g2col='gs2corr')
beta = sp.beta(phot["BPZ_Z_B"], clusterz)
import scipy
kappacut = scipy.array(
[False] * len(beta),
dtype=bool) #sp.calcWLViolationCut(r, beta, sigma_v = 1300)
for i in range(len(r)):
if 100 < r[i]:
kappacut[i] = True
#print kappacut
kappacut = (phot['BPZ_Z_B_MAX'] - phot['BPZ_Z_B_MIN'] < 2.0) * (
E < 1.4) * (phot['BPZ_ODDS'] > 0.6) * (phot['NFILT'] >
4) * sp.calcWLViolationCut(
r, beta, sigma_v=1300)
r = r[kappacut]
ngal = len(r)
E = E[kappacut]
Eerr = scipy.ones(len(r)) / 10. #sqrt(catalog['sigma2_gs'][kappacut])
beta = beta[kappacut]
Zs = phot['BPZ_Z_B'][kappacut]
weights = lens['weight'][kappacut]
import pylab, scipy
shears = {}
if len(sys.argv) != 6:
sys.stderr.write("wrong number of arguments!\n")
sys.exit(1)
catfile = sys.argv[1]
clusterz = float(sys.argv[2])
center = map(float, sys.argv[3].split(','))
pixscale = float(sys.argv[4]) # arcsec / pix
clustername = sys.argv[5]
catalog = ldac.openObjectFile(catfile)
r, E = sp.calcTangentialShear(catalog, center, pixscale)
beta = sp.beta(catalog["Z_BEST"], clusterz, calcAverage=False)
kappacut = sp.calcWLViolationCut(r, beta, sigma_v=1300)
radiuscut = r > 60 #arcseconds
largeradiuscut = r < 500
zcut = logical_and(catalog['Z_BEST'] > 1.2 * clusterz, catalog['Z_BEST'] < 1.2)
cleancut = logical_and(
kappacut, logical_and(radiuscut, logical_and(largeradiuscut, zcut)))
cleancat = catalog.filter(cleancut)
samples = sp.simpleBootstrap(cleancat, clusterz, pixscale, center,
beta[cleancut])
r500x = float(
subprocess.Popen(
"grep %s /nfs/slac/g/ki/ki05/anja/SUBARU/clusters.r500x.dat | awk '{print $2}'"