def GMRrichness(ra=None,dec=None,photoz=None,cat=None,plot=True,err=True,rw=True,bcg=True,radius=1.):
fra=cat.field('ra')
fdec=cat.field('dec')
imag=cat.field('model_counts')[:,3]
gmr=cat.field('gmr')
gmrerr=cat.field('gmr_err')
depth=12
h=es.htm.HTM(depth)
srad=np.rad2deg(radius/Da(0,photoz))
m1,m2,d12 = h.match(ra,dec,fra,fdec,srad,maxmatch=5000)
cimag=imag[m2[0]]
cgmr=gmr[m2[0]]
r12=np.deg2rad(d12)*Da(0,photoz)
if bcg is True:
indices=(imag[m2]<=limi(photoz))*(imag[m2]>cimag)
else:
indices=(imag[m2]<=limi(photoz))
ntot=len(m2[indices])
if ntot <= 10:
return 0, 0, 0
alpha=np.array([0.5,0.5])
mu=np.array([sts.scoreatpercentile(gmr[m2[indices]],per=70),sts.scoreatpercentile(gmr[m2[indices]],per=40)])
sigma=np.array([0.04,0.3])
if err is True:
if rw is False:
aic2=gmm.aic_ecgmm(gmr[m2[indices]],gmrerr[m2[indices]],alpha,mu,sigma)
aic1=gmm.wstat(gmr[m2[indices]],gmrerr[m2[indices]])[3]
else:
aic2,alpha,mu,sigma=rwgmm.aic2EM(gmr[m2[indices]],gmrerr[m2[indices]],r12[indices],alpha,mu,sigma)
aic1=rwgmm.aic1EM(gmr[m2[indices]],gmrerr[m2[indices]],r12[indices])[0]
else:
aic2=gmm.aic_ecgmm(gmr[m2[indices]],aalpha=alpha,mmu=mu,ssigma=sigma)
aic1=gmm.wstat(gmr[m2[indices]])[3]
if plot==True:
pl.hist(gmr[m2[indices]],bins=30,normed=True,facecolor='green',alpha=0.3)
pl.vlines(cgmr,0,3,color='green')
x=np.arange(-1,5,0.01)
srt=np.argsort(sigma)
alpha=alpha[srt]
mu=mu[srt]
sigma=sigma[srt]
z = gmrz(mu[0])
t=gmm.ecgmmplot(x,alpha,mu,sigma)
pl.xlabel('g - r')
pl.figtext(0.61,0.85,r'$\alpha$: '+str(np.round(alpha,4)))
pl.figtext(0.61,0.8,r'$\mu$: '+str(np.round(mu,4)))
pl.figtext(0.61,0.75,r'$\sigma$: '+str(np.round(sigma,4)))
pl.figtext(0.61,0.68,r'$Amplitude$: '+str(np.round(ntot*alpha[0],2)))
pl.figtext(0.61,0.61,r'$AIC_1$: '+str(aic1))
pl.figtext(0.61,0.54,r'$AIC_2$: '+str(aic2))
pl.figtext(0.61,0.47,'Photoz: '+str(photoz))
pl.figtext(0.61,0.4,'ridgeline Z: '+str(z))
pl.title('Total # of galaxies: '+str(ntot))
return ntot*alpha[0],aic1,aic2,cgmr,alpha,mu,sigma,z
def IMZrichness(ra, dec, photoz, cat, plot=True):
fra = cat.field('ra')
fdec = cat.field('dec')
imag = cat.field('model_counts')[:, 3]
imz = cat.field('imz')
imzerr = cat.field('imz_err')
depth = 12
h = es.htm.HTM(depth)
srad = np.rad2deg(1. / es.cosmology.Da(0, photoz, h=0.7) / (1 + photoz))
m1, m2, d12 = h.match(ra, dec, fra, fdec, srad, maxmatch=5000)
indices = (imag[m2] <= limi(photoz))
ntot = len(m2[indices])
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(imz[m2[indices]], per=70),
sts.scoreatpercentile(imz[m2[indices]], per=40)
])
sigma = np.array([0.04, 0.3])
aic2 = gmm.aic_ecgmm(imz[m2[indices]], imzerr[m2[indices]], alpha, mu,
sigma)
aic1 = gmm.wstat(imz[m2[indices]], imzerr[m2[indices]])[3]
if plot == True:
pl.hist(imz[m2[indices]], bins=30, normed=True, histtype='step')
x = np.arange(-1, 5, 0.01)
srt = np.argsort(sigma)
alpha = alpha[srt]
mu = mu[srt]
sigma = sigma[srt]
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('i - z')
pl.figtext(0.61, 0.85, r'$\alpha$: ' + str(np.round(alpha, 4)))
pl.figtext(0.61, 0.8, r'$\mu$: ' + str(np.round(mu, 4)))
pl.figtext(0.61, 0.75, r'$\sigma$: ' + str(np.round(sigma, 4)))
pl.figtext(0.61, 0.68, r'$Ngals$: ' + str(np.round(ntot * alpha[0])))
pl.figtext(0.61, 0.61, r'$AIC_1$: ' + str(aic1))
pl.figtext(0.61, 0.54, r'$AIC_2$: ' + str(aic2))
pl.figtext(0.61, 0.47, 'Photoz: ' + str(photoz))
pl.title('Total # of galaxies: ' + str(ntot))
return ntot * alpha[0]
z,alp1m,yerr = bp.bin_scatter(redshift,alp1,binsize=0.05,plot=False)
z,mu0m,yerr = bp.bin_scatter(redshift,mu0,binsize=0.05,plot=False)
z,mu1m,yerr = bp.bin_scatter(redshift,mu1,binsize=0.05,plot=False)
z,sgm0m,yerr = bp.bin_scatter(redshift,sgm0,binsize=0.05,plot=False)
z,sgm1m,yerr = bp.bin_scatter(redshift,sgm1,binsize=0.05,plot=False)
alpha = np.array(zip(alp0m,alp1m))
mu = np.array(zip(mu0m,mu1m))
sigma = np.array(zip(sgm0m,sgm1m))
x=np.arange(-0.5,2.5,0.01)
pl.figure(figsize=(15,15))
nn = len(z)
for i in range(nn):
pl.subplot(3,3,i+1)
gmm.ecgmmplot(x,alpha[i],mu[i],sigma[i])
pl.title('zbin: '+str(round(z[i],3))+' +/- 0.025')
pl.xlabel('g - r')
if galtype == 'truth':
pl.ylabel('DES Mock '+version+' : Truth')
else:
pl.ylabel('DES Mock '+version+' : Obs catalog')
if galtype == 'truth':
pl.savefig(catdir+'DES_Mock_'+version+'_gmr_distribution_truth_zbin.png')
else:
pl.savefig(catdir+'DES_mock_'+version+'_gmr_distribution_obs_zbin.png')
pl.close()
#------rmi ridgeline distribution ------
redshift = resRI[9]
def RMIrichness(ra=None,
dec=None,
photoz=None,
cat=None,
plot=True,
err=True,
rw=True,
bcg=True):
fra = cat.field('ra')
fdec = cat.field('dec')
imag = cat.field('imag')
rmi = cat.field('model_mag')[:, 2] - cat.field('model_mag')[:, 3]
rmierr = np.sqrt(
cat.field('model_magerr')[:, 2]**2 +
cat.field('model_magerr')[:, 3]**2)
depth = 12
h = es.htm.HTM(depth)
srad = np.rad2deg(1. / Da(0, photoz))
m1, m2, d12 = h.match(ra, dec, fra, fdec, srad, maxmatch=50000)
r12 = np.deg2rad(d12) * Da(0, photoz)
cimag = imag[m2[0]]
crmi = rmi[m2[0]]
if bcg is True:
indices = (imag[m2] <= limi0_2(photoz)) * (imag[m2] > cimag)
else:
indices = (imag[m2] <= limi0_2(photoz))
ntot = len(m2[indices])
if ntot <= 10:
return 'not enough galaxy brighter than 0.2 L*'
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(rmi[m2[indices]], per=70),
sts.scoreatpercentile(rmi[m2[indices]], per=40)
])
sigma = np.array([0.04, 0.3])
if err is True:
if rw is False:
aic2 = gmm.aic_ecgmm(rmi[m2[indices]], rmierr[m2[indices]], alpha,
mu, sigma)
aic1 = gmm.wstat(rmi[m2[indices]], rmierr[m2[indices]])[3]
else:
aic2, alpha, mu, sigma = rwgmm.aic2EM(rmi[m2[indices]],
rmierr[m2[indices]],
r12[indices], alpha, mu,
sigma)
aic1 = rwgmm.aic1EM(rmi[m2[indices]], rmierr[m2[indices]],
r12[indices])[0]
else:
aic2 = gmm.aic_ecgmm(rmi[m2[indices]],
aalpha=alpha,
mmu=mu,
ssigma=sigma)
aic1 = gmm.wstat(rmi[m2[indices]])[3]
srt = np.argsort(sigma)
alpha = alpha[srt]
mu = mu[srt]
sigma = sigma[srt]
z = rmiz(mu[0])
if plot == True:
pl.figure(figsize=(12, 6))
pl.subplot(1, 2, 1)
hh = pl.hist(rmi[m2[indices]],
bins=50,
normed=True,
facecolor='green',
alpha=0.3,
range=[-1, 3])
pl.vlines(crmi,
0,
hh[0].max() + 0.5,
color='red',
lw=2,
linestyle='dashed')
pl.grid()
x = np.arange(-1, 3, 0.01)
t = gmm.ecgmmplot(x, alpha, mu, sigma)
richness = ntot * alpha[0]
pl.xlabel('r - i')
pl.figtext(0.61, 0.85, 'Relative Weights: ' + str(np.round(alpha, 4)))
pl.figtext(0.61, 0.8, 'Mean Colors: ' + str(np.round(mu, 4)))
pl.figtext(0.61, 0.75, 'Mean Color Widths: ' + str(np.round(sigma, 4)))
pl.figtext(0.61, 0.68, 'Richness: ' + str(np.round(richness, 2)))
pl.figtext(0.61, 0.61, r'$AIC_1$: ' + str(aic1))
pl.figtext(0.61, 0.54, r'$AIC_2$: ' + str(aic2))
pl.figtext(0.61, 0.47, 'Test Photoz: ' + str(photoz))
pl.figtext(0.61, 0.4, 'Ridgeline Photoz: ' + str(round(z, 3)))
pl.figtext(0.61, 0.33,
'R200: ' + str(round(0.09 * richness**0.798, 2)) + ' Mpc')
pl.figtext(
0.61, 0.25, 'M200: ' + str(round(8.8 * (richness / 19.)**1.7, 2)) +
'x10^13 Solar Mass')
pl.title('Total # of galaxies: ' + str(ntot))
return richness, aic1, aic2, crmi, alpha, mu, sigma, z
def ZMYrichness(ra=None,
dec=None,
photoz=None,
cat=None,
plot=True,
err=True,
rw=True,
bcg=True):
fra = cat.field('ra')
fdec = cat.field('dec')
imag = cat.field('mag_z')
zmy = cat.field('mag_z') - cat.field('mag_y')
zmyerr = np.sqrt(cat.field('MAGERR_z')**2 + cat.field('MAGERR_y')**2)
depth = 10
h = es.htm.HTM(depth)
srad = np.rad2deg(1. / Da(0, photoz))
m1, m2, d12 = h.match(ra, dec, fra, fdec, srad, maxmatch=5000)
cimag = imag[m2[0]]
czmy = zmy[m2[0]]
r12 = np.deg2rad(d12) * Da(0, photoz)
if bcg is True:
indices = (imag[m2] <= limi(photoz)) * (imag[m2] > cimag)
else:
indices = (imag[m2] <= limi(photoz))
ntot = len(m2[indices])
if ntot <= 10:
return 0, 0, 0
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(zmy[m2[indices]], per=70),
sts.scoreatpercentile(zmy[m2[indices]], per=40)
])
sigma = np.array([0.04, 0.3])
if err is True:
if rw is False:
aic2 = gmm.aic_ecgmm(zmy[m2[indices]], zmyerr[m2[indices]], alpha,
mu, sigma)
aic1 = gmm.wstat(zmy[m2[indices]], zmyerr[m2[indices]])[3]
else:
aic2, alpha, mu, sigma = rwgmm.aic2EM(zmy[m2[indices]],
zmyerr[m2[indices]],
r12[indices], alpha, mu,
sigma)
aic1 = rwgmm.aic1EM(zmy[m2[indices]], zmyerr[m2[indices]],
r12[indices])[0]
else:
aic2 = gmm.aic_ecgmm(zmy[m2[indices]],
aalpha=alpha,
mmu=mu,
ssigma=sigma)
aic1 = gmm.wstat(zmy[m2[indices]])[3]
if plot == True:
pl.hist(zmy[m2[indices]],
bins=30,
normed=True,
facecolor='green',
alpha=0.3)
pl.vlines(czmy, 0, 3, color='pink')
x = np.arange(-1, 5, 0.01)
srt = np.argsort(sigma)
alpha = alpha[srt]
mu = mu[srt]
sigma = sigma[srt]
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('i - z')
pl.figtext(0.61, 0.85, r'$\alpha$: ' + str(np.round(alpha, 4)))
pl.figtext(0.61, 0.8, r'$\mu$: ' + str(np.round(mu, 4)))
pl.figtext(0.61, 0.75, r'$\sigma$: ' + str(np.round(sigma, 4)))
pl.figtext(0.61, 0.68,
r'$Amplitude$: ' + str(np.round(ntot * alpha[0], 2)))
pl.figtext(0.61, 0.61, r'$AIC_1$: ' + str(aic1))
pl.figtext(0.61, 0.54, r'$AIC_2$: ' + str(aic2))
pl.figtext(0.61, 0.47, 'Photoz: ' + str(photoz))
pl.title('Total # of galaxies: ' + str(ntot))
return ntot * alpha[0], aic1, aic2
def redsker(b, idx, err=True):
depth = 12
h = es.htm.HTM(depth)
ra = b.field('ra')
dec = b.field('dec')
photoz = b.field('z')
central = b.field('central')
gmr = b.field('omag')[:, 0] - b.field('omag')[:, 1]
rmi = b.field('omag')[:, 1] - b.field('omag')[:, 2]
imz = b.field('omag')[:, 2] - b.field('omag')[:, 3]
gmz = b.field('omag')[:, 0] - b.field('omag')[:, 3]
rmz = b.field('omag')[:, 1] - b.field('omag')[:, 3]
gmi = b.field('omag')[:, 0] - b.field('omag')[:, 2]
num = len(ra)
if err:
gmrerr = b.field('omagerr')[:, 0] - b.field('omagerr')[:, 1]
rmierr = b.field('omagerr')[:, 1] - b.field('omagerr')[:, 2]
imzerr = b.field('omagerr')[:, 2] - b.field('omagerr')[:, 3]
gmzerr = b.field('omagerr')[:, 0] - b.field('omagerr')[:, 3]
rmzerr = b.field('omagerr')[:, 1] - b.field('omagerr')[:, 3]
gmierr = b.field('omagerr')[:, 0] - b.field('omagerr')[:, 2]
else:
gmrerr = np.zeros(num)
rmierr = np.zeros(num)
imzerr = np.zeros(num)
gmzerr = np.zeros(num)
rmzerr = np.zeros(num)
gmierr = np.zeros(num)
iamag = b.field('amag')[:, 2]
imag = b.field('omag')[:, 2]
srad = np.rad2deg(1. / es.cosmology.Da(0, photoz[idx], h=0.7) /
(1 + photoz[idx]))
m1, m2, d12 = h.match(ra[idx], dec[idx], ra, dec, srad, maxmatch=5000)
indices = (imag[m2] <= limi(photoz[idx])) * (imag[m2] > imag[m1])
#indices=(iamag[m2]<=-20)*(iamag[m2]>iamag[m1])
ntot = len(m2[indices])
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(gmr[m2[indices]], per=80),
sts.scoreatpercentile(gmr[m2[indices]], per=30)
])
sigma = np.array([0.04, 0.3])
aic2 = gmm.aic_ecgmm(gmr[m2[indices]], gmrerr[m2[indices]], alpha, mu,
sigma)
aic1 = gmm.wstat(gmr[m2[indices]], gmrerr[m2[indices]])[3]
fig = pl.figure(figsize=(15, 8))
ax = fig.add_subplot(2, 3, 1)
pl.hist(gmr[m2[indices]], bins=30, normed=True, histtype='step')
x = np.arange(-1, 5, 0.01)
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('g - r')
pl.title('M200: ' + str(b[idx].field('m200')))
pl.text(0.1,
0.85,
r'$\alpha$: ' + str(np.round(alpha, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.8,
r'$\mu$: ' + str(np.round(mu, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.75,
r'$\sigma$: ' + str(np.round(sigma, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.68,
r'$Ngals$: ' + str(np.round(ntot * alpha[0])),
transform=ax.transAxes)
pl.text(0.1,
0.6,
r'$AIC$: ' + str(np.round(aic1)) + ', ' + str(np.round(aic2)),
transform=ax.transAxes)
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(rmi[m2[indices]], per=80),
sts.scoreatpercentile(rmi[m2[indices]], per=30)
])
sigma = np.array([0.04, 0.3])
aic2 = gmm.aic_ecgmm(rmi[m2[indices]], rmierr[m2[indices]], alpha, mu,
sigma)
aic1 = gmm.wstat(rmi[m2[indices]], rmierr[m2[indices]])[3]
ax = fig.add_subplot(2, 3, 2)
pl.hist(rmi[m2[indices]], bins=30, normed=True, histtype='step')
x = np.arange(-1, 5, 0.01)
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('r - i')
pl.title('photoz: ' + str(photoz[idx]))
pl.xlim(-0.2, 2.5)
pl.text(0.1,
0.85,
r'$\alpha$: ' + str(np.round(alpha, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.8,
r'$\mu$: ' + str(np.round(mu, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.75,
r'$\sigma$: ' + str(np.round(sigma, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.68,
r'$Ngals$: ' + str(np.round(ntot * alpha[0])),
transform=ax.transAxes)
pl.text(0.1,
0.6,
r'$AIC$: ' + str(np.round(aic1)) + ', ' + str(np.round(aic2)),
transform=ax.transAxes)
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(imz[m2[indices]], per=60),
sts.scoreatpercentile(imz[m2[indices]], per=30)
])
sigma = np.array([0.02, 0.3])
aic2 = gmm.aic_ecgmm(imz[m2[indices]], imzerr[m2[indices]], alpha, mu,
sigma)
aic1 = gmm.wstat(imz[m2[indices]], imzerr[m2[indices]])[3]
ax = fig.add_subplot(2, 3, 3)
pl.hist(imz[m2[indices]], bins=30, normed=True, histtype='step')
x = np.arange(-1, 5, 0.01)
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('i - z')
pl.title('Ntot: ' + str(ntot))
pl.xlim(-0.2, 2.5)
pl.text(0.1,
0.85,
r'$\alpha$: ' + str(np.round(alpha, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.8,
r'$\mu$: ' + str(np.round(mu, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.75,
r'$\sigma$: ' + str(np.round(sigma, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.68,
r'$Ngals$: ' + str(np.round(ntot * alpha[0])),
transform=ax.transAxes)
pl.text(0.1,
0.6,
r'$AIC$: ' + str(np.round(aic1)) + ', ' + str(np.round(aic2)),
transform=ax.transAxes)
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(gmz[m2[indices]], per=60),
sts.scoreatpercentile(gmz[m2[indices]], per=30)
])
sigma = np.array([0.02, 0.3])
aic2 = gmm.aic_ecgmm(gmz[m2[indices]], gmzerr[m2[indices]], alpha, mu,
sigma)
aic1 = gmm.wstat(gmz[m2[indices]], gmzerr[m2[indices]])[3]
ax = fig.add_subplot(2, 3, 4)
pl.hist(gmz[m2[indices]], bins=30, normed=True, histtype='step')
x = np.arange(-1, 5, 0.01)
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('g - z')
pl.text(0.1,
0.85,
r'$\alpha$: ' + str(np.round(alpha, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.8,
r'$\mu$: ' + str(np.round(mu, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.75,
r'$\sigma$: ' + str(np.round(sigma, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.68,
r'$Ngals$: ' + str(np.round(ntot * alpha[0])),
transform=ax.transAxes)
pl.text(0.1,
0.6,
r'$AIC$: ' + str(np.round(aic1)) + ', ' + str(np.round(aic2)),
transform=ax.transAxes)
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(rmz[m2[indices]], per=60),
sts.scoreatpercentile(rmz[m2[indices]], per=30)
])
sigma = np.array([0.02, 0.3])
aic2 = gmm.aic_ecgmm(rmz[m2[indices]], rmzerr[m2[indices]], alpha, mu,
sigma)
aic1 = gmm.wstat(rmz[m2[indices]], rmzerr[m2[indices]])[3]
ax = fig.add_subplot(2, 3, 5)
pl.hist(rmz[m2[indices]], bins=30, normed=True, histtype='step')
x = np.arange(-1, 5, 0.01)
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('r - z')
pl.text(0.1,
0.85,
r'$\alpha$: ' + str(np.round(alpha, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.8,
r'$\mu$: ' + str(np.round(mu, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.75,
r'$\sigma$: ' + str(np.round(sigma, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.68,
r'$Ngals$: ' + str(np.round(ntot * alpha[0])),
transform=ax.transAxes)
pl.text(0.1,
0.6,
r'$AIC$: ' + str(np.round(aic1)) + ', ' + str(np.round(aic2)),
transform=ax.transAxes)
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(gmi[m2[indices]], per=60),
sts.scoreatpercentile(gmi[m2[indices]], per=30)
])
sigma = np.array([0.02, 0.3])
aic2 = gmm.aic_ecgmm(gmi[m2[indices]], gmierr[m2[indices]], alpha, mu,
sigma)
aic1 = gmm.wstat(gmi[m2[indices]], gmierr[m2[indices]])[3]
ax = fig.add_subplot(2, 3, 6)
pl.hist(gmi[m2[indices]], bins=30, normed=True, histtype='step')
x = np.arange(-1, 5, 0.01)
t = gmm.ecgmmplot(x, alpha, mu, sigma)
pl.xlabel('g - i')
pl.text(0.1,
0.85,
r'$\alpha$: ' + str(np.round(alpha, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.8,
r'$\mu$: ' + str(np.round(mu, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.75,
r'$\sigma$: ' + str(np.round(sigma, 4)),
transform=ax.transAxes)
pl.text(0.1,
0.68,
r'$Ngals$: ' + str(np.round(ntot * alpha[0])),
transform=ax.transAxes)
pl.text(0.1,
0.6,
r'$AIC$: ' + str(np.round(aic1)) + ', ' + str(np.round(aic2)),
transform=ax.transAxes)
return ('Plot is done!')
# 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")
# non-error corrected GMM
alpha=np.array([0.5,0.5])
mu=np.array([0.3,0.1])
sigma=np.array([0.05,0.2])
xerr[:]=0
# 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")
# non-error corrected GMM
alpha=np.array([0.5,0.5])
mu=np.array([0.3,0.1])
sigma=np.array([0.05,0.2])
xerr[:]=0
z,alp1m,yerr = bp.bin_scatter(redshift,alp1,binsize=0.05,plot=False)
z,mu0m,yerr = bp.bin_scatter(redshift,mu0,binsize=0.05,plot=False)
z,mu1m,yerr = bp.bin_scatter(redshift,mu1,binsize=0.05,plot=False)
z,sgm0m,yerr = bp.bin_scatter(redshift,sgm0,binsize=0.05,plot=False)
z,sgm1m,yerr = bp.bin_scatter(redshift,sgm1,binsize=0.05,plot=False)
alpha = np.array(zip(alp0m,alp1m))
mu = np.array(zip(mu0m,mu1m))
sigma = np.array(zip(sgm0m,sgm1m))
x=np.arange(-0.5,2.5,0.01)
pl.figure(figsize=(15,15))
nn = len(z)
for i in range(nn):
pl.subplot(3,3,i+1)
gmm.ecgmmplot(x,alpha[i],mu[i],sigma[i])
pl.title('zbin: '+str(round(z[i],3))+' +/- 0.025')
pl.xlabel('g - r')
if galtype == 'input':
pl.ylabel('DC6B input catalog')
else:
pl.ylabel('DC6B output catalog')
if galtype == 'input':
pl.savefig(catdir+'dc6b_gmr_distribution_input.png')
else:
pl.savefig(catdir+'dc6b_gmr_distribution_output.png')
pl.close()
#------rmi ridgeline distribution ------
redshift = resRI[9]