def _ecgmm(c, e, alpha=[0.2,0.8], mu=[0.5,1.2], sigma=[0.5,0.05],
flavour='BIC', bootstrap=False):
"""
Error-Corrected Gaussian Mixture Model (ECGMM) by Hao et al. (2009)
Everything is coded in their script so here just execute it.
Parameters
----------
c : float array
galaxy colours
e : float array
galaxy colour uncertainties
alpha : float array-like
initial guess for the mixing of the Gaussian models
mu : float array-like
initial guess for the locations of the Gaussians
sigma : float array-like
initial guess for the widths of the Gaussians
flavour : {'AIC', 'BIC'}
which information criterion to use
bootstrap : False or int
whether to bootstrap the ECGMM and, if so, how many
bootstrap samples to use
"""
if bootstrap is not False:
gmm = ecgmmPy.bsecgmm(c, e, alpha, mu, sigma, nboot=100,
InfoCriteria=flavour)
elif flavour.upper() == 'AIC':
gmm = ecgmmPy.aic_ecgmm(c, e, alpha, mu, sigma)
elif flavour.upper() == 'BIC':
gmm = ecgmmPy.bic_ecgmm(c, e, alpha, mu, sigma)
return alpha, mu, sigma
def ecgmm(c, e, alpha = [0.2, 0.8], mu = [0.5, 1.2], sigma = [0.5, 0.05]): """ Error-Corrected Gaussian Mixture Model (ECGMM) by Hao et al. (2009) Everything is coded in their script so here just execute it. """ bic = ecgmmPy.bic_ecgmm(c, e, alpha, mu, sigma) return alpha, mu, sigma
bg=np.random.normal(mubg,sdbg,nbg)
xerr=np.random.uniform(0,0.2,ncl+nbg)
x=np.append(cl,bg)+np.random.uniform(0,1,ncl+nbg)*xerr
ec.wstat(x,xerr) # calculate the weighted statistics
# assign initial guess as two mixtures
alpha=np.array([0.5,0.5])
mu=np.array([0.3,0.1])
sigma=np.array([0.05,0.2])
#aic=ec.aic_ecgmm(x,xerr,alpha,mu,sigma)
bic=ec.bic_ecgmm(x,xerr,alpha,mu,sigma)
#make plots
pl.figure(figsize=(12,6))
pl.subplot(1,2,1)
y=np.arange(-1,1,0.0001)
pl.hist(x,bins=30,alpha=0.3,normed=True,facecolor='green')
ec.ecgmmplot(y,alpha,mu,sigma)
pl.xlabel='x'
pl.text(-0.9,3, r'$\mu=$'+str(mu))
pl.text(-0.9,2.6, r'$\sigma=$'+str(sigma))
pl.ylim(0,7)
pl.title("ECGMM")
bg=np.random.normal(mubg,sdbg,nbg)
xerr=np.random.uniform(0,0.2,ncl+nbg)
x=np.append(cl,bg)+np.random.uniform(0,1,ncl+nbg)*xerr
ec.wstat(x,xerr) # calculate the weighted statistics
# assign initial guess as two mixtures
alpha=np.array([0.5,0.5])
mu=np.array([0.3,0.1])
sigma=np.array([0.05,0.2])
#aic=ec.aic_ecgmm(x,xerr,alpha,mu,sigma)
bic=ec.bic_ecgmm(x,xerr,alpha,mu,sigma)
#make plots
pl.figure(figsize=(12,6))
pl.subplot(1,2,1)
y=np.arange(-1,1,0.0001)
pl.hist(x,bins=30,alpha=0.3,normed=True,facecolor='green')
ec.ecgmmplot(y,alpha,mu,sigma)
pl.xlabel='x'
pl.text(-0.9,3, r'$\mu=$'+str(mu))
pl.text(-0.9,2.6, r'$\sigma=$'+str(sigma))
pl.ylim(0,7)
pl.title("ECGMM")
