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tangential_shear_B&J_test.py
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tangential_shear_B&J_test.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 3 15:44:40 2018
@author: zehaojin
"""
import pyfits
import numpy as np
import matplotlib.pyplot as plt
import random
cluster=raw_input('which cluster?\n 1. J0416 (default)\n 2. J0717\n 3. J1149\n')
if cluster=="1" or cluster=="":
cluster='0416'
if cluster=="2":
cluster='0717'
if cluster=="3":
cluster='1149'
'''
http://iopscience.iop.org/article/10.1088/0004-637X/795/2/163/pdf page 13-shear to compare with
http://adsabs.harvard.edu/abs/2014MNRAS.442.1507G page 1530-shear for J0416
Bernstein & Jarvis (2002) https://arxiv.org/pdf/1507.05977.pdf http://iopscience.iop.org/article/10.1086/338085/pdf
e1 = (e1meas - R*e1psf)/(1-R)
e2 = (e2meas - R*e2psf)/(1-R)
R = mrr_cc_psf/mrr_cc*(4/mcr4_psf-1)/(4/mcr4-1)=(a^2+b^2)psf/(a^2+b^2)im
cut of R<0.5
change in x=change in RA cos(dec or dec of center)
change in y=change in DEC
'''
###show image?
show=False
###shuffle time
shuffle_time=1000
##load catalog
catalog=np.loadtxt('VLA-HFF_%s_compact_optical_rasort.txt' %cluster,dtype={'names': ('name', 'RA', 'DEC','IM_MAJ','IM_MIN','M_STAR','IM_PA','SIGMA_IM_MAJ','SIGMA_IM_MIN','ZBEST'),'formats': ('|S26',np.float,np.float,np.float,np.float,'|S26',np.float,np.float,np.float,'|S26')},usecols=(0,1,2,21,23,43,25,22,24,40))
#catalog=np.loadtxt('VLA-HFF_%s_compact_optical_rasort.txt' %cluster,dtype={'names': ('name', 'RA', 'DEC','DC_MAJ','DC_MIN','M_STAR','DC_PA','SIGMA_DC_MAJ','SIGMA_DC_MIN','ZBEST'),'formats': ('|S26',np.float,np.float,np.float,np.float,'|S26',np.float,np.float,np.float,'|S26')},usecols=(0,1,2,27,29,43,31,22,24,40))
ra,dec,a,b=np.loadtxt('VLA-HFF_%s_compact_optical_rasort.txt' %cluster,usecols=(1,2,21,23),unpack=True)
###ra,dec are for making plots
entries=catalog.size
if show==True:
for i in range(entries):
print catalog[i][3],catalog[i][7],catalog[i][4],catalog[i][8],catalog[i][9],catalog[i][4]/catalog[i][3]
print 'IM_MAJ,SIGMA_IM_MAJ,IM_MIN,SIGMA_IM_MIN,ZBEST,MIN/MAJ'
##catalog[i,(name_0,ra_1,dec_2,maj_3,min_4,mass_5,pa_6)]
########construct an array for results
result=np.zeros((5+shuffle_time,entries))
###[(0-phi,1-ellipticity,2-tangential shear,3-real[complex_shear],4-img[complex_shear],5~last-tangential_shear calculated after shuffle),i]
moreresult=np.zeros((3,entries))
###moreresult[(0-R,1-e1psf,2-e2psf),i]
##J0416 z=0.42
##RA:04h 16m 08.38s
##DEC:−24° 04′ 20.80″ from Zitrin et al (http://adsabs.harvard.edu/abs/2013ApJ...762L..30Z)
if cluster=='0416':
ra_center=4*15+16*(1.0/4)+8.38*(1.0/240)
dec_center=-(24+4*(1.0/60)+20.80*(1.0/3600))
print 'center of cluster: (',ra_center,',',dec_center,')'
B_MAJ = 0.000261401928209888*3600
B_MIN = 0.000140935584386377*3600
B_PA = 1.92392664856767
##J0717 z=0.548
##RA 07:17:32.63
##DEC 37:44:59.7 from http://iopscience.iop.org/article/10.1088/0004-637X/795/2/163/pdf
if cluster=='0717':
ra_center=7*15+17*(1.0/4)+32.63*(1.0/240)
dec_center=37+44*(1.0/60)+59.7*(1.0/3600)
print 'center of cluster: (',ra_center,',',dec_center,')'
B_MAJ = 0.000201486997608722*3600
B_MIN = 0.000170685105066699*3600
B_PA = 93.5441097813981
##J1149 z=0.544
##RA 11:49:35.69
##DEC 22:23:54.6 from http://iopscience.iop.org/article/10.1088/0004-637X/795/2/163/pdf
if cluster=='1149':
ra_center=11*15+49*(1.0/4)+35.69*(1.0/240)
dec_center=22+23*(1.0/60)+54.6*(1.0/3600)
print 'center of cluster: (',ra_center,',',dec_center,')'
B_MAJ = 0.000142663419847312*3600
B_MIN = 0.000134522499313709*3600
B_PA = 35.8609177934992
########Determine phi
for i in range(entries):
##catalog[i,(name_0,ra_1,dec_2,maj_3,min_4,mass_5,pa_6)]
ra_position=(catalog[i][1]-ra_center)*np.cos(dec_center*np.pi/180)
dec_position=catalog[i][2]-dec_center
if ra_position==0 and dec_position==0:
result[0][i]=0
elif ra_position==0 and dec_position>0:
result[0][i]=0
elif ra_position==0 and dec_position<0:
result[0][i]=np.pi
elif ra_position>0 and dec_position==0:
result[0][i]=np.pi/2
elif ra_position<0 and dec_position==0:
result[0][i]=np.pi*3/2
elif ra_position>0 and dec_position>0:
result[0][i]=np.arctan(float(ra_position)/dec_position)
elif ra_position>0 and dec_position<0:
result[0][i]=np.arctan(float(dec_position)/ra_position*-1)+(np.pi/2)
elif ra_position<0 and dec_position<0:
result[0][i]=np.arctan(float(ra_position)/dec_position)+(np.pi)
elif ra_position<0 and dec_position>0:
result[0][i]=np.arctan(float(dec_position)/ra_position*-1)+(np.pi*3/2)
#result[0][i]=(-result[0][i])+2*np.pi
result[0][i]+=np.pi/2
if result[0][i]>2*np.pi:
result[0][i]-=2*np.pi
plt.figure('phi J%s' %cluster)
plt.gca().invert_xaxis()
plt.scatter(ra,dec,c=result[0],cmap='gray')
plt.plot(ra_center,dec_center,marker='+', markersize=1000, color="red")
plt.colorbar()
plt.xlabel('RA(deg)')
plt.ylabel('DEC(deg)')
########Determine ellipticity
for i in range(entries):
##catalog[i,(name_0,ra_1,dec_2,maj_3,min_4,mass_5,pa_6)]
r=catalog[i][4]/catalog[i][3]
result[1][i]=(1-r*r)/(1+r*r)
#result[1][i]=(1-r)/(1+r)
result[1]=np.nan_to_num(result[1])
plt.figure('ellipticity J%s' %cluster)
plt.gca().invert_xaxis()
plt.scatter(ra,dec,c=result[1],cmap='gray')
plt.plot(ra_center,dec_center,marker='+', markersize=1000, color="red")
plt.colorbar()
plt.xlabel('RA(deg)')
plt.ylabel('DEC(deg)')
########Determine tangential shear https://www.astro.umd.edu/~richard/ASTR680/Schneider_weak_lensing.pdf
####complex and mean complex shear
#psf
r=B_MIN/B_MAJ
complex_shear_psf=((1-r*r)/(1+r*r))*np.exp(2j*B_PA*np.pi/180)
#complex_shear_psf=(1-r/1+r)*np.exp(2j*B_PA*np.pi/180)
moreresult[1]=np.real(complex_shear_psf)
moreresult[2]=np.imag(complex_shear_psf)
print 'psf_shear: ',moreresult[1][0],'+',moreresult[2][0],'i'
real_part_sum=0
imag_part_sum=0
avg_a=np.average(a)
avg_b=np.average(b)
avg_r=np.average(b/a)
for i in range(entries):
##catalog[i,(name_0,ra_1,dec_2,maj_3,min_4,mass_5,pa_6)]
complex_shear=result[1][i]*np.exp(2j*catalog[i][6]*np.pi/180)
##result[(0-phi,1-ellipticity,2-tangential shear,3-real[complex_shear](e1),4-imag[complex_shear](e2)),i]
##moreresult[(0-R,1-e1psf,2-e2psf),i]
result[3][i]=np.real(complex_shear)
result[4][i]=np.imag(complex_shear)
real_part_sum+=result[3][i]
imag_part_sum+=result[4][i]
###R
#moreresult[0][i]=(B_MAJ**2+B_MIN**2)/(avg_a**2+avg_b**2)
moreresult[0][i]=(B_MAJ**2+B_MIN**2)/(catalog[i][3]**2+catalog[i][4]**2)
real_noise=real_part_sum/entries
imag_noise=imag_part_sum/entries
print 'mean complex shear before correction: ',real_noise,'+',imag_noise,'i'
moreresult1=moreresult
c=0
print 'total source ',entries
for i in range(entries):
if moreresult[0][i]>=0.7:
result=np.delete(result,i-c,1)
moreresult1=np.delete(moreresult1,i-c,1)
ra=np.delete(ra,i-c,0)
dec=np.delete(dec,i-c,0)
c+=1
moreresult=moreresult1
entries=result[0].size
print 'after cut ',entries
'''
'''
result[3]=(result[3]-moreresult[0]*moreresult[1])/(1-moreresult[0])
result[4]=(result[4]-moreresult[0]*moreresult[2])/(1-moreresult[0])
'''
'''
print 'mean after correction ',np.average(result[3]),'+',np.average(result[4]),'i'
###randomize stuff and get tangential shear(randomized)
random_array=np.zeros((shuffle_time,entries))
for i in range(shuffle_time):
random_list=list(range(entries))
for j in range(10):
random.shuffle(random_list)
random_array[i]=random_list
random_array=random_array.astype(int)
for j in range(shuffle_time):
for i in range(entries):
###[(0-phi,1-ellipticity,2-tangential shear,3-real[complex_shear],4-img[complex_shear],5~end-tangential_shear calculated after shuffle),i]
complex_shear=result[3][random_array[j][i]]+1j*result[4][random_array[j][i]]
result[5+j][i]=-np.real(complex_shear*np.exp(-2j*result[0][i]))
###calculate tangential shear
for i in range(entries):
complex_shear=result[3][i]+1j*result[4][i]
result[2][i]=-np.real(complex_shear*np.exp(-2j*result[0][i]))
plt.figure('Tangential Shear J%s' %cluster)
plt.gca().invert_xaxis()
plt.scatter(ra,dec,c=result[2],cmap='gray')
plt.plot(ra_center,dec_center,marker='+', markersize=1000, color="red")
plt.colorbar()
plt.xlabel('RA(deg)')
plt.ylabel('DEC(deg)')
plt.show()
if show==False:
plt.close('all')
##save data to fits file
data=np.zeros((3+shuffle_time,entries))
data[0]=ra
data[1]=dec
data[2]=result[2]
data[3:3+shuffle_time]=result[5:5+shuffle_time]
tangential_shear_hdu=pyfits.PrimaryHDU(data)
tangential_shear_hdu.writeto('fits/J%s_ra[0]_dec[1]_tangential_shear[2]_randomized[3].fits' %cluster,clobber=True)
##5/20 6 last2mon 7 oneweek
'''
plt.figure('Shear')
plt.gca().invert_xaxis()
plt.scatter(ra,dec,c=result[1],cmap='gray')
plt.plot(ra_center,dec_center,marker='+', markersize=1000, color="red")
plt.colorbar()
plt.xlabel('RA(deg)')
plt.ylabel('DEC(deg)')
'''
'''
########Determine tangential shear
for i in range(entries):
complex_shear=result[1][i]*np.exp(2j*result[0][i])
shear_1=np.real(complex_shear)
shear_2=np.imag(complex_shear)
result[2][i]=-((shear_1*np.cos(2*result[0][i]))+(shear_2*np.sin(2*result[0][i])))
result[2][i]=-np.real(result[1][i]*np.exp(-2j*result[0][i]))
plt.figure('Tangential Shear')
plt.gca().invert_xaxis()
plt.scatter(ra,dec,c=result[2],cmap='gray')
plt.plot(ra_center,dec_center,marker='+', markersize=1000, color="red")
plt.colorbar()
plt.xlabel('RA(deg)')
plt.ylabel('DEC(deg)')
'''