def bias_alpha(Args, gal_hlr, rt_g=[[0.005,0.005],[0.01,0.01]]):
"""Plot CG bias for the 4 HSM methods: Regauss, BJ, Linearization & KSB
at different redshfts. Functions are imported from cg_fns_reloaded.py
@param Args Class containing parameters
@param rt_g shear values applied to measure bias
"""
gtrue = np.array(rt_g)
m_a_re, m_a_ksb =[],[]
c_a_re, c_a_ksb =[],[]
alphas = np.linspace(-1, 1, 10)
for alpha in alphas:
#REGAUSS
input_p = Args
input_p.alpha = alpha
input_p.rt_g = rt_g
gcg, gnocg = cg.calc_cg_new(input_p)
fit_fin = np.polyfit(gtrue.T[0],gcg.T-gnocg.T,1)
m_a_re.append(fit_fin[0])
c_a_re.append(fit_fin[1])
#Plots
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)
plt.plot(alphas, np.array(m_a_re).T[0],
marker='s', color='b', label='REGAUSS 1', markersize=12)
plt.plot(alphas, np.array(m_a_re).T[1],
marker='^', color='g', label='REGAUSS 2', markersize=12)
plt.axhline(0.0, color='k', linestyle='-.')
plt.axvline(0, color='k', linestyle='-.')
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title(r'Multiplicative bias for {0} using {1}'.format(Args.telescope,
Args.shear_est),
size=22)
plt.ylabel(r' $\rm m_{CG}$', size=22)
plt.xlabel(r'PSF size $\lambda$ scaling exponent, $\alpha$', size=20)
plt.xticks(fontsize = 16)
plt.yticks(fontsize = 16)
def bias_all(Args, rt_g=[[0.005,0.005],[0.01,0.01]]):
"""Plot CG bias for Regauss, KSB & model fitting
at different redshfts. Functions are imported from cg_fns_reloaded.py
@param Args Class containing parameters
@param rt_g shear values applied to measure bias
"""
gtrue=np.array(rt_g)
m_z_re, m_z_fit, m_z_ksb =[],[],[]
c_z_re, c_z_fit,c_z_ksb =[],[],[]
redshifts=np.linspace(0.,1.2,15)
for z in redshifts:
#REGAUSS
input_p = Args
input_p.shear_est = 'REGAUSS'
input_p.redshift = z
input_p.rt_g = rt_g
gcg,gnocg=cg.calc_cg_new(input_p)
fit_fin = np.polyfit(gtrue.T[0],gcg.T-gnocg.T,1)
m_z_re.append(fit_fin[0])
c_z_re.append(fit_fin[1])
#KSB
input_p = Args
input_p.shear_est = 'KSB'
input_p.redshift = z
input_p.rt_g = rt_g
gcg,gnocg = cg.calc_cg_new(input_p, calc_weight=True)
fit_fin = np.polyfit(gtrue.T[0],gcg.T-gnocg.T,1)
m_z_ksb.append(fit_fin[0])
c_z_ksb.append(fit_fin[1])
#fit
input_p = Args
input_p.shear_est = 'fit'
input_p.redshift = z
input_p.rt_g = rt_g
gcg,gnocg = cg.calc_cg_new(input_p)
fit_fin = np.polyfit(gtrue.T[0],gcg.T-gnocg.T,1)
m_z_fit.append(fit_fin[0])
c_z_fit.append(fit_fin[1])
#Plots
plt.rc('legend',**{'fontsize':12})
plt.figure(figsize=[18,14])
plt.subplots_adjust(hspace=0.5)
plt.subplots_adjust(wspace = 0.5)
plt.subplot(2,2,1)
if Args.telescope is 'Euclid':
plt.plot(redshifts, -np.array(m_z_re).T[0],label='REGAUSS', linewidth=2.5)
plt.plot(redshifts, -np.array(m_z_ksb).T[0]*10,label='KSB$\\times 10$',
linewidth=2.5)
plt.plot(redshifts, -np.array(m_z_fit).T[0],label='Model Fitting',
linewidth=2.5)
plt.ylabel(r'-$\rm m_{CG}$',size=22)
else :
plt.plot(redshifts, np.array(m_z_re).T[0],label='REGAUSS')
plt.plot(redshifts, np.array(m_z_ksb).T[0],label='KSB$\\times 10$',
linewidth=2.5)
plt.plot(redshifts, np.array(m_z_fit).T[0],label='Model Fitting',
linewidth=2.5)
plt.ylabel(r'$\rm m_{CG}$',size=22)
plt.xlabel(r'redshift',size=20)
plt.xticks(fontsize = 16)
plt.yticks(fontsize = 16)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.subplot(2,2,2)
plt.plot(redshifts, np.array(c_z_re).T[0],label='REGAUSS',linewidth=2.5)
plt.plot(redshifts, np.array(c_z_ksb).T[0],label='KSB', linewidth=2.5)
plt.plot(redshifts, np.array(c_z_fit).T[0],label='Model Fitting',
linewidth=2.5)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.ylabel(r'$\rm c_{CG}$',size=22)
plt.xlabel(r'redshift',size=20)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.xticks(fontsize = 16)
plt.yticks(fontsize = 16)
plt.suptitle(r'Bias in shape Measurement for {0}'.format(Args.telescope), size=24)