def plotProfile(datfilebase, c, label, labelZ = True):
mass, concen, redshift = readtxtfile.readtxtfile('%s.dat' % datfilebase)[0]
cat = ldac.openObjectFile('%s.cat' % datfilebase)
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
r_mpc = np.ascontiguousarray(cat['r_mpc'], dtype='<d')
clean = r_mpc > 0.05
rho_c_over_sigma_c = 1.5 * nfwutils.global_cosmology.angulardist(zlens) * nfwutils.global_cosmology.beta([1e6], zlens)[0] * nfwutils.global_cosmology.hubble2(zlens) / nfwutils.global_cosmology.v_c**2
gamma = nfwmodeltools.NFWShear(r_mpc[clean], concen, rscale, rho_c_over_sigma_c)
kappa = nfwmodeltools.NFWKappa(r_mpc[clean], concen, rscale,rho_c_over_sigma_c)
gpred = cat['beta_s'][clean]*gamma / (1 - (cat['beta_s2'][clean]*kappa/cat['beta_s'][clean]))
signalpred = gpred / (cat['beta_s'][clean]*nfwutils.global_cosmology.beta([1e6], zlens)*nfwutils.global_cosmology.angulardist(zlens))
# print signalpred
if labelZ:
flabel='%s z=%1.2f' % (label, zlens)
else:
flabel =label
pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c, label=flabel)
pylab.plot(cat['r_mpc'][clean]*nfwutils.global_cosmology.h, signalpred, marker='None', linestyle=':', color=c, linewidth=2)
def deltaShear(zspec, zphot, zcluster = 0.2):
ginfpredict = nfwmodeltools.NFWShear(ones(1), 4.0, 1., zcluster)
beta_s_spec = nfwutils.beta_s(zspec, zcluster)
beta_s_phot = nfwutils.beta_s(zphot, zcluster)
return (beta_s_spec - beta_s_phot)*ginfpredict
def reducedShear(scaledm200):
m200 = 1e14 * scaledm200
c = duffy(m200, zcluster)
rs = nfwutils.rscaleConstM(m200, c, zcluster, 200)
gamma = tools.NFWShear(r_mpc, c, rs, rho_c_over_sigma_c)
kappa = tools.NFWKappa(r_mpc, c, rs, rho_c_over_sigma_c)
g = beta_s * gamma / (1 - beta_cor * kappa)
return g
def NFWModel(r, r_scale):
nfw_shear = beta_s * tools.NFWShear(r, concentration, r_scale,
zcluster, D_lens)
nfw_kappa = beta_s * tools.NFWKappa(r, concentration, r_scale,
zcluster, D_lens)
g = (1 +
(beta_s2 / beta_s**2 - 1) * nfw_kappa) * nfw_shear / (1 -
nfw_kappa)
return g
def create_nfwmodel_shapedata(concentration, scale_radius, zcluster, ngals, zs,
rs):
realBetas = nfwutils.beta_s(zs, zcluster)
gamma = realBetas * tools.NFWShear(rs, concentration, scale_radius,
zcluster)
kappa = realBetas * tools.NFWKappa(rs, concentration, scale_radius,
zcluster)
g = gamma / (1 - kappa)
return g, gamma, kappa
def NFWModel(coords, r_scale):
r = coords[:, 0]
beta_s = coords[:, 1]
nfw_shear = beta_s * tools.NFWShear(r, concentration, r_scale,
zcluster)
nfw_kappa = beta_s * tools.NFWKappa(r, concentration, r_scale,
zcluster)
g = nfw_shear / (1 - nfw_kappa)
return g
def createPerfectProfile(m200, c, zcluster, r_mpc, beta_s):
rho_c_over_sigma_c = 1.5 * nfwutils.global_cosmology.angulardist(
zcluster) * nfwutils.global_cosmology.beta(
[1e6], zcluster)[0] * nfwutils.global_cosmology.hubble2(
zcluster) / nfwutils.global_cosmology.v_c**2
r_scale = nfwutils.rscaleConstM(m200, c, zcluster, 200)
nfw_shear_inf = tools.NFWShear(r_mpc, c, r_scale, rho_c_over_sigma_c)
nfw_kappa_inf = tools.NFWKappa(r_mpc, c, r_scale, rho_c_over_sigma_c)
g = beta_s * nfw_shear_inf / (1 - (beta_s * nfw_kappa_inf))
return g
def scaleShear(controller, rs, concen = 4., cosmology = nfwutils.std_cosmology):
#assumes BentVoigt3Shapedistro
inputcat = controller.inputcat
z_b = inputcat['z_b']
zcluster = controller.zcluster
cluster_exclusion = np.logical_and(zcluster-0.05 < z_b, z_b < zcluster + 0.1)
inputcat = inputcat.filter(np.logical_not(cluster_exclusion))
r_mpc = inputcat['r_mpc']
ghats = inputcat['ghats']
size = inputcat['size']
z_b = inputcat['z_b']
comovingdist = nfwutils.ComovingDistMemoization(cosmology)
pivot, m_slope, m_b, m_cov, c = controller.modelbuilder.psfDependence('interp', controller.psfsize)
betainf = nfwutils.beta([1e6], zcluster, comovingdist = comovingdist)
#matching definitions with Taylor et al 2011 -> need to get rid of beta_inf in shear amp
shear = nfwmodeltools.NFWShear(r_mpc, concen, rs, zcluster, comovingdist = comovingdist) / betainf
kappa = nfwmodeltools.NFWKappa(r_mpc, concen, rs, zcluster, comovingdist = comovingdist)
m = np.zeros_like(size)
m[size >= pivot] = m_b
m[size < pivot] = m_slope*(size[size < pivot] - pivot) + m_b
beta_s = nfwutils.beta_s(z_b, zcluster, comovingdist = comovingdist)
estimators = (ghats - c)*(1-beta_s*kappa)/((1+m)*shear)
comoving_cluster = comovingdist(zcluster)
scaledZ = np.array([comovingdist(zi)/comoving_cluster for zi in z_b])
estimators[scaledZ < 1] = ((ghats - c)/(1+m))[scaledZ < 1]
return scaledZ, estimators
def residual(binbase):
mass, concen, redshift = readtxtfile.readtxtfile('%s.dat' % binbase)[0]
cat = ldac.openObjectFile('%s.cat' % binbase)
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
fig = pylab.figure()
# pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat']/gpred, cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])*gpred), fmt='bo')
# pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat']/gpred, cat['ghatdistrosigma']/(gpred), fmt='bo')
pylab.errorbar(cat['r_mpc'], cat['ghat'], cat['ghatdistrosigma']/np.sqrt(cat['ndat']), fmt='bo')
#
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.xlabel('Radius [Mpc/h]', fontsize=16)
pylab.ylabel('<g_meas/g_pred> - 1)', fontsize=16)
# pylab.ylabel('<g_meas - g_pred>', fontsize=16)
pylab.axis([0.05, 10, -.55, 0.35])
# ax2 = ax.twinx()
# pylab.plot(cat['r_mpc'], gpred, 'k--')
# ax2.errorbar(cat['r_mpc'], cat['ghat'], cat['ghatdistrosigma']/np.sqrt(cat['ndat']), fmt='rs')
# ax2.set_ylabel('<g_meas - g_pred>', color='r', fontsize=16)
# ax2.set_ylim(-0.2, 0.1)
#
# pylab.title('Redshift=%1.2f Mass=%1.2fx10^14 Concen=%1.2f' % (zlens, mass/1e14, concen))
#
pylab.tight_layout()
pylab.savefig('%s.png' % binbase)
return fig
def profileFromMass(mcrelation, mdelta, delta, r_mpc, simcat):
zcluster = simcat.hdu.header['ZLENS']
concentration = mcrelation(mdelta, zcluster, delta)
r_scale = nfwutils.rscaleConstM(mdelta, concentration, zcluster, delta)
nfw_shear_inf = tools.NFWShear(r_mpc, concentration, r_scale, zcluster)
nfw_kappa_inf = tools.NFWKappa(r_mpc, concentration, r_scale, zcluster)
beta_s = np.mean(simcat['beta_s'])
beta_s2 = np.mean(simcat['beta_s']**2)
g = beta_s * nfw_shear_inf / (1 - ((beta_s2 / beta_s) * nfw_kappa_inf))
return g
def createNLikelihood(mcluster,
zcluster,
z0,
sigz0,
rmin=0.75,
rmax=3.0,
c=4.0,
massrad=1.5,
gsig=0.25,
npoints=200000):
rs = nfwutils.RsMassInsideR(mcluster, c, zcluster, massrad)
linmin = rmin / np.sqrt(2)
linmax = rmax / np.sqrt(2)
r_points = np.sqrt(
np.random.uniform(linmin, linmax, size=npoints)**2 +
np.random.uniform(linmin, linmax, size=npoints)**2)
z_points = z0 + sigz0 * np.random.standard_normal(npoints)
gamma_inf = nfwmodeltools.NFWShear(r_points, c, rs, zcluster)
kappa_inf = nfwmodeltools.NFWKappa(r_points, c, rs, zcluster)
beta_s = nfwutils.beta_s(z_points, zcluster)
g0 = beta_s * gamma_inf / (1 - beta_s * kappa_inf)
ghat_points = g0 + gsig * np.random.standard_normal(npoints)
dzt = 0.01
pdzrange = np.arange(z0 - 5 * sigz0, z0 + 5 * sigz0, dzt)
pdz = stats.Gaussian(pdzrange, z0, sigz0) * dzt
betas = np.array(nfwutils.beta_s(pdzrange, zcluster))
def nloglike(Mguess):
#units of 1e14
Mguess = Mguess * 1e14
rs_guess = nfwutils.RsMassInsideR(Mguess, c, zcluster, massrad)
return -pdzperturbtools.nloglike_loop(r_points, z_points, ghat_points,
betas, pdz, gsig, rs_guess, c,
zcluster)
return nloglike
def __call__(self, x, m200, c200):
if m200 == 0.:
return np.zeros_like(x)
isNegative=m200 < 0
if isNegative:
m200 = np.abs(m200)
r_scale = nfwutils.rscaleConstM(m200*self.massScale, c200, self.zcluster, self.overdensity)
nfw_shear_inf = tools.NFWShear(x, c200, r_scale, self.rho_c_over_sigma_c)
nfw_kappa_inf = tools.NFWKappa(x, c200, r_scale, self.rho_c_over_sigma_c)
if isNegative:
nfw_shear_inf = -nfw_shear_inf
g = self.beta_s*nfw_shear_inf / (1 - ((self.beta_s2/self.beta_s)*nfw_kappa_inf) )
return g
def alteredCosmology(cat, om):
cosmology = nfwutils.Cosmology(om, 1. - om)
comovingdist = nfwutils.ComovingDistMemoization(cosmology)
betas = nfwutils.beta_s(cat['z'], 0.3, comovingdist)
dl = nfwutils.angulardist(0.3, comovingdist=comovingdist)
r_mpc = cat['r_pix'] * 0.2 * (1. / 3600.) * (np.pi / 180.) * dl
shears = tools.NFWShear(r_mpc,
4.0,
0.5,
0.3,
dl,
Omega_m=om,
Omega_l=1 - om)
kappa = tools.NFWKappa(r_mpc,
4.0,
0.5,
0.3,
dl,
Omega_m=om,
Omega_l=1 - om)
g = betas * shears / (1 - betas * kappa)
scale_radii = beta_method(cat['r_pix'],
r_mpc,
g,
np.mean(betas),
np.mean(betas**2),
4,
0.3,
Omega_m=om,
Omega_l=1 - om)
mass = nfwutils.massInsideR(scale_radii, 4.0, 0.3, 1, cosmology)
return mass
def multibinresidual(binbase, fig = None):
matplotlib.rcParams['figure.figsize'] = [16,16]
if fig is None:
fig = pylab.figure()
curplot = 1
for curm in range(4):
for curc in range(4):
pylab.subplot(4,4,curplot)
colori = 0
# for curz in range(3):
for curz in [0]:
mass, concen, redshift = readtxtfile.readtxtfile('%s_%d_%d_%d.dat' % (binbase, curz, curm, curc))[0]
cat = ldac.openObjectFile('%s_%d_%d_%d.cat' % (binbase, curz, curm, curc))
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
pylab.errorbar(cat['r_mpc']*0.72, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c[colori], label='BCC z=%1.1f' % redshift)
pylab.plot(cat['r_mpc']*0.72, gpred, 'k-', linewidth=2)
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.legend(loc='lower center', fontsize=10)
pylab.axis([0.05, 10, -.55, 0.35])
# pylab.axis([0.05, 10, -.10, 0.05])
colori+= 1
pylab.title('M=%1.1fx10^14 C=%1.1f' % ( mass/1e14, concen))
curplot += 1
for i in range(4):
pylab.subplot(4,4,13+i)
pylab.xlabel('Radius [Mpc/h]')
pylab.subplot(4,4,4*i+1)
# pylab.ylabel('<g_m-g_p>/g_p(<M>,<c>)')
# pylab.ylabel('<g_m/g_p - 1>')
pylab.tight_layout()
# pylab.savefig('%s_multibin_fracresid.png' % binbase)
pylab.savefig('%s_multibin_resid.png' % binbase)
return fig
def __init__(self, filebase):
print 'Loading %s' % filebase
#the file in this case is a configuration file with information about mass, concentration, redshift, etc
super(AnalyticSim, self).__init__()
with open(filebase) as input:
config = yaml.load(input)
max_dist = config['max_dist']
gridlength = config['gridlength']
zcluster = config['zcluster']
m200 = config['m200']
c200 = config['c200']
rscale = nfwutils.rscaleConstM(m200, c200, zcluster, 200.)
xs = np.linspace(-max_dist, max_dist, gridlength)
ys = np.linspace(-max_dist, max_dist, gridlength)
x_mpc, y_mpc = [grid.flatten() for grid in np.meshgrid(xs, ys, indexing='ij')]
r_mpc = np.sqrt(x_mpc**2 + y_mpc**2)
Dl = nfwutils.global_cosmology.angulardist(zcluster)
x_arcmin = (x_mpc/Dl)*(180./np.pi)*60.
y_arcmin = (y_mpc/Dl)*(180./np.pi)*60.
rho_c_over_sigma_c = 1.5 * nfwutils.global_cosmology.angulardist(zcluster) * nfwutils.global_cosmology.beta([1e6], zcluster)[0] * nfwutils.global_cosmology.hubble2(zcluster) / nfwutils.global_cosmology.v_c**2
gamma_t_inf = nfwmodeltools.NFWShear(r_mpc,
c200,
rscale,
rho_c_over_sigma_c)
kappa_inf = nfwmodeltools.NFWKappa(r_mpc,
c200,
rscale,
rho_c_over_sigma_c)
posangle = np.arctan2(y_mpc, x_mpc)
cos2phi = np.cos(2*posangle)
sin2phi = np.sin(2*posangle)
gamma1_inf = -gamma_t_inf*cos2phi
gamma2_inf = -gamma_t_inf*sin2phi
self.zcluster = zcluster
self.x_mpc = x_mpc
self.y_mpc = y_mpc
self.x_arcmin = x_arcmin
self.y_arcmin = y_arcmin
self.gamma1_inf = gamma1_inf
self.gamma2_inf = gamma2_inf
self.kappa_inf = kappa_inf
def BK11multibinresidual(dirbase):
setBK11()
matplotlib.rcParams['figure.figsize'] = [16,16]
fig = pylab.figure()
curplot = 1
for curm in range(4):
for curc in range(4):
pylab.subplot(4,4,curplot)
colori = 1
for snap in [124,141]:
try:
mass, concen, redshift = readtxtfile.readtxtfile('%s_%d/bk11stack_%d_%d.dat' % (dirbase, snap,
curm, curc))[0]
cat = ldac.openObjectFile('%s_%d/bk11stack_%d_%d.cat' % (dirbase, snap,
curm, curc))
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c[colori], label='M=%1.1fx10^14' % (mass/1e14))
pylab.plot(cat['r_mpc']*nfwutils.global_cosmology.h, gpred, 'k-', linewidth=2)
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.axis([0.05, 10, -0.03, 0.4])
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
# pylab.legend(loc='lower center', fontsize=10)
# pylab.title('M=%1.1fx10^14 C=%1.1f' % ( mass/1e14, concen))
except:
pass
colori+= 1
curplot += 1
for i in range(4):
pylab.subplot(4,4,13+i)
pylab.xlabel('Radius [Mpc/h]')
pylab.subplot(4,4,4*i+1)
pylab.ylabel('<g_m/g_p - 1>')
pylab.tight_layout()
pylab.savefig('%s_multibin_shearprofile.png' % dirbase)
# pylab.savefig('%s_multibin_resid.png' % binbase)
return fig
def MXXLmultibinresidualoverplot(binbase, fig = None):
setMXXL()
if fig is None:
matplotlib.rcParams['figure.figsize'] = [16,8]
fig = pylab.figure()
curplot = 5
for curc in range(4):
pylab.subplot(2,4,curplot)
colori = 0
for curm in range(2):
try:
mass, concen = readtxtfile.readtxtfile('%s_%d_%d.dat' % (binbase, curm, curc))[0]
cat = ldac.openObjectFile('%s_%d_%d.cat' % (binbase, curm, curc))
zlens = cat.hdu.header['ZLENS']
rscale = nfwutils.rscaleConstM(mass/nfwutils.global_cosmology.h, concen, zlens, 200)
gamma = nfwmodeltools.NFWShear(cat['r_mpc'], concen, rscale, zlens)
kappa = nfwmodeltools.NFWKappa(cat['r_mpc'], concen, rscale, zlens)
gpred = cat['beta_s']*gamma / (1 - (cat['beta_s2']*kappa/cat['beta_s']))
pylab.errorbar(cat['r_mpc']*nfwutils.global_cosmology.h, cat['ghat'], cat['ghatdistrosigma']/(np.sqrt(cat['ndat'])),
linestyle='None', marker='o', color=c[colori], label='M=%1.1fx10^14' % (mass/1e14))
pylab.plot(cat['r_mpc']*nfwutils.global_cosmology.h, gpred, 'k-', linewidth=2)
ax = pylab.gca()
ax.set_xscale('log')
pylab.axhline(0.0, c='k', linewidth=2)
pylab.axis([0.05, 10, -0.03, 0.4])
except:
pass
colori+= 1
pylab.title('C=%1.1f' % (concen))
curplot += 1
for i in range(4):
pylab.subplot(2,4,4+i+1)
pylab.xlabel('Radius [Mpc/h]')
pylab.text(0.1, 0.03, 'MXXL')
pylab.minorticks_on()
pylab.subplot(2,4,5)
pylab.ylabel('<g_m/g_p-1>')
pylab.subplot(2,4,8)
pylab.legend(loc='upper right')
pylab.tight_layout()
pylab.savefig('%s_shearstack.png' % binbase)
return fig