmpl.wavelength, galtmpl.flux )
data=[spec, isnflux, igalflux]
global_mcmc.mcmc(likelihood,prior,t0,N,sig,data, snt + '_' + galt +'.txt')
#read in results from mcmc
p1, p2, l = np.loadtxt(snt + '_' + galt + '.txt', unpack=True)
i = np.array(l).argmax()
n1 = p1[i]
n2 = p2[i]
fout.write('%s %s %f %f %f\n' % (snt,galt, n1, n2, l[i]))
fout.close()
fin=open('final_params.dat', 'r')
fin_lines=file.readlines(fin)
norm1, norm2,like=np.loadtxt('final_params.dat',usecols=(2, 3, 4), unpack=True)
k=like.argmax()
print fin_lines[k]
norm1=float(fin_lines[k].split()[2])
