def SIS(theta, zs, zl, s=0.2, sigma_v=1000., w=-1.):
c = 300000. # km s^-1
import advanced_calc, math
radius = 2 * math.pi / (360. * 60. * 60) * theta
einstein_radius = 4 * math.pi * (sigma_v / c)**2. * (advanced_calc.compute(
zs, w) - advanced_calc.compute(zl, w)) / advanced_calc.compute(zs, w)
kappa = einstein_radius / (2. * radius)
decrement = (5. * s - 2) * kappa
return kappa, decrement
def angdist_ratio(z):
#d_cluster = dict_DCMR['%.2f' % cluster_z + '_' + '%.2f' % w + '_' + '%.2f' % WM] #advanced_calc.compute(clusterz,w)
key = '%.2f' % z + '_' + '%.2f' % w + '_' + '%.2f' % WM
if key in dict_DCMR:
DS = dict_DCMR[key]
else:
DS = advanced_calc.compute(z, w, WM=WM) #dict_DCMR[]
dict_DCMR[key] = DS
ratio = (DS - d_cluster) / DS
if ratio < 0: ratio = 0
return ratio / norm * 0.1
omega_lambda = 0.7
import scipy
#for w in scipy.arange(-2,0,0.5):
p, redchi, chisq = fit_mass(zs_f[:],
scipy.array(amps_f)[:],
scipy.array(ampErrs_f)[:], clusterz, w,
omega_m, omega_lambda, dict_DCMR)
#x.append(w)
#y.append(redchi)
print 'finished fitting'
print redchi
fit_val[str(w)] = redchi
d_cluster = advanced_calc.compute(clusterz, w)
import scipy
ratios = []
zsrat = []
infinite = p[0] * (advanced_calc.compute(10000, w) -
d_cluster) / advanced_calc.compute(10000, w)
for z in scipy.arange(clusterz, 2.4, 0.02):
zsrat.append(z)
#ratios.append((p[0] * (advanced_calc.compute(z,w) - d_cluster)/advanced_calc.compute(z,w)) / infinite)
DS = dict_DCMR[
'%.2f' % z + '_' + '%.2f' % w + '_' + '%.2f' %
WM] #str(float('%.2f' % z)) + '_' + str(w) + '_' + str(WM)]
ratios.append((p[0] * (DS - d_cluster) / DS) / infinite)
z = 1.0
#cluster_z = 0.55
zs = scipy.array(zs)
amps_t = scipy.array(amps)[zs > 0] / max
ampErrs_t = scipy.array(ampErrs)[zs > 0] / max
zs_f = zs[zs > 0]
calc_z = zs_f[0]
print amps_t, ampErrs_t
amps_f = []
ampErrs_f = []
import advanced_calc
d_cluster = advanced_calc.compute(cluster_z, -1.)
base = (advanced_calc.compute(calc_z, -1.) -
d_cluster) / advanced_calc.compute(calc_z, -1.)
#for i in range(len(amps_t)):
i = 0
corr = (advanced_calc.compute(zs_f[i], -1.) -
d_cluster) / advanced_calc.compute(zs_f[i], -1.) / base
amps_f.append(amps_t[i] / corr)
ampErrs_f.append(ampErrs_t[i] / corr)
i = 1
corr = (advanced_calc.compute(zs_f[i], -1.) -
d_cluster) / advanced_calc.compute(zs_f[i], -1.) / base
ampErrs_f.append(
((ampErrs_t[0] * corr)**2. + (ampErrs_t[i])**2.)**0.5 / amps[i])
amps_f.append(amps_t[i] / corr)
import scipy
for w in [-1]: #scipy.arange(-10,10,2):
color = 'black'
print 'fitting'
omega_m = 0.27
omega_lambda = 0.73
p, redchi = fit_mass(zs_f[:],
scipy.array(amps_f)[:],
scipy.array(ampErrs_f)[:], clusterz, w,
omega_m, omega_lambda)
print 'finished fitting'
fit_val[str(w)] = redchi
d_cluster = advanced_calc.compute(clusterz, w)
import scipy
ratios = []
zsrat = []
for z in scipy.arange(clusterz, 2, 0.01):
zsrat.append(z)
ratios.append(p[0] *
(advanced_calc.compute(z, w) - d_cluster) /
advanced_calc.compute(z, w))
print p[0], w
print ratios, zsrat
pylab.plot(scipy.array(zsrat),
scipy.array(ratios),
color=color,
def run(C, U, P):
import pickle
f = open('DA.pickle', 'r')
m = pickle.Unpickler(f)
dict_DCMR = m.load()
import scipy, pylab, math
r = scipy.arange(0.8, 1.2, 0.02)
masses = dict(zip(r, scipy.zeros(len(r))))
masses_onepoint = dict(zip(r, scipy.zeros(len(r))))
zs = scipy.arange(0.01000, 4.0100, 0.0100)
def parse(i, Z, Z_onepoint):
import scipy
from scipy import stats
an = scipy.stats.norm.rvs() * 0.3
true_shear = (angdist_ratio(Z) + an)
y = scipy.array([float(x) for x in P[i + 1][:-1].split(' ')[1:-1]])
vec = zip(zs, y)
for m in masses.keys():
prob = 0
for v in vec:
if v[1] > 0:
prob += v[1] * math.exp(
-1. * (m * angdist_ratio(v[0]) - true_shear)**2. /
0.3**2.) / (2. * math.pi)**0.5
#print true_shear, angdist_ratio(v[0]), v[1], prob, m
#raw_input()
#print prob, m
masses[m] += math.log(prob)
masses_onepoint[m] += math.exp(
-1. * (m * angdist_ratio(Z_onepoint) - true_shear)**2. /
0.3**2.) / (2. * math.pi)**0.5
import advanced_calc
cluster_z = 0.5
WM = 0.27
w = -1
d_cluster = advanced_calc.compute(cluster_z, w)
DS = advanced_calc.compute(0.75, w, WM=WM) #dict_DCMR[]
norm = (DS - d_cluster) / DS
def angdist_ratio(z):
#d_cluster = dict_DCMR['%.2f' % cluster_z + '_' + '%.2f' % w + '_' + '%.2f' % WM] #advanced_calc.compute(clusterz,w)
key = '%.2f' % z + '_' + '%.2f' % w + '_' + '%.2f' % WM
if key in dict_DCMR:
DS = dict_DCMR[key]
else:
DS = advanced_calc.compute(z, w, WM=WM) #dict_DCMR[]
dict_DCMR[key] = DS
ratio = (DS - d_cluster) / DS
if ratio < 0: ratio = 0
return ratio / norm * 0.1
CZ = []
UZ = []
fracs = []
vals = []
num = 0
for i in range(40000, 60000):
if i % 10: print i
cosmos_z = C.field('zp_best')[i]
our_z = U.field('BPZ_Z_B')[i]
mag = U.field('BPZ_M_0')[i]
if mag < 25 and U.field(
'BPZ_ODDS'
)[i] > 0.5 and our_z > cluster_z + 0.1 and our_z < 1. and cosmos_z > 0 and cosmos_z < 1.5: # and not (0.18 < cosmos_z < 0.025): # and our_z > 0.5:
num += 1
CZ.append(cosmos_z)
UZ.append(our_z)
frac = parse(i, cosmos_z, our_z)
fracs.append(frac)
vals.append(angdist_ratio(our_z) / angdist_ratio(cosmos_z))
#print w, str(float(w)), '%.2f' % z + '_' + '%.2f' % w + '_' + '%.2f' % WM
import pylab
#pylab.hist(vals,bins=40)
pylab.scatter(masses_onepoint.keys(), masses.values())
pylab.title(str(num))
pylab.show()
pylab.scatter(masses.keys(), masses.values())
pylab.title(str(num))
pylab.show()
''' fit for each cosmology '''
#for w,color in [[-1.,'orange'],[-0.5,'red'],[-1.5,'blue'],[0,'black']]:
import scipy
for w in [-1]: #scipy.arange(-10,10,2):
color = 'black'
print 'fitting'
omega_m = 0.27
omega_lambda = 0.73
p, redchi = fit_mass(zs_f[:],scipy.array(amps_f)[:],scipy.array(ampErrs_f)[:],clusterz,w,omega_m,omega_lambda)
print 'finished fitting'
fit_val[str(w)] = redchi
d_cluster = advanced_calc.compute(clusterz,w)
import scipy
ratios = []
zsrat = []
infinite = p[0] * (advanced_calc.compute(10000,w) - d_cluster)/advanced_calc.compute(10000,w)
for z in scipy.arange(clusterz,3,0.02):
zsrat.append(z)
ratios.append((p[0] * (advanced_calc.compute(z,w) - d_cluster)/advanced_calc.compute(z,w)) / infinite)
print p[0], w
print ratios, zsrat
pylab.plot(scipy.array(zsrat), scipy.array(ratios), color=color) #,label=str(w))
pylab.legend()