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 bic1EM(c, delta, r):
"""
Input: color, color errors, radius to the center
BIC,mu,sigma
"""
mu, sigma, aic, bic = ec.wstat(c, delta)
lkhood_b = sum(np.log(pcz2(c, delta, r, mu, sigma)))
BIC = -2. * lkhood_b + 2. * np.log(len(c))
return BIC, mu, sigma
def ecgmmRidgeZbin(z=None,color=None,colorErr=None,mag=None):
#--define some quantity to be returned ----
ok = (color>=-1) * (color <= 5.)
color = color[ok]
colorErr = colorErr[ok]
mag = mag[ok]
z = z[ok]
alpha0 = []
alpha1 = []
mu0 = []
mu1 = []
sigma0 = []
sigma1 = []
ntot = []
amp = []
aic1 = []
aic2 = []
zmd=[]
#-----------------------------------------
startTime=time.time()
h,rev = es.stat.histogram(z,binsize=0.006,rev=True)
for i in range(h.size):
if rev[i] != rev[i+1]:
indices = rev[rev[i]:rev[i+1]]
zmed = np.mean(z[indices])
ok = mag[indices] <= limz(zmed)
indices = indices[ok]
alpha=np.array([0.5,0.5])
mu=np.array([sts.scoreatpercentile(color[indices],per=70),sts.scoreatpercentile(color[indices],per=40)])
sigma=np.array([0.04,0.3])
a2 = gmm.aic_ecgmm(color[indices],colorErr[indices],alpha,mu,sigma)
a1 = gmm.wstat(color[indices],colorErr[indices])[2]
if a2 < a1:
srt=np.argsort(sigma)
alpha0.append(alpha[srt[0]])
alpha1.append(alpha[srt[1]])
mu0.append(mu[srt[0]])
mu1.append(mu[srt[1]])
sigma0.append(sigma[srt[0]])
sigma1.append(sigma[srt[1]])
aic1.append(a1)
aic2.append(a2)
amp.append(len(indices)*alpha[srt[0]])
zmd.append(zmed)
print a2, a1
endTime=time.time()
elapseTime=endTime-startTime
print '---elapsed time: ' + str(elapseTime)
return np.array(alpha0),np.array(alpha1),np.array(mu0),np.array(mu1),np.array(sigma0),np.array(sigma1),np.array(aic1),np.array(aic2),np.array(amp),np.array(zmd)
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]
def ecgmmRidge(ra_c=None,
dec_c=None,
photoz_c=None,
r200_c=None,
ra=None,
dec=None,
color=None,
colorErr=None,
mag=None,
candidateIdx=None):
#--define some quantity to be returned ----
rac = ra_c[candidateIdx]
decc = dec_c[candidateIdx]
photozc = photoz_c[candidateIdx]
r200c = r200_c[candidateIdx]
ok = (color >= -1) * (color <= 5.) * abs(colorErr) < 0.5
color = color[ok]
colorErr = colorErr[ok]
mag = mag[ok]
ra = ra[ok]
dec = dec[ok]
BCGalpha0 = []
BCGalpha1 = []
BCGmu0 = []
BCGmu1 = []
BCGsigma0 = []
BCGsigma1 = []
BCGntot = []
BCGamp = []
BCGaic1 = []
BCGaic2 = []
BCGphotoz = []
#-----------------------------------------
Ncandidates = len(photozc)
ridgeZ = np.zeros(Ncandidates)
depth = 10
h = es.htm.HTM(depth)
Cosmo = es.cosmology.Cosmo(h=0.7)
DA = Cosmo.Da(0, photozc)
srad = np.rad2deg(r200c / DA)
m1, m2, d12 = h.match(rac, decc, ra, dec, srad, maxmatch=5000)
r12 = np.deg2rad(d12) * DA[m1]
indices = (mag[m2] <= limz(photozc[m1])) # no bcg assumed
m1 = m1[indices]
m2 = m2[indices]
h, rev = es.stat.histogram(m1, binsize=1, rev=True)
startTime = time.time()
for i in range(h.size):
if rev[i] != rev[i + 1]:
print i
indx = rev[rev[i]:rev[i + 1]]
alpha = np.array([0.5, 0.5])
mu = np.array([
sts.scoreatpercentile(color[m2[indx]], per=70),
sts.scoreatpercentile(color[m2[indx]], per=40)
])
sigma = np.array([0.04, 0.3])
aic2 = gmm.aic_ecgmm(color[m2[indx]], colorErr[m2[indx]], alpha,
mu, sigma)
aic1 = gmm.wstat(color[m2[indx]], colorErr[m2[indx]])[2]
print aic2, aic1
if aic2 < aic1:
srt = np.argsort(sigma)
BCGalpha0.append(alpha[srt[0]])
BCGalpha1.append(alpha[srt[1]])
BCGmu0.append(mu[srt[0]])
BCGmu1.append(mu[srt[1]])
BCGsigma0.append(sigma[srt[0]])
BCGsigma1.append(sigma[srt[1]])
BCGaic1.append(aic1)
BCGaic2.append(aic2)
BCGamp.append(len(indx) * alpha[srt[0]])
BCGphotoz.append(photozc[m1[indx[0]]])
endTime = time.time()
elapseTime = endTime - startTime
print '---elapsed time: ' + str(elapseTime)
return np.array(BCGalpha0), np.array(BCGalpha1), np.array(
BCGmu0), np.array(BCGmu1), np.array(BCGsigma0), np.array(
BCGsigma1), np.array(BCGaic1), np.array(BCGaic2), np.array(
BCGamp), np.array(BCGphotoz)
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!')
mucl=0.5 # mean of each mixture mubg=0 sdcl=0.04 # width of each mixture sdbg=0.3 cl=np.random.normal(mucl,sdcl,ncl) 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')
mucl=0.5 # mean of each mixture mubg=0 sdcl=0.04 # width of each mixture sdbg=0.3 cl=np.random.normal(mucl,sdcl,ncl) 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')
def ecgmmRidge(ra_c=None, dec_c=None,photoz_c=None,r200_c=None,ra=None, dec=None,color=None,colorErr=None,mag=None,candidateIdx=None):
#--define some quantity to be returned ----
rac = ra_c[candidateIdx]
decc = dec_c[candidateIdx]
photozc = photoz_c[candidateIdx]
r200c = r200_c[candidateIdx]
ok = (color>=-1) * (color <= 5.)*abs(colorErr) < 0.5
color = color[ok]
colorErr = colorErr[ok]
mag = mag[ok]
ra = ra[ok]
dec = dec[ok]
BCGalpha0 = []
BCGalpha1 = []
BCGmu0 = []
BCGmu1 = []
BCGsigma0 = []
BCGsigma1 = []
BCGntot = []
BCGamp = []
BCGaic1 = []
BCGaic2 = []
BCGphotoz=[]
#-----------------------------------------
Ncandidates = len(photozc)
ridgeZ = np.zeros(Ncandidates)
depth = 10
h=es.htm.HTM(depth)
Cosmo = es.cosmology.Cosmo(h=0.7)
DA=Cosmo.Da(0,photozc)
srad=np.rad2deg(r200c/DA)
m1,m2,d12 = h.match(rac,decc,ra,dec,srad,maxmatch=5000)
r12=np.deg2rad(d12)*DA[m1]
indices=(mag[m2]<=limz(photozc[m1])) # no bcg assumed
m1 = m1[indices]
m2 = m2[indices]
h,rev = es.stat.histogram(m1, binsize=1, rev=True)
startTime=time.time()
for i in range(h.size):
if rev[i] != rev[i+1]:
print i
indx = rev[ rev[i]:rev[i+1]]
alpha=np.array([0.5,0.5])
mu=np.array([sts.scoreatpercentile(color[m2[indx]],per=70),sts.scoreatpercentile(color[m2[indx]],per=40)])
sigma=np.array([0.04,0.3])
aic2 = gmm.aic_ecgmm(color[m2[indx]],colorErr[m2[indx]],alpha,mu,sigma)
aic1 = gmm.wstat(color[m2[indx]],colorErr[m2[indx]])[2]
print aic2, aic1
if aic2 < aic1:
srt=np.argsort(sigma)
BCGalpha0.append(alpha[srt[0]])
BCGalpha1.append(alpha[srt[1]])
BCGmu0.append(mu[srt[0]])
BCGmu1.append(mu[srt[1]])
BCGsigma0.append(sigma[srt[0]])
BCGsigma1.append(sigma[srt[1]])
BCGaic1.append(aic1)
BCGaic2.append(aic2)
BCGamp.append(len(indx)*alpha[srt[0]])
BCGphotoz.append(photozc[m1[indx[0]]])
endTime=time.time()
elapseTime=endTime-startTime
print '---elapsed time: ' + str(elapseTime)
return np.array(BCGalpha0),np.array(BCGalpha1),np.array(BCGmu0),np.array(BCGmu1),np.array(BCGsigma0),np.array(BCGsigma1),np.array(BCGaic1),np.array(BCGaic2),np.array(BCGamp),np.array(BCGphotoz)
