def bias_KSB_weight(Args, gal_hlr):
"""Make plots to analyze CG bias with weight function"""
mcg_w_ksb, ccg_w_ksb = [],[]
weights=np.linspace(0.5,2, 15)
rt_g=[[0.005,0.005],[0.01,0.01]]
gtrue=np.array(rt_g)
for w in weights:
input_args = Args
input_args.shear_est='KSB'
input_args.sig_w = w*gal_hlr
input_args.rt_g = rt_g
gcgw,gnocgw=cg.calc_cg(input_args)
fit_fin = np.polyfit(gtrue.T[0],gcgw.T-gnocgw.T,1)
mcg_w_ksb.append(fit_fin[0])
ccg_w_ksb.append(fit_fin[1])
k=0
plt.rc('legend',**{'fontsize':16})
plt.figure(figsize=[12,8])
#plt.subplots_adjust(hspace=0.4)
#plt.subplots_adjust(wspace = 0.4)
#plt.subplot(221)
if Args.telescope is 'Euclid':
plt.plot(weights,-np.array(mcg_w_ksb).T[k],label='KSB',linewidth=2.5)
plt.ylabel(r'-$\rm m_{CG}$', size=22)
else:
plt.plot(weights,np.array(mcg_w_ksb).T[k],label='KSB',linewidth=2.5)
plt.ylabel(r'$\rm m_{CG}$', size=22)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.xlabel(r'weight function size/galaxy size',size=20)
plt.title(r'Dependence of $\rm m_{CG}$ '+'on weight function size for {0}'.format(Args.telescope),
size=22)
plt.xticks(fontsize = 16)
plt.yticks(fontsize = 16)
def cg_unit_tests():
""" Sanity checks for cg_fns_reloaded.py
Compute the CG bias for various galaxy parameters and test is they match
expected values. Test only for Euclid for the time being """
T=6
# bulge_frac=0
input_args = cg.Eu_Args(shear_est='REGAUSS', bulge_frac=0.)
g1,g2 = cg.calc_cg(input_args)
print 'NO Disk : gcg,gncg'
print g1.T[0],g2.T[0]
print 'CG,S bias', (g1/g2-1).T[0]
range_print((g1/g2-1).T[0],1e-05)
# bulge_frac=1
input_args = cg.Eu_Args(shear_est='REGAUSS', bulge_frac=1.)
g1,g2 = cg.calc_cg(input_args)
print 'NO Bulge: gcg,gncg'
print g1.T[0],g2.T[0]
print 'CG,S bias', (g1/g2-1).T[0]
range_print((g1/g2-1).T[0],1e-05)
#bulge disk have same SED
input_args = cg.Eu_Args(shear_est='REGAUSS', bulge_frac=1.)
input_args.bulge_SED_name = 'E'
input_args.disk_SED_name = 'E'
g1,g2 = cg.calc_cg(input_args)
print 'NO Bulge: gcg,gncg'
print g1.T[0],g2.T[0]
print 'CG,S bias', (g1/g2-1).T[0]
range_print((g1/g2-1).T[0],1e-05)
# REGAUSS
input_args = cg.Eu_Args(shear_est='REGAUSS')
g1_r,g2_r = cg.calc_cg(input_args)
print 'Default parameters, REGAUSS : gcg,gncg'
print g1_r.T[0],g2_r.T[0]
print 'CG,S bias', (g1_r/g2_r-1).T[0]
# KSB
input_args = cg.Eu_Args(shear_est='KSB')
g1_k,g2_k = cg.calc_cg(input_args)
print 'Default parameters, KSB : gcg,gncg'
print g1_k.T[0],g2_k.T[0]
print 'CG,S bias', (g1_k/g2_k-1).T[0]
val=(g1_r/g2_r-1).T[0]-(g1_k/g2_k-1).T[0]
range_print(val,1e-02)
# KSB with REGAUSS weight size
input_args = cg.Eu_Args(shear_est='KSB',sig_w=0.19)
g1_k,g2_k = cg.calc_cg(input_args)
print 'With weight sigma = 0.19, KSB : gcg,gncg'
print g1_k.T[0],g2_k.T[0]
print 'CG,S bias', (g1_k/g2_k-1).T[0]
# S13 with REGAUSS weight size
input_args = cg.Eu_Args(shear_est='S13',sig_w=0.19)
g1_s,g2_s = cg.calc_cg(input_args)
print 'With weight sigma = 0.19, S13 : gcg,gncg'
print g1_s.T[0],g2_s.T[0]
print 'CG,S bias', (g1_s/g2_s-1).T[0]
val=(g1_s/g2_s-1).T[0]-(g1_k/g2_k-1).T[0]
range_print(val,1e-02)
#interchange bulge and disk labels
input_args = cg.Eu_Args(shear_est='REGAUSS', bulge_frac=0.75,
bulge_n=1.0, disk_n=1.5,
disk_HLR=0.17,bulge_HLR=1.2)
input_args.bulge_SED_name='Im'
input_args.disk_SED_name='E'
g1_r2,g2_r2 = cg.calc_cg(input_args)
val=(g1_r/g2_r-1).T[0]-(g1_r2/g2_r2-1).T[0]
range_print(val,1e-07)
#large weight function
input_args = cg.Eu_Args(shear_est='KSB',sig_w=3.)
g1_k2,g2_k2 = cg.calc_cg(input_args)
print 'Large weight, KSB : gcg,gncg'
print g1_k2.T[0],g2_k2.T[0]
print 'CG,S bias', (g1_k2/g2_k2-1).T[0]
val=(g1_k2/g2_k2-1).T[0]
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
#bias must be small and negative
if (np.all(val<0.) and np.all(val>-1e03)):
print colored('PASS', 'green')
else:
print colored('FAIL', 'red')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
