

In [46]:

    
from astropy.io import fits,ascii
import numpy as np
from matplotlib import pyplot as plt
import os
import sys
import warnings
warnings.filterwarnings('ignore')
from astropy.table import Table
from astropy.coordinates import SkyCoord
from scipy.stats import ks_2samp
from astropy import units as u



In [4]:

    
sys.path.append('/home/rfinn/github/APPSS/')



In [5]:

    
from a100sdss import ks, anderson



In [6]:

    
sys.path.append('/home/rfinn/github/LCS/python/Python3/')



In [7]:

    
from LCScommon import *









    



Running on coma



In [8]:

    
homedir = os.getenv("HOME")



In [9]:

    
def colormass(x1,y1,x2,y2,name1,name2, figname, hexbinflag=False,contourflag=False, \
             xmin=7.9, xmax=11.6, ymin=-1.2, ymax=1.2, contour_bins = 40, ncontour_levels=5,\
             xlabel='$\log_{10}(M_\star/M_\odot) $', ylabel='$(g-i)_{corrected} $', color2='c',\
             nhistbin=50, alphagray=.1):
    fig = plt.figure(figsize=(8,8))
    nrow = 4
    ncol = 4
    
    # for purposes of this plot, only keep data within the 
    # window specified by [xmin:xmax, ymin:ymax]
    
    keepflag1 = (x1 >= xmin) & (x1 <= xmax) & (y1 >= ymin) & (y1 <= ymax)
    keepflag2 = (x2 >= xmin) & (x2 <= xmax) & (y2 >= ymin) & (y2 <= ymax)
    
    x1 = x1[keepflag1]
    y1 = y1[keepflag1]
    
    x2 = x2[keepflag2]
    y2 = y2[keepflag2]
    
    ax1 = plt.subplot2grid((nrow,ncol),(1,0),rowspan=nrow-1,colspan=ncol-1, fig=fig)
    if hexbinflag:
        #t1 = plt.hist2d(x1,y1,bins=100,cmap='gray_r')
        #H, xbins,ybins = np.histogram2d(x1,y1,bins=20)
        #extent = [xbins[0], xbins[-1], ybins[0], ybins[-1]]
        #plt.contour(np.log10(H.T+1),  10, extent = extent, zorder=1,colors='k')
        #plt.hexbin(xvar2,yvar2,bins='log',cmap='Blues', gridsize=100)

        plt.hexbin(x1,y1,bins='log',cmap='gray_r', gridsize=75,label=name1)
    else:
        plt.plot(x1,y1,'k.',alpha=alphagray,label=name1, zorder=2)
    if contourflag:
        H, xbins,ybins = np.histogram2d(x2,y2,bins=contour_bins)
        extent = [xbins[0], xbins[-1], ybins[0], ybins[-1]]
        plt.contour((H.T), levels=ncontour_levels, extent = extent, zorder=1,colors=color2, label='__nolegend__')
        #plt.legend()
    else:
        plt.plot(x2,y2,'c.',color=color2,alpha=.3, label=name2)
        
        
        #plt.legend()
    #sns.kdeplot(agc['LogMstarTaylor'][keepagc],agc['gmi_corrected'][keepagc])#,bins='log',gridsize=200,cmap='blue_r')
    #plt.colorbar()
    
    plt.axis([xmin,xmax,ymin,ymax])
    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)
    plt.xlabel(xlabel,fontsize=22)
    plt.ylabel(ylabel,fontsize=22)
    #plt.axis([7.9,11.6,-.05,2])
    ax2 = plt.subplot2grid((nrow,ncol),(0,0),rowspan=1,colspan=ncol-1, fig=fig, sharex = ax1, yticks=[])
    t = plt.hist(x1, normed=True, bins=nhistbin,color='k',histtype='step',lw=1.5, label=name1)
    t = plt.hist(x2, normed=True, bins=nhistbin,color=color2,histtype='step',lw=1.5, label=name2)
    #plt.legend()
    ax2.legend(fontsize=10,loc='upper left')
    ax2.xaxis.tick_top()
    ax3 = plt.subplot2grid((nrow,ncol),(1,ncol-1),rowspan=nrow-1,colspan=1, fig=fig, sharey = ax1, xticks=[])
    t=plt.hist(y1, normed=True, orientation='horizontal',bins=nhistbin,color='k',histtype='step',lw=1.5, label=name1)
    t=plt.hist(y2, normed=True, orientation='horizontal',bins=nhistbin,color=color2,histtype='step',lw=1.5, label=name2)
    
    plt.yticks(rotation='horizontal')
    ax3.yaxis.tick_right()
    plt.savefig(figname)

    print('############################################################# ')
    print('KS test comparising galaxies within range shown on the plot')
    print('')
    print('STELLAR MASS')
    t = ks(x1,x2,run_anderson=False)
    print('')
    print('COLOR')
    t = ks(y1,y2,run_anderson=False)



In [10]:

    
def plotsalim07():
    #plot the main sequence from Salim+07 for a Chabrier IMF

    lmstar=np.arange(8.5,11.5,0.1)

    #use their equation 11 for pure SF galaxies
    lssfr = -0.35*(lmstar - 10) - 9.83

    #use their equation 12 for color-selected galaxies including
    #AGN/SF composites.  This is for log(Mstar)>9.4
    #lssfr = -0.53*(lmstar - 10) - 9.87

    lsfr = lmstar + lssfr -.3
    sfr = 10.**lsfr

    plt.plot(lmstar, lsfr, 'w-', lw=4)
    plt.plot(lmstar, lsfr, c='salmon',ls='-', lw=2, label='$Salim+07$')
    plt.plot(lmstar, lsfr-np.log10(5.), 'w--', lw=4)
    plt.plot(lmstar, lsfr-np.log10(5.), c='salmon',ls='--', lw=2)
        
def plotelbaz():
    #plot the main sequence from Elbaz+13
        
    xe=np.arange(8.5,11.5,.1)
    xe=10.**xe

    #I think that this comes from the intercept of the
    #Main-sequence curve in Fig. 18 of Elbaz+11.  They make the
    #assumption that galaxies at a fixed redshift have a constant
    #sSFR=SFR/Mstar.  This is the value at z=0.  This is
    #consistent with the value in their Eq. 13

    #This is for a Salpeter IMF
    ye=(.08e-9)*xe   
        
        
    plt.plot(log10(xe),np.log19(ye),'w-',lw=3)
    plt.plot(log10(xe),np.log10(ye),'k-',lw=2,label='$Elbaz+2011$')
    #plot(log10(xe),(2*ye),'w-',lw=4)
    #plot(log10(xe),(2*ye),'k:',lw=2,label='$2 \ SFR_{MS}$')
    plt.plot(log10(xe),np.log10(ye/5.),'w--',lw=4)
    plt.plot(log10(xe),np.log10(ye/5.),'k--',lw=2,label='$SFR_{MS}/5$')



In [11]:

    
lcs = fits.getdata(homedir+'/github/LCS/tables/LCS_all_size.fits')



In [12]:

    
mstar = lcs['MSTAR_50']
sfr = np.log10(lcs['SFR_ZDIST'])
core = (lcs['DR_R200'] < 0.6) & (lcs['DELTA_V'] < 3.)
ssfr = sfr-mstar
sf_flag = ssfr > -11.2



In [13]:

    
plt.figure(figsize=(8,6))
plt.plot(mstar[core],(sfr[core]),'ro',label='Core')
plt.plot(mstar[~core],(sfr[~core]),'bo',label='External')
plt.xlabel('M*')
plt.ylabel('SFR')
#plt.gca().set_yscale('log')
xl = np.linspace(8,12,100)
yl = -11.2 + xl
plt.plot(xl,yl,'k--')









    Out[13]:





[<matplotlib.lines.Line2D at 0x7f89549e9b50>]



In [14]:

    
plt.figure()
mybins = np.linspace(-14,-8,20)
plt.hist(ssfr[core],bins=mybins,color='r',histtype='step',label='core')
plt.hist(ssfr[~core],bins=mybins,color='b',histtype='step',label='external')









    Out[14]:





(array([ 0.,  0.,  0.,  1.,  7., 37., 33., 39., 31., 44., 70., 85., 83.,
        36., 17.,  6.,  4.,  2.,  0.]),
 array([-14.        , -13.68421053, -13.36842105, -13.05263158,
        -12.73684211, -12.42105263, -12.10526316, -11.78947368,
        -11.47368421, -11.15789474, -10.84210526, -10.52631579,
        -10.21052632,  -9.89473684,  -9.57894737,  -9.26315789,
         -8.94736842,  -8.63157895,  -8.31578947,  -8.        ]),
 <a list of 1 Patch objects>)



In [15]:

    
flag1 = core & sf_flag #& (mstar > 9.5)  & (mstar < 10.)
flag2 = ~core & sf_flag #& (mstar > 9.5) & (mstar < 10.)
colormass(mstar[flag1],(sfr[flag1]),mstar[flag2],(sfr[flag2]),'core','external','sfr-mstar.pdf',ymin=-3,ymax=1,nhistbin=20,ylabel='$\log_{10}(SFR)$')









    



############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.11
p-vale = 0.11276 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.09
p-vale = 0.29821 (prob that samples are from same distribution)

So if we compare the core and external samples in the SFR-M* plane, we find no significant differences in either mass or SFR distribution.

Reading in LCSbase instead



In [16]:

    
%run ~/github/LCS/python/Python3/LCSbase.py









    



nothing happening here
(1800, 1800)
updating table



In [17]:

    
sfr = g.SFR_NUV_BEST
sfr = g.SFR_BEST
sfr = g.logSFR_NUV_BEST
#sfr = np.log10(g.s['SFR_ZDIST'])
mstar = g.logstellarmass
ssfr = sfr - mstar

sf_flag = ssfr > -11



In [18]:

    
core = g.membflag
core = (g.s['DR_R200'] < 0.6) & (g.s['DELTA_V'] < 3.)

Comparing UV and IR SFRs



In [19]:

    
plt.figure()
sfr_uv = g.logSFR_NUV_BEST
sfr_ir = np.log10(g.SFR_BEST)
plt.plot(sfr_ir,sfr_uv,'bo')
plt.xlabel('SFR IR')
plt.ylabel('SFR UV+IR')
xmin=-2
xmax=1
plt.axis([xmin,xmax,xmin,xmax])
xl = np.linspace(xmin,xmax,100)
plt.plot(xl,xl,'k-',lw=3)









    Out[19]:





[<matplotlib.lines.Line2D at 0x7f8957ff0810>]

Using Kennicutt & Evans SFR Conversions from LCS catalog

Switched from chary & elbaz to kennicutt and Evans SFRs. The plot below shows UV, IR, and UV+IR SFRs. The IR is sensitive to highests SFRs, and UV misses or underestimates these. However, the UV detects lower SFRs that IR tracers miss. Thus, the optimum tracer of SF is UV+IR.



In [20]:

    
# compare distribution of SFRs
plt.figure()
mybins = np.linspace(-5,1,20)
plt.hist(g.logSFR_NUVIR_KE,histtype='step',color='c',bins=mybins,label='UV+IR')
plt.hist(g.logSFR_IR_KE,histtype='step',color='r',bins=mybins,label='IR')
plt.hist(g.logSFR_NUV_KE,histtype='step',color='b',bins=mybins,label='UV')
plt.legend(loc='upper left')









    Out[20]:





<matplotlib.legend.Legend at 0x7f8957ff5650>

UV+IR probes to lower SFRs than IR alone.

SFR- Mstar with Kennicutt & Evans SFRs



In [21]:

    
sfr = g.logSFR_NUVIR_KE
mstar = g.logstellarmass
ssfr = sfr - mstar
flag1 = core #& sf_flag #& (mstar > 9.5)  & (mstar < 10.)
flag2 = ~core #& sf_flag #& (mstar > 9.5) & (mstar < 10.)
colormass(mstar[flag1],(sfr[flag1]),mstar[flag2],(sfr[flag2]),'core','external','sfr-mstar.pdf',ymin=-3,ymax=1,nhistbin=20,ylabel='$\log_{10}(SFR)$')









    



############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.11
p-vale = 0.00005 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.17
p-vale = 0.00000 (prob that samples are from same distribution)

This difference we see between the core and external sample is do to the higher fraction of galaxies on the passive sequence. The SFRs on the passive sequence are not reliable and should be thought of as upper limits (according to Leroy+2019).

We also need to apply mass cuts.

plot distribution of sSFR to see where to cut the passive galaxies



In [22]:

    
plt.figure()
sfr = g.logSFR_NUVIR_KE
mstar = g.logstellarmass
ssfr = sfr - mstar
flag1 = core & (mstar > 9.5)  #& (mstar < 10.)
flag2 = ~core &  (mstar > 9.5) #& (mstar < 10.)
plt.hist(ssfr[~core],bins=np.linspace(-14,-8,30),color='b',histtype='step')
plt.hist(ssfr[core],bins=np.linspace(-14,-8,30),color='r',histtype='step')
plt.axvline(x=-11.5,color='k')
plt.axvline(x=-11.,color='k')









    Out[22]:





<matplotlib.lines.Line2D at 0x7f8954494ed0>

Checking Redshift distribution to see if sample is in Leroy

Our internal analysis for LCS is limited by :

the size of the external comparison sample
the fact that the external sample is not a true field sample.

We are looking for other field samples to compare with.

Leroy+2019

This is a reprocessing of galaxies with $z < 3500$~km/s. There are only 18 galaxies in common with LCS. However, we can use this as a field comparison.

Salim+2017

The GALEX-SDSS-WISE Legacy survey has SFR and Mstar for SDSS galaxies out to $z < ??$

We have $\sim$1500 galaxies in common b/w LCS and GSWLC. This is definitely a better field sample. And we can use the SFRs and Mstar values from GSW for all comparisons (including LCS galaxies) so that there is in no potential systematics related to how these quantities are calculated.



In [24]:

    
plt.figure()
plt.hist(g.s['ZDIST']*3.e5)
plt.xlabel('Recession Velocity')
plt.ylabel('number')
plt.title('Recession Velocity Distribution of LCS')









    Out[24]:





Text(0.5, 1.0, 'Recession Velocity Distribution of LCS')

Make LCS catalog to upload to IRSA

need NSAID, RA, DEC, radius (10")
matched to z0MGS using LCS irsa
https://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-scan?submit=Select&projshort=Contributed_Data_Sets



In [25]:

    
search_radius = 30.*np.ones(len(g.s)) # units are arcsec
newtable = Table([g.s['NSAID'],g.s['RA'],g.s['DEC'],search_radius],names=['galid','ra','dec','major'])
newtable.write('/home/rfinn/research/lcs_sample.txt',format='ipac',overwrite=True)



In [27]:

    
lcsmgs = ascii.read('/home/rfinn/research/LCS/tables/lcs_z0mgs_match.tbl',format='ipac')



In [28]:

    
# number of matches
print(sum(lcsmgs['ra'] > 0))

We have 18 galaxies that overlap between the Local Cluster Survey and the z0MGS. We compare the stellar mass and SFR estimates from LCS and z0MGS for these galaxies. Both the stellar mass and SFRs are consistent.



In [29]:

    
# compare SFRs
plt.figure(figsize=(8,6))
sfr1 = g.logSFR_NUVIR_KE
sfr2 = lcsmgs['logsfr']
plt.plot(sfr1,sfr2,'bo')
xl = np.linspace(-1.5,1,10)
plt.plot(xl,xl,'k-')
plt.xlabel('$\log_{10}(SFR \ LCS)$',fontsize=20)
plt.ylabel('$ \log_{10}(SFR \ z0MGS)$',fontsize=20)









    Out[29]:





Text(0, 0.5, '$ \\log_{10}(SFR \\ z0MGS)$')



In [30]:

    
# compare SFRs
plt.figure()
sfr1 = g.logstellarmass
sfr2 = lcsmgs['logmass']
plt.plot(sfr1,sfr2,'bo')
xl = np.linspace(9,12,10)
plt.plot(xl,xl,'k-')
plt.xlabel('$\log_{10}(M_\star \ LCS/M_\odot)$',fontsize=20)
plt.ylabel('$\log_{10}(M_\star \ z0MGS/M_\odot)$',fontsize=20)









    Out[30]:





Text(0, 0.5, '$\\log_{10}(M_\\star \\ z0MGS/M_\\odot)$')

The z0MGS SFRs are systematically higher than the K&E SFRs that I calculated for the LCS galaxies.

The slope of the z0MGS vs Moustakas stellar masses is less than 1, meaning that z0MGS has lower masses for high mass galaxies and higher masses for low-mass galaxies. Not sure what to make of this...

GSWLC is a better comparison sample, so skip z0MGS for now.

downloaded full z0MGS catalog from irsa



In [32]:

    
mgs = ascii.read('/home/rfinn/research/z0mgs/z0mgs_full_catalog.tbl',format='ipac')



In [33]:

    
mgs.columns









    Out[33]:





<TableColumns names=('pgc_name','pgc','ra','dec','gl_deg','gb_deg','resolution','has_fuv','has_nuv','has_wise1','has_wise2','has_wise3','has_wise4','time_fuv','time_nuv','afuv','anuv','rms_fuv','std_fuv','rms_nuv','std_nuv','rms_wise1','std_wise1','rms_wise2','std_wise2','rms_wise3','std_wise3','rms_wise4','std_wise4','sat_effects_fuv','star_area_fuv','star_flux_fuv','star_flag_fuv','sat_effects_nuv','star_area_nuv','star_flux_nuv','star_flag_nuv','sat_effects_wise1','star_area_wise1','star_flux_wise1','star_flag_wise1','sat_effects_wise2','star_area_wise2','star_flux_wise2','star_flag_wise2','sat_effects_wise3','star_area_wise3','star_flux_wise3','star_flag_wise3','sat_effects_wise4','star_area_wise4','star_flux_wise4','star_flag_wise4','galaxy_mask_overlap','galaxy_overlap_flag','flux_fuv','rms_flux_fuv','std_flux_fuv','flux_nuv','rms_flux_nuv','std_flux_nuv','flux_wise1','rms_flux_wise1','std_flux_wise1','flux_wise2','rms_flux_wise2','std_flux_wise2','flux_wise3','rms_flux_wise3','std_flux_wise3','flux_wise4','rms_flux_wise4','std_flux_wise4','dist_mpc','e_dist_dex','mtol','method_mtol','logmass','e_logmass','logsfr','e_logsfr','method_sfr','deltams','absbtc','complete_sample','summary_url')>



In [34]:

    
plt.figure(figsize=(8,6))
plt.plot(mgs['ra'],mgs['dec'],'k.',alpha=.05)
plt.gca().invert_xaxis()
ra = mgs['ra']
dec = mgs['dec']
region1 = (ra > 225) & (dec > 50)
region2 = (ra < 150) & (dec < -50)
region3 = (ra < 100) & (dec > 60)
plt.plot(ra[region1],dec[region1],'c.',alpha=.1)
plt.plot(ra[region2],dec[region2],'c.',alpha=.1)
plt.plot(ra[region3],dec[region3],'c.',alpha=.1)
print(sum(region1)+sum(region2) + sum(region3))
fieldsample = region1 | region2 | region3
plt.xlabel('RA (deg)',fontsize=20)
plt.ylabel('DEC (deg)',fontsize=20)
plt.title('Leroy+2019 z0MGS')
plt.savefig('z0mgs-positions.pdf')

The cyan regions are areas that we selected "by eye" to be relatively low density regions. This is a first attempt at creating a field sample.

A more quantitative comparison is to calculate local density, and then select a "field" sample according to local density.



In [36]:

    
pos = SkyCoord(ra=mgs['ra'],dec=mgs['dec'], distance=mgs['dist_mpc'],frame='icrs')



In [66]:

    
NN = 10
idx, d2d, d3d = pos.match_to_catalog_3d(pos,nthneighbor=NN)



In [75]:

    
plt.figure(figsize=(10,8))
densN = NN/d3d
plt.scatter(mgs['ra'],mgs['dec'],c=np.log10(densN*u.Mpc),vmin=-.5,vmax=1.,s=10)
plt.colorbar(label='$\log_{10}(N/d_N)$')
plt.xlabel('RA')
plt.ylabel('DEC')
plt.gca().invert_xaxis()



In [79]:

    
plt.figure()
t = plt.hist(np.log10(densN*u.Mpc), bins=20)
plt.xlabel('$ \log_{10} (N/d_N)$')









    Out[79]:





Text(0.5, 0, '$ \\log_{10} (N/d_N)$')

Seems reasonable to select "field" as galaxies with $\log_{10}(N/d_N) < 0.5$

Comparison of LCS with GSWLC

The GSWLC provides a better comparison sample for us.
I matched to GSWLC-X2 - used 5 arcsecond radius. found 1496 matches.
- I didn't note how I did the match. I don't see any python programs, so I probably used topcat.
we can use the GSWLC SFRs and Stellar masses for both the LCS and comparison galaxies. This removes significant uncertainty associated with mixing methods for calculating SFR and Mstar.
redshift limit $0.01 < z < 0.3$
mag limit r petrosian $r = 18.0$
main sequence: $log10(sSFR) > -11$
explanation of GSWLC-2 catalog http://pages.iu.edu/~salims/gswlc/table2.pdf

Stellar Mass Limit

convert r to Mr at maximum distance of LCS ($z=.0433$)
then plot Mstar vs Mr, and estimate Mstar limit from Mr limit

SFR Limit

Salim+2018 use $log10(sSFR) > -11$ to select star-forming galaxies
$log10(sSFR) < -11.5$ should be considered upper limits GSWLC-A (Salim+2018, pg 11, col2)
- we use this as our sSFR limit
for SF galaxies, SFR errors are typically below 0.1dex

Environment Catalogs

yang+07
tempel+2012 - http://www.aai.ee/~elmo/dr8groups/readme.txt
decarvalha+2017



In [12]:

    
# this catalog is a joined catalog between LCS and GSWLC
# it contains 1505 rows - the number of matches b/w the two catalogs
lcsgsw = fits.getdata('/home/rfinn/research/LCS/tables/lcs-gswlc-x2-match.fits')
#lcsgsw['sampleflag'] = lcsgsw['sampleflag'] == 84



In [38]:

    
print('number of matches between LCS and GSWLC = ',len(lcsgsw))
print('number with UV = ',sum(lcsgsw['flag_uv'] == 2))
print('number with mid IR = ',sum(lcsgsw['flag_midir'] == 1))
print('number with SDSS galaxy data = ',sum(lcsgsw['flag_mgs'] == 1))









    



number of matches between LCS and GSWLC =  1505
number with UV =  546
number with mid IR =  437
number with SDSS galaxy data =  1477

DETERMINING STELLAR MASS LIMIT



In [26]:

    
zmax = max(lcsgsw['ZDIST'])
print(zmax)
# assume hubble flow
dmax = zmax*3.e5/70
r_limit = 18.
# abs r
# m - M = 5logd_pc - 5
# M = m - 5logd_pc + 5
## r = 22.5 - np.log10(lcsgsw['NMGY'][:,4])
Mr = r_limit - 5*np.log10(dmax*1.e6) +5
print(Mr)









    



0.04320573
-18.337822841201557



In [38]:

    
ssfr = lcsgsw['logSFR'] - lcsgsw['logMstar']
flag = (lcsgsw['logMstar'] > 0) & (ssfr > -11.5)
Mr = lcsgsw['ABSMAG'][:,4]
plt.figure()
plt.plot(Mr[flag],lcsgsw['logMstar'][flag],'bo',alpha=.2,markersize=3)
plt.axvline(x=-18.33)
plt.axhline(y=9.8)
plt.axhline(y=9.5)
plt.xlabel('Mr')
plt.ylabel('logMstar GSWLC')
plt.grid(True)

Compare SFRs and Stellar Masses



In [219]:

    
plt.figure()
plt.plot((lcsgsw['MSTAR_50']),lcsgsw['logMstar'],'ko',color='0.5',alpha=.5)
flag = lcsgsw['sampleflag']
plt.plot(lcsgsw['MSTAR_50'][flag],lcsgsw['logMstar'][flag],'bo',alpha=.5)
axis([8,12,8,12])
xl = np.linspace(8,12,100)
plt.plot(xl,xl,'k--',lw=3)
plt.xlabel('Moustakas Mstar')
plt.ylabel('GSWLC Mstar')









    Out[219]:





Text(0, 0.5, 'GSWLC Mstar')

The fact that GSWLC and JM's stellar mass values agree is reassuring. However, we will be using GSWLC measurements for both the LCS and comparison samples, so this is not as critical.



In [274]:

    
plt.figure()
mybins=np.arange(8,12,.125)
plt.hist(lcsgsw['logMstar'],bins=mybins)
plt.axvline(x=9.5,c='k')
plt.title('Distribution of GSWLC Mstar for LCS Galaxies')
#print(sum(lcsgsw.sampleflag))









    Out[274]:





Text(0.5, 1.0, 'Distribution of GSWLC Mstar for LCS Galaxies')



In [287]:

    
plt.figure()
flag = lcsgsw['logMstar']> 0
x = lcsgsw['Z']*3.e5
y = lcsgsw['logMstar']
plt.plot(x[flag],y[flag],'k.')
plt.xlim([3000,15000])
plt.axhline(y=9.5)
plt.title('Mstar limits of LCS+GSWLC')
plt.xlabel('vr (km/s)',fontsize=16)
plt.ylabel('GSWLC logMstar',fontsize=16)
print('number of galaxies with logMstar > 9.5 = ',sum(y > 9.5))









    



number of galaxies with logMstar > 9.5 =  1025



In [40]:

    
plt.figure()
flag = lcsgsw['logMstar']> 0
x = lcsgsw['Z']*3.e5
y = lcsgsw['logSFR']
plt.plot(x[flag],y[flag],'k.')
plt.xlim([3000,15000])
#plt.axhline(y=9.5)
plt.title('SFR limits of LCS+GSWLC')
plt.xlabel('vr (km/s)',fontsize=16)
plt.ylabel('GSWLC logSFR',fontsize=16)









    Out[40]:





Text(0, 0.5, 'GSWLC logSFR')

make SFR-Mstar plot using 11,000 < vr < 13,000. Set sSFR limits using this plot.



In [55]:

    
plt.figure()
x = lcsgsw['Z']*3.e5
flag = (lcsgsw['logMstar']> 0) #& (x < 13000) & (x > 10000)
flag2 = (lcsgsw['logMstar']> 0) & (x < 13000) & (x > 10000)
y = lcsgsw['logSFR']
x = lcsgsw['logMstar']
plt.plot(x[flag],y[flag],'k.',alpha=.5)
plt.plot(x[flag2],y[flag2],'c.')
#plt.xlim([3000,15000])
#plt.axhline(y=9.5)
xl = np.linspace(8.5,10.5,20)
ssfr = -10
plt.plot(xl,xl+ssfr,'b--',lw=3)
plotsalim07()
plt.title('SFR - Mstar of LCS+GSWLC')
plt.xlabel('GSWLC logMstar',fontsize=16)
plt.ylabel('GSWLC logSFR',fontsize=16)









    Out[55]:





Text(0, 0.5, 'GSWLC logSFR')



In [207]:

    
plt.figure(figsize=(8,6))
flag = lcsgsw['sampleflag'] & ~lcsgsw['membflag']
flag = lcsgsw['lirflag']
plt.plot(lcsgsw['logSFR_NUVIR_KE'][flag],lcsgsw['logSFR'][flag],'bo')
axis([-2,1.5,-2,1.5])
xl = np.linspace(-1.5,1,20)
plt.plot(xl,xl,'c-',lw=3,label='1:1')
offset=.18
plt.plot(xl,xl+offset,'c--',lw=3,label='1:1+'+str(offset))
plt.xlabel('SFR LCS',fontsize=16)
plt.ylabel('SFR GSWLC',fontsize=16)
plt.legend()









    Out[207]:





<matplotlib.legend.Legend at 0x7f0fd33961d0>

need to check which IMF is used in Kennicutt & Evans, and by GSWLC

SFR - Mstar with GSWLC Values for SFR and Mstar

Comparing core and external samples using GSWLC measures of SFR and stellar mass.
results are sensitive to mass and ssfr cuts.
if we use log(Mstar) > 9.5 and log(sSFR)> -1.5, then we detect an offset in SFR between cluster and field



In [236]:

    
masscut = 9.25
#core = (g.s['DR_R200'] < 0.6) & (g.s['DELTA_V'] < 3.)

mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar
mybins = np.arange(-14,-8,.2)
t=plt.hist(ssfr,bins=mybins)



In [244]:

    
plt.figure()
flag = mstar > -20
print(sum(flag))
print(sum(flag & (mstar > 9.5)))
plt.plot(mstar[flag],sfr[flag],'k.',alpha=.5)









    



1496
1025






    Out[244]:





[<matplotlib.lines.Line2D at 0x7f0fd24da410>]



In [295]:

    
masscut = 9.5
ssfrcut = -11.5
#core = (g.s['DR_R200'] < 0.6) & (g.s['DELTA_V'] < 3.)
mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar
ssfr_flag = (ssfr < -7) & (ssfr > ssfrcut)
#core = (g.s['DR_R200'] < 0.6) & (g.s['DELTA_V'] < 3.)
flag1 = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & ssfr_flag
#fieldflag = (lcsgsw['DR_R200'] > 3) & (lcsgsw['DELTA_V'] > 3)
#print('number in field = ',print(sum(fieldflag)))
flag2 = ~lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & ssfr_flag
#flag2 = fieldflag &  (lcsgsw['logMstar']> masscut)  & ssfr_flag
print('number in core sample = ',sum(flag1))
print('number in external sample = ',sum(flag2))
colormass(mstar[flag1],sfr[flag1],mstar[flag2],sfr[flag2],'core','external','sfr-mstar-gswlc.pdf',ymin=-3,ymax=1.6,xmin=8.5,xmax=11.5,nhistbin=15,ylabel='$\log_{10}(SFR)$')
#plt.plot(xl,xl+ssfr_lower_limit,'k--',c='0.5'6
#plt.axis([9,12,-2,1.5]),









    



8
number in field =  None
number in core sample =  220
number in external sample =  222
############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.11
p-vale = 0.13052 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.20
p-vale = 0.00030 (prob that samples are from same distribution)

Creating Field Sample from Full GSWLC

for a quick test, use entire GSWLC catalog



In [43]:

    
gsw = ascii.read('/home/rfinn/research/GSWLC/GSWLC-X2.dat')
gsw = gsw[((gsw['Z']*3.e5 < 13000.) & (gsw['logMstar']> 0))] 
print('number is GSWLC = ',len(gsw))









    



number is GSWLC =  65611

measure local densities

cut GSWLC by local density to create a field sample

could also try matching to Yang catalog - see if it matters.



In [44]:

    
print(sum(gsw['flag_mgs']))



In [47]:

    
pos = SkyCoord(ra=gsw['RA']*u.deg,dec=gsw['DEC']*u.deg, distance=gsw['Z']*3.e5/70*u.Mpc,frame='icrs')
NN = 10
idx, d2d, d3d = pos.match_to_catalog_3d(pos,nthneighbor=NN)



In [48]:

    
plt.figure(figsize=(14,8))
densN = NN/d3d
plt.scatter(gsw['RA'],gsw['DEC'],c=np.log10(densN*u.Mpc),vmin=-.5,vmax=1.,s=10)
plt.colorbar(label='$\log_{10}(N/d_N)$')
plt.xlabel('RA')
plt.ylabel('DEC')
plt.gca().invert_xaxis()
plt.axhline(y=5)
plt.axhline(y=60)
plt.axvline(x=135)
plt.axvline(x=225)









    Out[48]:





<matplotlib.lines.Line2D at 0x7fc1f3db8310>

cutting GSWLC to main SDSS survey area, and then recalculating local densities



In [49]:

    
ramin=135
ramax=225
decmin=5
decmax=60
gsw_position_flag = (gsw['RA'] > ramin) & (gsw['RA'] < ramax) & (gsw['DEC'] > decmin) & (gsw['DEC'] < decmax)



In [50]:

    
gsw = gsw[gsw_position_flag]



In [51]:

    
pos = SkyCoord(ra=gsw['RA']*u.deg,dec=gsw['DEC']*u.deg, distance=gsw['Z']*3.e5/70*u.Mpc,frame='icrs')
NN = 10
idx, d2d, d3d = pos.match_to_catalog_3d(pos,nthneighbor=NN)



In [52]:

    
plt.figure(figsize=(14,8))
densN = NN/d3d
plt.scatter(gsw['RA'],gsw['DEC'],c=np.log10(densN*u.Mpc),vmin=-.5,vmax=1.,s=10)
plt.colorbar(label='$\log_{10}(N/d_N)$')
plt.xlabel('RA')
plt.ylabel('DEC')
plt.gca().invert_xaxis()
plt.axhline(y=5)
plt.axhline(y=60)
plt.axvline(x=135)
plt.axvline(x=225)









    Out[52]:





<matplotlib.lines.Line2D at 0x7fc1ea345a90>



In [53]:

    
# make a histogram of local densities
plt.figure()
t = plt.hist(np.log10(densN*u.Mpc), bins=20)
plt.xlabel('$ \log_{10} (N/d_N)$')
print('median local density =  %.3f'%(np.median(np.log10(densN*u.Mpc))))
plt.axvline(x = (np.median(np.log10(densN*u.Mpc))),c='k')
gsw_density_flag = np.log10(densN*u.Mpc) < np.median(np.log10(densN*u.Mpc))
gsw_density_flag = np.log10(densN*u.Mpc) < .2









    



median local density =  0.488

Could also use Yang catalog to remove all galaxies in halos with log M_halo > 13.5 or 14

To determine SFR M* limits

read Salim GSWLC paper - do they mention any limits?
things that aren't detected in GALEX or WISE
stellar mass



In [55]:

    
# repeat above, but only keep galaxies in redshift range of LCS
# so that we can judge the mass limit where we can probe SF a factor of 
# 5 (or N) below the main sequence

# redshift range of LCS
# 0.0137 < z < 0.0433.
zmin = 0.0137
zmax = 0.0433
zflag = (gsw['Z'] > zmin) & (gsw['Z'] < zmax)
plt.figure(figsize=(8,6))
x = gsw['logMstar'][zflag]
y = gsw['logSFR'][zflag]
plt.plot(x,y,'k.',alpha=.1)
#plt.hexbin(x,y,gridsize=30,vmin=5,cmap='gray_r')
#plt.colorbar()
xl=np.linspace(8,12,50)
ssfr_limit = -10
#plt.plot(xl,xl+ssfr_limit,'r-')
plotsalim07()
plt.xlabel('logMstar',fontsize=16)
plt.ylabel('logSFR',fontsize=16)









    Out[55]:





Text(0, 0.5, 'logSFR')



In [56]:

    
field_gsw = zflag & gsw_density_flag

The Figure above shows the Salim+07 fit to SF main sequence, minus 0.3 dex. Apparently there is a normalization offset b/w Salim+07 and GSWLC from Salim+16. The solid line is the mass-dependent fit to the MS, and the dashed line is a factor of 5 below the MS. The function to plot the lines is from Greg.

lssfr = -0.35*(lmstar - 10) - 9.83

#use their equation 12 for color-selected galaxies including
#AGN/SF composites.  This is for log(Mstar)>9.4
#lssfr = -0.53*(lmstar - 10) - 9.87

lsfr = lmstar + lssfr -.3
sfr = 10.**lsfr

Plot samples vs distance from MS.

Comparing LCS vs GSWLC with correct mass and sSFR cuts

According to Salim+2016, the mass limit is log10Mstar = 9.8, and the ssfr cut is -11.5.

When we apply these cuts and compare the LCS and GSWLC in the SFR-Mstar plane, the LCS galaxies have a similar distribution of stellar masses, but significantly lower SFRs.

We can also compare sSFRs.



In [58]:

    
#masscut = 8.5
masscut = 9.8
ssfrcut = -11.5



In [ ]:

    
mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar
gswssfr = gsw['logSFR'] - gsw['logMstar']



In [77]:

    
flag1 = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr > ssfrcut)
flag2 = (gsw['logMstar'] > masscut) & (gswssfr > ssfrcut)  & field_gsw
print('number in core sample = ',sum(flag1))
print('number in external sample = ',sum(flag2))
colormass(mstar[flag1],sfr[flag1],gsw['logMstar'][flag2],gsw['logSFR'][flag2],'LCS core','GSWLC','sfr-mstar-gswlc-field.pdf',ymin=-2,ymax=1.6,xmin=9.75,xmax=11.5,nhistbin=10,ylabel='$\log_{10}(SFR)$',contourflag=False,alphagray=.8)
plt.savefig(homedir+'/research/LCS/plots/lcscore-gsw-sfms.pdf')
plt.savefig(homedir+'/research/LCS/plots/lcscore-gsw-sfms.png')









    



number in core sample =  137
number in external sample =  1159
############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.10
p-vale = 0.14328 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.34
p-vale = 0.00000 (prob that samples are from same distribution)

LCS Infall vs GSWLC Field

CONCLUSION:

LCS infall galaxies have significantly lower SFRs



In [78]:

    
lcsinfall = ~lcsgsw['membflag'] & (lcsgsw['DELTA_V'] < 3.)
mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar
gswssfr = gsw['logSFR'] - gsw['logMstar']
flag1 = lcsinfall &  (lcsgsw['logMstar']> masscut)  & (ssfr > ssfrcut)
flag2 = (gsw['logMstar'] > masscut) & (gswssfr > ssfrcut)  & field_gsw
print('number in infall sample = ',sum(flag1))
print('number in GSWLC field sample = ',sum(flag2))
colormass(mstar[flag1],sfr[flag1],gsw['logMstar'][flag2],gsw['logSFR'][flag2],'LCS infall','GSWLC','lcsinfall-gswlc-sfms.pdf',ymin=-2,ymax=1.6,xmin=9.75,xmax=11.5,nhistbin=15,ylabel='$\log_{10}(SFR)$',contourflag=False,alphagray=.8)
plt.savefig(homedir+'/research/LCS/plots/lcsinfall-gsw-sfms.pdf')
plt.savefig(homedir+'/research/LCS/plots/lcsinfall-gsw-sfms.png')









    



number in infall sample =  118
number in GSWLC field sample =  1159
############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.13
p-vale = 0.04050 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.26
p-vale = 0.00000 (prob that samples are from same distribution)

Splitting LCS by Cluster/group vs GSWLC Field

CONCLUSIONS:

Group members have lower SFRs compared to GSWLC sample.



In [80]:

    
sigma_split = 600
group = lcsgsw['CLUSTER_SIGMA'] < sigma_split
cluster = lcsgsw['CLUSTER_SIGMA'] > sigma_split



In [81]:

    
flag1 = lcsgsw['membflag'] &   (lcsgsw['logMstar']> masscut)  & (ssfr > ssfrcut) & group
flag2 = (gsw['logMstar'] > masscut) & (gswssfr > ssfrcut)  & field_gsw
print('number in core sample = ',sum(flag1))
print('number in external sample = ',sum(flag2))
colormass(mstar[flag1],sfr[flag1],gsw['logMstar'][flag2],gsw['logSFR'][flag2],'LCS core groups','GSWLC','sfr-mstar-gswlc-field.pdf',ymin=-2,ymax=1.6,xmin=9.5,xmax=11.5,nhistbin=15,ylabel='$\log_{10}(SFR)$',contourflag=False,alphagray=.8)
plt.savefig(homedir+'/research/LCS/plots/lcsgroup-gsw-sfms.pdf')
plt.savefig(homedir+'/research/LCS/plots/lcsgroup-gsw-sfms.png')









    



number in core sample =  32
number in external sample =  1159
############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.16
p-vale = 0.38027 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.50
p-vale = 0.00000 (prob that samples are from same distribution)



In [82]:

    
flag1 = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr > ssfrcut) & cluster
flag2 = (gsw['logMstar'] > masscut) & (gswssfr > ssfrcut)  & field_gsw
print('number in core sample = ',sum(flag1))
print('number in external sample = ',sum(flag2))
colormass(mstar[flag1],sfr[flag1],gsw['logMstar'][flag2],gsw['logSFR'][flag2],'LCS core cluster','GSWLC','sfr-mstar-gswlc-field.pdf',ymin=-2,ymax=1.6,xmin=9.5,xmax=11.5,nhistbin=15,ylabel='$\log_{10}(SFR)$',contourflag=False,alphagray=.8)
plt.savefig(homedir+'/research/LCS/plots/lcscluster-gsw-sfms.pdf')
plt.savefig(homedir+'/research/LCS/plots/lcscluster-gsw-sfms.png')









    



number in core sample =  105
number in external sample =  1159
############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.12
p-vale = 0.10553 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.32
p-vale = 0.00000 (prob that samples are from same distribution)

Comparing sSFRs of LCS vs GSWLC



In [99]:

    
flag1 = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr > ssfrcut)
flag2 = (gsw['logMstar'] > masscut) & (gswssfr > ssfrcut)  & field_gsw
flag3 = ~lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr > ssfrcut)
print('number in core sample = ',sum(flag1))
print('number in external sample = ',sum(flag2))
ks(ssfr[flag1],gswssfr[flag2])
plt.figure(figsize=(6,8))
plt.subplot(2,1,1)
mybins = np.linspace(-11.5,-8.75,20)
t = plt.hist(ssfr[flag1],bins=mybins,histtype='step',normed=True,lw=2,label='LCS Core')
t = plt.hist(gswssfr[flag2],bins=mybins,histtype='step',normed=True,lw=2,label='GSWLC')
#t = plt.hist(ssfr[flag3],bins=mybins,histtype='step',normed=True,lw=2,label='LCS External')
plt.legend()
#plt.xlabel('$\log_{10}(sSFR)$',fontsize=20)
plt.ylabel('$Normalized \ Frequency$',fontsize=20)
plt.subplot(2,1,2)
mybins = np.linspace(-11.5,-8.75,20)
#t = plt.hist(ssfr[flag1],bins=mybins,histtype='step',normed=True,lw=2,label='LCS Core')
t = plt.hist(ssfr[flag3],bins=mybins,histtype='step',normed=True,lw=2,label='LCS External')
t = plt.hist(gswssfr[flag2],bins=mybins,histtype='step',normed=True,lw=2,label='GSWLC')
plt.legend()
plt.xlabel('$\log_{10}(sSFR)$',fontsize=20)
plt.ylabel('$Normalized \ Frequency$',fontsize=20)









    



number in core sample =  137
number in external sample =  1159
KS Test:
D =   0.33
p-vale = 0.00000 (prob that samples are from same distribution)
Anderson-Darling test Test:
D =  39.14
p-vale = 0.04896 (prob that samples are from same distribution)






    Out[99]:





Text(0, 0.5, '$Normalized \\ Frequency$')



In [ ]:

Looking at Distance from the main sequence for LCS vs GSWLC

CONCLUSION: not much difference



In [72]:

    
sfms_dist_lcs = lcsgsw['logSFR'] - ((-0.35*(lcsgsw['logMstar'] - 10) -9.83+ lcsgsw['logMstar']-.3))
sfms_dist_gsw = gsw['logSFR'] - ((-0.35*(gsw['logMstar'] - 10) -9.83+ gsw['logMstar']-.3))



In [73]:

    
mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar

lcs_flag = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr_cut_lcs)
gsw_flag = (gsw['logMstar'] > masscut) & (ssfr_cut_gsw)

plt.figure(figsize=(8,6))
#mybins = np.arange(-1,1.5,20)
t = plt.hist(sfms_dist_lcs[lcs_flag],histtype='step',normed=True,lw=2,label='LCS')#,bins=mybins)
t = plt.hist(sfms_dist_gsw[gsw_flag],histtype='step',normed=True,lw=2,label='GSWLC')#,bins=mybins)
plt.legend(loc='upper right')

plt.xlabel('$\log_{10}(SFR) - \log_{10}(SFR_{MS})$',fontsize=20)
plt.ylabel('$Frequency$',fontsize=20)

# KS test
ks_2samp(sfms_dist_lcs[lcs_flag],sfms_dist_gsw[gsw_flag])









    Out[73]:





Ks_2sampResult(statistic=0.09121373796175454, pvalue=0.48079983165134577)

Repeat, but for cluster and groups separately

Define cluster as velocity dispersion > 700 km/s.



In [234]:

    
sigma_split = 600
group = lcsgsw['CLUSTER_SIGMA'] < sigma_split
cluster = lcsgsw['CLUSTER_SIGMA'] > sigma_split



In [235]:

    
mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar

lcs_flag = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr_cut_lcs) & group
gsw_flag = (gsw['logMstar'] > masscut) & (ssfr_cut_gsw)

plt.figure(figsize=(8,6))
#mybins = np.arange(-1,1.5,20)
t = plt.hist(sfms_dist_lcs[lcs_flag],histtype='step',normed=True,lw=2,label='LCS')#,bins=mybins)
t = plt.hist(sfms_dist_gsw[gsw_flag],histtype='step',normed=True,lw=2,label='GSWLC')#,bins=mybins)
plt.legend(loc='upper right')

plt.xlabel('$\log_{10}(SFR) - \log_{10}(SFR_{MS})$',fontsize=20)
plt.ylabel('$Frequency$',fontsize=20)

# KS test
ks_2samp(sfms_dist_lcs[lcs_flag],sfms_dist_gsw[gsw_flag])









    Out[235]:





Ks_2sampResult(statistic=0.3636488321288114, pvalue=2.0486979262773031e-07)



In [236]:

    
mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar

lcs_flag = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr_cut_lcs) & cluster
gsw_flag = (gsw['logMstar'] > masscut) & (ssfr_cut_gsw)

plt.figure(figsize=(8,6))
mybins = np.arange(-1,1.5,20)
t = plt.hist(sfms_dist_lcs[lcs_flag],histtype='step',normed=True,lw=2,label='LCS')#,bins=mybins)
t = plt.hist(sfms_dist_gsw[gsw_flag],histtype='step',normed=True,lw=2,label='GSWLC')#,bins=mybins)
plt.legend(loc='upper right')

plt.xlabel('$\log_{10}(SFR) - \log_{10}(SFR_{MS})$',fontsize=20)
plt.ylabel('$Frequency$',fontsize=20)

# KS test
ks_2samp(sfms_dist_lcs[lcs_flag],sfms_dist_gsw[gsw_flag])









    Out[236]:





Ks_2sampResult(statistic=0.14784326997609093, pvalue=5.6320797302554005e-05)

Comparing Each Cluster Separately



In [237]:

    
clusters = set(lcsgsw['CLUSTER'])
print(clusters)









    



{'Coma', 'MKW8', 'Hercules', 'MKW11', 'A1367', 'A2052', 'A2063', 'NGC6107', 'AWM4'}



In [238]:

    
mstar = lcsgsw['logMstar']
sfr = lcsgsw['logSFR']
ssfr = sfr - mstar

for c in clusters:
    lcs_flag = lcsgsw['membflag'] &  (lcsgsw['logMstar']> masscut)  & (ssfr_cut_lcs) & (lcsgsw['CLUSTER'] == c)
    gsw_flag = (gsw['logMstar'] > masscut) & (ssfr_cut_gsw)

    plt.figure(figsize=(8,6))
    mybins = np.arange(-1,1.5,20)
    t = plt.hist(sfms_dist_lcs[lcs_flag],histtype='step',normed=True,lw=2,label='LCS')#,bins=mybins)
    t = plt.hist(sfms_dist_gsw[gsw_flag],histtype='step',normed=True,lw=2,label='GSWLC')#,bins=mybins)
    plt.legend(loc='upper right')

    plt.xlabel('$\log_{10}(SFR) - \log_{10}(SFR_{MS})$',fontsize=20)
    plt.ylabel('$Frequency$',fontsize=20)
    plt.title(c,fontsize=22)
    # KS test
    t = ks_2samp(sfms_dist_lcs[lcs_flag],sfms_dist_gsw[gsw_flag])
    print(c,sum(lcs_flag),sum(gsw_flag),t)









    



Coma 141 24981 Ks_2sampResult(statistic=0.20749045870606347, pvalue=1.1427847135524038e-05)
MKW8 16 24981 Ks_2sampResult(statistic=0.4786487730675313, pvalue=0.0013155388464662888)
Hercules 26 24981 Ks_2sampResult(statistic=0.12075177134622317, pvalue=0.843264696149085)
MKW11 14 24981 Ks_2sampResult(statistic=0.25086208375508245, pvalue=0.341966123474646)
A1367 12 24981 Ks_2sampResult(statistic=0.1674472599175373, pvalue=0.889708035176863)
A2052 19 24981 Ks_2sampResult(statistic=0.23312454307378871, pvalue=0.25346706607229447)
A2063 44 24981 Ks_2sampResult(statistic=0.11259557263520281, pvalue=0.6335374786599515)
NGC6107 20 24981 Ks_2sampResult(statistic=0.42266522557143427, pvalue=0.0015853150902229044)
AWM4 11 24981 Ks_2sampResult(statistic=0.4040270605660301, pvalue=0.055214688850238884)

Creating Field Sample from LCS local densities



In [256]:

    
plt.figure()
x = lcsgsw['DELTA_V']
y = lcsgsw['DR_R200']
plt.plot(x,y,'k.')
#plt.gca().set_xscale('log')
plt.xlim(0,5)
print(len(x))



In [ ]:

SFR - Mstar for Most Massive Clusters vs Field

selecting Coma and A2063 - most massive clusters



In [ ]:



In [34]:

    
sfr = g.logSFR_NUVIR_KE
mstar = g.logstellarmass
ssfr = sfr - mstar
sf_flag = ssfr > -99
ssfr_lower_limit = -11.5
xl = np.linspace(8.5,11.5)



In [51]:

    
flag1 = core & g.clusterflag & sf_flag & (mstar > masscut)  #& (mstar < 10.)
flag2 = ~core & sf_flag & (mstar > masscut) #& (mstar < 10.)
plt.figure()
plt.plot(mstar[flag1],ssfr[flag1],'k.')
plt.plot(mstar[flag2],ssfr[flag2],'c.')
plt.ylim(-12.5,-9)
xl = np.linspace(9,12,20)
plt.plot(xl,-.25*(xl-9)-11.1,'m-',lw=3)
plt.xlabel('$\log_{10}(M_star/M_\odot) $')
plt.ylabel('$\log_{10}(SFR/M_star) $')
plt.savefig('ssfr_cut.pdf')
ssfr_flag = ssfr > (-.25*(mstar-9)-11.1)



In [96]:

    
plt.figure(figsize=(10,8))

flag1 = core & g.clusterflag & sf_flag & (mstar > masscut)  #& (mstar < 10.)
flag2 = ~core & sf_flag & (mstar > masscut) #& (mstar < 10.)
plt.plot(mgs['logmass'][fieldsample],mgs['logsfr'][fieldsample],'k.',alpha=.2)
plt.plot(mstar[flag2],sfr[flag2],'c.',alpha=.2)
plt.xlabel('$\log_{10}(M_\star/M_\odot) $')
plt.ylabel('$\log_{10}(SFR) $')
plt.ylim(-4,2)
plt.savefig('sfms_with_mgs.pdf')



In [136]:

    
masscut = 9.5
#core = (g.s['DR_R200'] < 0.6) & (g.s['DELTA_V'] < 3.)
core = g.membflag
#core = (g.s['DR_R200'] < 0.6) & (g.s['DELTA_V'] < 3.)
flag1 = core &  (mstar > masscut)  & (ssfr > -11.)
flag2 = fieldsample & (mgs['logmass'] > masscut) & (mgs['logsfr']-mgs['logmass'] > -11.)
colormass(mstar[flag1],sfr[flag1],mgs['logmass'][flag2],mgs['logsfr'][flag2],'core','mgs','sfr-mstar-cluster-mgs.pdf',ymin=-1.6,ymax=1.6,xmin=9,xmax=11.5,nhistbin=15,ylabel='$\log_{10}(SFR)$')
#plt.plot(xl,xl+ssfr_lower_limit,'k--',c='0.5'6
#plt.axis([9,12,-2,1.5]),









    



############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.11
p-vale = 0.18009 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.30
p-vale = 0.00000 (prob that samples are from same distribution)

Still to do

bootstrap our external sample, selecting galaxies that have some distribution in SFR-Mass plane as the z0MGS sample.
this will let us include size in the SF MS plot, using bootstrapped external sample
incorporate SF in the disk shrinking model. apply to SF-stellar mass plane. where do objects go in main sequence. do we reproduce low SF tail seen in core sample.
what if we split LCS into low and high mass clusters

Results

pop of galaxies with low sSFRs in clusters
we show that SF is suppressed in infall regions because the external sample is similar in SF-M* to core sample.
drop in SFRs can be explained by disk shrinking?



In [128]:

    
masscut = 9.5
ssfr_cut = -11
core = (g.s['DR_R200'] < 0.6) & (g.s['DELTA_V'] < 3.)
flag1 = core  & (mstar > masscut)  & (ssfr > ssfr_cut)
flag2 = ~core & sf_flag & (mstar > masscut) & (ssfr > ssfr_cut)
colormass(mstar[flag1],(sfr[flag1]),mstar[flag2],(sfr[flag2]),'core','external','sfr-mstar.pdf',ymin=-1.5,ymax=1.5,xmin=9,xmax=11.5,nhistbin=15,ylabel='$\log_{10}(SFR)$')
#plt.plot(xl,xl+ssfr_lower_limit,'k--',c='0.5')









    



############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.15
p-vale = 0.08799 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.14
p-vale = 0.13513 (prob that samples are from same distribution)



In [103]:

    
plt.figure()
mybins = np.linspace(-13,-8,20)
plt.hist(ssfr[core],bins=mybins,color='r',histtype='step',label='core')
plt.hist(ssfr[~core],bins=mybins,color='b',histtype='step',label='external')









    Out[103]:





(array([  4.,   5.,  59., 141.,  99.,  72.,  53.,  51.,  59.,  80.,  84.,
         47.,  26.,  11.,   7.,   4.,   1.,   1.,   0.]),
 array([-13.        , -12.73684211, -12.47368421, -12.21052632,
        -11.94736842, -11.68421053, -11.42105263, -11.15789474,
        -10.89473684, -10.63157895, -10.36842105, -10.10526316,
         -9.84210526,  -9.57894737,  -9.31578947,  -9.05263158,
         -8.78947368,  -8.52631579,  -8.26315789,  -8.        ]),
 <a list of 1 Patch objects>)



In [22]:

    
flag1 = g.membflag & sf_flag 
flag2 = ~core & sf_flag 
colormass(mstar[flag1],(sfr[flag1]),mstar[flag2],(sfr[flag2]),'core','external','sfr-mstar.pdf',ymin=-3,ymax=1,nhistbin=20,ylabel='$\log_{10}(SFR)$')









    



############################################################# 
KS test comparising galaxies within range shown on the plot

STELLAR MASS
KS Test:
D =   0.09
p-vale = 0.00081 (prob that samples are from same distribution)

COLOR
KS Test:
D =   0.18
p-vale = 0.00000 (prob that samples are from same distribution)

Ratio of two different SFR measures, comparing field and cluster

seems like we get many more passive field galaxies when using UV+IR sfr



In [105]:

    
sfr_ir = np.log10(g.s['SFR_ZDIST'])
sfr_uvir =  g.logSFR_NUV_ZDIST
sfr_ratio = sfr_ir - sfr_uvir









    



/home/rfinn/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log10
  """Entry point for launching an IPython kernel.



In [109]:

    
plt.figure()
mybins = np.linspace(-1,1,20)
plt.hist(sfr_ratio[core],bins=mybins,color='r',histtype='step',label='core')
plt.hist(sfr_ratio[~core],bins=mybins,color='b',histtype='step',label='external')
plt.legend(loc='upper left')









    Out[109]:





<matplotlib.legend.Legend at 0x7f254fc59990>



In [97]:

    
nuv24 = g.s.ABSMAG[:,1] - g.s.fcmag1



In [100]:

    
plt.figure()
mybins = np.linspace(-20,0,20)
plt.hist(nuv24[core],bins=mybins,color='r',histtype='step',label='core')
plt.hist(nuv24[~core],bins=mybins,color='b',histtype='step',label='external')









    Out[100]:





(array([  0.,   0.,   2.,  18.,  47., 103., 121.,  57.,  25.,   2.,   2.,
          1.,   2.,   2.,   1.,   0.,   1.,   1.,   0.]),
 array([-20.        , -18.94736842, -17.89473684, -16.84210526,
        -15.78947368, -14.73684211, -13.68421053, -12.63157895,
        -11.57894737, -10.52631579,  -9.47368421,  -8.42105263,
         -7.36842105,  -6.31578947,  -5.26315789,  -4.21052632,
         -3.15789474,  -2.10526316,  -1.05263158,   0.        ]),
 <a list of 1 Patch objects>)



In [99]:

    
plt.figure()
plt.hist(nuv24)









    Out[99]:





(array([  2.,   0.,   1.,   5.,  51., 803.,   8., 567., 352.,  11.]),
 array([-66.16591454, -59.51406432, -52.8622141 , -46.21036388,
        -39.55851367, -32.90666345, -26.25481323, -19.60296301,
        -12.95111279,  -6.29926258,   0.35258764]),
 <a list of 10 Patch objects>)



In [23]:

    
sfr = g.SFR_NUV_BEST
sfr = g.SFR_BEST
sfr = g.logSFR_NUV_BEST
#sfr = np.log10(g.s['SFR_ZDIST'])
mstar = g.logstellarmass
ssfr = sfr - mstar
sf_flag = ssfr > -11.8



In [24]:

    
inner_cluster = g.membflag & (g.s['DR_R200'] < .5) 
outer_cluster = g.membflag & (g.s['DR_R200'] >= .5)

flags = [inner_cluster & g.galfitflag, outer_cluster & g.galfitflag, ~g.membflag & g.galfitflag]
plt.figure(figsize=(14,4))
all_axes=[]
for i in range(len(flags)):
    plt.subplot(1,3,i+1)
    plt.scatter(mstar[flags[i]],sfr[flags[i]],c=g.sizeratio[flags[i]],vmin=.1,vmax=1)
    if i > 0:
        plt.yticks([])
    plt.axis([8,12,-2,1])
    all_axes.append(plt.gca())
    xl = np.linspace(8,12,100)
    yl = -11.8 + xl
    plt.plot(xl,yl,'k--')
    yl = -10.4 + xl
    plt.plot(xl,yl,'k-')
    xbin,ybin,ybinerr= binxycolor(mstar[flags[i]& sf_flag],sfr[flags[i]& sf_flag],g.sizeratio[flags[i] & sf_flag],6,use_median=True)
    
    print(xbin,ybin)
    plt.scatter(xbin,ybin,c=ybinerr,s=300,vmin=.1,vmax=1,marker='s')
plt.colorbar(ax=all_axes)









    



[ 8.069622  9.062393  9.729434 10.248668 10.885409 11.372235] [ 0.08951795 -1.0090549  -0.5784401  -0.42475212 -0.25589257  0.35476857]
[ 7.9588914  8.949782   9.357066   9.858171  10.342478  11.0165415] [-0.9262373  -1.0165286  -0.9824431  -0.6500736  -0.43450513 -0.22929856]
[ 7.2959633  8.451033   9.239992   9.795658  10.494736  11.000428 ] [-1.4427719  -0.8278848  -0.90380096 -0.5443306  -0.12281922 -0.10765491]






    Out[24]:





<matplotlib.colorbar.Colorbar at 0x7f0fda2c1e10>



In [153]:

    
# size vs color for SF field pop

plt.figure()
flag = ~g.membflag & g.galfitflag & sf_flag

plt.scatter(g.sizeratio[flag],g.NUVr[flag],c=g.s['SERSIC_N'][flag],vmin=1,vmax=4)
plt.colorbar()









    Out[153]:





<matplotlib.colorbar.Colorbar at 0x7f254793f890>



In [155]:

    
inner_cluster = g.membflag & (g.s['DR_R200'] < .5) 
outer_cluster = g.membflag & (g.s['DR_R200'] >= .5)

flags = [inner_cluster & g.galfitflag, outer_cluster & g.galfitflag, ~g.membflag & g.galfitflag]
plt.figure(figsize=(14,4))
all_axes=[]
for i in range(len(flags)):
    plt.subplot(1,3,i+1)
    plt.scatter(mstar[flags[i]],sfr[flags[i]],c=g.s['SERSIC_N'][flags[i]],vmin=1,vmax=4)
    if i > 0:
        plt.yticks([])
    plt.axis([8,12,-2,1])
    all_axes.append(plt.gca())
    xl = np.linspace(8,12,100)
    yl = -11.8 + xl
    plt.plot(xl,yl,'k--')
    yl = -10.4 + xl
    plt.plot(xl,yl,'k-')
    xbin,ybin,ybinerr= binxycolor(mstar[flags[i]& sf_flag],sfr[flags[i]& sf_flag],g.s['SERSIC_N'][flags[i] & sf_flag],6,use_median=True)
    
    print(xbin,ybin)
    plt.scatter(xbin,ybin,c=ybinerr,s=300,vmin=1,vmax=4,marker='s')
plt.colorbar(ax=all_axes)









    



[ 8.069622  8.945342  9.516651 10.056835 10.519292 11.085979] [ 0.08951795 -1.2655075  -0.5784401  -0.27247596 -0.284118    0.4257636 ]
[ 7.9588914  8.809036   9.318189   9.757981  10.265377  10.72887  ] [-0.9262373  -1.0489608  -0.9824431  -0.72975135 -0.13846084  0.00188851]
[ 7.2959633  8.399897   8.913061   9.466389  10.02633   10.55502  ] [-1.4427719  -0.77720845 -0.91076326 -0.75322783 -0.34723625  0.11348807]






    Out[155]:





<matplotlib.colorbar.Colorbar at 0x7f25476b5e90>



In [191]:

    
def sfr_mstar(flags,colorvar,plotbinned=True,v1=.1,v2=1):

    plt.figure(figsize=(14,4))
    all_axes=[]
    for i in range(len(flags)):
        plt.subplot(1,3,i+1)
        plt.scatter(mstar[flags[i]],sfr[flags[i]],c=colorvar[flags[i]],vmin=v1,vmax=v2)
        if i > 0:
            plt.yticks([])
        plt.axis([8,12,-2,1])
        all_axes.append(plt.gca())
        xl = np.linspace(8,12,100)
        # chi by eye split b/w SF and quiescent
        yl = -11.8 + xl
        plt.plot(xl,yl,'k--')
        # chi by eye fit to main sequence in field
        yl = -10.4 + xl
        plt.plot(xl,yl,'k-')
        if plotbinned:
            xbin,ybin,ybinerr= binxycolor(mstar[flags[i]& sf_flag],sfr[flags[i]& sf_flag],colorvar[flags[i] & sf_flag],6,use_median=True)
 
            plt.scatter(xbin,ybin,c=ybinerr,s=300,vmin=v1,vmax=v2,marker='s')
    plt.colorbar(ax=all_axes)
    
def size_vs_size(flags,colorvar,plotbinned=True,v1=1,v2=10):
    xmin=0
    xmax=15
    plt.figure(figsize=(14,4))
    all_axes=[]
    for i in range(len(flags)):
        plt.subplot(1,3,i+1)
        #plt.scatter(g.s['Rd'][flags[i]],g.s['fre1'][flags[i]],c=colorvar[flags[i]],vmin=v1,vmax=v2)
        plt.scatter(g.s['Rd'][flags[i]],g.s['fre1'][flags[i]],c=colorvar[flags[i]],vmin=v1,vmax=v2)
        if i > 0:
            plt.yticks([])
        plt.axis([xmin,xmax,xmin,xmax])
        #plt.gca().set_xscale('log')
        #plt.gca().set_yscale('log')
        all_axes.append(plt.gca())
        xl = np.linspace(xmin,xmax,100)
        # chi by eye split b/w SF and quiescent
        #yl = -11.8 + xl
        plt.plot(xl,xl,'k--')
        # chi by eye fit to main sequence in field
        #yl = -10.4 + xl
        #plt.plot(xl,yl,'k-')
        if plotbinned:
            xbin,ybin,ybinerr= binxycolor(mstar[flags[i]& sf_flag],sfr[flags[i]& sf_flag],colorvar[flags[i] & sf_flag],6,use_median=True)
 
            plt.scatter(xbin,ybin,c=ybinerr,s=300,vmin=v1,vmax=v2,marker='s')
    plt.colorbar(ax=all_axes)



In [176]:

    
inner_cluster = g.membflag & (g.s['DR_R200'] < .5) 
outer_cluster = g.membflag & (g.s['DR_R200'] >= .5)
flags = [inner_cluster & g.galfitflag, outer_cluster & g.galfitflag, ~g.membflag & g.galfitflag]
sfr_mstar(flags,g.s['SERSIC_N'],v1=1,v2=4)



In [169]:

    
myclusters = set(g.s['CLUSTER'])
for c in myclusters:
    inner_cluster = g.membflag & (g.s['DR_R200'] < .5) 
    outer_cluster = g.membflag & (g.s['DR_R200'] >= .5)
    flags = [inner_cluster & g.galfitflag, outer_cluster & g.galfitflag, ~g.membflag & g.galfitflag]
    #flags = [inner_cluster , outer_cluster, ~g.membflag ]
    for i in range(len(flags)):
        flags[i] = flags[i] & (g.s['CLUSTER'] == c)
    sfr_mstar(flags,g.sizeratio,plotbinned=False)
    plt.title(c)

Removing galfit flag to see about what we are cutting out with this.

Hercules looses A LOT of points. This was the cluster where I had PSF problems and ended up using the standard MIPS PRF rather than one from the actual scan.

Perhaps we should make a measurement using the galfit models without the PRF folded in.



In [168]:

    
myclusters = set(g.s['CLUSTER'])
for c in myclusters:
    inner_cluster = g.membflag & (g.s['DR_R200'] < .5) 
    outer_cluster = g.membflag & (g.s['DR_R200'] >= .5)
    flags = [inner_cluster & g.galfitflag, outer_cluster & g.galfitflag, ~g.membflag & g.galfitflag]
    flags = [inner_cluster , outer_cluster, ~g.membflag ]
    for i in range(len(flags)):
        flags[i] = flags[i] & (g.s['CLUSTER'] == c)
    sfr_mstar(flags,g.sizeratio,plotbinned=False)
    plt.title(c)

Comparing 24um sizes with and without convolution.



In [190]:

    
# Disk scale length vs Re24 w/out PRF
inner_cluster = g.membflag & (g.s['DR_R200'] < .5) 
outer_cluster = g.membflag & (g.s['DR_R200'] >= .5)
flags = [inner_cluster & g.galfitflag, outer_cluster & g.galfitflag, ~g.membflag & g.galfitflag]
flags = [inner_cluster , outer_cluster, ~g.membflag]
size_vs_size(flags,g.sizeratio,plotbinned=False)



In [192]:

    
myclusters = set(g.s['CLUSTER'])
for c in myclusters:
    inner_cluster = g.membflag & (g.s['DR_R200'] < .5) 
    outer_cluster = g.membflag & (g.s['DR_R200'] >= .5)
    flags = [inner_cluster & g.galfitflag, outer_cluster & g.galfitflag, ~g.membflag & g.galfitflag]
    #flags = [inner_cluster , outer_cluster, ~g.membflag ]
    for i in range(len(flags)):
        flags[i] = flags[i] & (g.s['CLUSTER'] == c)
    size_vs_size(flags,g.sizeratio,plotbinned=False,v1=.1,v2=1)
    plt.title(c)

Matching GSWLC-2 and Group Catalogs

matched GSWLC-X2 and Tempel+2012 GALAXY group catalog using topcat
- matching by SDSS objid yielded only a few thousand matches, so I concluded that GSWLC is using DR7 ids whereas tempel is using DR8. Should look into this more closely b/c (1) why would salim use DR7 in a 2018 paper? (2) salim does not specifically mention which DR the ids correspond to.
- matched by RA, DEC, 1 arcsec offset
  - best match, symmetric
  - join 1 and 2
  - 514,186 matches
  - saved as fits-basic: ~/research/GSWLC/GSWLC-X2-Tempel2012-groups.fits



In [105]:

    
# read in matched catalog that has GSWLC and Tempel group galaxies
gg = Table(fits.getdata('/home/rfinn/research/GSWLC/GSWLC-X2-Tempel2012-groups.fits'))
gg = gg[gg['redshift'] < 0.04]



In [106]:

    
# catalog with group info
tgroups = Table(fits.getdata('/home/rfinn/research/sdss-tempel/dr8_ggc.fits',2))



In [109]:

    
plt.figure()
mybins = np.linspace(0,200,100)
t = plt.hist(tgroups['nrich'],bins=mybins)



In [110]:

    
gg









    Out[110]:




Table length=514186

id idcl nrich redshift dist distcl ra_1 dec_1 lon lat eta lam mag_u mag_g mag_r mag_i mag_z absmag_u absmag_g absmag_r absmag_i absmag_z kcor_u kcor_g kcor_r kcor_i kcor_z lumr w rank den1 den2 den4 den8 den16 edgedist morf hcearly dr8objid dr8specobjid iddr7 zobs distcor flagfc ObjID GLXID plate MJD fiber_id RA_2 DEC_2 Z chisq logMstar logMstar_err logSFR logSFR_err AFUV AFUV_err AB AB_err AV AV_err flag_sed UV_survey flag_uv flag_midir flag_mgs Separation
int32 int32 int16 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 int16 float32 float32 float32 float32 float32 float32 int16 float32 int64 int64 int32 float32 float32 int16 int64 int64 int16 int32 int16 float64 float64 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 float32 int16 int16 int16 int16 int16 float64
16 1 2 0.104439 306.3157 308.8412 251.16074 28.222496 48.56338 38.87968 20.517765 53.70146 19.29954 17.5629 16.73604 16.37341 16.00028 -18.22351 -20.15187 -20.87932 -21.17534 -21.62663 0.301386 0.263509 0.119865 0.052196 0.040841 1.61335 1.812 1 61.980377 10.358891 3.6414793 5.4820952 3.2901235 10.7944 1 0.08454 1237662302450024704 1898274722890672128 571291 0.104394 308.5762 0 1237662302450024731 3778029842680774769 1686 53472 27 251.16074 28.222484 0.1044 0.66 11.011 0.024 -0.954 0.596 1.248 0.594 0.246 0.04 0.18 0.036 0 2 2 6 1 0.04506360017438818
17 1 2 0.106202 311.3657 308.8412 251.17737 28.137957 48.462173 38.845753 20.438244 53.773224 19.80018 18.22746 17.40944 16.93972 16.64743 -17.78977 -19.47799 -20.26045 -20.60851 -21.02504 0.368287 0.254186 0.17439 0.051671 0.086398 0.9123873 1.812 2 38.71778 8.553819 3.4010985 5.1016965 3.219225 11.0503 1 0.0208 1237662302450024960 1898275547524392960 571320 0.106156 309.1071 1 1237662302450024875 6371478762349924061 1686 53472 30 251.17736 28.137953 0.1062 0.15 10.759 0.062 0.206 0.256 3.002 0.938 0.825 0.171 0.618 0.183 0 1 2 7 1 0.02900791424883003
18 2 3 0.132497 386.2322 387.9647 251.34805 28.46555 48.91581 38.7754 21.000711 53.635235 20.61649 18.66415 17.68126 17.26858 16.89503 -17.54496 -19.69511 -20.49315 -20.82019 -21.28428 0.507843 0.415484 0.174733 0.08776 0.065152 1.130477 2.559 2 61.09396 16.477825 6.3557687 4.2470684 2.2704484 11.3725 1 0.48339 1237659152610492672 1898275822402299904 571684 0.132455 387.90866 0 1237659152610492765 -99 1686 53472 31 251.34804 28.465542 0.1325 0.88 10.788 0.029 -1.346 0.868 1.635 0.715 0.266 0.105 0.18 0.072 0 2 0 0 1 0.049092403110811415
19 2 3 0.133721 389.6974 387.9647 251.35022 28.482853 48.937973 38.77752 21.022816 53.623775 21.14881 18.63368 17.56442 17.14632 16.81766 -17.03879 -19.77514 -20.60835 -20.96562 -21.35924 0.533996 0.465046 0.173089 0.110927 0.062743 1.25702 2.559 1 63.639896 16.667818 6.4811325 4.273681 2.273468 11.2985 0 0.37689 1237659152610492672 1898276647036020736 571688 0.13368 388.02075 0 1237659152610492746 -99 1686 53472 34 251.35021 28.482844 0.1337 0.7 10.856 0.026 -1.387 0.829 2.268 0.804 0.395 0.109 0.27 0.077 0 2 0 0 1 0.0445205440503209
22 2 3 0.133109 387.9639 387.9647 251.33757 28.495668 48.950703 38.79128 21.023628 53.60682 19.96002 18.64266 17.6602 17.29719 17.07429 -18.12773 -19.69588 -20.48092 -20.76989 -21.06878 0.43415 0.394761 0.141441 0.066061 0.028905 1.117814 2.559 3 63.61239 16.57515 6.419016 4.2665396 2.2731717 11.3298 0 0.59931 1237659152610492672 1898277471669741568 571660 0.133067 387.96466 0 1237659152610492720 -99 1686 53472 37 251.33756 28.495651 0.1331 1.22 10.811 0.047 -0.775 0.69 1.688 0.877 0.282 0.107 0.197 0.075 0 2 0 6 1 0.07048647425396412
21 3 2 0.139811 406.8983 405.6975 251.56398 28.53981 49.064293 38.60779 21.321783 53.708347 18.86582 18.08535 17.35154 16.99901 16.8454 -19.0461 -20.22903 -20.86916 -21.10735 -21.39504 0.175765 0.274361 0.12213 0.006402 0.022005 1.598314 2.81 1 60.06423 15.979706 5.129575 3.4810004 2.618589 10.4915 1 0.21858 1237662302986895616 1898277196791834624 572056 0.139774 405.97498 0 1237662302986895706 2878048789020543928 1686 53472 36 251.56399 28.539792 0.1398 0.96 11.052 0.023 0.362 0.083 1.197 0.35 0.229 0.081 0.161 0.065 0 2 2 6 1 0.07255010812356293
97487 3 2 0.13896 404.4965 405.6975 251.53249 28.570827 49.0948 38.641846 21.322384 53.666782 18.83956 17.92746 17.40542 17.09212 16.94814 -19.06352 -20.28695 -20.7935 -20.95586 -21.27928 0.166922 0.174394 0.100355 -0.051976 0.008992 1.490731 2.81 2 65.77574 16.47748 4.9649625 3.4422574 2.6373043 10.5813 1 0.13945 1237662302986830336 1511014621568329728 572000 0.138922 405.42047 0 1237662302986830308 2878048789020544316 1342 52793 207 251.53249 28.57084 0.1389 0.21 10.687 0.049 0.919 0.068 1.86 0.303 0.271 0.064 0.168 0.05 0 2 2 2 1 0.04683095094840111
23 4 2 0.063235 187.1078 186.8625 251.6357 28.509396 49.045 38.539474 21.36619 53.773193 17.54103 16.07032 15.3497 14.97246 14.7903 -18.91487 -20.42364 -21.12309 -21.41729 -21.67736 0.231808 0.1322 0.084505 0.000842 0.025065 2.019465 1.269 1 38.214947 8.209964 2.473889 1.4912746 1.256844 5.3662 0 0.12294 1237662302986895872 1898277746547648512 572184 0.063199 187.09985 0 1237662302986895795 2878048789020543613 1686 53472 38 251.6357 28.509378 0.0632 1.01 11.015 0.027 0.298 0.121 2.952 0.538 0.434 0.116 0.282 0.096 0 2 2 7 1 0.06436688629151503
25 4 2 0.063067 186.6173 186.8625 251.57397 28.425804 48.92496 38.57294 21.203096 53.798622 18.01591 16.73657 16.27709 15.96611 15.83089 -18.40732 -19.69057 -20.20828 -20.35374 -20.62368 0.199145 0.065379 0.09708 -0.069061 0.011983 0.8695822 1.269 2 32.661793 7.807244 2.4758716 1.5008337 1.2766114 5.5284 1 0.05443 1237662302986895872 1898278296303462400 572076 0.063029 186.62524 0 1237662302986895852 2878048789020542878 1686 53472 40 251.57397 28.425804 0.063 1.09 10.299 0.054 0.409 0.074 2.479 0.396 0.646 0.099 0.457 0.087 0 2 2 2 1 0.01460158902986252
27 5 4 0.107642 315.4893 318.0026 250.58606 27.509233 47.516888 39.205605 19.061684 53.86495 20.37864 18.4994 17.42471 17.07623 16.77213 -17.30153 -19.3648 -20.25968 -20.56937 -20.92229 0.402255 0.349702 0.124327 0.084449 0.041028 0.9117445 1.877 4 97.03725 19.319965 3.848274 2.2723925 1.8786135 15.4976 2 0.93898 1237662301376348416 1898278846059276288 570189 0.107583 317.8129 2 1237662301376348538 -99 1686 53472 42 250.58609 27.509247 0.1076 1.09 10.695 0.034 -1.13 0.765 1.704 0.726 0.274 0.123 0.187 0.085 0 1 0 0 1 0.10867884228156727
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
576468 388757 1 0.147332 428.0726 428.0726 210.6439 47.58472 93.43591 65.264946 18.02429 16.972193 18.94964 18.30195 17.76207 17.45383 17.31482 -18.97049 -20.03344 -20.54798 -20.72349 -21.04153 0.085829 0.179907 0.092704 -0.041504 0.012292 1.189033 3.188 1 36.861954 7.1475315 2.0999005 2.1414974 1.6807028 128.1986 1 0.0 1237661212042395904 1448000784405915648 0 0.146842 428.0726 0 1237661151919734959 3748615707614515274 1286 52725 340 210.6439 47.584702 0.1468 0.36 10.914 0.036 0.491 0.072 0.914 0.259 0.269 0.107 0.203 0.092 0 2 2 1 1 0.06838679035531006
576472 388761 1 0.113021 330.8691 330.8691 215.71976 46.965702 86.84904 63.246975 18.748646 20.402637 19.41107 18.30164 17.74937 17.45667 17.29531 -18.1468 -19.45763 -19.98209 -20.15215 -20.43806 0.204022 0.159043 0.083752 -0.040009 -0.011538 0.7060486 1.977 1 27.932364 9.566907 2.6807125 1.1563162 0.8084043 93.4016 1 0.14681 1237662498386739456 1449176437264246784 424189 0.112576 330.8691 0 1237661957225382065 6374610214417597148 1287 52728 521 215.71978 46.96569 0.1126 0.88 10.166 0.052 0.434 0.066 2.434 0.405 0.425 0.083 0.273 0.063 0 1 3 2 1 0.07109624609791332
576476 388763 1 0.108786 318.7622 318.7622 216.70955 46.597713 85.277054 62.94877 18.68146 21.171307 20.0191 17.89479 17.29475 17.21819 17.24389 -17.63993 -19.774 -20.39735 -20.17727 -20.39621 0.376534 0.149137 0.126768 -0.170966 -0.018798 1.035004 1.882 1 26.812565 9.955991 4.0241594 2.30039 1.0036465 89.7458 0 0.56819 1237662468320067584 1449199252130523136 428709 0.108347 318.7622 0 1237661957225709602 6374610213343857780 1287 52728 604 216.70945 46.597681 0.1083 0.8 10.282 0.048 0.382 0.082 1.243 0.24 0.646 0.141 0.535 0.123 0 1 3 2 1 0.2712455127771415
576477 388764 1 0.116256 340.0983 340.0983 219.47818 45.701645 81.31784 61.889057 18.686966 23.288177 20.1601 18.75685 17.7641 17.3286 17.00948 -17.6281 -19.26099 -20.10165 -20.48043 -20.86731 0.381964 0.358161 0.157247 0.099357 0.068308 0.7882452 2.055 1 21.375921 6.9040575 2.8544965 0.7513229 0.5774786 94.4139 1 0.17097 1237658206132306176 1450327351110952960 442298 0.115839 340.0983 0 1237661957226627224 -99 1288 52731 612 219.47818 45.701639 0.1158 1.49 10.748 0.032 -2.007 0.62 1.332 0.761 0.232 0.069 0.161 0.043 0 1 0 6 1 0.02123155503986127
576480 388766 1 0.146856 426.734 426.734 222.3252 42.304726 72.94569 61.6406 16.353813 26.643187 19.83864 18.5195 17.72928 17.4021 17.13284 -18.32095 -19.95687 -20.60626 -20.82974 -21.21554 0.330994 0.327625 0.125114 0.019947 0.011655 1.254594 3.162 1 33.795273 7.2341847 1.372415 0.4184083 0.3127752 129.8289 1 0.11407 1237658203449458688 1452488166387771392 455191 0.146437 426.734 0 1237661435930673160 6374786045009988701 1290 52734 281 222.32524 42.304721 0.1464 1.84 10.768 0.05 0.265 0.135 1.652 0.51 0.547 0.131 0.427 0.121 0 1 3 7 1 0.12014956999153588
576483 388767 1 0.137989 401.7574 401.7574 215.15308 44.95166 83.81945 64.81328 16.602634 20.823343 20.46196 18.63352 17.70614 17.29463 17.04238 -17.76626 -19.78139 -20.5367 -20.83814 -21.20311 0.510029 0.395895 0.16587 0.054423 0.049849 1.176736 2.746 1 35.287754 6.3592453 1.8709384 2.6414092 2.1769989 126.3782 0 0.76725 1237661210433487104 1449103044863092736 421715 0.137514 401.7574 0 1237658203446902802 -99 1287 52728 254 215.15308 44.951674 0.1375 0.11 10.796 0.05 -1.385 0.819 2.355 0.918 0.354 0.111 0.234 0.07 0 1 0 6 1 0.05078285187298792
576484 388768 1 0.113693 332.7875 332.7875 225.23367 43.390003 73.36376 59.256996 18.576609 27.993847 18.91322 18.17945 17.74286 17.37465 17.26306 -18.54485 -19.54366 -20.04427 -20.21775 -20.54141 0.09319 0.110379 0.12665 -0.069215 0.046215 0.7476665 1.992 1 11.454626 6.710798 2.9277315 1.4660515 0.8866329 89.3271 1 0.06399 1237661212584116224 1452580250486597632 468472 0.113324 332.7875 0 1237662498390016125 6374610207975147026 1290 52734 616 225.23367 43.389996 0.1133 0.57 10.456 0.047 0.394 0.093 1.364 0.215 0.269 0.107 0.182 0.084 0 1 3 1 1 0.027170684989730475
576486 388769 1 0.154885 449.2627 449.2627 226.68652 42.75094 71.502235 58.476864 18.566946 29.232668 19.46567 18.39187 17.7445 17.44911 17.25587 -18.72579 -20.1322 -20.70318 -20.83447 -21.21759 0.269878 0.264842 0.123399 -0.042257 0.015977 1.371746 3.61 1 47.237476 14.607214 6.5659237 3.5854876 1.6193584 115.7614 1 0.385 1237661212584640768 1453680311937296384 475200 0.154528 449.2627 0 1237662498390605973 6374786041788761828 1291 52738 522 226.68649 42.750939 0.1545 0.14 10.844 0.05 0.816 0.097 1.864 0.257 0.882 0.141 0.72 0.131 0 1 3 7 1 0.08841777291812682
576487 388770 1 0.143519 417.3458 417.3458 219.55759 43.24185 76.64903 63.074196 16.290852 24.406784 20.29389 18.61894 17.76307 17.36787 17.13032 -18.00334 -19.85428 -20.56199 -20.81425 -21.19807 0.509174 0.372303 0.163871 0.019486 0.043771 1.204473 3.001 1 26.736942 6.5976777 2.7531867 1.0925767 0.616932 129.761 0 0.43573 1237661209898188800 1451363365992556544 442667 0.143075 417.3458 0 1237661435929690314 6374786039641281693 1289 52734 285 219.55762 43.241856 0.1431 0.02 10.826 0.037 0.086 0.183 1.714 0.612 0.311 0.093 0.218 0.079 0 1 2 6 1 0.08870170569276291
576489 388771 1 0.148589 431.6054 431.6054 235.14748 37.76601 60.559525 53.104458 17.904324 37.364117 19.47353 18.48107 17.746 17.39538 16.99607 -18.56297 -19.9735 -20.62507 -20.90979 -21.41889 0.187291 0.281436 0.135528 0.068143 0.051632 1.276519 3.259 1 41.667095 18.691345 10.446997 4.305711 2.3753824 108.049 1 0.11719 1237658205064724736 1456924421246707712 514895 0.148315 431.6054 0 1237662504291860686 6374926797530726855 1294 52753 36 235.1475 37.765996 0.1483 0.11 10.743 0.053 0.715 0.102 2.306 0.346 0.786 0.142 0.595 0.13 0 1 3 7 1 0.08504954109834578



In [ ]:

id	idcl	nrich	redshift	dist	distcl	ra_1	dec_1	lon	lat	eta	lam	mag_u	mag_g	mag_r	mag_i	mag_z	absmag_u	absmag_g	absmag_r	absmag_i	absmag_z	kcor_u	kcor_g	kcor_r	kcor_i	kcor_z	lumr	w	rank	den1	den2	den4	den8	den16	edgedist	morf	hcearly	dr8objid	dr8specobjid	iddr7	zobs	distcor	flagfc	ObjID	GLXID	plate	MJD	fiber_id	RA_2	DEC_2	Z	chisq	logMstar	logMstar_err	logSFR	logSFR_err	AFUV	AFUV_err	AB	AB_err	AV	AV_err	flag_sed	UV_survey	flag_uv	flag_midir	flag_mgs	Separation
int32	int32	int16	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	int16	float32	float32	float32	float32	float32	float32	int16	float32	int64	int64	int32	float32	float32	int16	int64	int64	int16	int32	int16	float64	float64	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	int16	int16	int16	int16	int16	float64
16	1	2	0.104439	306.3157	308.8412	251.16074	28.222496	48.56338	38.87968	20.517765	53.70146	19.29954	17.5629	16.73604	16.37341	16.00028	-18.22351	-20.15187	-20.87932	-21.17534	-21.62663	0.301386	0.263509	0.119865	0.052196	0.040841	1.61335	1.812	1	61.980377	10.358891	3.6414793	5.4820952	3.2901235	10.7944	1	0.08454	1237662302450024704	1898274722890672128	571291	0.104394	308.5762	0	1237662302450024731	3778029842680774769	1686	53472	27	251.16074	28.222484	0.1044	0.66	11.011	0.024	-0.954	0.596	1.248	0.594	0.246	0.04	0.18	0.036	0	2	2	6	1	0.04506360017438818
17	1	2	0.106202	311.3657	308.8412	251.17737	28.137957	48.462173	38.845753	20.438244	53.773224	19.80018	18.22746	17.40944	16.93972	16.64743	-17.78977	-19.47799	-20.26045	-20.60851	-21.02504	0.368287	0.254186	0.17439	0.051671	0.086398	0.9123873	1.812	2	38.71778	8.553819	3.4010985	5.1016965	3.219225	11.0503	1	0.0208	1237662302450024960	1898275547524392960	571320	0.106156	309.1071	1	1237662302450024875	6371478762349924061	1686	53472	30	251.17736	28.137953	0.1062	0.15	10.759	0.062	0.206	0.256	3.002	0.938	0.825	0.171	0.618	0.183	0	1	2	7	1	0.02900791424883003
18	2	3	0.132497	386.2322	387.9647	251.34805	28.46555	48.91581	38.7754	21.000711	53.635235	20.61649	18.66415	17.68126	17.26858	16.89503	-17.54496	-19.69511	-20.49315	-20.82019	-21.28428	0.507843	0.415484	0.174733	0.08776	0.065152	1.130477	2.559	2	61.09396	16.477825	6.3557687	4.2470684	2.2704484	11.3725	1	0.48339	1237659152610492672	1898275822402299904	571684	0.132455	387.90866	0	1237659152610492765	-99	1686	53472	31	251.34804	28.465542	0.1325	0.88	10.788	0.029	-1.346	0.868	1.635	0.715	0.266	0.105	0.18	0.072	0	2	0	0	1	0.049092403110811415
19	2	3	0.133721	389.6974	387.9647	251.35022	28.482853	48.937973	38.77752	21.022816	53.623775	21.14881	18.63368	17.56442	17.14632	16.81766	-17.03879	-19.77514	-20.60835	-20.96562	-21.35924	0.533996	0.465046	0.173089	0.110927	0.062743	1.25702	2.559	1	63.639896	16.667818	6.4811325	4.273681	2.273468	11.2985	0	0.37689	1237659152610492672	1898276647036020736	571688	0.13368	388.02075	0	1237659152610492746	-99	1686	53472	34	251.35021	28.482844	0.1337	0.7	10.856	0.026	-1.387	0.829	2.268	0.804	0.395	0.109	0.27	0.077	0	2	0	0	1	0.0445205440503209
22	2	3	0.133109	387.9639	387.9647	251.33757	28.495668	48.950703	38.79128	21.023628	53.60682	19.96002	18.64266	17.6602	17.29719	17.07429	-18.12773	-19.69588	-20.48092	-20.76989	-21.06878	0.43415	0.394761	0.141441	0.066061	0.028905	1.117814	2.559	3	63.61239	16.57515	6.419016	4.2665396	2.2731717	11.3298	0	0.59931	1237659152610492672	1898277471669741568	571660	0.133067	387.96466	0	1237659152610492720	-99	1686	53472	37	251.33756	28.495651	0.1331	1.22	10.811	0.047	-0.775	0.69	1.688	0.877	0.282	0.107	0.197	0.075	0	2	0	6	1	0.07048647425396412
21	3	2	0.139811	406.8983	405.6975	251.56398	28.53981	49.064293	38.60779	21.321783	53.708347	18.86582	18.08535	17.35154	16.99901	16.8454	-19.0461	-20.22903	-20.86916	-21.10735	-21.39504	0.175765	0.274361	0.12213	0.006402	0.022005	1.598314	2.81	1	60.06423	15.979706	5.129575	3.4810004	2.618589	10.4915	1	0.21858	1237662302986895616	1898277196791834624	572056	0.139774	405.97498	0	1237662302986895706	2878048789020543928	1686	53472	36	251.56399	28.539792	0.1398	0.96	11.052	0.023	0.362	0.083	1.197	0.35	0.229	0.081	0.161	0.065	0	2	2	6	1	0.07255010812356293
97487	3	2	0.13896	404.4965	405.6975	251.53249	28.570827	49.0948	38.641846	21.322384	53.666782	18.83956	17.92746	17.40542	17.09212	16.94814	-19.06352	-20.28695	-20.7935	-20.95586	-21.27928	0.166922	0.174394	0.100355	-0.051976	0.008992	1.490731	2.81	2	65.77574	16.47748	4.9649625	3.4422574	2.6373043	10.5813	1	0.13945	1237662302986830336	1511014621568329728	572000	0.138922	405.42047	0	1237662302986830308	2878048789020544316	1342	52793	207	251.53249	28.57084	0.1389	0.21	10.687	0.049	0.919	0.068	1.86	0.303	0.271	0.064	0.168	0.05	0	2	2	2	1	0.04683095094840111
23	4	2	0.063235	187.1078	186.8625	251.6357	28.509396	49.045	38.539474	21.36619	53.773193	17.54103	16.07032	15.3497	14.97246	14.7903	-18.91487	-20.42364	-21.12309	-21.41729	-21.67736	0.231808	0.1322	0.084505	0.000842	0.025065	2.019465	1.269	1	38.214947	8.209964	2.473889	1.4912746	1.256844	5.3662	0	0.12294	1237662302986895872	1898277746547648512	572184	0.063199	187.09985	0	1237662302986895795	2878048789020543613	1686	53472	38	251.6357	28.509378	0.0632	1.01	11.015	0.027	0.298	0.121	2.952	0.538	0.434	0.116	0.282	0.096	0	2	2	7	1	0.06436688629151503
25	4	2	0.063067	186.6173	186.8625	251.57397	28.425804	48.92496	38.57294	21.203096	53.798622	18.01591	16.73657	16.27709	15.96611	15.83089	-18.40732	-19.69057	-20.20828	-20.35374	-20.62368	0.199145	0.065379	0.09708	-0.069061	0.011983	0.8695822	1.269	2	32.661793	7.807244	2.4758716	1.5008337	1.2766114	5.5284	1	0.05443	1237662302986895872	1898278296303462400	572076	0.063029	186.62524	0	1237662302986895852	2878048789020542878	1686	53472	40	251.57397	28.425804	0.063	1.09	10.299	0.054	0.409	0.074	2.479	0.396	0.646	0.099	0.457	0.087	0	2	2	2	1	0.01460158902986252
27	5	4	0.107642	315.4893	318.0026	250.58606	27.509233	47.516888	39.205605	19.061684	53.86495	20.37864	18.4994	17.42471	17.07623	16.77213	-17.30153	-19.3648	-20.25968	-20.56937	-20.92229	0.402255	0.349702	0.124327	0.084449	0.041028	0.9117445	1.877	4	97.03725	19.319965	3.848274	2.2723925	1.8786135	15.4976	2	0.93898	1237662301376348416	1898278846059276288	570189	0.107583	317.8129	2	1237662301376348538	-99	1686	53472	42	250.58609	27.509247	0.1076	1.09	10.695	0.034	-1.13	0.765	1.704	0.726	0.274	0.123	0.187	0.085	0	1	0	0	1	0.10867884228156727
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
576468	388757	1	0.147332	428.0726	428.0726	210.6439	47.58472	93.43591	65.264946	18.02429	16.972193	18.94964	18.30195	17.76207	17.45383	17.31482	-18.97049	-20.03344	-20.54798	-20.72349	-21.04153	0.085829	0.179907	0.092704	-0.041504	0.012292	1.189033	3.188	1	36.861954	7.1475315	2.0999005	2.1414974	1.6807028	128.1986	1	0.0	1237661212042395904	1448000784405915648	0	0.146842	428.0726	0	1237661151919734959	3748615707614515274	1286	52725	340	210.6439	47.584702	0.1468	0.36	10.914	0.036	0.491	0.072	0.914	0.259	0.269	0.107	0.203	0.092	0	2	2	1	1	0.06838679035531006
576472	388761	1	0.113021	330.8691	330.8691	215.71976	46.965702	86.84904	63.246975	18.748646	20.402637	19.41107	18.30164	17.74937	17.45667	17.29531	-18.1468	-19.45763	-19.98209	-20.15215	-20.43806	0.204022	0.159043	0.083752	-0.040009	-0.011538	0.7060486	1.977	1	27.932364	9.566907	2.6807125	1.1563162	0.8084043	93.4016	1	0.14681	1237662498386739456	1449176437264246784	424189	0.112576	330.8691	0	1237661957225382065	6374610214417597148	1287	52728	521	215.71978	46.96569	0.1126	0.88	10.166	0.052	0.434	0.066	2.434	0.405	0.425	0.083	0.273	0.063	0	1	3	2	1	0.07109624609791332
576476	388763	1	0.108786	318.7622	318.7622	216.70955	46.597713	85.277054	62.94877	18.68146	21.171307	20.0191	17.89479	17.29475	17.21819	17.24389	-17.63993	-19.774	-20.39735	-20.17727	-20.39621	0.376534	0.149137	0.126768	-0.170966	-0.018798	1.035004	1.882	1	26.812565	9.955991	4.0241594	2.30039	1.0036465	89.7458	0	0.56819	1237662468320067584	1449199252130523136	428709	0.108347	318.7622	0	1237661957225709602	6374610213343857780	1287	52728	604	216.70945	46.597681	0.1083	0.8	10.282	0.048	0.382	0.082	1.243	0.24	0.646	0.141	0.535	0.123	0	1	3	2	1	0.2712455127771415
576477	388764	1	0.116256	340.0983	340.0983	219.47818	45.701645	81.31784	61.889057	18.686966	23.288177	20.1601	18.75685	17.7641	17.3286	17.00948	-17.6281	-19.26099	-20.10165	-20.48043	-20.86731	0.381964	0.358161	0.157247	0.099357	0.068308	0.7882452	2.055	1	21.375921	6.9040575	2.8544965	0.7513229	0.5774786	94.4139	1	0.17097	1237658206132306176	1450327351110952960	442298	0.115839	340.0983	0	1237661957226627224	-99	1288	52731	612	219.47818	45.701639	0.1158	1.49	10.748	0.032	-2.007	0.62	1.332	0.761	0.232	0.069	0.161	0.043	0	1	0	6	1	0.02123155503986127
576480	388766	1	0.146856	426.734	426.734	222.3252	42.304726	72.94569	61.6406	16.353813	26.643187	19.83864	18.5195	17.72928	17.4021	17.13284	-18.32095	-19.95687	-20.60626	-20.82974	-21.21554	0.330994	0.327625	0.125114	0.019947	0.011655	1.254594	3.162	1	33.795273	7.2341847	1.372415	0.4184083	0.3127752	129.8289	1	0.11407	1237658203449458688	1452488166387771392	455191	0.146437	426.734	0	1237661435930673160	6374786045009988701	1290	52734	281	222.32524	42.304721	0.1464	1.84	10.768	0.05	0.265	0.135	1.652	0.51	0.547	0.131	0.427	0.121	0	1	3	7	1	0.12014956999153588
576483	388767	1	0.137989	401.7574	401.7574	215.15308	44.95166	83.81945	64.81328	16.602634	20.823343	20.46196	18.63352	17.70614	17.29463	17.04238	-17.76626	-19.78139	-20.5367	-20.83814	-21.20311	0.510029	0.395895	0.16587	0.054423	0.049849	1.176736	2.746	1	35.287754	6.3592453	1.8709384	2.6414092	2.1769989	126.3782	0	0.76725	1237661210433487104	1449103044863092736	421715	0.137514	401.7574	0	1237658203446902802	-99	1287	52728	254	215.15308	44.951674	0.1375	0.11	10.796	0.05	-1.385	0.819	2.355	0.918	0.354	0.111	0.234	0.07	0	1	0	6	1	0.05078285187298792
576484	388768	1	0.113693	332.7875	332.7875	225.23367	43.390003	73.36376	59.256996	18.576609	27.993847	18.91322	18.17945	17.74286	17.37465	17.26306	-18.54485	-19.54366	-20.04427	-20.21775	-20.54141	0.09319	0.110379	0.12665	-0.069215	0.046215	0.7476665	1.992	1	11.454626	6.710798	2.9277315	1.4660515	0.8866329	89.3271	1	0.06399	1237661212584116224	1452580250486597632	468472	0.113324	332.7875	0	1237662498390016125	6374610207975147026	1290	52734	616	225.23367	43.389996	0.1133	0.57	10.456	0.047	0.394	0.093	1.364	0.215	0.269	0.107	0.182	0.084	0	1	3	1	1	0.027170684989730475
576486	388769	1	0.154885	449.2627	449.2627	226.68652	42.75094	71.502235	58.476864	18.566946	29.232668	19.46567	18.39187	17.7445	17.44911	17.25587	-18.72579	-20.1322	-20.70318	-20.83447	-21.21759	0.269878	0.264842	0.123399	-0.042257	0.015977	1.371746	3.61	1	47.237476	14.607214	6.5659237	3.5854876	1.6193584	115.7614	1	0.385	1237661212584640768	1453680311937296384	475200	0.154528	449.2627	0	1237662498390605973	6374786041788761828	1291	52738	522	226.68649	42.750939	0.1545	0.14	10.844	0.05	0.816	0.097	1.864	0.257	0.882	0.141	0.72	0.131	0	1	3	7	1	0.08841777291812682
576487	388770	1	0.143519	417.3458	417.3458	219.55759	43.24185	76.64903	63.074196	16.290852	24.406784	20.29389	18.61894	17.76307	17.36787	17.13032	-18.00334	-19.85428	-20.56199	-20.81425	-21.19807	0.509174	0.372303	0.163871	0.019486	0.043771	1.204473	3.001	1	26.736942	6.5976777	2.7531867	1.0925767	0.616932	129.761	0	0.43573	1237661209898188800	1451363365992556544	442667	0.143075	417.3458	0	1237661435929690314	6374786039641281693	1289	52734	285	219.55762	43.241856	0.1431	0.02	10.826	0.037	0.086	0.183	1.714	0.612	0.311	0.093	0.218	0.079	0	1	2	6	1	0.08870170569276291
576489	388771	1	0.148589	431.6054	431.6054	235.14748	37.76601	60.559525	53.104458	17.904324	37.364117	19.47353	18.48107	17.746	17.39538	16.99607	-18.56297	-19.9735	-20.62507	-20.90979	-21.41889	0.187291	0.281436	0.135528	0.068143	0.051632	1.276519	3.259	1	41.667095	18.691345	10.446997	4.305711	2.3753824	108.049	1	0.11719	1237658205064724736	1456924421246707712	514895	0.148315	431.6054	0	1237662504291860686	6374926797530726855	1294	52753	36	235.1475	37.765996	0.1483	0.11	10.743	0.053	0.715	0.102	2.306	0.346	0.786	0.142	0.595	0.13	0	1	3	7	1	0.08504954109834578