def beta_method(r_pix,
r_mpc,
ghat,
beta_s,
beta_s2,
concentration,
zcluster,
pixscale=__DEFAULT_PIXSCALE__,
fitrange=(0.5, 3.),
Omega_m=0.3,
Omega_l=0.7):
cosmology = nfwutils.Cosmology(Omega_m, Omega_l)
comovingdist = nfwutils.ComovingDistMemoization(cosmology=cosmology)
def NFWModel(r, r_scale):
nfw_shear = beta_s * tools.NFWShear(r, concentration, r_scale,
zcluster, comovingdist)
nfw_kappa = beta_s * tools.NFWKappa(r, concentration, r_scale,
zcluster, comovingdist)
g = (1 +
(beta_s2 / beta_s**2 - 1) * nfw_kappa) * nfw_shear / (1 -
nfw_kappa)
return g
minPix = fitrange[0] * 3600. * (180. / np.pi) / (pixscale * D_lens)
maxPix = fitrange[1] * 3600. * (180. / np.pi) / (pixscale * D_lens)
profile = sp.shearprofile(np.zeros_like(r_pix),
r_pix,
ghat,
np.zeros_like(ghat),
sigma2=None,
range=(minPix, maxPix),
bins=15,
center=(0, 0),
logbin=True)
profile_r_mpc = profile.r * pixscale * (1. / 3600.) * (np.pi /
180.) * D_lens
keepBins = np.logical_and(np.isfinite(profile.E),
np.isfinite(profile.Eerr))
rs, isCon = leastsq.leastsq(NFWModel, [0.4],
profile_r_mpc[keepBins],
profile.E[keepBins],
profile.Eerr[keepBins],
fullOutput=False)
if not isCon:
return None
return rs
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 getCosmology(self):
return nfwutils.Cosmology(omega_m=0.27, omega_l=0.73, h=0.7)
mean_ghat, covar = sr.calcCovariance(medians)
return mean_ghat, covar
#################################
if __name__ == '__main__':
# bootstrap = int(sys.argv[1])
# outdir = sys.argv[2]
#
# data = calcBootstrapCovar(bootstrap)
#
# output = open('%s/scan_%d.pkl' % (outdir, bootstrap), 'wb')
# cPickle.dump(data, output)
# output.close()
#
omega_l = float(sys.argv[1])
outdir = sys.argv[2]
cosmology = nfwutils.Cosmology(1-omega_l, omega_l)
data = stackClusters(doPlot = False, cosmology = cosmology)
output = open('%s/scan_%4.3f.pkl' % (outdir, omega_l), 'wb')
cPickle.dump(data, output)
output.close()
import nfwutils
omega_ms = np.arange(0.15, 0.39, 0.005)
ws = np.arange(-1.76, -0.2, 0.005)
zclusters = [0.25, 1.0]
zsource = 1.5
c = [(.9, .6, 0), (.35, .7, .9), (0, .6, .5), (0.95, 0.9, 0.25)]
scalings = []
for zcluster in zclusters:
scaling = np.zeros_like(omega_ms)
for i, omega_m in enumerate(omega_ms):
cosmo = nfwutils.Cosmology(omega_m=omega_m, omega_l=1. - omega_m)
dl = cosmo.angulardist(zcluster)
ds = cosmo.angulardist(zsource)
dls = cosmo.angulardist(zcluster, zsource)
scaling[i] = dl * ds / dls
scalings.append(scaling)
hydro_scalings = []
mgas_scalings = []
for zcluster in zclusters:
hydro_scaling = np.zeros_like(omega_ms)
mgas_scaling = np.zeros_like(omega_ms)
for i, omega_m in enumerate(omega_ms):
cosmo = nfwutils.Cosmology(omega_m=omega_m, omega_l=1. - omega_m)
dl = cosmo.angulardist(zcluster)
hydro_scaling[i] = dl
mgas_scaling[i] = dl**(5 / 2)
hydro_scalings.append(hydro_scaling)
mgas_scalings.append(mgas_scaling)
def publicationStack(data = None):
if data is None:
data = {}
if 'standard' not in data:
stddata = {}
stdfig, stddata = stackClusters(data = stddata)
data['standard'] = stddata
else:
stddata = data['standard']
if 'eds' not in data:
edsdata = {}
edsfig, edsdata = stackClusters(data = edsdata, cosmology = nfwutils.Cosmology(omega_m = 1.0, omega_l = 0.0), outdir = '/u/ki/dapple/subaru/doug/publication/eds_cosmos_2012-05-17')
data['eds'] = edsdata
else:
edsdata = data['eds']
stdzbins = stddata['zbins']
stdbincenters = (stdzbins[1:] + stdzbins[:-1])/2.
edszbins = edsdata['zbins']
edsbincenters = (edszbins[1:] + edszbins[:-1])/2.
stdmaxlike = stddata['maxlike']
stdsig1 = stddata['sig1']
stdsig2 = stddata['sig2']
edsmaxlike = edsdata['maxlike']
edssig1 = edsdata['sig1']
edssig2 = edsdata['sig2']
fig = pylab.figure()
ax = fig.add_axes([0.125, 0.12, 0.95 - 0.125, 0.95 - 0.12])
xplot = np.arange(0.01, np.max(np.hstack([stdzbins, edszbins])), 0.01)
ax.axhline(0.0, linestyle=':', linewidth=1.25, color='k')
ax.plot(xplot, sr.shearScaling(xplot), 'k-', linewidth=1.5, label='Expected Scaling')
ax.errorbar(edsbincenters, edsmaxlike, edssig1, fmt='rs', mfc = 'None',label='$\mathbf{\Omega_{\Lambda} = 0.0}$', elinewidth=1.5,
markeredgewidth=1.5, markeredgecolor='r', marker='None')
ax.plot(edsbincenters, edsmaxlike, 'rs', markerfacecolor='None', markeredgecolor='r', markeredgewidth=1.5)
ax.errorbar(stdbincenters, stdmaxlike, stdsig1, fmt='bo', label='$\mathbf{\Omega_{\Lambda} = 0.7}$', elinewidth=1.5, markeredgewidth=1.5,
markeredgecolor='b', marker='None')
ax.plot(stdbincenters, stdmaxlike, 'bo')
thexrange = (0., 5.0)
theyrange = (-0.15, 1.75)
ax.legend(loc='upper left', numpoints=1)
ax.set_xlabel('$x = \omega_s/\omega_l$', size='x-large')
ax.set_ylabel('$\gamma_t(x)/\gamma_t(x=\infty)$', size='x-large')
fig.savefig('publication/pubstack.pdf')
######
fig2 = pylab.figure()
ax = fig2.add_axes([0.125, 0.12, 0.95 - 0.125, 0.95 - 0.12])
ax.axhline(0.0, linestyle=':', linewidth=1.25, color='k')
ax.plot(xplot, sr.shearScaling(xplot), 'k-', linewidth=1.5, label='Expected Scaling')
ax.errorbar(stdbincenters, stdmaxlike, stdsig1, fmt='bo', label='$\mathbf{\Omega_{\Lambda} = 0.7}$', elinewidth=1.5, markeredgewidth=1.5,
markeredgecolor='b', marker='None')
ax.plot(stdbincenters, stdmaxlike, 'bo')
ax.set_xlim(*thexrange)
ax.set_ylim(*theyrange)
ax.text(0.3, 1.5, '$\mathbf{\Omega_{\Lambda} = 0.7}$', size='xx-large', weight='bold')
ax.set_xlabel('$x = \omega_s/\omega_l$ ', size='x-large')
ax.set_ylabel('$\gamma_t(x)/\gamma_t(x=\infty)$', size='x-large')
fig2.savefig('publication/pubstack_lcdm.pdf')
###################
fig3 = pylab.figure()
ax = fig3.add_axes([0.125, 0.12, 0.95 - 0.125, 0.95 - 0.12])
ax.axhline(0.0, linestyle=':', linewidth=1.25, color='k')
ax.plot(xplot, sr.shearScaling(xplot), 'k-', linewidth=1.5, label='Expected Scaling')
ax.errorbar(edsbincenters, edsmaxlike, edssig1, fmt='bo', label='$\mathbf{\Omega_{\Lambda} = 0.0}$', elinewidth=1.5, markeredgewidth=1.5,
markeredgecolor='b', marker='None')
ax.plot(edsbincenters, edsmaxlike, 'bo')
ax.set_xlim(*thexrange)
ax.set_ylim(*theyrange)
ax.text(0.3, 1.5, '$\mathbf{\Omega_{\Lambda} = 0.0}$', size='xx-large', weight='bold')
ax.set_xlabel('$x = \omega_s/\omega_l$', size='x-large')
ax.set_ylabel('$\gamma_t(x)/\gamma_t(x=\infty)$', size='x-large')
fig3.savefig('publication/pubstack_eds.pdf')
return fig, fig2, fig3, data
# Utility to parse Stefan's 2D and 3D mass profiles
##################
import numpy as np
import readtxtfile
import varcontainer
import re, os.path
import nfwutils
import scipy.interpolate as interp
#################
h = .73
#m_p = 7.18*8.60657e8/h #M_sol
m_p = 8.456e9
cosmo = nfwutils.Cosmology(omega_m=0.25, omega_l=0.75, h=0.73)
################
profile_start_template = re.compile('r_lo')
class MXXLProfile(object):
def __init__(self, filename):
self.parseFile(filename)
self._massEnclosed = None
self._overdensitymass = None
self._overdensityradius = None
###
def setBK11():
nfwutils.global_cosmology.set_cosmology(nfwutils.Cosmology(omega_m = 0.27, omega_l = 0.73, h = 0.7))
def setBCC():
nfwutils.global_cosmology.set_cosmology(nfwutils.Cosmology(omega_m = 0.23, omega_l = 0.77, h=.72))
def setMXXL():
nfwutils.global_cosmology.set_cosmology(nfwutils.Cosmology(omega_m = 0.25, omega_l = 0.75, h=0.73))
