return t1 * t2 def fn_get_A_tSZ(nu, nu_ref = 150e9): ysz_Tsz_conv_fac_ref = compton_y_to_delta_Tcmb(nu_ref) ysz_Tsz_conv_fac = compton_y_to_delta_Tcmb(nu) return ysz_Tsz_conv_fac/ysz_Tsz_conv_fac_ref import numpy as np import modules.scl_cmb as scl_cmb import scipy as sc from pylab import * sims = scl_cmb.simulations() h=6.62607004e-34 #Planck constant in m2 kg / s k_B=1.38064852e-23 #Boltzmann constant in m2 kg s-2 / K-1 Tcmb = 2.73 #Kelvin #pol = 1 which_exp = sys.argv[1]#'act' pol = int(sys.argv[2]) print '\n\tExp = %s; Pol = %s\n' %(which_exp, pol) if which_exp == 'spt': nuarr = [90e9,150e9, 220e9] #noisearr = [3,2,4] noisearr = [4.5,2.5,4.5]
"""
input: takes in mass(in 1e14 solar mass), redshift of a cluster, and params file
output: lensing convergence profile in output.pkl.gz
ex: python PC_kappa_gen.py 2 0.7
"""
import modules.scl_cmb as scl_cmb, sys
import numpy as np, pickle, gzip
clus_mass = float(sys.argv[1]) * 1e14
clus_redshift = float(sys.argv[2])
sims = scl_cmb.simulations()
# read the params file
paramfile = 'params/params.txt'
params = np.recfromtxt(paramfile, usecols=[0], delimiter='=')
paramvals = np.recfromtxt(paramfile, usecols=[1], delimiter='=')
param_dict = {}
for p, pval in zip(params, paramvals):
tmp = pval.strip()
try:
float(tmp)
if tmp.find('.') > -1:
param_dict[p.strip()] = float(tmp)
else:
param_dict[p.strip()] = int(tmp)
except:
if tmp == 'None':
y_ip = fitting_func(p1,p1,x_ip, cluster_mass,return_fit = 1)
linds, uinds = np.where(x_ip<=cluster_mass)[0], np.where(x_ip>=cluster_mass)[0]
value_for_error = 1.
interp_type = 'linear'
fninterp = interp1d(y_ip[linds], x_ip[linds], kind = interp_type, bounds_error = 0, fill_value = 0.)
l_err = fninterp(value_for_error)
fninterp = interp1d(y_ip[uinds], x_ip[uinds], kind = interp_type, bounds_error = 0, fill_value = 0.)
u_err = fninterp(value_for_error)
width = (u_err - l_err)/2.
if width == 0:
width = default_width
return width
sims = scl.simulations()
sims.tqulen = 1
ipfolder = sys.argv[1]
noofsims = int(sys.argv[2])
minrich = float(sys.argv[3])
maxrich = float(sys.argv[4])
# Covariance matrix calculation
files = sorted(glob.glob('%s/st*'%(ipfolder)))
cov_file = '%s/kappa_COV_%s_simsJK.pkl.gz' %(ipfolder,noofsims)
data_for_cov = pickle.load(gzip.open(files[0]))
boxsize = data_for_cov['param_dict']['boxsize']
dx,dy = data_for_cov['param_dict']['reso_arcmin'],data_for_cov['param_dict']['reso_arcmin']
nx,ny = int(boxsize/dx), int(boxsize/dy)