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functions.py
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functions.py
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# -*- coding: utf-8 -*-
"""
Created on Thu May 24 18:19:47 2018
@author: zehaojin
"""
import pyfits
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import random
def facts(cluster):
##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),http://iopscience.iop.org/article/10.1088/2041-8205/762/2/L30/pdf
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))
z=0.396
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)
z=0.548
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)
z=0.544
B_MAJ = 0.000142663419847312*3600
B_MIN = 0.000134522499313709*3600
B_PA = 35.8609177934992
return ra_center,dec_center,z,B_MAJ,B_MIN,B_PA
def loadcatalog(cluster):
##load catalog
catalog=np.loadtxt('VLA-HFF_%s_compact_optical_rasort.txt' %cluster,dtype={'names': ('name', 'RA', 'DEC','IM_MAJ','IM_MIN','IM_PA','DC_MAJ','DC_MIN','DC_PA','SUBARU_ID','SUBARU_RA','SUBARU_DEC'),'formats': ('|S26',np.float,np.float,np.float,np.float,np.float,np.float,np.float,np.float,'|S26','|S26','|S26')},usecols=(0,1,2,21,23,25,27,29,31,50,51,52))
##catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8,subaru_id_9)]
#catalog=np.loadtxt('VLA-HFF_%s_compact_rasort.txt' %cluster,dtype={'names': ('name', 'RA', 'DEC','IM_MAJ','IM_MIN','IM_PA','DC_MAJ','DC_MIN','DC_PA'),'formats': ('|S26',np.float,np.float,np.float,np.float,np.float,np.float,np.float,np.float)},usecols=(0,1,2,21,23,25,27,29,31))
return catalog
def supplementary_loadcatalog(catalog,cluster):
sup_catalog=np.loadtxt('VLA-HFF_%s_compact_rasort.txt' %cluster,dtype={'names': ('name', 'RA', 'DEC','IM_MAJ','IM_MIN','IM_PA','DC_MAJ','DC_MIN','DC_PA','SUBARU_ID','SUBARU_RA','SUBARU_DEC'),'formats': ('|S26',np.float,np.float,np.float,np.float,np.float,np.float,np.float,np.float,'|S26','|S26','|S26')},usecols=(0,1,2,21,23,25,27,29,31,28,30,32))
catalog=np.append(catalog,sup_catalog)
return catalog
def z_cut(cluster,catalog,z,behind1_infrontof2_within3):
z_within_sigma=0.3
##catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8,subaru_id_9)]
if cluster=='0416':
subarucatalog=np.loadtxt('hlsp_clash_subaru_suprimecam_macs%s_photoz-cat.txt' %cluster,usecols=(0,12,13,17,15)) #(0_id,1_zb,2_zmin,3_zml,4_tb)
if cluster=='0717':
subarucatalog=np.loadtxt('hlsp_clash_subaru_suprimecam_macs%s_photoz-cat.txt' %cluster,usecols=(0,20,21,25,23))
if cluster=='1149':
subarucatalog=np.loadtxt('hlsp_clash_subaru_suprimecam_macs%s_photoz-cat.txt' %cluster,usecols=(0,16,17,21,19))
#subarucols=(0,12,11,13,14,17,19,10)
#(0_id,12_zbest,11_uncertainty in z,13_zbmin,14_zbmax,17_zml,19_Poorness of BPZ fit,10_z_Subaru band magnitude)
entries=catalog.size
cut_list=np.array([])
subaru_zb=np.array([])
subaru_zmin=np.array([])
subaru_zml=np.array([])
subaru_tb=np.array([])
for i in range(entries):
if catalog[i][9]=='-':
subaru_zb=np.append(subaru_zb,0)
subaru_zmin=np.append(subaru_zmin,0)
subaru_zml=np.append(subaru_zml,0)
subaru_tb==np.append(subaru_tb,0)
else:
subaru_index=np.int(catalog[i][9])-1
subaru_zb=np.append(subaru_zb,subarucatalog[subaru_index][1])
subaru_zmin=np.append(subaru_zmin,subarucatalog[subaru_index][2])
subaru_zml=np.append(subaru_zml,subarucatalog[subaru_index][3])
subaru_tb=np.append(subaru_tb,subarucatalog[subaru_index][4])
#if subaru_zmin[i]<z and subaru_zb[i]<z and subaru_zml[i]<z:
#if subaru_zml[i]<z:
#if behind1_infrontof2_within3==1 and subaru_zb[i]<=(z+z_within_sigma):
if behind1_infrontof2_within3==1 and (subaru_zmin[i])<=(z):
#if subaru_zb[i]<z or subaru_zml[i]<z:
#if subaru_zb[i]<z and subaru_zml[i]<z:
#if (subaru_zmin[i])<z:
cut_list=np.append(cut_list,i)
if behind1_infrontof2_within3==2 and (subaru_zb[i])>=(z-z_within_sigma):
#if behind1_infrontof2_within3==2 and (subaru_zb[i])>=(z):
cut_list=np.append(cut_list,i)
if behind1_infrontof2_within3==3 and (subaru_zb[i]>(z+z_within_sigma) or subaru_zb[i]<(z-z_within_sigma)):
cut_list=np.append(cut_list,i)
catalog=np.delete(catalog,cut_list,0)
#R=np.delete(R,cut_list,0)
subaru_tb=np.delete(subaru_tb,cut_list)
'''
bins=100
plt.figure('J'+cluster+' zmin histogram '+str(bins)+'bins')
plt.hist(subaru_zmin,bins,color='red',label='zmin')
plt.axvline(x=z,color='g')
plt.xlim(0,5)
plt.xlabel('zmin')
plt.figure('J'+cluster+' zb histogram '+str(bins)+'bins')
plt.hist(subaru_zb,bins,color='red',label='zb')
plt.axvline(x=z,color='g')
plt.xlim(0,5)
plt.xlabel('zb')
plt.figure('J'+cluster+' zml histogram '+str(bins)+'bins')
plt.hist(subaru_zml,bins,color='blue',label='zml')
plt.axvline(x=z,color='g')
plt.xlim(0,5)
plt.xlabel('zml')
plt.show()
'''
'''
for i in range(entries):
print cluster,subaru_zb[i],subaru_zmin[i],subaru_zml[i]
'''
return catalog,subaru_zb,subaru_zml,subaru_tb
def position_cut(catalog,cluster):
##catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8)]
'''
J0717: https://arxiv.org/pdf/0905.3650.pdf page3
ra1:7:17:35, 7:17:36.5 dec1:37:45:45,37:46:15
ra2:7:17:30.7,7:17:32.2 dec2:37:44:45,37:45:00
J1149: https://arxiv.org/pdf/1608.01329.pdf page2 (main)
http://www.mergingclustercollaboration.org/macs-j114952223.html
ra1:11:49:43 ,11:49:47 dec1:22:21:00,22:22:08
ra2:11:49:21.5,11:49:24.5 dec2:22:22:54,22:24:06
J0416: no cuts are done
'''
J0717_ra_cut1=np.array([7*15+17*(1.0/4)+35*(1.0/240),7*15+17*(1.0/4)+36.5*(1.0/240)])
J0717_dec_cut1=np.array([37+45*(1.0/60)+45*(1.0/3600),37+46*(1.0/60)+15*(1.0/3600)])
J0717_ra_cut2=np.array([7*15+17*(1.0/4)+30.7*(1.0/240),7*15+17*(1.0/4)+32.2*(1.0/240)])
J0717_dec_cut2=np.array([37+44*(1.0/60)+45*(1.0/3600),37+45*(1.0/60)+0*(1.0/3600)])
J1149_ra_cut1=np.array([11*15+49*(1.0/4)+43*(1.0/240),11*15+49*(1.0/4)+47*(1.0/240)])
J1149_dec_cut1=np.array([22+21*(1.0/60)+0*(1.0/3600),22+22*(1.0/60)+8*(1.0/3600)])
J1149_ra_cut2=np.array([11*15+49*(1.0/4)+21.5*(1.0/240),11*15+49*(1.0/4)+24.5*(1.0/240)])
J1149_dec_cut2=np.array([22+22*(1.0/60)+54*(1.0/3600),22+24*(1.0/60)+6*(1.0/3600)])
entries=catalog.size
cut_list=np.array([])
if cluster=='0717':
for i in range(entries):
if (J0717_ra_cut1[0] <= catalog[i][1] <= J0717_ra_cut1[1]) and (J0717_dec_cut1[0] <= catalog[i][2] <= J0717_dec_cut1[1]):
cut_list=np.append(cut_list,i)
if (J0717_ra_cut2[0] <= catalog[i][1] <= J0717_ra_cut2[1]) and (J0717_dec_cut2[0] <= catalog[i][2] <= J0717_dec_cut2[1]):
cut_list=np.append(cut_list,i)
catalog=np.delete(catalog,cut_list,0)
#print 'position cut',cut_list
if cluster=='1149':
for i in range(entries):
if (J1149_ra_cut1[0] <= catalog[i][1] <= J1149_ra_cut1[1]) and (J1149_dec_cut1[0] <= catalog[i][2] <= J1149_dec_cut1[1]):
cut_list=np.append(cut_list,i)
if (J1149_ra_cut2[0] <= catalog[i][1] <= J1149_ra_cut2[1]) and (J1149_dec_cut2[0] <= catalog[i][2] <= J1149_dec_cut2[1]):
cut_list=np.append(cut_list,i)
catalog=np.delete(catalog,cut_list,0)
#print 'position cut',cut_list
return catalog
def ellipticity_cut(catalog,ellipticity_cutter):
#catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8)]
entries=catalog.size
cut_list=np.array([])
for i in range(entries):
if (catalog[i][6]/catalog[i][7])>=ellipticity_cutter:
#print catalog[i][7],catalog[i][6]
cut_list=np.append(cut_list,i)
catalog=np.delete(catalog,cut_list,0)
return catalog
def DC_cutter(catalog,DC_cut):
'''
definition of DC and IM datas: http://sundog.stsci.edu/first/catalogs/readme.html
'''
####DC_MIN>DC_cut are accepted
catalog1=catalog
c=0
entries=catalog.size
for i in range(entries):
if catalog[i][7]<=DC_cut:
catalog1=np.delete(catalog1,i-c,0)
c+=1
return catalog1
def R_cutter(catalog,R_cut,B_MAJ,B_MIN):
'''
see functions.apply_Bernstein_Jarvis_correction for reference
'''
entries=catalog.size
R=np.zeros(entries)
for i in range(entries):
##catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8)]
R[i]=(B_MAJ**2+B_MIN**2)/(catalog[i][3]**2+catalog[i][4]**2)
R1=R
c=0
for i in range(entries):
if R[i]>=R_cut:
catalog=np.delete(catalog,i-c,0)
R1=np.delete(R1,i-c,0)
c+=1
return catalog,R1
def Look_for_Jet_cut(catalog,ra_center,dec_center,cluster,Jet_cut):
entries=catalog.size
cluster_list=('0416','0717','1149')
if cluster==cluster_list[0]:
Jet_cut=Jet_cut[0]
if cluster==cluster_list[1]:
Jet_cut=Jet_cut[1]
if cluster==cluster_list[2]:
Jet_cut=Jet_cut[2]
bins=100
r_list=np.array([])
factor=np.cos(dec_center*np.pi/180)
for i in range(entries-1):
ra1=catalog[i][1]
dec1=catalog[i][2]
for j in range(i,entries):
if i!=j:
ra2=catalog[j][1]
dec2=catalog[j][2]
r=np.sqrt(((ra1-ra2)*factor)**2+(dec1-dec2)**2)
r_list=np.append(r_list,r)
r_list*=3600
plt.figure('J'+cluster+' r histogram '+str(bins)+'bins')
plt.hist(r_list,bins,(0,300))
plt.xlabel('arcsec')
plt.axvline(x=Jet_cut,color='r')
return r_list
def Jet_cutter(catalog,Jet_cut,ra_center,dec_center,cluster,subaru_zb,subaru_zml):
'''
The intention would be to cut both sources when they are too close in position.
The idea is that adjacent sources may in fact be a single radio source with a compact core and jets coming out.
The jet alignment would be something strongly anisotropic,
in a way that is completely unrelated to the gravitational effect of the galaxy cluster.
'''
cluster_list=('0416','0717','1149')
z_limit=1
entries=catalog.size
if cluster==cluster_list[0]:
Jet_cut=Jet_cut[0]
if cluster==cluster_list[1]:
Jet_cut=Jet_cut[1]
if cluster==cluster_list[2]:
Jet_cut=Jet_cut[2]
Jet_cut=Jet_cut/3600.0 ##arcsec to deg
Jet_cut=Jet_cut**2 ##deg^2, r^2
cut_list=np.array([])
factor=np.cos(dec_center*np.pi/180)
for i in range(entries-1):
ra1=catalog[i][1]
dec1=catalog[i][2]
for j in range(i,entries):
if i!=j:
ra2=catalog[j][1]
dec2=catalog[j][2]
r=((ra1-ra2)*factor)**2+(dec1-dec2)**2
if r<Jet_cut:
if np.abs(subaru_zb[i]-subaru_zb[j])<z_limit or np.abs(subaru_zml[i]-subaru_zml[j])<z_limit:
cut_list=np.append(cut_list,i)
cut_list=np.append(cut_list,j)
'''
cut_list=np.array([])
factor=np.cos(dec_center*np.pi/180)
for i in range(entries-1):
r_list=np.array([])
j_list=np.array([])
ra1=catalog[i][1]
dec1=catalog[i][2]
for j in range(i,entries):
if i!=j:
ra2=catalog[j][1]
dec2=catalog[j][2]
r=((ra1-ra2)*factor)**2+(dec1-dec2)**2
r_list=np.append(r_list,r)
j_list=np.append(j_list,j)
if np.min(r_list)<Jet_cut:
cut_list=np.append(cut_list,i)
cut_list=np.append(cut_list,j_list[np.argmin(r_list)])
'''
cut_list=np.unique(cut_list)
cut_list=np.sort(cut_list)
c=0
for i in cut_list:
catalog=np.delete(catalog,i-c,0)
c+=1
return catalog,cut_list
def get_phi_and_grid(catalog,ra_center,dec_center):
'''
change in x=change in RA cos(dec or dec of center)
change in y=change in DEC
definition of phi: https://www.astro.umd.edu/~richard/ASTR680/Schneider_weak_lensing.pdf
'''
entries=catalog.size
result=np.zeros((6,entries))
########Determine phi
for i in range(entries):
##catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8)]
ra_position=(catalog[i][1]-ra_center)*np.cos(dec_center*np.pi/180)
dec_position=catalog[i][2]-dec_center
result[1][i]=ra_position
result[2][i]=dec_position
result[3][i]=catalog[i][6]
result[4][i]=catalog[i][7]
result[5][i]=catalog[i][8]
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
return result ###grid[(phi,x,y,dc_a,dc_b,dc_PA),i]
def get_complex_shear(catalog,DC_or_not):
'''
https://www.astro.umd.edu/~richard/ASTR680/Schneider_weak_lensing.pdf
'''
entries=catalog.size
##catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8)]
if DC_or_not==False:###IM
a_index=3
b_index=4
pa_index=5
if DC_or_not==True:###DC
a_index=6
b_index=7
pa_index=8
result=np.zeros((3,entries))
###result[(0-ellipcity,e1-1,e2-2),i]
########Determine ellipticity
for i in range(entries):
r=catalog[i][b_index]/catalog[i][a_index]
result[0][i]=(1-r*r)/(1+r*r)
result[0]=np.nan_to_num(result[0])
real_part_sum=0
imag_part_sum=0
for i in range(entries):
##catalog[i,(name_0,ra_1,dec_2,im_maj_3,im_min_4,im_pa_5,dc_maj_6,dc_min_7,dc_pa_8)]
complex_shear=result[0][i]*np.exp(2j*np.deg2rad(90-catalog[i][pa_index]))
complex_shear/=2
result[1][i]=np.real(complex_shear)
result[2][i]=np.imag(complex_shear)
real_part_sum+=result[1][i]
imag_part_sum+=result[2][i]
real_avg=real_part_sum/entries
imag_avg=imag_part_sum/entries
if DC_or_not==False:###IM
return result
if DC_or_not==True:###DC
return result,real_avg,imag_avg
###complex_shear[(0-ellipcity,e1-1,e2-2),i]
def shuffle_complex_shear_get_tangential_shear(complex_shear,grid,shuffle_time,DC_or_not,random_array=0):
'''
formulaism for tangential shear: https://www.astro.umd.edu/~richard/ASTR680/Schneider_weak_lensing.pdf
note the thing calculated is X. for shear, shear=X/2
'''
###randomize stuff and get tangential shear(randomized)
entries=complex_shear[0].size
tangential_shear=np.zeros((7+shuffle_time,entries))
###tangential_shear[(0-x,1-y,2-tangential_shear,3-cross_shear,4-dc_a,5-dc_a,6-dc_PA,7~end-shuffled_tangential_shear),i]
###complex_shear[(0-ellipcity,e1-1,e2-2),i]
###grid[(phi,x,y,dc_a,dc_b,dc_PA),i]
tangential_shear[0]=grid[1]
tangential_shear[1]=grid[2]
tangential_shear[4]=grid[3]
tangential_shear[5]=grid[4]
tangential_shear[6]=grid[5]
##tangential shear
for i in range(entries):
complex_shear_complex=complex_shear[1][i]+1j*complex_shear[2][i]
tangential_shear[2][i]=-np.real(complex_shear_complex*np.exp(-2j*grid[0][i]))
tangential_shear[3][i]=np.imag(complex_shear_complex*np.exp(-2j*grid[0][i]))
##randomized tangential shear
if DC_or_not==True:
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):
complex_shear_complex=complex_shear[1][random_array[j][i]]+1j*complex_shear[2][random_array[j][i]]
tangential_shear[7+j][i]=-np.real(complex_shear_complex*np.exp(-2j*grid[0][i]))
return tangential_shear,random_array
def apply_Bernstein_Jarvis_correction(complex_shear,R,B_MAJ,B_MIN,B_PA):
'''
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
'''
#psf
r=B_MIN/B_MAJ
complex_shear_psf=((1-r*r)/(1+r*r))*np.exp(2j*np.deg2rad(90-B_PA))
complex_shear_psf/=2
e1_psf=np.real(complex_shear_psf)
e2_psf=np.imag(complex_shear_psf)
#print 'psf_shear: ',e1_psf,'+',e2_psf,'i'
###complex_shear[(0-ellipcity,e1-1,e2-2),i]
complex_shear[1]=(complex_shear[1]-R*e1_psf)/(1-R)
complex_shear[2]=(complex_shear[2]-R*e2_psf)/(1-R)
real_avg=np.average(complex_shear[1])
imag_avg=np.average(complex_shear[2])
return complex_shear,real_avg,imag_avg
def averaged_tangential_shear(cluster,data_DC,data_IM,seperate_DC_IM,ring,shuffle_time,show_scatter):
'''
https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot
'''
J0717_ra_cut1=(7*15+17*(1.0/4)+35*(1.0/240),7*15+17*(1.0/4)+36.5*(1.0/240))
J0717_dec_cut1=(37+45*(1.0/60)+45*(1.0/3600),37+46*(1.0/60)+15*(1.0/3600))
J0717_ra_cut2=(7*15+17*(1.0/4)+30.7*(1.0/240),7*15+17*(1.0/4)+32.2*(1.0/240))
J0717_dec_cut2=(37+44*(1.0/60)+45*(1.0/3600),37+45*(1.0/60)+0*(1.0/3600))
J1149_ra_cut1=(11*15+49*(1.0/4)+43*(1.0/240),11*15+49*(1.0/4)+47*(1.0/240))
J1149_dec_cut1=(22+21*(1.0/60)+0*(1.0/3600),22+22*(1.0/60)+8*(1.0/3600))
J1149_ra_cut2=(11*15+49*(1.0/4)+21.5*(1.0/240),11*15+49*(1.0/4)+24.5*(1.0/240))
J1149_dec_cut2=(22+22*(1.0/60)+54*(1.0/3600),22+24*(1.0/60)+6*(1.0/3600))
data_fmt_DC='ro' ##red circles
error_bar_color_DC='g' ##green
shuffled_data_color_DC='c' ##cyan
data_fmt_IM='yo' ##yellow circles
error_bar_color_IM='b' ##blue
shuffled_data_color_IM='m'##magenta
entries=data_DC[0].size
###tangential_shear[(0-x,1-y,2-tangential_shear,3-cross_shear,4-dc_a,5-dc_b,6-dc_PA,7~end-shuffled_tangential_shear),i]
ellipses=[]
for i in range(entries):
ellipses.append(patches.Ellipse((data_DC[0][i],data_DC[1][i]),data_DC[5][i]/3600*30,data_DC[4][i]/3600*30,-data_DC[6][i],edgecolor='black',fill=False))
newrow=np.zeros((1,entries))
data_DC=np.vstack([data_DC,newrow])
data_IM=np.vstack([data_IM,newrow])
###old data(ra[0] dec[1] tangential_shear[2] randomized_shear[3:-2] radius^2[-1],[i])
###new data=tangential_shear(x[0] y[1] tangential_shear[2] cross_shear[3] randomized_shear[4:-2] radius^2[-1],[i])
data_DC[-1]=data_DC[0]**2+data_DC[1]**2
data_IM[-1]=data_IM[0]**2+data_IM[1]**2
##sort according to data[-1] (radius^2)
data_DC=data_DC[:,np.argsort(data_DC[-1])]
data_IM=data_IM[:,np.argsort(data_IM[-1])]
result_DC=np.zeros((len(ring),max(ring)))
result2_DC=np.zeros((shuffle_time,len(ring),max(ring)))
result3_DC=np.zeros((len(ring),max(ring)))
error_mean_DC=np.zeros((len(ring),max(ring)))
error_stdev_DC=np.zeros((len(ring),max(ring)))
result_IM=np.zeros((len(ring),max(ring)))
result2_IM=np.zeros((shuffle_time,len(ring),max(ring)))
result3_IM=np.zeros((len(ring),max(ring)))
error_mean_IM=np.zeros((len(ring),max(ring)))
error_stdev_IM=np.zeros((len(ring),max(ring)))
count=-1
for ring_quantity in ring:
count+=1
###################DC
xticks=np.array([])
if show_scatter==True:
plt.figure('J'+cluster+' tangential shear scatter with '+str(ring_quantity)+' rings(DC)')
plt.title('J'+cluster+'$\gamma $ with'+str(ring_quantity)+' rings(DC)')
plt.xlim(-0.20,0.25)
plt.ylim(-0.20,0.20)
plt.gca().invert_xaxis()
ax=plt.gca()
plt.scatter(data_DC[0],data_DC[1],c=data_DC[2],cmap='rainbow',edgecolors='none')
scatter_xticks=np.linspace(-0.20,0.25,20)
scatter_xticks=np.append(scatter_xticks,0)
scatter_xticks=np.append(scatter_xticks,0)
scatter_yticks=np.linspace(-0.20,0.20,20)
plt.xticks(scatter_xticks,np.round(scatter_xticks*60,2))
plt.yticks(scatter_yticks,np.round(scatter_yticks*60,2))
for ellipse in ellipses:
ax.add_patch(ellipse)
plt.plot(0,0,marker='+', markersize=1000, color="red")
plt.colorbar()
plt.xlabel("$\Delta x(arcmin)$")
plt.ylabel("$\Delta y(arcmin)$")
point_in_each_ring=entries//ring_quantity
for i in range(ring_quantity-1):
for j in range(point_in_each_ring):
index=i*point_in_each_ring+j
result_DC[count][i]+=data_DC[2][index]
result3_DC[count][i]+=data_DC[3][index]
#print index
result_DC[count][i]/=point_in_each_ring
result3_DC[count][i]/=point_in_each_ring
radius=(np.sqrt(data_DC[-1][index])+np.sqrt(data_DC[-1][index+1]))/2
print cluster,'bin radius(arcmin) ',radius*60
xticks=np.append(xticks,radius)
if show_scatter==True:
circle=plt.Circle((0,0),radius,color='r',fill=False)
ax.add_patch(circle)
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)
width1=(J0717_ra_cut1[1]-J0717_ra_cut1[0])*np.cos(np.deg2rad(dec_center))
height1=J0717_dec_cut1[1]-J0717_dec_cut1[0]
width2=(J0717_ra_cut2[1]-J0717_ra_cut2[0])*np.cos(np.deg2rad(dec_center))
height2=J0717_dec_cut2[1]-J0717_dec_cut2[0]
xy1=((J0717_ra_cut1[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J0717_dec_cut1[0]-dec_center)
xy2=((J0717_ra_cut2[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J0717_dec_cut2[0]-dec_center)
rect1=patches.Rectangle(xy1,width1,height1,fill=False,edgecolor='b')
ax.add_patch(rect1)
rect2=patches.Rectangle(xy2,width2,height2,fill=False,edgecolor='b')
ax.add_patch(rect2)
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)
width1=(J1149_ra_cut1[1]-J1149_ra_cut1[0])*np.cos(np.deg2rad(dec_center))
height1=J1149_dec_cut1[1]-J1149_dec_cut1[0]
width2=(J1149_ra_cut2[1]-J1149_ra_cut2[0])*np.cos(np.deg2rad(dec_center))
height2=J1149_dec_cut2[1]-J1149_dec_cut2[0]
xy1=((J1149_ra_cut1[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J1149_dec_cut1[0]-dec_center)
xy2=((J1149_ra_cut2[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J1149_dec_cut2[0]-dec_center)
rect1=patches.Rectangle(xy1,width1,height1,fill=False,edgecolor='b')
ax.add_patch(rect1)
rect2=patches.Rectangle(xy2,width2,height2,fill=False,edgecolor='b')
ax.add_patch(rect2)
for j in range(point_in_each_ring*(ring_quantity-1),entries):
result_DC[count][ring_quantity-1]+=data_DC[2][j]
result3_DC[count][ring_quantity-1]+=data_DC[3][j]
#print j
result_DC[count][ring_quantity-1]/=len(range(point_in_each_ring*(ring_quantity-1),entries))
result3_DC[count][ring_quantity-1]/=len(range(point_in_each_ring*(ring_quantity-1),entries))
###
for time in range(shuffle_time):
for i in range(ring_quantity-1):
for j in range(point_in_each_ring):
index=i*point_in_each_ring+j
result2_DC[time][count][i]+=data_DC[7+time][index]
result2_DC[time][count][i]/=point_in_each_ring
for j in range(point_in_each_ring*(ring_quantity-1),entries):
result2_DC[time][count][ring_quantity-1]+=data_DC[7+time][j]
result2_DC[time][count][ring_quantity-1]/=len(range(point_in_each_ring*(ring_quantity-1),entries))
###calculate mean and stdev
error_mean_DC[count]=np.average(result2_DC[:,count,:],0)
error_stdev_DC[count]=np.std(result2_DC[:,count,:],0)
###################IM
xticks=np.array([])
if show_scatter==True:
plt.figure('J'+cluster+' tangential shear scatter with '+str(ring_quantity)+' rings(IM)')
plt.title('J'+cluster+'$\gamma $ with'+str(ring_quantity)+' rings(IM)')
plt.gca().invert_xaxis()
plt.scatter(data_IM[0],data_IM[1],c=data_IM[2],cmap='rainbow')
plt.plot(0,0,marker='+', markersize=1000, color="red")
plt.colorbar()
plt.xlabel('$\Delta x^\circ$')
plt.ylabel('$\Delta y^\circ$')
point_in_each_ring=entries//ring_quantity
for i in range(ring_quantity-1):
for j in range(point_in_each_ring):
index=i*point_in_each_ring+j
result_IM[count][i]+=data_IM[2][index]
result3_IM[count][i]+=data_IM[3][index]
#print index
result_IM[count][i]/=point_in_each_ring
result3_IM[count][i]/=point_in_each_ring
radius=(np.sqrt(data_IM[-1][index])+np.sqrt(data_IM[-1][index+1]))/2
xticks=np.append(xticks,radius)
if show_scatter==True:
circle=plt.Circle((0,0),radius,color='r',fill=False)
ax=plt.gca()
ax.add_patch(circle)
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)
width1=(J0717_ra_cut1[1]-J0717_ra_cut1[0])*np.cos(np.deg2rad(dec_center))
height1=J0717_dec_cut1[1]-J0717_dec_cut1[0]
width2=(J0717_ra_cut2[1]-J0717_ra_cut2[0])*np.cos(np.deg2rad(dec_center))
height2=J0717_dec_cut2[1]-J0717_dec_cut2[0]
xy1=((J0717_ra_cut1[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J0717_dec_cut1[0]-dec_center)
xy2=((J0717_ra_cut2[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J0717_dec_cut2[0]-dec_center)
rect1=patches.Rectangle(xy1,width1,height1,fill=False,edgecolor='b')
ax.add_patch(rect1)
rect2=patches.Rectangle(xy2,width2,height2,fill=False,edgecolor='b')
ax.add_patch(rect2)
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)
width1=(J1149_ra_cut1[1]-J1149_ra_cut1[0])*np.cos(np.deg2rad(dec_center))
height1=J1149_dec_cut1[1]-J1149_dec_cut1[0]
width2=(J1149_ra_cut2[1]-J1149_ra_cut2[0])*np.cos(np.deg2rad(dec_center))
height2=J1149_dec_cut2[1]-J1149_dec_cut2[0]
xy1=((J1149_ra_cut1[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J1149_dec_cut1[0]-dec_center)
xy2=((J1149_ra_cut2[0]-ra_center)*np.cos(np.deg2rad(dec_center)),J1149_dec_cut2[0]-dec_center)
rect1=patches.Rectangle(xy1,width1,height1,fill=False,edgecolor='b')
ax.add_patch(rect1)
rect2=patches.Rectangle(xy2,width2,height2,fill=False,edgecolor='b')
ax.add_patch(rect2)
for j in range(point_in_each_ring*(ring_quantity-1),entries):
result_IM[count][ring_quantity-1]+=data_IM[2][j]
result3_IM[count][ring_quantity-1]+=data_IM[3][j]
#print j
result_IM[count][ring_quantity-1]/=len(range(point_in_each_ring*(ring_quantity-1),entries))
result3_IM[count][ring_quantity-1]/=len(range(point_in_each_ring*(ring_quantity-1),entries))
###
for time in range(shuffle_time):
for i in range(ring_quantity-1):
for j in range(point_in_each_ring):
index=i*point_in_each_ring+j
result2_IM[time][count][i]+=data_IM[7+time][index]
result2_IM[time][count][i]/=point_in_each_ring
for j in range(point_in_each_ring*(ring_quantity-1),entries):
result2_IM[time][count][ring_quantity-1]+=data_IM[7+time][j]
result2_IM[time][count][ring_quantity-1]/=len(range(point_in_each_ring*(ring_quantity-1),entries))
###calculate mean and stdev
error_mean_IM[count]=np.average(result2_IM[:,count,:],0)
error_stdev_IM[count]=np.std(result2_IM[:,count,:],0)
########back to heaven, or it's stil hell?
xticks=(xticks+np.insert(xticks,0,0)[0:ring_quantity-1])/2
xticks=np.append(xticks,xticks[-1]+(xticks[-1]-xticks[-2]))##add last tick
xticks=xticks*60 ##deg to arcmin
if seperate_DC_IM==False:
plt.figure('J'+cluster+'averaged tangential shear for '+str(ring_quantity)+' rings')
plt.xlabel('$r$ (arcmin)')
plt.ylabel('$\overline{\gamma}$')
#plt.ylim(-0.20,0.20)
#plt.scatter(np.array(range(ring_quantity))+1,result[count][:ring_quantity], color="red",marker='s',label="actual data")
plt.scatter(xticks,result3_DC[count][:ring_quantity],s=100, color="r",marker='x',label="DC cross shear")
plt.scatter(xticks,result3_IM[count][:ring_quantity],s=100, color="b",marker='x',label="IM cross shear")
plt.errorbar(xticks,result_DC[count][:ring_quantity],error_stdev_DC[count][:ring_quantity],ecolor=error_bar_color_DC,fmt=data_fmt_DC,label="DC actual data")
plt.errorbar(xticks,result_IM[count][:ring_quantity],error_stdev_IM[count][:ring_quantity],ecolor=error_bar_color_IM,fmt=data_fmt_IM,label="IM actual data")
plt.scatter(xticks,error_mean_DC[count][:ring_quantity], color=shuffled_data_color_DC,marker='s',label="DC shuffled data")
plt.scatter(xticks,error_mean_IM[count][:ring_quantity], color=shuffled_data_color_IM,marker='s',label="IM shuffled data")
plt.xticks(xticks)
plt.title('J'+cluster+' $\overline{\gamma}$ for'+str(ring_quantity)+' rings')
#plt.legend(loc='center left', bbox_to_anchor=(0.8, 1))
plt.legend(loc=0)
if seperate_DC_IM==True:
plt.figure('J'+cluster+'averaged tangential shear for '+str(ring_quantity)+' rings(DC)')
plt.xlabel('$r$ (arcmin)')
plt.ylabel('$\overline{\gamma}$')
plt.errorbar(xticks,result_DC[count][:ring_quantity],error_stdev_DC[count][:ring_quantity],ecolor=error_bar_color_DC,fmt=data_fmt_DC,label="DC actual data")
plt.scatter(xticks,error_mean_DC[count][:ring_quantity], color=shuffled_data_color_DC,marker='s',label="DC shuffled data")
plt.scatter(xticks,result3_DC[count][:ring_quantity],s=100,color="r",marker='x',label="DC cross shear")
plt.xticks(xticks)
plt.title('J'+cluster+' $\overline{\gamma}$ for'+str(ring_quantity)+' rings(DC)')
#plt.legend(loc='center left', bbox_to_anchor=(0.8, 1))
plt.legend(loc=0)
plt.figure('J'+cluster+'averaged tangential shear for '+str(ring_quantity)+' rings(IM)')
plt.xlabel('$r$ (arcmin)')
plt.ylabel('$\overline{\gamma}$')
plt.errorbar(xticks,result_IM[count][:ring_quantity],error_stdev_IM[count][:ring_quantity],ecolor=error_bar_color_IM,fmt=data_fmt_IM,label="IM actual data")
plt.scatter(xticks,error_mean_IM[count][:ring_quantity], color=shuffled_data_color_IM,marker='s',label="IM shuffled data")
plt.scatter(xticks,result3_IM[count][:ring_quantity],s=100,color="b",marker='x',label="IM cross shear")
plt.xticks(xticks)
plt.title('J'+cluster+' $\overline{\gamma}$ for'+str(ring_quantity)+' rings(IM)')
#plt.legend(loc='center left', bbox_to_anchor=(0.8, 1))
plt.legend(loc=0)
print 'J',cluster
print 'DC averged tangential shear:\n',result_DC
print 'DC mean of randomized tangential shear:\n',error_mean_DC
print 'DC standard deviation(1/2length of whole error bar):\n',error_stdev_DC
print 'DC ratio of std: ',error_stdev_DC/np.min(error_stdev_DC)
print 'IM averged tangential shear:\n',result_IM
print 'IM mean of randomized tangential shear:\n',error_mean_IM
print 'IM standard deviation(1/2length of whole error bar):\n',error_stdev_IM
print 'IM ratio of std: ',error_stdev_IM/np.min(error_stdev_IM)
plt.show()
return
def seperate_quardants_plot(cluster,data_DC,data_IM,ring,shuffle_time):
data_fmt_DC='ro' ##red circles
error_bar_color_DC='g' ##green
shuffled_data_color_DC='c' ##cyan
data_fmt_IM='yo' ##yellow circles
error_bar_color_IM='b' ##blue
shuffled_data_color_IM='m'##magenta
entries=data_DC[0].size
newrow=np.zeros((1,entries))
data_DC=np.vstack([data_DC,newrow])
data_IM=np.vstack([data_IM,newrow])
###old data(ra[0] dec[1] tangential_shear[2] randomized_shear[3:-2] radius^2[-1],[i])
###new data=tangential_shear(x[0] y[1] tangential_shear[2] randomized_shear[3:-2] radius^2[-1],[i])
data_DC[-1]=data_DC[0]**2+data_DC[1]**2
data_IM[-1]=data_IM[0]**2+data_IM[1]**2
##sort according to data[-1] (radius^2)
data_DC=data_DC[:,np.argsort(data_DC[-1])]
data_IM=data_IM[:,np.argsort(data_IM[-1])]
result_DC_1=np.zeros((len(ring),max(ring)))
result2_DC_1=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_DC_1=np.zeros((len(ring),max(ring)))
error_stdev_DC_1=np.zeros((len(ring),max(ring)))
result_DC_2=np.zeros((len(ring),max(ring)))
result2_DC_2=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_DC_2=np.zeros((len(ring),max(ring)))
error_stdev_DC_2=np.zeros((len(ring),max(ring)))
result_DC_3=np.zeros((len(ring),max(ring)))
result2_DC_3=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_DC_3=np.zeros((len(ring),max(ring)))
error_stdev_DC_3=np.zeros((len(ring),max(ring)))
result_DC_4=np.zeros((len(ring),max(ring)))
result2_DC_4=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_DC_4=np.zeros((len(ring),max(ring)))
error_stdev_DC_4=np.zeros((len(ring),max(ring)))
result_IM_1=np.zeros((len(ring),max(ring)))
result2_IM_1=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_IM_1=np.zeros((len(ring),max(ring)))
error_stdev_IM_1=np.zeros((len(ring),max(ring)))
result_IM_2=np.zeros((len(ring),max(ring)))
result2_IM_2=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_IM_2=np.zeros((len(ring),max(ring)))
error_stdev_IM_2=np.zeros((len(ring),max(ring)))
result_IM_3=np.zeros((len(ring),max(ring)))
result2_IM_3=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_IM_3=np.zeros((len(ring),max(ring)))
error_stdev_IM_3=np.zeros((len(ring),max(ring)))
result_IM_4=np.zeros((len(ring),max(ring)))
result2_IM_4=np.zeros((shuffle_time,len(ring),max(ring)))
error_mean_IM_4=np.zeros((len(ring),max(ring)))
error_stdev_IM_4=np.zeros((len(ring),max(ring)))
count=-1
for ring_quantity in ring:
count+=1
###################DC
xticks=np.array([])