From b0163c79c05ec48c1f2db9a09b1a58ad873df847 Mon Sep 17 00:00:00 2001 From: Clancy James Date: Wed, 23 Oct 2024 15:50:57 +0800 Subject: [PATCH 1/3] Added all 171020 data and files to this directory --- papers/171020/171020.py | 374 ++++ papers/171020/Data/P.csv | 24 + papers/171020/Data/R-MAG_CANDIDATES.csv | 27 + .../171020/Data/R-MAG_CANDIDATES_w_priors.csv | 27 + papers/171020/Data/cand_pos_liklihood.csv | 28 + papers/171020/Data/candidate_zs.dat | 23 + papers/171020/Data/including_mr_prior.csv | 25 + papers/171020/Data/modR-MAG_CANDIDATES.csv | 26 + papers/171020/Data/mod_P.csv | 23 + .../171020/Data/mod_cand_pos_likelihood.csv | 26 + papers/171020/Data/object_search_2.csv | 50 + papers/171020/Data/object_search_approx.csv | 25 + papers/171020/Data/second_cut.csv | 114 ++ papers/171020/Data/z_mag_priors.csv | 114 ++ papers/171020/Data/zpriors_added.csv | 114 ++ papers/171020/README.txt | 55 + papers/171020/errors_misc_functions.py | 1578 +++++++++++++++++ papers/171020/galaxy_redshifts.dat | 26 + papers/171020/mag_prior.py | 234 +++ papers/171020/match_galaxies.py | 39 + papers/171020/paper_mag_prior.py | 187 ++ papers/171020/plot_prior.py | 44 + .../171020/rmag_HG171020 _field - Sheet1.xlsx | Bin 0 -> 9860 bytes papers/171020/use_z_priors.py | 84 + 24 files changed, 3267 insertions(+) create mode 100644 papers/171020/171020.py create mode 100644 papers/171020/Data/P.csv create mode 100644 papers/171020/Data/R-MAG_CANDIDATES.csv create mode 100644 papers/171020/Data/R-MAG_CANDIDATES_w_priors.csv create mode 100644 papers/171020/Data/cand_pos_liklihood.csv create mode 100644 papers/171020/Data/candidate_zs.dat create mode 100644 papers/171020/Data/including_mr_prior.csv create mode 100644 papers/171020/Data/modR-MAG_CANDIDATES.csv create mode 100644 papers/171020/Data/mod_P.csv create mode 100644 papers/171020/Data/mod_cand_pos_likelihood.csv create mode 100644 papers/171020/Data/object_search_2.csv create mode 100644 papers/171020/Data/object_search_approx.csv create mode 100644 papers/171020/Data/second_cut.csv create mode 100644 papers/171020/Data/z_mag_priors.csv create mode 100644 papers/171020/Data/zpriors_added.csv create mode 100644 papers/171020/README.txt create mode 100644 papers/171020/errors_misc_functions.py create mode 100644 papers/171020/galaxy_redshifts.dat create mode 100644 papers/171020/mag_prior.py create mode 100644 papers/171020/match_galaxies.py create mode 100644 papers/171020/paper_mag_prior.py create mode 100644 papers/171020/plot_prior.py create mode 100644 papers/171020/rmag_HG171020 _field - Sheet1.xlsx create mode 100644 papers/171020/use_z_priors.py diff --git a/papers/171020/171020.py b/papers/171020/171020.py new file mode 100644 index 0000000..e698ed5 --- /dev/null +++ b/papers/171020/171020.py @@ -0,0 +1,374 @@ + +from zdm import cosmology as cos +import argparse +import survey +import numpy as np + +from zdm import pcosmic +import matplotlib.pyplot as plt +import matplotlib.colors as colors +import matplotlib.cm as cm +import matplotlib +import os +import sys + +import scipy as sp + +#import ne2001 +#import frb +#from frb import igm +#from frb import dlas +import time +from matplotlib.ticker import NullFormatter +import iteration as it + +import beams + +import pickle + +matplotlib.rcParams['image.interpolation'] = None + +defaultsize=14 +ds=4 +font = {'family' : 'normal', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + +#### we now test some simple update steps ###### + # pset defined as: + # [0]: log10 Emin + # [1]: log10 Emax + # [2]: alpha (spectrum: nu^alpha) + # [3]: gamma + # [4]: sfr n + # [5:} parameters passed to dm function +from zdm.misc_functions import * +from errors_misc_functions import * +import pcosmic + +#import igm +defaultsize=14 +ds=4 +font = {'family' : 'normal', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + +def main(): + zs=[] + ### loads 171020 info ### + with open('Data/candidate_zs.dat', 'r') as infile: + for line in infile: + info=line.split() + z=float(info[4]) + zs.append(z) + candidate_zs=np.array(zs) + + + #cos.set_cosmology(Omega_m=1.2) setup for cosmology + cos.init_dist_measures(this_ZMIN=0,this_ZMAX=0.2,this_NZ=1000) + + #parser.add_argument(", help + # get the grid of p(DM|z) + zDMgrid, zvals,dmvals=get_zdm_grid(new=False,plot=False,method='analytic',nz=400,zmax=0.1,ndm=200,dmmax=200,datdir='171020') + + # NOTE: if this is new, we also need new surveys and grids! + + # constants of beam method + thresh=0 + method=2 + + # constants of intrinsic width distribution + Wlogmean=1.70267 + Wlogsigma=0.899148 + DMhalo=50 + + #prefix='Std' + #prefix='' + #Wbins=5 + #Wscale=3.5 + #Nbeams=[5,5,10] + + #### these surveys combine samples! #### + NewSurveys=False + sprefix='171020' + + Wbins=1 + Wscale=1.4 + Nbeams=[50,20,20] + + if NewSurveys: + #load the lat50 survey data + lat50=survey.survey() + #lat50.process_survey_file('Surveys/CRAFT_FE.dat') + lat50.process_survey_file('Surveys/171020.dat') + lat50.init_DMEG(DMhalo) + #efficiencies=lat50.get_efficiency(dmvals) + lat50.init_beam(nbins=Nbeams[0],method=2,thresh=thresh) # tells the survey to use the beam file + pwidths,pprobs=survey.make_widths(lat50,Wlogmean,Wlogsigma,Wbins,scale=Wscale) + + # 171020 is unresolved in time + # hence the effective true width is 0 + # thus we could hard-code it + #pprobs=np.array([1.]) + #pwidths=np.array([0.2]) # no smearing here + # note: this is roughly what one gets anyway with nbins=0 + efficiencies=lat50.get_efficiency_from_wlist(dmvals,pwidths,pprobs) + + names=['ASKAP/FE','ASKAP/ICS','Parkes/Mb'] + + surveys=[lat50] + with open('171020/'+sprefix+'surveys.pkl', 'wb') as output: + pickle.dump(surveys, output, pickle.HIGHEST_PROTOCOL) + pickle.dump(names, output, pickle.HIGHEST_PROTOCOL) + else: + with open('171020/'+sprefix+'surveys.pkl', 'rb') as infile: + surveys=pickle.load(infile) + names=pickle.load(infile) + lat50=surveys[0] + print("Initialised surveys ",names) + dirnames=['ASKAP_FE','ASKAP_ICS','Parkes_Mb'] + + #### these are outdated - it's mean efficiencies vs efficiency of the mean #### + # update this for width distribution? + plot_paper_efficiencies=False + if plot_paper_efficiencies: + plot_efficiencies_paper(surveys[0],'PaperPlots/lat50_compare_efficiencies.pdf',"ASKAP Fly's Eye") + plot_efficiencies_paper(surveys[1],'PaperPlots/ICS_compare_efficiencies.pdf',"ASKAP ICS") + plot_efficiencies_paper(surveys[2],'PaperPlots/Parkes_compare_efficiencies.pdf',"Parkes") + + + PlotEfficiencies=False + if PlotEfficiencies: + for i,s in enumerate(surveys): + savename='OtherPlots/'+names[i]+'_efficiencies.pdf' + plot_efficiencies(s,savename=savename) + + + ############################ BEST n=0 ######################### + ######## gets a grid of the best rates for no steller evolution ####### + n0set=np.array([30. , 41.6 , 1.25 ,-0.9 , 0. , 2.25 , 0.5 , 1.66]) + NewN0=False + #prefix='' use this for standard + prefix='171020' + if NewN0: + n0grids=initialise_grids(surveys,zDMgrid, zvals,dmvals,n0set,wdist=True) + n0rates=[] + for g in n0grids: + n0rates.append(g.rates) + + #allgrids.append(grids) + with open('Data/'+prefix+'n0rates.pkl', 'wb') as output: + pickle.dump(n0rates, output, pickle.HIGHEST_PROTOCOL) + #allgrids.append(grids) + with open('Data/'+prefix+'n0grids.pkl', 'wb') as output: + pickle.dump(n0grids, output, pickle.HIGHEST_PROTOCOL) + else: + with open('Data/'+prefix+'n0rates.pkl', 'rb') as infile: + n0rates=pickle.load(infile) + with open('Data/'+prefix+'n0grids.pkl', 'rb') as infile: + n0grids=pickle.load(infile) + + + ############################ 90% ranges ######################### + + NSYS=12 + temp=np.array([[30 , 41.6, 1.5, -1.2, 2.6, 2.0, 0.5],[ + 30, 42.51, 1.5, -1.2, 1.69, 2.0, 0.5],[ +30 , 42.0, 1.2, -1.1, 1.48, 2.0, 0.5],[ + 30, 42.0, 1.88, -1.05, 1.8, 2.0, 0.5],[ +30 , 42.08, 1.5, -1.34, 2.02, 2.25, 0.5],[ + 30, 41.92, 1.5, -0.96, 1.4, 2.0, 0.54],[ +30 , 41.8, 1.5, -1.0, 1.11, 2.25, 0.5],[ + 30, 42.0, 1.75, -1.14, 2.28, 2.0, 0.5],[ +30 , 42.18, 1.5, -1.1, 1.78, 1.77, 0.59],[ + 30, 41.8, 1.5, -1.1, 1.47, 2.41, 0.63],[ +30 , 42.08, 1.5, -1.1, 1.6, 2.0, 0.36],[ + 30, 42.0, 1.5, -1.1, 1.6, 2.0, 0.81]]) + + params=np.zeros([NSYS,8]) + for i in np.arange(NSYS): + params[i,0:7]=temp[i,:] + params[i,-1]=0. + + #generates zdm grids for 1 sigma error parameter sets + NewGrids=False + + if NewGrids: + all_grids=[] + all_rates=[] + for i in np.arange(NSYS): + pset=params[i] + + print("About to do pset ",pset) + grids=initialise_grids(surveys,zDMgrid, zvals,dmvals,pset,wdist=True) + print("Initialised grids for pset ",i) + rates=[] + for j,g in enumerate(grids): + rates.append(g.rates) + all_rates.append(rates) + + all_grids.append(grids) + with open('Data/'+prefix+'errorrates.pkl', 'wb') as output: + pickle.dump(all_rates, output, pickle.HIGHEST_PROTOCOL) + with open('Data/'+prefix+'errorgrids.pkl', 'wb') as output: + pickle.dump(all_grids, output, pickle.HIGHEST_PROTOCOL) + else: + with open('Data/'+prefix+'errorrates.pkl', 'rb') as infile: + all_rates=pickle.load(infile) + with open('Data/'+prefix+'errorgrids.pkl', 'rb') as infile: + all_grids=pickle.load(infile) + + # changes all_rates to be first ordered by telescope, then byerror + + inv_all_rates=[[],[],[]] + for i, rset in enumerate(all_rates): + for j,s in enumerate(rset): + inv_all_rates[j].append(s) + inv_all_grids=[[],[],[]] + for i, rset in enumerate(all_grids): + for j,s in enumerate(rset): + inv_all_grids[j].append(s) + + + ###################### BEST GRIDS ################# + + # gets best grids # + bestset=np.array([30. , 41.7 , 1.55 , -1.09 , 1.67 , 2.11 , 0.53 , 3.15]) + NewBest=False + if NewBest: + bestgrids=initialise_grids(surveys,zDMgrid, zvals,dmvals,bestset,wdist=True) + bestrates=[] + for g in bestgrids: + bestrates.append(g.rates) + + #allgrids.append(grids) + with open('Data/'+prefix+'bestrates.pkl', 'wb') as output: + pickle.dump(bestrates, output, pickle.HIGHEST_PROTOCOL) + with open('Data/'+prefix+'bestgrids.pkl', 'wb') as output: + pickle.dump(bestgrids, output, pickle.HIGHEST_PROTOCOL) + else: + with open('Data/'+prefix+'bestrates.pkl', 'rb') as infile: + bestrates=pickle.load(infile) + with open('Data/'+prefix+'bestgrids.pkl', 'rb') as infile: + bestgrids=pickle.load(infile) + #with open('Pickle/tempgrids.pkl', 'rb') as infile: + # bestgrids=pickle.load(infile) + print("Initialised grids") + + #compare_dm_fits(surveys,bestrates,all_rates,n0rates,zvals,dmvals) + #compare_z_fits(surveys,bestrates,all_rates,n0rates,zvals,dmvals) + + ############### adds hypothetical Emin of 10^36, tries this out ######## + # gets best grids # + Eminset=np.array([39.0 , 41.7 , 1.55, -1.09 , 1.67 , 2.11 , 0.53 , 3.15]) + NewEmin=False + if NewEmin: + Emingrids=initialise_grids(surveys,zDMgrid, zvals,dmvals,Eminset,wdist=True) + Eminrates=[] + for g in Emingrids: + Eminrates.append(g.rates) + + #allgrids.append(grids) + with open('Data/Eminrates.pkl', 'wb') as output: + pickle.dump(Eminrates, output, pickle.HIGHEST_PROTOCOL) + with open('Data/Emingrids.pkl', 'wb') as output: + pickle.dump(Emingrids, output, pickle.HIGHEST_PROTOCOL) + else: + with open('Data/Eminrates.pkl', 'rb') as infile: + Eminrates=pickle.load(infile) + with open('Data/Emingrids.pkl', 'rb') as infile: + Emingrids=pickle.load(infile) + #with open('Pickle/tempgrids.pkl', 'rb') as infile: + # bestgrids=pickle.load(infile) + print("Initialised Emin grid") + + plot_psnrs=False + if plot_psnrs: + ### generates snr plots ### + gridsets=[bestgrids,n0grids,Emingrids] + labels=['best','n0','Emin'] + psets=[bestset,n0set,Eminset] + error_plot_psnrs(gridsets,labels,surveys,psets,plot='Emin/PSNR/snr_hist_comparison.pdf') + + compare_z_dm_fits=False + if compare_z_dm_fits: + ### all these generate results to go into the paper ### + compare_z_fits(surveys,bestrates,all_rates,n0rates,Eminrates,zvals,dmvals,outdir='Emin') + compare_dm_fits(surveys,bestrates,all_rates,n0rates,Eminrates,zvals,dmvals,outdir='Emin') + for i in np.arange(3): + use_this=[] + print("Doing z, DM dists for survey ",i) + for item in all_rates: + use_this.append([item[i]]) + if i==0: + compare_dm_fits([surveys[i]],[bestrates[i]],use_this,[n0rates[i]],[Eminrates[i]],zvals,dmvals,outdir='Emin/FE') + compare_z_fits2([surveys[i]],[bestrates[i]],use_this,[n0rates[i]],[Eminrates[i]],zvals,dmvals,outdir='Emin/FE',xmax=0.8,ymax=20) + elif i==1: + compare_dm_fits([surveys[i]],[bestrates[i]],use_this,[n0rates[i]],[Eminrates[i]],zvals,dmvals,outdir='Emin/ICS') + compare_z_fits2([surveys[i]],[bestrates[i]],use_this,[n0rates[i]],[Eminrates[i]],zvals,dmvals,outdir='Emin/ICS',xmax=1,ymax=4) + elif i==2: + compare_dm_fits([surveys[i]],[bestrates[i]],use_this,[n0rates[i]],[Eminrates[i]],zvals,dmvals,outdir='Emin/Pks') + compare_z_fits2([surveys[i]],[bestrates[i]],use_this,[n0rates[i]],[Eminrates[i]],zvals,dmvals,outdir='Emin/Pks',xmax=2,ymax=6) + + + doSNR=False # Done! + if doSNR: + error_get_source_counts(bestgrids[0],inv_all_grids[0],Emingrids[0],plot='Emin/lat50_source_counts.pdf',Slabel='ASKAP/FE',load=True,tag='lat50') + error_get_source_counts(bestgrids[2],inv_all_grids[2],Emingrids[2],plot='Emin/Parkes_source_counts.pdf',Slabel='Parkes/Mb',load=True,tag='Pks') + + + plot_z_given_dm_priors=True + # reads in redshifts of FRB candidates + cands=np.genfromtxt('Data/P.csv', delimiter=',') + lp=np.array(cands[:,-3],dtype='float') + zcands=np.array(cands[1:,-4],dtype='float') + zcands[0]=0.00867 + + print("zcands are ") + for z in zcands: + print(z) + print("pos likelihood is ") + for l in lp: + print(l) + print("p(z) is ",) + + if plot_z_given_dm_priors: + for i,s in enumerate(surveys): + basename='p_z_g_dmsnr_'+dirnames[i]+'.pdf' + #dirname='ErrorPlots/'+dirnames[i]+'/' + dirname='Data/' + temp=[] + for j,r in enumerate(all_grids): + temp.append(r[i]) + print("Evaluating on DM ",[surveys[i].DMEGs[0]]) + error_get_zgdmsnr_priors(surveys[i],bestgrids[i],temp,Emingrids[i],dirname,basename,dmvals,zvals,z_evaluate=zcands,dm_evaluate=[surveys[i].DMEGs[0]]) + exit() + for i,s in enumerate(surveys): + basename='p_z_g_dm_'+dirnames[i]+'.pdf' + #dirname='ErrorPlots/'+dirnames[i]+'/' + dirname='Data/' + temp=[] + for j,r in enumerate(all_rates): + temp.append(r[i]) + error_get_zgdm_priors(surveys[i],bestrates[i],temp,Eminrates[i],dirname,basename,dmvals,zvals) + + exit() + + + ######## calculate SNR ####### + # this was a test! Does a plot for every FRB individually + #calc_psnr_1D(Emingrids[0],surveys[0],Eminset,doplot='Emin/Emin_psnr_per_frb.pdf') + #calc_psnr_1D(bestgrids[0],surveys[0],bestset,doplot='Emin/psnr_per_frb.pdf') + + # OK redo this one when we have all the others. Generate a histogram. Huzzah! + t0=time.process_time() + err_get_source_counts([bestgrids[0]],plot='ErrorPlots/psnr.pdf') + t1=time.process_time() + print("Took ",t1-t0," seconds") + # this takes a long time + + + +main() diff --git a/papers/171020/Data/P.csv b/papers/171020/Data/P.csv new file mode 100644 index 0000000..5fd58b4 --- /dev/null +++ b/papers/171020/Data/P.csv @@ -0,0 +1,24 @@ +wiseX_host,RA_host,DEC_host,zPhoto_ANN,positional_likelihood,redshift_probability,P +J221524.61-193504.8,333.8525451,-19.5846943,-0.006412,0.0021506404129229594,0.65,0.0013979162683999237 +J221427.49-200156.0,333.6145763,-20.0322477,0.047019,0.00755113744981839,0.05,0.00037755687249091955 +J221430.33-200013.6,333.6264155,-20.0037953,0.048971,0.00755113744981839,0.037,0.00027939208564328044 +J221359.13-195106.0,333.4963953,-19.8516715,0.055101,0.009988529917797744,0.012,0.00011986235901357292 +J221445.43-194502.2,333.6892954,-19.7506157,0.056032,0.010275281972854138,0.0091,9.350506595297266e-05 +J221413.69-194032.1,333.5570813,-19.6755901,0.056289,0.007025425348881667,0.0091,6.393137067482317e-05 +J221338.59-193704.2,333.4108319,-19.6178467,0.036444,0.0003823360734085261,0.16,6.117377174536418e-05 +J221415.90-202202.4,333.5662652,-20.3673573,0.046004,0.00019116803670426305,0.058,1.1087746128847257e-05 +J221459.58-201030.4,333.7482807,-20.175112,0.055571,0.00043012808258459183,0.0091,3.914165551519786e-06 +J221404.53-195840.5,333.5189094,-19.9779194,0.066262,0.010800994073790862,0.0003,3.240298222137258e-06 +J221501.93-200730.5,333.7580599,-20.1251575,0.058634,0.0005735041101127892,0.0041,2.3513668514624357e-06 +J221419.92-201529.1,333.5830269,-20.2580925,0.063239,0.0004779200917606576,0.0012,5.735041101127891e-07 +J221358.74-193215.9,333.4947776,-19.5377563,0.067499,0.0005257121009367234,0.00018,9.462817816861022e-08 +J221437.97-192453.2,333.6582404,-19.4148032,0.068376,0.0005735041101127892,0.00011,6.308545211240681e-08 +J221552.85-195808.4,333.9702173,-19.9690231,0.070473,0.0001433760275281973,3.6e-05,5.161536991015103e-09 +J221449.49-192207.4,333.7062152,-19.3687435,0.070665,0.0001433760275281973,1.9e-05,2.7241445230357486e-09 +J221357.33-195652.7,333.4888951,-19.9479875,0.076738,0.009653985853565283,1.8e-07,1.7377174536417508e-09 +J221559.40-192629.3,333.9975223,-19.4414966,0.069531,4.779200917606576e-05,3.6e-05,1.7205123303383675e-09 +J221528.16-193851.9,333.8673352,-19.647757,0.076014,0.0019116803670426305,4.5e-07,8.602561651691836e-10 +J221421.41-192756.0,333.5892417,-19.4655629,0.076212,0.0002867520550563946,4.5e-07,1.2903842477537757e-10 +J221416.08-193057.8,333.5670365,-19.5160643,0.077144,0.00043012808258459183,1.8e-07,7.742305486522653e-11 +J221503.26-192544.4,333.7635872,-19.4290001,0.07949,0.0008602561651691837,2.4e-08,2.064614796406041e-11 +J221249.30-194822.9,333.2054474,-19.8063775,0.077736,4.779200917606576e-05,6.8e-08,3.2498566239724718e-12 diff --git a/papers/171020/Data/R-MAG_CANDIDATES.csv b/papers/171020/Data/R-MAG_CANDIDATES.csv new file mode 100644 index 0000000..271c4c4 --- /dev/null +++ b/papers/171020/Data/R-MAG_CANDIDATES.csv @@ -0,0 +1,27 @@ +Object Name,SkyMapperID,R-mag,RA,DEC +WISEA J221142.57-194059.0,3869857,15.452 ± 0.029,332.92792,-19.68286 +WISEA J221145.17-195400.1,3858532,16.128 ± 0.045,332.93842,-19.90072 +WISEA J221149.26-194704.0,3869760,15.752 ± 0.032,332.95521,-19.7845 +WISEA J221151.41-193329.2,3869907,16.343 ± 0.015,332.96421,-19.55808 +WISEA J221152.88-194139.0,3869859,16.193 ± 0.036,332.97021,-19.69372 +WISEA J221211.44-195557.8,3858550,16.034 ± 0.033,333.04704,-19.93294 +WISEA J221220.88-195835.8,3858726,15.766 ± 0.040,333.08679,-19.97669 +WISEA J221232.13-194917.7,3869965,15.884 ± 0.017,333.13405,-19.82146 +WISEA J221232.55-201832.1,3858178,16.232 ± 0.021,333.13558,-20.309 +WISEA J221233.47-193736.7,3870097,16.228 ± 0.020,333.13925,-19.627 +WISEA J221241.55-192515.5,3870537,15.793 ± 0.019,333.17313,-19.42097 +WISEA J221246.95-201055.2,3858605,16.329 ± 0.024,333.1955,-20.18178 +WISEA J221254.49-192811.7,3870533,16.807 ± 0.020,333.22708,-19.47036 +WISEA J221258.42-191529.1,3870688,15.242 ± 0.034,333.2435,-19.25813 +WISEA J221304.50-191722.7,3870737,16.886 ± 0.032,333.26887,-19.28964 +WISEA J221312.66-194532.5,3870131,14.749 ± 0.072,333.30295,-19.75911 +WISEA J221319.95-200202.0,3858799,16.035 ± 0.011,333.33308,-20.03394 +WISEA J221322.11-193412.1,3870552,16.540 ± 0.052,333.34208,-19.57003 +WISEA J221355.53-202723.5,5735766,16.568 ± 0.031,333.48146,-20.45656 +WISEA J221412.12-191801.2,5744322,15.862 ± 0.017,333.55071,-19.30053 +WISEA J221437.05-191904.9,5744363,15.922 ± 0.056,333.65435,-19.31814 +WISEA J221501.14-192536.9,5744348,15.643 ± 0.015,333.75484,-19.42702 +ESO 601- G 036,5744413,15.304394945118 ± 0.016185704779628 ,333.85313,-19.58527 +WISEA J221655.11-193432.7,5745348,16.175 ± 0.031,334.22962,-19.57575 +2MASX J22165626-1955155,5744652,16.485 ± 0.051,334.2345,-19.92092 +2MASX J22172928-1954557,5744838,16.020 ± 0.036,334.37208,-19.91557 diff --git a/papers/171020/Data/R-MAG_CANDIDATES_w_priors.csv b/papers/171020/Data/R-MAG_CANDIDATES_w_priors.csv new file mode 100644 index 0000000..79f8065 --- /dev/null +++ b/papers/171020/Data/R-MAG_CANDIDATES_w_priors.csv @@ -0,0 +1,27 @@ +ObjectName,SkyMapperID,R-mag,RA,DEC,magprior +WISEAJ221142.57-194059.0,3869857,15.452±0.029,332.92792,-19.68286,0.0637 +WISEAJ221145.17-195400.1,3858532,16.128±0.045,332.93842,-19.90072,0.0268 +WISEAJ221149.26-194704.0,3869760,15.752±0.032,332.95521,-19.7845,0.0432 +WISEAJ221151.41-193329.2,3869907,16.343±0.015,332.96421,-19.55808,0.0205 +WISEAJ221152.88-194139.0,3869859,16.193±0.036,332.97021,-19.69372,0.0247 +WISEAJ221211.44-195557.8,3858550,16.034±0.033,333.04704,-19.93294,0.0302 +WISEAJ221220.88-195835.8,3858726,15.766±0.040,333.08679,-19.97669,0.0425 +WISEAJ221232.13-194917.7,3869965,15.884±0.017,333.13405,-19.82146,0.0365 +WISEAJ221232.55-201832.1,3858178,16.232±0.021,333.13558,-20.309,0.0235 +WISEAJ221233.47-193736.7,3870097,16.228±0.020,333.13925,-19.627,0.0237 +WISEAJ221241.55-192515.5,3870537,15.793±0.019,333.17313,-19.42097,0.0410 +WISEAJ221246.95-201055.2,3858605,16.329±0.024,333.1955,-20.18178,0.0208 +WISEAJ221254.49-192811.7,3870533,16.807±0.020,333.22708,-19.47036,0.0116 +WISEAJ221258.42-191529.1,3870688,15.242±0.034,333.2435,-19.25813,0.0838 +WISEAJ221304.50-191722.7,3870737,16.886±0.032,333.26887,-19.28964,0.0105 +WISEAJ221312.66-194532.5,3870131,14.749±0.072,333.30295,-19.75911,0.1614 +WISEAJ221319.95-200202.0,3858799,16.035±0.011,333.33308,-20.03394,0.0302 +WISEAJ221322.11-193412.1,3870552,16.540±0.052,333.34208,-19.57003,0.0160 +WISEAJ221355.53-202723.5,5735766,16.568±0.031,333.48146,-20.45656,0.0155 +WISEAJ221412.12-191801.2,5744322,15.862±0.017,333.55071,-19.30053,0.0376 +WISEAJ221437.05-191904.9,5744363,15.922±0.056,333.65435,-19.31814,0.0348 +WISEAJ221501.14-192536.9,5744348,15.643±0.015,333.75484,-19.42702,0.0497 +ESO601-G036,5744413,15.304394945118±0.016185704779628,333.85313,-19.58527,0.0772 +WISEAJ221655.11-193432.7,5745348,16.175±0.031,334.22962,-19.57575,0.0253 +2MASXJ22165626-1955155,5744652,16.485±0.051,334.2345,-19.92092,0.0172 +2MASXJ22172928-1954557,5744838,16.020±0.036,334.37208,-19.91557,0.0307 diff --git a/papers/171020/Data/cand_pos_liklihood.csv b/papers/171020/Data/cand_pos_liklihood.csv new file mode 100644 index 0000000..fcea6f0 --- /dev/null +++ b/papers/171020/Data/cand_pos_liklihood.csv @@ -0,0 +1,28 @@ +Obj_Name,RA_host,DEC_host,positional_likelihood +WISEA J221142.57-194059.0,332.928,-19.683,0.000 +WISEA J221145.17-195400.1,332.938,-19.901,0.000 +WISEA J221149.26-194704.0,332.955,-19.785,0.000 +WISEA J221151.41-193329.2,332.964,-19.558,0.000 +WISEA J221152.88-194139.0,332.970,-19.694,0.000 +WISEA J221211.44-195557.8,333.047,-19.933,0.000 +WISEA J221220.88-195835.8,333.087,-19.977,0.000 +WISEA J221232.13-194917.7,333.134,-19.821,0.000 +WISEA J221232.55-201832.1,333.136,-20.309,0.000 +WISEA J221233.47-193736.7,333.139,-19.627,0.000 +WISEA J221241.55-192515.5,333.173,-19.421,0.000 +ESO 601- G 029,333.189,-19.562,0.000 +WISEA J221246.95-201055.2,333.195,-20.182,0.008 +WISEA J221254.49-192811.7,333.227,-19.470,0.000 +WISEA J221258.42-191529.1,333.243,-19.258,0.000 +WISEA J221304.50-191722.7,333.269,-19.290,0.000 +WISEA J221312.66-194532.5,333.303,-19.759,0.102 +WISEA J221319.95-200202.0,333.333,-20.034,0.398 +WISEA J221322.11-193412.1,333.342,-19.570,0.000 +WISEA J221355.53-202723.5,333.481,-20.457,0.000 +WISEA J221412.12-191801.2,333.551,-19.301,0.000 +WISEA J221437.05-191904.9,333.654,-19.318,0.000 +WISEA J221501.14-192536.9,333.755,-19.427,0.141 +ESO 601- G 036,333.853,-19.585,0.352 +WISEA J221655.11-193432.7,334.230,-19.576,0.000 +2MASX J22165626-1955155,334.235,-19.921,0.000 +2MASX J22172928-1954557,334.372,-19.916,0.000 diff --git a/papers/171020/Data/candidate_zs.dat b/papers/171020/Data/candidate_zs.dat new file mode 100644 index 0000000..e32b7b9 --- /dev/null +++ b/papers/171020/Data/candidate_zs.dat @@ -0,0 +1,23 @@ +1 J221404.53-195840.5 22h14m04.5383s -19d58m40.5098s 0.066 0.693 +2 J221445.43-194502.2 22h14m45.4309s -19d45m02.2165s 0.056 0.66 +3 J221359.13-195106.0 22h13m59.1349s -19d51m06.0174s 0.055 0.641 +4 J221357.33-195652.7 22h13m57.3348s -19d56m52.755s 0.077 0.62 +5 J221430.33-200013.6 22h14m30.3397s -20d00m13.6631s 0.049 0.485 +6 J221427.49-200156.0 22h14m27.4983s -20d01m56.0917s 0.047 0.485 +7 J221413.69-194032.1 22h14m13.6995s -19d40m32.1244s 0.056 0.451 +8 J221524.61-193504.8 22h15m24.6108s -19d35m04.8995s 0.006 0.138 +9 J221528.16-193851.9 22h15m28.1604s -19d38m51.9252s 0.076 0.123 +10 J221503.26-192544.4 22h15m03.2609s -19d25m44.4004s 0.079 0.055 +11 J221501.93-200730.5 22h15m01.9344s -20d07m30.567s 0.059 0.037 +12 J221437.97-192453.2 22h14m37.9777s -19d24m53.2915s 0.068 0.037 +13 J221358.74-193215.9 22h13m58.7466s -19d32m15.9227s 0.067 0.034 +14 J221419.92-201529.1 22h14m19.9265s -20d15m29.133s 0.063 0.031 +15 J221459.58-201030.4 22h14m59.5874s -20d10m30.4032s 0.056 0.028 +16 J221416.08-193057.8 22h14m16.0888s -19d30m57.8315s 0.077 0.028 +17 J221338.59-193704.2 22h13m38.5997s -19d37m04.2481s 0.036 0.025 +18 J221421.41-192756.0 22h14m21.418s -19d27m56.0264s 0.076 0.018 +19 J221415.90-202202.4 22h14m15.9036s -20d22m02.4863s 0.046 0.012 +20 J221552.85-195808.4 22h15m52.8522s -19d58m08.4832s 0.07 0.009 +21 J221449.49-192207.4 22h14m49.4916s -19d22m07.4766s 0.071 0.009 +22 J221559.40-192629.3 22h15m59.4054s -19d26m29.3878s 0.07 0.003 +23 J221249.30-194822.9 22h12m49.3074s -19d48m22.959s 0.078 0.003 diff --git a/papers/171020/Data/including_mr_prior.csv b/papers/171020/Data/including_mr_prior.csv new file mode 100644 index 0000000..f2ad067 --- /dev/null +++ b/papers/171020/Data/including_mr_prior.csv @@ -0,0 +1,25 @@ +rmag_HG171020 _field - Sheet1 +#host_name,ra_deg,dec_deg,z_photo_WISE,rmag,rmag_err,Mag prior (1/\sum m),Positional lik,p(z),product (raw),product (normed) +J221524.61-193504.8,333.853,-19.585,0.0087,19.2233,0.0889,0.00570296590878147,0.00215064041292296,32.908590259394100,0.000403624812463784,0.483248388812653 +J221427.49-200156.0,333.615,-20.032,0.047,20.3648,0.0606,0.0017511167428884,0.00755113744981839,2.8030534380456200,0.0000370645603822628,0.044376333004515 +J221430.33-200013.6,333.626,-20.004,0.049,20.0347,0.0336,0.00244445951977035,0.00755113744981839,2.0832661217234600,0.0000384538631785027,0.0460397053174252 +J221359.13-195106.0,333.496,-19.852,0.0551,17.5727,0.0029,0.0360789145387826,0.00998852991779774,0.6330606145126230,0.000228139419807582,0.273144771189109 +J221445.43-194502.2,333.689,-19.751,0.056,20.6629,0.0671,0.00130275443942884,0.0102752819728541,0.5063921634276870,0.00000677865118449814,0.00811588426201036 +J221338.59-193704.2,333.411,-19.618,0.0364,18.2049,0.0067,0.0174568451067372,0.00702542534888167,0.4750683741579500,0.0000582632225362917,0.0697568820059156 +J221413.69-194032.1,333.557,-19.676,0.0563,18.3037,0.0064,0.0156186638338704,0.000382336073408526,8.841295688647030,0.0000527964921494267,0.0632117228136454 +J221415.90-202202.4,333.566,-20.367,0.046,18.5523,0.007,0.0118355357185103,0.000191168036704263,3.22933127709492,0.00000730660785260168,0.00874799160859927 +J221459.58-201030.4,333.748,-20.175,0.0556,NaN,NaN,0.00129789512943858,0.000430128082584592,0.5665181162490530,0.000000316265051346076,0.000378655057323701 +J221404.53-195840.5,333.519,-19.978,0.0663,19.7793,0.0365,0.00317812629673928,0.0108009940737909,0.014658872105804000,0.000000503193978394121,0.000602459689816533 +J221501.93-200730.5,333.758,-20.125,0.0586,18.6564,0.0837,0.01054938984715,0.000573504110112789,0.25239988064855100,0.00000152704917128792,0.00182829208927486 +J221419.92-201529.1,333.583,-20.258,0.0632,18.6821,0.0697,0.0102550237455036,0.000477920091760658,0.05351459771262990,0.000000262279425671044,0.00031401961911252 +J221358.74-193215.9,333.495,-19.538,0.0675,19.7689,0.0228,0.00321253679722543,0.000525712100936723,0.007990987275477960,0.0000000134957344367695,0.0000161580550080713 +J221437.97-192453.2,333.658,-19.415,0.0684,17.7779,0.0061,0.0284288848378469,0.000573504110112789,0.0050541490848270500,0.0000000824032626376548,0.0000986590582959406 +J221552.85-195808.4,333.97,-19.969,0.0705,17.8776,0.0065,0.0253441575390886,0.000143376027528197,0.001504139100920670,0.00000000546565737924438,0.00000654387451108156 +J221449.49-192207.4,333.706,-19.369,0.0707,19.8369,0.0542,0.0029944569553945,0.000143376027528197,0.0013369501476994800,0.000000000573997276160543,0.000000687230443525625 +J221559.40-192629.3,333.998,-19.441,0.0695,15.1775,0.0048,0.70617956213222,0.00965398585356528,1.28230207737972E-05,0.0000000874202709539762,0.000104665778177017 +J221357.33-195652.7,333.489,-19.948,0.0767,17.2688,0.004,0.0515918269998431,4.77920091760658E-05,0.0026442604715819200,0.00000000651989241026467,0.00000780607981768948 +J221528.16-193851.9,333.867,-19.648,0.076,20.6667,0.0627,0.00129789512943858,0.00191168036704263,2.46605385664198E-05,0.0000000000611867575887756,0.000000073257146509295 +J221421.41-192756.0,333.589,-19.466,0.0762,17.519,0.0024,0.0384168405690495,0.000286752055056395,2.07251801901164E-05,0.000000000228310822919669,0.000000273349987209522 +J221416.08-193057.8,333.567,-19.516,0.0771,19.7686,0.0507,0.0032135352210089,0.000430128082584592,8.81231946001844E-06,0.0000000000121806676864826,0.0000000145835633796255 +J221503.26-192544.4,333.764,-19.429,0.0795,18.4509,0.0565,0.0132472198469478,0.000860256165169184,7.93698988041709E-07,0.00000000000904499568743994,0.0000000108293134064074 +J221249.30-194822.9,333.205,-19.806,0.0777,18.8426,0.0357,0.00860139314433543,4.77920091760658E-05,4.94611100461563E-06,0.00000000000203323672750062,0.00000000243433590378576 \ No newline at end of file diff --git a/papers/171020/Data/modR-MAG_CANDIDATES.csv b/papers/171020/Data/modR-MAG_CANDIDATES.csv new file mode 100644 index 0000000..970e035 --- /dev/null +++ b/papers/171020/Data/modR-MAG_CANDIDATES.csv @@ -0,0 +1,26 @@ +WISEA J221142.57-194059.0 3869857 15.452 err 0.029 332.92792 -19.68286 +WISEA J221145.17-195400.1 3858532 16.128 err 0.045 332.93842 -19.90072 +WISEA J221149.26-194704.0 3869760 15.752 err 0.032 332.95521 -19.7845 +WISEA J221151.41-193329.2 3869907 16.343 err 0.015 332.96421 -19.55808 +WISEA J221152.88-194139.0 3869859 16.193 err 0.036 332.97021 -19.69372 +WISEA J221211.44-195557.8 3858550 16.034 err 0.033 333.04704 -19.93294 +WISEA J221220.88-195835.8 3858726 15.766 err 0.040 333.08679 -19.97669 +WISEA J221232.13-194917.7 3869965 15.884 err 0.017 333.13405 -19.82146 +WISEA J221232.55-201832.1 3858178 16.232 err 0.021 333.13558 -20.309 +WISEA J221233.47-193736.7 3870097 16.228 err 0.020 333.13925 -19.627 +WISEA J221241.55-192515.5 3870537 15.793 err 0.019 333.17313 -19.42097 +WISEA J221246.95-201055.2 3858605 16.329 err 0.024 333.1955 -20.18178 +WISEA J221254.49-192811.7 3870533 16.807 err 0.020 333.22708 -19.47036 +WISEA J221258.42-191529.1 3870688 15.242 err 0.034 333.2435 -19.25813 +WISEA J221304.50-191722.7 3870737 16.886 err 0.032 333.26887 -19.28964 +WISEA J221312.66-194532.5 3870131 14.749 err 0.072 333.30295 -19.75911 +WISEA J221319.95-200202.0 3858799 16.035 err 0.011 333.33308 -20.03394 +WISEA J221322.11-193412.1 3870552 16.540 err 0.052 333.34208 -19.57003 +WISEA J221355.53-202723.5 5735766 16.568 err 0.031 333.48146 -20.45656 +WISEA J221412.12-191801.2 5744322 15.862 err 0.017 333.55071 -19.30053 +WISEA J221437.05-191904.9 5744363 15.922 err 0.056 333.65435 -19.31814 +WISEA J221501.14-192536.9 5744348 15.643 err 0.015 333.75484 -19.42702 +ESO 601-G036 5744413 15.304394945118 err 0.016185704779628 333.85313 -19.58527 +WISEA J221655.11-193432.7 5745348 16.175 err 0.031 334.22962 -19.57575 +2MASX J22165626-1955155 5744652 16.485 err 0.051 334.2345 -19.92092 +2MASX J22172928-1954557 5744838 16.020 err 0.036 334.37208 -19.91557 diff --git a/papers/171020/Data/mod_P.csv b/papers/171020/Data/mod_P.csv new file mode 100644 index 0000000..fc20696 --- /dev/null +++ b/papers/171020/Data/mod_P.csv @@ -0,0 +1,23 @@ +J221524.61-193504.8,333.8525451,-19.5846943,0.00867,0.0021506404129229594,0.65,0.0013979162683999237 +J221427.49-200156.0,333.6145763,-20.0322477,0.047019,0.00755113744981839,0.05,0.00037755687249091955 +J221430.33-200013.6,333.6264155,-20.0037953,0.048971,0.00755113744981839,0.037,0.00027939208564328044 +J221359.13-195106.0,333.4963953,-19.8516715,0.055101,0.009988529917797744,0.012,0.00011986235901357292 +J221445.43-194502.2,333.6892954,-19.7506157,0.056032,0.010275281972854138,0.0091,9.350506595297266e-05 +J221413.69-194032.1,333.5570813,-19.6755901,0.056289,0.007025425348881667,0.0091,6.393137067482317e-05 +J221338.59-193704.2,333.4108319,-19.6178467,0.036444,0.0003823360734085261,0.16,6.117377174536418e-05 +J221415.90-202202.4,333.5662652,-20.3673573,0.046004,0.00019116803670426305,0.058,1.1087746128847257e-05 +J221459.58-201030.4,333.7482807,-20.175112,0.055571,0.00043012808258459183,0.0091,3.914165551519786e-06 +J221404.53-195840.5,333.5189094,-19.9779194,0.066262,0.010800994073790862,0.0003,3.240298222137258e-06 +J221501.93-200730.5,333.7580599,-20.1251575,0.058634,0.0005735041101127892,0.0041,2.3513668514624357e-06 +J221419.92-201529.1,333.5830269,-20.2580925,0.063239,0.0004779200917606576,0.0012,5.735041101127891e-07 +J221358.74-193215.9,333.4947776,-19.5377563,0.067499,0.0005257121009367234,0.00018,9.462817816861022e-08 +J221437.97-192453.2,333.6582404,-19.4148032,0.068376,0.0005735041101127892,0.00011,6.308545211240681e-08 +J221552.85-195808.4,333.9702173,-19.9690231,0.070473,0.0001433760275281973,3.6e-05,5.161536991015103e-09 +J221449.49-192207.4,333.7062152,-19.3687435,0.070665,0.0001433760275281973,1.9e-05,2.7241445230357486e-09 +J221357.33-195652.7,333.4888951,-19.9479875,0.076738,0.009653985853565283,1.8e-07,1.7377174536417508e-09 +J221559.40-192629.3,333.9975223,-19.4414966,0.069531,4.779200917606576e-05,3.6e-05,1.7205123303383675e-09 +J221528.16-193851.9,333.8673352,-19.647757,0.076014,0.0019116803670426305,4.5e-07,8.602561651691836e-10 +J221421.41-192756.0,333.5892417,-19.4655629,0.076212,0.0002867520550563946,4.5e-07,1.2903842477537757e-10 +J221416.08-193057.8,333.5670365,-19.5160643,0.077144,0.00043012808258459183,1.8e-07,7.742305486522653e-11 +J221503.26-192544.4,333.7635872,-19.4290001,0.07949,0.0008602561651691837,2.4e-08,2.064614796406041e-11 +J221249.30-194822.9,333.2054474,-19.8063775,0.077736,4.779200917606576e-05,6.8e-08,3.2498566239724718e-12 diff --git a/papers/171020/Data/mod_cand_pos_likelihood.csv b/papers/171020/Data/mod_cand_pos_likelihood.csv new file mode 100644 index 0000000..6ab5742 --- /dev/null +++ b/papers/171020/Data/mod_cand_pos_likelihood.csv @@ -0,0 +1,26 @@ +WISEA J221142.57-194059.0,332.928,-19.683,0.000 +WISEA J221145.17-195400.1,332.938,-19.901,0.000 +WISEA J221149.26-194704.0,332.955,-19.785,0.000 +WISEA J221151.41-193329.2,332.964,-19.558,0.000 +WISEA J221152.88-194139.0,332.970,-19.694,0.000 +WISEA J221211.44-195557.8,333.047,-19.933,0.000 +WISEA J221220.88-195835.8,333.087,-19.977,0.000 +WISEA J221232.13-194917.7,333.134,-19.821,0.000 +WISEA J221232.55-201832.1,333.136,-20.309,0.000 +WISEA J221233.47-193736.7,333.139,-19.627,0.000 +WISEA J221241.55-192515.5,333.173,-19.421,0.000 +WISEA J221246.95-201055.2,333.195,-20.182,0.008 +WISEA J221254.49-192811.7,333.227,-19.470,0.000 +WISEA J221258.42-191529.1,333.243,-19.258,0.000 +WISEA J221304.50-191722.7,333.269,-19.290,0.000 +WISEA J221312.66-194532.5,333.303,-19.759,0.102 +WISEA J221319.95-200202.0,333.333,-20.034,0.398 +WISEA J221322.11-193412.1,333.342,-19.570,0.000 +WISEA J221355.53-202723.5,333.481,-20.457,0.000 +WISEA J221412.12-191801.2,333.551,-19.301,0.000 +WISEA J221437.05-191904.9,333.654,-19.318,0.000 +WISEA J221501.14-192536.9,333.755,-19.427,0.141 +ESO 601-G036,333.853,-19.585,0.352 +WISEA J221655.11-193432.7,334.230,-19.576,0.000 +2MASX J22165626-1955155,334.235,-19.921,0.000 +2MASX J22172928-1954557,334.372,-19.916,0.000 diff --git a/papers/171020/Data/object_search_2.csv b/papers/171020/Data/object_search_2.csv new file mode 100644 index 0000000..9162868 --- /dev/null +++ b/papers/171020/Data/object_search_2.csv @@ -0,0 +1,50 @@ +#No.,Object Name,RA,DEC,Type,Velocity,Redshift,Redshift Flag,Magnitude and Filter,Separation,References,Notes,Photometry Points,Positions,Redshift Points,Diameter Points,Associations +1,WISEA J221312.66-194532.5,333.30295,-19.75911,G,18461.00,0.061579,,16.07,11.983,10,0,32,7,3,2,0 +2,WISEA J221319.95-200202.0,333.33308,-20.03394,G,18570.00,0.061942,PHOT,,15.281,1,0,31,5,1,2,0 +3,WISEA J221322.11-193412.1,333.34208,-19.57003,G,23474.00,0.078301,PHOT,18.23,18.143,2,0,28,5,1,2,0 +4,WISEA J221232.13-194917.7,333.13405,-19.82146,G,17696.00,0.059029,PHOT,17.69,20.668,3,0,32,6,1,2,0 +5,ESO 601-G029,333.18858,-19.56156,G,20470.00,0.068281,,15.62,23.987,11,0,42,9,3,14,0 +6,WISEA J221220.88-195835.8,333.08679,-19.97669,G,19876.00,0.066299,,16.92,24.848,4,0,32,6,3,2,0 +7,ESO 601-G036,333.85313,-19.58527,G,2600.00,0.008672,,15.25,24.888,15,1,11,6,3,12,1 +8,WISEA J221211.44-195557.8,333.04704,-19.93294,G,15894.00,0.053017,,16.94,26.247,3,0,26,5,2,2,0 +9,WISEA J221246.95-201055.2,333.19550,-20.18178,G,20423.00,0.068124,PHOT,18.00,27.052,2,0,32,6,1,2,0 +10,WISEA J221501.14-192536.9,333.75484,-19.42702,G,19960.00,0.066579,,16.66,28.315,3,0,31,5,3,2,0 +11,WISEA J221241.55-192515.5,333.17313,-19.42097,G,15748.00,0.05253,,17.63,30.877,6,0,31,8,4,2,0 +12,WISEA J221149.26-194704.0,332.95521,-19.78450,G,20346.00,0.067867,,15.5R,30.893,4,0,27,5,2,2,0 +13,WISEA J221152.88-194139.0,332.97021,-19.69372,G,21027.00,0.070138,PHOT,17.40,31.066,2,0,32,6,1,2,0 +14,WISEA J221145.17-195400.1,332.93842,-19.90072,G,19829.00,0.066143,PHOT,17.14,31.946,2,0,44,7,1,2,0 +15,WISEA J221412.12-191801.2,333.55071,-19.30053,G,15411.00,0.051404,PHOT,15.7r,32.097,1,0,31,5,1,2,0 +16,WISEA J221437.05-191904.9,333.65435,-19.31814,G,18208.00,0.060737,PHOT,17.12,32.119,3,0,28,5,1,2,0 +17,WISEA J221142.57-194059.0,332.92792,-19.68286,G,19704.00,0.065725,,17.59,33.542,3,0,25,5,1,0,0 +18,WISEA J221151.41-193329.2,332.96421,-19.55808,G,15622.00,0.052109,,18.04,34.479,5,0,26,6,2,0,0 +19,WISEA J221232.55-201832.1,333.13558,-20.30900,G,23359.00,0.077918,PHOT,18.05,35.161,1,0,28,5,1,2,0 +20,WISEA J221131.72-193830.8,332.88233,-19.64193,G,20164.00,0.06726,,16.85,36.725,6,0,32,6,3,2,0 +21,WISEA J221258.42-191529.1,333.24350,-19.25813,G,20422.00,0.06812,,17.04,37.436,5,0,32,7,3,2,0 +22,WISEA J221133.38-193359.8,332.88917,-19.56667,G,23355.00,0.077905,PHOT,17.87,38.034,2,0,28,5,1,2,0 +23,WISEA J221117.80-194054.2,332.82404,-19.68186,G,19806.00,0.066066,,16.46,39.237,6,0,32,6,4,2,0 +24,WISEA J221158.99-201738.9,332.99579,-20.29419,G,19930.00,0.066479,,14.3I,39.65,4,0,31,5,3,2,1 +25,FGCE 1714,333.34575,-20.52508,G,23815.00,0.079438,,17.3,42.404,6,0,32,7,3,2,0 +26,WISEA J221655.11-193432.7,334.22962,-19.57575,G,18944.00,0.06319,PHOT,,44.016,2,0,27,4,1,2,0 +27,WISEA J221049.28-194341.0,332.70521,-19.72828,G,20428.00,0.068139,PHOT,17.76,45.314,2,0,28,5,1,2,0 +28,WISEA J221413.39-190223.7,333.55575,-19.04011,G,23227.00,0.077477,PHOT,16.57,47.698,3,0,32,6,1,2,0 +29,WISEA J221101.25-192428.6,332.75563,-19.40844,G,19578.00,0.065305,,17.57,49.191,3,0,31,6,3,2,0 +30,WISEA J221119.87-202203.2,332.83288,-20.36758,G,18533.00,0.061819,,18.16,49.401,2,0,32,6,2,2,1 +31,2MASX J22172928-1954557,334.37208,-19.91557,G,15403.00,0.051379,,16.30,49.455,4,0,19,4,2,2,0 +32,WISEA J221032.17-193812.3,332.63408,-19.63683,G,23774.00,0.079303,PHOT,18.25,50.304,1,0,32,6,1,2,0 +33,WISEA J221216.80-190418.9,333.07008,-19.07181,G,18810.00,0.062743,,16.70,51.762,7,0,32,6,3,2,0 +34,WISEA J221221.15-203700.8,333.08812,-20.61758,G,23751.00,0.079226,PHOT,17.82,52.458,1,0,44,7,1,2,0 +35,WISEA J221149.76-203300.2,332.95736,-20.55022,G,23517.00,0.078444,,16.62,52.762,6,0,28,5,3,2,0 +36,WISEA J221224.13-190158.6,333.10083,-19.03281,G,23448.00,0.078215,PHOT,17.55,53.077,1,0,32,6,1,2,0 +37,2MFGC 16774,333.57325,-18.94897,G,18898.00,0.063037,,17.25,53.223,5,0,32,6,3,2,0 +38,WISEA J221430.60-185707.7,333.62746,-18.95219,G,21367.00,0.071274,PHOT,18.17,53.358,1,0,34,7,1,2,0 +39,WISEA J221035.95-201355.1,332.65042,-20.23236,G,23767.00,0.079278,PHOT,17.63,53.542,2,0,44,7,1,2,0 +40,WISEA J221037.91-201716.1,332.65809,-20.28786,G,20256.00,0.067567,,16.15,54.728,7,0,32,7,3,2,0 +41,WISEA J221002.99-194712.2,332.51242,-19.78658,G,17090.00,0.057006,PHOT,16.84,55.819,4,0,36,7,1,2,0 +42,WISEA J221110.31-191040.5,332.79296,-19.17792,G,21865.00,0.072934,PHOT,16.65,56.084,3,0,32,6,1,2,0 +43,WISEA J221304.26-204451.4,333.26779,-20.74725,G,21250.00,0.070884,PHOT,17.60,56.371,2,0,32,6,1,2,0 +44,WISEA J221425.23-204641.7,333.60521,-20.77814,G,23626.00,0.078808,,17.05,56.997,4,0,32,6,3,2,2 +45,WISEA J221548.00-185855.2,333.95008,-18.98194,G,21644.00,0.072197,PHOT,18.20,57.081,2,0,28,5,1,2,0 +46,WISEA J221303.14-185407.7,333.26279,-18.90253,G,20194.00,0.06736,,16.80,57.44,4,0,32,6,3,2,0 +47,[VCV96] 2213-208,334.08738,-20.62307,G,22185.00,0.074,,18.9,57.783,3,0,4,2,1,0,0 +48,GSM 100,332.93438,-19.01643,G,15929.00,0.053133,,16.77,58.538,3,0,0,1,1,0,2 +49,WISEA J221445.79-185207.0,333.69079,-18.86867,G,20636.00,0.068833,PHOT,17.74,58.879,2,0,32,6,1,2,0 diff --git a/papers/171020/Data/object_search_approx.csv b/papers/171020/Data/object_search_approx.csv new file mode 100644 index 0000000..80fcd76 --- /dev/null +++ b/papers/171020/Data/object_search_approx.csv @@ -0,0 +1,25 @@ +#No.,Object Name,RA,DEC,Type,Velocity,Redshift,Redshift Flag,Magnitude and Filter,Separation,References,Notes,Photometry Points,Positions,Redshift Points,Diameter Points,Associations +1,WISEA J221312.66-194532.5,333.30295,-19.75911,G,18461.00,0.061579,,16.07,11.983,10,0,32,7,3,2,0 +2,WISEA J221319.95-200202.0,333.33308,-20.03394,G,18570.00,0.061942,PHOT,,15.281,1,0,31,5,1,2,0 +3,WISEA J221322.11-193412.1,333.34208,-19.57003,G,23474.00,0.078301,PHOT,18.23,18.143,2,0,28,5,1,2,0 +4,WISEA J221232.13-194917.7,333.13405,-19.82146,G,17696.00,0.059029,PHOT,17.69,20.668,3,0,32,6,1,2,0 +5,ESO 601-G029,333.18858,-19.56156,G,20470.00,0.068281,,15.62,23.987,11,0,42,9,3,14,0 +6,WISEA J221220.88-195835.8,333.08679,-19.97669,G,19876.00,0.066299,,16.92,24.848,4,0,32,6,3,2,0 +7,ESO 601-G036,333.85313,-19.58527,G,2600.00,0.008672,,15.25,24.888,15,1,11,6,3,12,1 +8,WISEA J221211.44-195557.8,333.04704,-19.93294,G,15894.00,0.053017,,16.94,26.247,3,0,26,5,2,2,0 +9,WISEA J221246.95-201055.2,333.19550,-20.18178,G,20423.00,0.068124,PHOT,18.00,27.052,2,0,32,6,1,2,0 +10,WISEA J221501.14-192536.9,333.75484,-19.42702,G,19960.00,0.066579,,16.66,28.315,3,0,31,5,3,2,0 +11,WISEA J221241.55-192515.5,333.17313,-19.42097,G,15748.00,0.05253,,17.63,30.877,6,0,31,8,4,2,0 +12,WISEA J221149.26-194704.0,332.95521,-19.78450,G,20346.00,0.067867,,15.5R,30.893,4,0,27,5,2,2,0 +13,WISEA J221152.88-194139.0,332.97021,-19.69372,G,21027.00,0.070138,PHOT,17.40,31.066,2,0,32,6,1,2,0 +14,WISEA J221145.17-195400.1,332.93842,-19.90072,G,19829.00,0.066143,PHOT,17.14,31.946,2,0,44,7,1,2,0 +15,WISEA J221412.12-191801.2,333.55071,-19.30053,G,15411.00,0.051404,PHOT,15.7r,32.097,1,0,31,5,1,2,0 +16,WISEA J221437.05-191904.9,333.65435,-19.31814,G,18208.00,0.060737,PHOT,17.12,32.119,3,0,28,5,1,2,0 +17,WISEA J221142.57-194059.0,332.92792,-19.68286,G,19704.00,0.065725,,17.59,33.542,3,0,25,5,1,0,0 +18,WISEA J221151.41-193329.2,332.96421,-19.55808,G,15622.00,0.052109,,18.04,34.479,5,0,26,6,2,0,0 +19,WISEA J221232.55-201832.1,333.13558,-20.30900,G,23359.00,0.077918,PHOT,18.05,35.161,1,0,28,5,1,2,0 +20,WISEA J221131.72-193830.8,332.88233,-19.64193,G,20164.00,0.06726,,16.85,36.725,6,0,32,6,3,2,0 +21,WISEA J221258.42-191529.1,333.24350,-19.25813,G,20422.00,0.06812,,17.04,37.436,5,0,32,7,3,2,0 +22,WISEA J221133.38-193359.8,332.88917,-19.56667,G,23355.00,0.077905,PHOT,17.87,38.034,2,0,28,5,1,2,0 +23,WISEA J221117.80-194054.2,332.82404,-19.68186,G,19806.00,0.066066,,16.46,39.237,6,0,32,6,4,2,0 +24,WISEA J221158.99-201738.9,332.99579,-20.29419,G,19930.00,0.066479,,14.3I,39.65,4,0,31,5,3,2,1 diff --git a/papers/171020/Data/second_cut.csv b/papers/171020/Data/second_cut.csv new file mode 100644 index 0000000..f6f58cc --- /dev/null +++ b/papers/171020/Data/second_cut.csv @@ -0,0 +1,114 @@ +J221258.68-204401.8,3325021301351052049,284056252201331,-0.29986786761,-0.0720231680632,-0.951258074992,12734292953977,333.2445393,-20.7338586,34.22261,-53.381756,0.041,16.216999,16.190001,18.431,18.110001,0.055808,0.055808,0 +J221231.15-203946.6,3325021301351054428,284056252206352,-0.238484931538,-0.0289922910515,-0.970713337958,12734295991657,333.1297963,-20.6629661,34.280014,-53.25778,0.041,14.091,14.059,17.531,16.466,0.077429898,0.077430002,0 +J221306.08-203932.5,3325021301351054460,284056252206646,-0.228071487565,-0.0622702903056,-0.971651072919,12734284785041,333.2753548,-20.6590315,34.356857,-53.386066,0.039,14.731,14.698,17.916,17.416,0.061081901,0.061082002,0 +J221315.32-202831.7,3325021301351054505,284056252219955,-0.0525689090031,-0.0165508911462,-0.998480133908,12734278487417,333.3138382,-20.475493,34.66801,-53.363113,0.032,14.972,14.876,17.360001,17.120001,0.0485131,0.048512999,0 +J221210.65-202716.4,3325021301351057099,284056252221573,-0.0341860398629,-0.0012164803948,-0.999414746166,12734297543141,333.0443904,-20.4545666,34.5689,-53.116848,0.040,15.445,15.401,18.410999,17.42,0.075704902,0.075704999,0 +J221100.78-202732.2,3325021301351057191,284056252221230,-0.0373323841145,0.00975354510493,-0.999255303441,12734245659022,332.7532712,-20.4589473,34.419876,-52.859226,0.031,15.871,15.798,18.646,17.635,0.067479499,0.067479998,0 +J221338.59-193704.2,3328019701241016594,284056252286293,0.661770099138,0.281361659122,-0.694907153988,12732061719390,333.4108319,-19.6178467,36.074528,-53.17267,0.025,16.969999,16.544001,19.011999,18.266001,0.036443599,0.036444001,1 +J221419.92-201529.1,3328019701351000069,284056252235895,0.135640159513,0.0877480720825,-0.986864744012,12732117345172,333.5830269,-20.2580925,35.148247,-53.533855,0.026,14.513,14.269,16.816,16.419001,0.063239299,0.063239001,0 +J221415.90-202202.4,3328019701351000684,284056252228060,0.0449510007714,0.0280222037883,-0.99859609634,12731936694640,333.5662652,-20.3673573,34.96547,-53.553608,0.024,16.924,16.684,19.097,18.43,0.046004299,0.046004001,0 +J221217.50-202631.2,3328019701351002874,284056252222528,-0.0216058732089,-0.00138720130618,-0.999765603487,12734284108085,333.0729381,-20.4420043,34.602776,-53.1383,0.036,14.439,14.414,17.712999,16.636,0.079436697,0.079437003,0 +J221155.07-202217.6,3328019701351002888,284056252227769,0.0487402932397,-0.00143130830627,-0.998810460083,12734150215526,332.9794637,-20.3715716,34.668163,-53.032974,0.033,14.709,14.589,16.83,16.386,0.035179801,0.035179999,0 +J221305.21-202311.1,3328019701351002979,284056252226700,0.0327598984673,0.0088172950412,-0.999424356498,12734279733459,333.2717398,-20.38642,34.788822,-53.297653,0.026,15.245,15.225,17.952,17.004,0.0620826,0.062082998,0 +J221125.18-202120.2,3328019701351005901,284056252228899,0.0639135840474,-0.00991300526422,-0.997906201053,12734131846375,332.8549472,-20.3556293,34.632103,-52.917175,0.033,15.749,15.692,18.677,17.826,0.076002397,0.076002002,0 +J221101.75-201302.8,3328019701351005963,284056252238849,0.195177778128,-0.0501475413727,-0.979484996832,12734137982844,332.7573285,-20.2174457,34.801037,-52.78652,0.026,15.986,15.978,18.641001,18.014,0.066913001,0.066913001,0 +J221116.87-201906.6,3328019701351006334,284056252231543,0.0998660052001,-0.0190525005107,-0.994818467475,12734132044316,332.820306,-20.3185091,34.673462,-52.874626,0.031,16.757,16.559,18.243999,18.554001,0.039997499,0.039997999,0 +J221024.01-195344.4,3328019701351016716,284056252264092,0.459658479117,-0.199097255362,-0.865490823454,12734388760267,332.6000748,-19.8956853,35.225,-52.54313,0.024,16.429001,16.167,18.121,18.209999,0.043521099,0.043520998,0 +J221111.58-195130.5,3328019701351018833,284056252267044,0.521010482975,-0.111347956723,-0.846256290472,12734124091582,332.7982733,-19.8584747,35.382893,-52.70726,0.028,13.954,13.947,17.785999,16.792,0.068537198,0.068536997,0 +J221152.02-195901.7,3328019701351018849,284056252257293,0.422430122603,-0.0177656997194,-0.906221369992,12734117432392,332.9667901,-19.9838155,35.271904,-52.89781,0.027,14.22,14.041,17.355,16.820999,0.076587602,0.076587997,0 +J221133.33-195451.8,3328019701351018906,284056252262736,0.481154736991,-0.0579847015866,-0.874715892992,12734119855199,332.8888881,-19.9144052,35.341324,-52.806034,0.028,14.911,14.81,18.030001,17.461,0.076160498,0.076159999,0 +J221136.18-200235.1,3328019701351019210,284056252252434,0.366227145161,-0.0397317032088,-0.929676863166,12734164768709,332.9007547,-20.0430906,35.145752,-52.858227,0.025,16.309999,16.305,18.719,18.544001,0.077462703,0.077463001,0 +J221211.14-195600.3,3328019701351021254,284056252261340,0.467572527111,0.0175997483777,-0.883779486494,12732032539525,333.0464442,-19.933435,35.391212,-52.952312,0.028,14.116,14.009,16.181,15.814,0.0301445,0.030144,0 +J221230.16-195918.8,3328019701351021347,284056252256913,0.415638106656,0.0488034426776,-0.908219790732,12732086916118,333.1257042,-19.9885663,35.344677,-53.040657,0.026,15.175,14.988,17.891001,17.472,0.078153998,0.078153998,0 +J221157.18-200840.7,3328019701351021576,284056252244322,0.272167958231,-0.0055886058598,-0.962233531944,12734165088401,332.9882905,-20.1446473,35.03006,-52.9687,0.026,16.334999,16.254999,17.726999,17.83,0.033358399,0.033358,0 +J221357.33-195652.7,3328019701351023874,284056252260131,0.403561281861,0.21013624611,-0.890494834266,12732099586328,333.4888951,-19.9479875,35.593487,-53.35042,0.025,15.461,15.43,18.504999,17.601999,0.076738,0.076738,0 +J221404.53-195840.5,3328019701351023906,284056252257775,0.373637826119,0.209040803011,-0.903712740626,12732099477151,333.5189094,-19.9779194,35.561382,-53.386818,0.025,15.654,15.598,18.1,17.875999,0.066261597,0.066261999,0 +J221359.13-195106.0,3328019701351024098,284056252267598,0.475768131513,0.252288416274,-0.842612152803,12732102212387,333.4963953,-19.8516715,35.74984,-53.325703,0.026,16.191999,15.939,18.576,17.663,0.055101,0.055101,0 +J221329.43-193306.7,3328019701351026849,284056252291819,0.713384398097,0.271648397342,-0.645979758795,12732182999849,333.3726569,-19.551862,36.158363,-53.11682,0.025,16.518999,16.437,19.118,18.163,0.060293399,0.060293,0 +J221358.74-193215.9,3328019701351026913,284056252292992,0.682969663741,0.360748112715,-0.635148201275,12732397467871,333.4947776,-19.5377563,36.24451,-53.22063,0.022,16.709999,16.584,19.003,18.642,0.067499101,0.067498997,0 +J221413.69-194032.1,3328019701351027035,284056252281688,0.578454078321,0.353269845707,-0.735249138311,12732095628081,333.5570813,-19.6755901,36.05969,-53.321754,0.026,16.587,16.52,18.957001,18.343,0.056288902,0.056288999,0 +J221249.30-194822.9,3328019701351029025,284056252271258,0.565019713004,0.112551873145,-0.817364545212,12732093321131,333.2054474,-19.8063775,35.671833,-53.05225,0.030,14.612,14.57,18.224001,17.334,0.077735797,0.077735998,0 +J221254.76-193316.2,3328019701351029880,284056252291609,0.743393239136,0.165728247636,-0.647997407358,12732188704757,333.2281692,-19.5545081,36.078823,-52.989307,0.026,16.941999,16.799999,19.354,18.701,0.072269797,0.072269998,0 +J221125.02-193205.7,3328019701351031647,284056252293260,0.764950493839,-0.11917689268,-0.632967305811,12732177327978,332.8542669,-19.534936,35.915413,-52.650505,0.026,14.607,14.595,17.077999,16.070999,0.046887498,0.046888001,0 +J221136.68-193442.9,3328019701351031660,284056252289524,0.741635832275,-0.0788788451878,-0.666148947359,12732177675453,332.9028617,-19.5785892,35.87259,-52.7082,0.026,13.864,13.636,16.816,16.344,0.078338198,0.078337997,0 +J221034.11-194619.1,3328019701351031682,284056252274049,0.563745596724,-0.216514172351,-0.797064937972,12734390771662,332.6421282,-19.7719752,35.4385,-52.54016,0.027,14.237,14.179,17.722,16.98,0.061746601,0.061747,0 +J221148.87-194803.4,3328019701351031691,284056252271670,0.579650725743,-0.0320086645742,-0.814236133771,12732043060206,332.9536567,-19.8009681,35.55163,-52.826633,0.032,14.348,14.225,17.41,16.907,0.062687904,0.062688001,0 +J221139.05-193427.7,3328019701351031765,284056252289908,0.745174039507,-0.0718181863438,-0.662991552702,12732177758179,332.9127403,-19.5743637,35.884266,-52.715576,0.026,15.144,15.139,18.136999,17.124001,0.073818699,0.073818997,0 +J221104.31-193528.3,3328019701351031778,284056252288521,0.716074071139,-0.175866472186,-0.675506409002,12732163145623,332.7679898,-19.5912072,35.78352,-52.592503,0.025,15.306,15.124,17.996,17.209,0.075825199,0.075824998,0 +J221132.06-194009.9,3328019701351032001,284056252282118,0.676971552233,-0.0852133405965,-0.731059644661,12732030808396,332.8836055,-19.6694296,35.721172,-52.721134,0.028,16.245001,15.946,18.004999,18.089001,0.0477219,0.047722001,0 +J221147.91-193809.9,3328019701351032014,284056252284846,0.705065297267,-0.0417625730889,-0.707911586343,12732037553680,332.9496582,-19.6360929,35.80709,-52.76884,0.028,16.364,15.771,18.347,18.76,0.05483,0.05483,0 +J221057.31-194241.6,3328019701351032133,284056252278875,0.627315551474,-0.17362662349,-0.759163351654,12734108783366,332.7388036,-19.7115827,35.581505,-52.606293,0.027,16.523001,16.062,18.795,18.584,0.075669497,0.075669996,0 +J221058.27-193209.0,3328019701351032708,284056252293127,0.746396864837,-0.203362020915,-0.633668374318,12731696031148,332.7428196,-19.5358419,35.856503,-52.551754,0.026,16.940001,16.511999,19.188999,18.528999,0.0588562,0.058855999,0 +J221019.06-193223.3,3328019701351034347,284056252292778,0.701094264241,-0.320999401688,-0.636730882527,12733639839392,332.5794545,-19.5398071,35.766357,-52.40786,0.024,14.255,14.103,17.472,17.232,0.062660299,0.062660001,0 +J221055.36-192551.2,3328019701351044999,284056252301441,0.803608009495,-0.229461762576,-0.54914612499,12731707207601,332.7306833,-19.4308981,36.012856,-52.505966,0.028,14.12,13.946,17.297001,17.030001,0.064996302,0.064995997,0 +J221053.92-192642.3,3328019701351045010,284056252300376,0.794654358948,-0.23207805325,-0.5609493979,12731693468066,332.7246748,-19.4450881,35.987774,-52.50537,0.027,14.25,14.15,17.745001,16.91,0.077566899,0.077567004,0 +J221101.52-192749.7,3328019701351045039,284056252298877,0.79129860487,-0.2041210229,-0.576351564534,12731693629041,332.7563678,-19.463813,35.975174,-52.539795,0.028,14.753,14.657,17.888,17.436001,0.0687989,0.068798997,0 +J221236.65-192411.1,3328019701351047582,284056252303551,0.841867161111,0.122005839817,-0.525713094846,12732214643277,333.1527422,-19.4031091,36.276123,-52.871693,0.026,14.273,14.118,17.683001,17.055,0.078838103,0.078837998,0 +J221156.26-191756.7,3328019701351048862,284056252311888,0.900374801536,-0.0219518666418,-0.434561080068,12732219329874,332.9844453,-19.2991068,36.34966,-52.68716,0.027,16.215,16.134001,19.159,18.153999,0.078580096,0.07858,0 +J221416.08-193057.8,3328019701351050374,284056252294734,0.666674012689,0.416315250581,-0.618245398639,12732266843498,333.5670365,-19.5160643,36.316666,-53.27759,0.022,15.445,15.339,18.174999,17.827999,0.077143803,0.077143997,0 +J221421.41-192756.0,3328019701351050428,284056252298717,0.682526052454,0.447579766168,-0.577780702896,12732276840717,333.5892417,-19.4655629,36.407974,-53.280426,0.023,15.745,15.65,18.632,17.712999,0.076211996,0.076211996,0 +J221315.05-191927.2,3328019701351050788,284056252309933,0.848677542312,0.266164134547,-0.457059167566,12732409388841,333.3127282,-19.3242419,36.483612,-52.98727,0.024,16.577,16.462999,19.157,18.263,0.063661598,0.063662,0 +J221224.13-190158.6,3328019701351055404,284056252333048,0.979092608117,0.0900874521293,-0.182378495713,12732395272937,333.1005741,-19.0329609,36.82417,-52.69972,0.025,13.858,13.758,16.829,16.103001,0.060065601,0.060066,0 +J221258.35-190002.0,3328019701351055620,284056252335539,0.96160257289,0.229545306976,-0.150430860719,12732423850291,333.2431473,-19.00056,36.950974,-52.81517,0.025,16.046,15.499,17.923,17.750999,0.0586375,0.058637999,0 +J221247.19-190316.8,3328019701351055697,284056252331322,0.961811847525,0.182822652431,-0.203700387132,12732393014070,333.1966619,-19.054692,36.84191,-52.792492,0.024,16.297001,16.237,18.568001,17.775999,0.038392,0.038392,0 +J221147.89-190323.8,3328019701351058132,284056252331155,0.976920545886,-0.0579449599524,-0.205593357484,12732198313833,332.9495768,-19.0566259,36.706917,-52.573746,0.026,15.203,14.839,17.298,17.208,0.065505899,0.065505996,0 +J221150.31-190232.1,3328019701351058170,284056252332272,0.980300321045,-0.0482655835744,-0.191524708594,12732198489922,332.9596255,-19.0422713,36.73446,-52.57775,0.026,15.396,15.318,18.079,17.802,0.0719551,0.071955003,0 +J221119.83-190831.6,3328019701351058320,284056252324387,0.942684364893,-0.167805450763,-0.288422465978,12732202450872,332.8326584,-19.1421348,36.51276,-52.499104,0.030,15.938,15.815,18.462999,17.968,0.069401696,0.069402002,0 +J221125.30-191134.1,3328019701351058331,284056252320443,0.930599029646,-0.143886445766,-0.336574117762,12732203397772,332.855419,-19.1928323,36.446426,-52.536556,0.030,16.098,15.996,18.052999,17.809,0.042613,0.042613,0 +J221139.54-190422.6,3328019701351058353,284056252329902,0.970841604427,-0.0915863289021,-0.221536731652,12732211115634,332.9147626,-19.072946,36.663166,-52.548412,0.026,16.153999,15.852,18.715,17.945,0.075778902,0.075778998,0 +J221147.46-191029.1,3328019701351058598,284056252321944,0.945821444317,-0.0578317881655,-0.319495351684,12732209034088,332.9477528,-19.1747528,36.523163,-52.612434,0.028,16.681999,16.337,19.016001,18.181,0.0573989,0.057399001,0 +J221448.42-185410.4,3330018201351000047,284056252343401,0.76827000819,0.637900272503,-0.0533332621937,12731525039076,333.7017711,-18.9029145,37.352226,-53.188507,0.024,13.933,13.77,16.572001,16.134001,0.0573912,0.057390999,0 +J221444.43-185432.7,3330018201351000072,284056252342899,0.778498156076,0.624819318861,-0.0595099971874,12731524980668,333.685143,-18.9091011,37.333443,-53.175896,0.024,14.434,14.237,17.212999,16.794001,0.071198702,0.071199,0 +J221458.56-185326.8,3330018201351000183,284056252344386,0.74105614878,0.67017587199,-0.0412320865059,12731528195082,333.7440358,-18.8907997,37.394363,-53.2218,0.024,15.504,15.446,18.330999,17.393999,0.070268102,0.070267998,0 +J221358.43-185333.6,3330018201351002755,284056252344223,0.884007927837,0.465479232392,-0.0431169077305,12732365455023,333.4934869,-18.8926862,37.254414,-53.000126,0.025,15.864,15.859,18.646999,17.690001,0.0632338,0.063234001,0 +J221543.75-204526.9,3341021301351045316,284056252199510,-0.199466855888,-0.263860984007,-0.943710948607,12731899923127,333.9323166,-20.757473,34.519207,-54.00095,0.030,16.103001,16.075001,18.500999,18.195999,0.068483204,0.068483002,0 +J221539.40-204536.7,3341021301351045360,284056252199299,-0.205812134503,-0.262232154027,-0.942802027303,12731899839758,333.9141848,-20.7602102,34.505898,-53.98566,0.030,16.136,16.113001,18.542,17.836,0.047815099,0.047814999,0 +J221710.89-202655.0,3341021301351061584,284060547157265,-0.00792462448912,-0.0271289484307,-0.999600530454,12853177734721,334.2954154,-20.4486187,35.200497,-54.227726,0.029,14.701,14.614,17.646,17.176001,0.060841899,0.060842,0 +J221655.83-202749.8,3341021301351061590,284060547156371,-0.0147884363977,-0.0408996393561,-0.99905381319,12852994240099,334.2326652,-20.4638571,35.14413,-54.176716,0.030,13.907,13.824,17.791,16.858,0.078358702,0.078359,0 +J221639.82-203006.9,3341021301351061727,284060547154074,-0.0327612556672,-0.0746248076442,-0.996673385925,12852993138820,334.165938,-20.5019422,35.04886,-54.129337,0.031,15.585,15.465,18.212,17.438,0.065468103,0.065467998,0 +J221518.09-204346.2,3341021301351064324,284056252201640,-0.208335501975,-0.221748915501,-0.952587915676,12731905093761,333.8253972,-20.7295174,34.51203,-53.897213,0.030,14.955,14.945,18.080999,17.247999,0.069260202,0.069260001,0 +J221456.75-203524.6,3341021301351064816,284056252211565,-0.126211485132,-0.112418915636,-0.985612828867,12731951177534,333.7364848,-20.5901863,34.69282,-53.77493,0.034,16.447001,16.372999,19.128,18.309999,0.069802299,0.069802001,0 +J221555.58-203030.0,3341021301351065464,284056252217504,-0.049471103167,-0.0726211473411,-0.996131908389,12731923318643,333.981606,-20.5083362,34.94631,-53.967396,0.031,16.729,16.533001,18.819,18.202999,0.035236299,0.035236001,0 +J221413.61-202754.1,3341021301351066991,284056252220784,-0.0381417275273,-0.0232745562027,-0.99900125308,12731942992017,333.5567161,-20.4650493,34.804607,-53.57589,0.029,14.598,14.587,17.551001,16.544001,0.066910997,0.066910997,0 +J221424.77-203733.0,3341021301351067423,284056252209024,-0.169057703922,-0.114277535148,-0.978958700715,12731934845749,333.6032354,-20.6258541,34.56998,-53.667484,0.036,16.386,16.284,18.757999,18.584999,0.077046297,0.077045999,0 +J221552.85-195808.4,3344019701241010174,284056252258451,0.249648569503,0.357649076887,-0.899868173427,12732004904420,333.9702173,-19.9690231,35.807495,-53.785126,0.029,16.382999,16.139999,18.993,18.027,0.0704735,0.070473999,1 +J221802.14-201515.6,3344019701351005349,284060547169143,0.0116715744384,0.164842798916,-0.986250792646,12853143418275,334.5089313,-20.2543352,35.62498,-54.35594,0.028,14.411,14.266,17.052,16.556,0.057429101,0.057429001,0 +J221720.58-201222.6,3344019701351005532,284060547172285,0.0512918817262,0.206142410067,-0.977176775021,12853122374720,334.335752,-20.2062935,35.613914,-54.18662,0.029,15.843,15.746,18.275999,17.514,0.051794101,0.051794,0 +J221659.23-201522.1,3344019701351005592,284060547169025,0.0534030492673,0.154492948985,-0.986549564413,12853112250113,334.2468085,-20.256153,35.48768,-54.12348,0.028,15.928,15.669,18.219999,17.875999,0.064908601,0.064909004,0 +J221732.22-201058.5,3344019701351006550,284060547173656,0.0456471006448,0.230716657518,-0.971949672641,12853114562325,334.384291,-20.1829393,35.676704,-54.222282,0.029,16.955999,16.905001,18.843,18.152,0.022397799,0.022398001,0 +J221648.32-201402.7,3344019701351007936,284060547170451,0.0683948104824,0.172096728805,-0.982702836993,12853111953818,334.2013367,-20.2340876,35.500103,-54.076008,0.028,14.027,14.017,17.513,16.622999,0.0583717,0.058371998,0 +J221548.77-202123.1,3344019701351007943,284056252228821,0.0374459260378,0.0517495286947,-0.99795780918,12731911530144,333.9532281,-20.3564322,35.176952,-53.894306,0.030,14.173,14.115,17.976,17.219,0.062739797,0.062739998,0 +J221625.51-201353.9,3344019701351008013,284060547170601,0.0855053702943,0.166952401799,-0.982250338348,12731998199269,334.106294,-20.2316598,35.455505,-53.990726,0.030,15.059,14.669,17.309999,17.145,0.076247402,0.076246999,0 +J221640.90-202632.0,3344019701351008160,284060547157654,-0.00870432955675,-0.0200724603816,-0.999760636843,12852995616773,334.1704471,-20.4422325,35.147842,-54.11458,0.031,16.033001,15.799,18.454,17.997,0.069151297,0.069150999,0 +J221602.01-201021.0,3344019701351008268,284056252242159,0.132630624997,0.206371634846,-0.969443069831,12731996645936,334.0084065,-20.1725062,35.500755,-53.88476,0.029,16.212,16.164,18.971001,18.066,0.066377901,0.066377997,0 +J221602.10-202444.3,3344019701351009036,284056252224753,0.00433452145951,0.0067498588213,-0.999967825147,12731911809679,334.0087702,-20.4123304,35.11483,-53.96137,0.032,16.892,16.834999,19.319,18.604,0.066220999,0.066220999,0 +J221459.58-201030.4,3344019701351011282,284056252241955,0.179380804345,0.163613006012,-0.970079023223,12732146477589,333.7482807,-20.175112,35.36418,-53.65431,0.030,16.796,16.236,18.794001,18.646,0.055571102,0.055571001,0 +J221501.93-200730.5,3344019701351013404,284056252245924,0.213018372777,0.19814443016,-0.956745503076,12732151810297,333.7580599,-20.1251575,35.44898,-53.646976,0.031,14.102,13.93,16.771,16.405001,0.058634501,0.058634002,0 +J221430.33-200013.6,3344019701351013615,284056252255795,0.329871438695,0.234302174588,-0.914487465697,12732158162676,333.6264155,-20.0037953,35.57524,-53.49078,0.028,15.871,15.417,17.51,17.547001,0.048970699,0.048971001,0 +J221427.49-200156.0,3344019701351013869,284056252253269,0.311100664307,0.215474204558,-0.925628026714,12732158326901,333.6145763,-20.0322477,35.52394,-53.48946,0.028,16.622999,16.27,18.698,18.360001,0.047018901,0.047019001,0 +J221645.10-195445.7,3344019701351016506,284060547190085,0.185569656632,0.449303360978,-0.873893810684,12731969259818,334.1879408,-19.9127115,36.010864,-53.960293,0.030,15.796,15.626,18.452999,17.646999,0.071464904,0.071465001,0 +J221558.63-200327.8,3344019701351016725,284056252251257,0.19597246473,0.295677580869,-0.934970353134,12732007187837,333.9943029,-20.0577314,35.677784,-53.83528,0.028,16.629999,16.375,19.014,18.191999,0.056721799,0.056722,0 +J221729.39-195455.0,3344019701351018738,284060547189899,0.0995053358337,0.473525299729,-0.87514140495,12853105008755,334.3724939,-19.9152839,36.10322,-54.12514,0.027,14.056,13.985,16.325001,15.944,0.028028199,0.028028,0 +J221659.66-195139.4,3344019701351019125,284060547193147,0.172459688125,0.501859531652,-0.847581657696,12853098951520,334.2486187,-19.8609673,36.125744,-53.99729,0.030,16.153999,16.073,18.888,18.238001,0.078367896,0.078368001,0 +J221709.46-193829.7,3344019701351032068,284056252284404,0.200964543246,0.673039249765,-0.711780458171,12853093279830,334.2894525,-19.6415857,36.499462,-53.96117,0.027,14.259,14.053,17.306,16.872,0.077686198,0.077685997,0 +J221815.16-193544.9,3344019701351032516,284060547208463,0.0120746369429,0.734128025956,-0.678903707936,12853639181050,334.5631715,-19.5958244,36.71938,-54.189034,0.027,16.490999,16.385,18.739,18.212,0.0494349,0.049435001,0 +J221618.08-194206.2,3344019701351034642,284056252279690,0.318096560031,0.576398134648,-0.75271493201,12731976797073,334.0753402,-19.7017322,36.289806,-53.790905,0.028,14.285,14.071,17.346001,16.93,0.079287603,0.079287998,0 +J221601.96-193251.4,3344019701351034758,284056252292158,0.414330125958,0.644362331486,-0.642750132233,12732239493682,334.0081734,-19.5476387,36.499954,-53.680023,0.027,15.689,15.499,17.837999,17.608,0.055362601,0.055362999,0 +J221648.05-193438.1,3344019701351035025,284060547209463,0.276555170447,0.693595443354,-0.665163587892,12732229855719,334.2002092,-19.5772688,36.55489,-53.860485,0.024,16.378,15.932,18.68,18.250999,0.069989301,0.069989003,0 +J221524.61-193504.8,3344019701351037393,284056252288973,0.493022303827,0.554169826494,-0.670689802615,12732267228707,333.8525451,-19.5846943,36.35863,-53.554127,0.027,14.662,14.577,15.016,14.974,0.00867,0.00867,0 +J221445.43-194502.2,3344019701351037744,284056252275821,0.482516054509,0.390570612055,-0.783985238471,12731993266601,333.6892954,-19.7506157,36.00978,-53.464005,0.026,16.437,15.87,18.305,18.422001,0.056032199,0.056031998,0 +J221528.16-193851.9,3344019701351037937,284056252283874,0.456184365296,0.528293771311,-0.716101610145,12731986547850,333.8673352,-19.647757,36.266262,-53.588223,0.026,16.239,16.221001,18.653999,18.454,0.076013699,0.076013997,0 +J221503.26-192544.4,3344019701351040204,284056252301593,0.609527896225,0.573284365621,-0.547558928206,12732286048652,333.7635872,-19.4290001,36.558094,-53.423027,0.024,14.248,14.085,17.424999,16.771,0.079489902,0.079489999,0 +J221449.49-192207.4,3344019701351040419,284056252306479,0.665508636599,0.557591862001,-0.49617493895,12732280767657,333.7062152,-19.3687435,36.622684,-53.351784,0.024,16.025,15.619,18.18,18.120001,0.070665397,0.070665002,0 +J221437.97-192453.2,3344019701351040614,284056252302673,0.672554629263,0.510662811342,-0.535624648209,12732273886551,333.6582404,-19.4148032,36.52453,-53.324684,0.022,16.506001,15.917,18.728001,17.879,0.068375602,0.068375997,0 +J221638.72-192651.0,3344019701351042887,284060547217001,0.335709416694,0.755227594033,-0.562965779381,12731493568707,334.161335,-19.4475258,36.74119,-53.7825,0.025,14.116,14.018,17.089001,16.502001,0.054687999,0.054687999,0 +J221559.40-192629.3,3344019701351042894,284056252300645,0.456236264444,0.693191957593,-0.557972562887,12732242469674,333.9975223,-19.4414966,36.663036,-53.635,0.026,14.14,14.133,17.302,16.219999,0.069531403,0.069531001,0 +J221621.59-191829.9,3344019701351042975,284060547225497,0.421681012017,0.791252096428,-0.442837717457,12731502694449,334.0899753,-19.3083171,36.92424,-53.672127,0.025,15.032,15.001,17.540001,17.013,0.045552298,0.045552,0 +J221612.70-192222.1,3344019701351043065,284056252306112,0.435407904752,0.748813125639,-0.499698768609,12732236559318,334.052939,-19.3728069,36.801853,-53.661064,0.026,15.639,15.481,18.396,17.469999,0.076146401,0.076145999,0 +J221611.23-191443.8,3344019701351043150,284056252316080,0.468650952733,0.794736353817,-0.385694714021,12731502922566,334.0468108,-19.2455166,37.000523,-53.61247,0.025,15.952,15.789,18.533001,17.757999,0.065723501,0.065724,0 +J221649.58-192707.1,3344019701351043472,284060547216715,0.300270801356,0.767298826976,-0.566648000062,12731495093934,334.2066034,-19.4519879,36.758385,-53.8242,0.026,16.537001,16.077999,18.767,18.193001,0.057471801,0.057472002,0 +J221548.31-192225.0,3344019701351043474,284056252306066,0.508882634815,0.700449003378,-0.500409489968,12732235505319,333.9513209,-19.3736276,36.74599,-53.571106,0.025,16.582001,16.242001,18.753,18.079,0.043149199,0.043148998,0 +J221731.80-192117.3,3344019701351045590,284060547222783,0.17136608339,0.858100052374,-0.484043350929,12852768449934,334.3825066,-19.3548255,37.00856,-53.94759,0.023,14.059,13.873,17.325001,16.847,0.074181497,0.074181996,0 +J221754.26-191814.6,3344019701351045848,284060547225719,0.0928508166429,0.893661872519,-0.43902982069,12852812657684,334.4760898,-19.3040745,37.140694,-54.013443,0.024,16.045,16.031,18.469999,18.106001,0.066279501,0.06628,0 +J221658.52-190749.9,3344019701351058868,284060547236302,0.316566583671,0.907130748166,-0.277307417561,12731553632772,334.2438556,-19.1305464,37.290325,-53.74807,0.025,15.759,15.737,18.396999,17.632999,0.059107799,0.059108,0 +J221552.86-190559.2,3344019701351062201,284056252327731,0.554515677654,0.794473177729,-0.247638311061,12731530579476,333.9702577,-19.0997978,37.189434,-53.494705,0.023,16.434999,16.224001,18.757999,18.52,0.072344899,0.072345003,0 diff --git a/papers/171020/Data/z_mag_priors.csv b/papers/171020/Data/z_mag_priors.csv new file mode 100644 index 0000000..aee8f0a --- /dev/null +++ b/papers/171020/Data/z_mag_priors.csv @@ -0,0 +1,114 @@ +J221258.68-204401.8,3325021301351052049,284056252201331,-0.29986786761,-0.0720231680632,-0.951258074992,12734292953977,333.2445393,-20.7338586,34.22261,-53.381756,0.041,16.216999,16.190001,18.431,18.110001,0.055808,0.055808,0, 0.003028431996363633,0.0025960934124943484,0.002444034631493081,0.002829650261845749,0.0030457425614378525,0.0011965240519883115,0.004394144717380802,0.0020757842330807064,0.002766523052650797,0.006378071597664387,0.003798352898542122,0.0004647659042081986,0.00949215130184012,0.002733004585157247 +J221231.15-203946.6,3325021301351054428,284056252206352,-0.238484931538,-0.0289922910515,-0.970713337958,12734295991657,333.1297963,-20.6629661,34.280014,-53.25778,0.041,14.091,14.059,17.531,16.466,0.077429898,0.077430002,0, 4.5338634355400396e-08,3.216682201482422e-08,2.8824081410563447e-08,3.553217883259604e-08,3.966041625090356e-08,8.059201856809263e-09,9.637762730867511e-08,1.6736846657828382e-08,3.4878538371243055e-08,3.079705602179236e-07,1.0057382352486143e-07,4.20331438853676e-10,1.3807826822111328e-06,0.01917792386628246 +J221306.08-203932.5,3325021301351054460,284056252206646,-0.228071487565,-0.0622702903056,-0.971651072919,12734284785041,333.2753548,-20.6590315,34.356857,-53.386066,0.039,14.731,14.698,17.916,17.416,0.061081901,0.061082002,0, 0.0006788260517588788,0.0005606385492331068,0.0005214302320786469,0.0006142867533947117,0.0006677849595478494,0.00022314326179446206,0.001077118807809328,0.00040733060396803934,0.0006007023414729073,0.0018399418564173462,0.0009467657712872504,5.88533606498381e-05,0.0033625034975094527,0.006096175022452466 +J221315.32-202831.7,3325021301351054505,284056252219955,-0.0525689090031,-0.0165508911462,-0.998480133908,12734278487417,333.3138382,-20.475493,34.66801,-53.363113,0.032,14.972,14.876,17.360001,17.120001,0.0485131,0.048512999,0, 0.01241232478833886,0.011115756623628771,0.010643054406402676,0.011993111778118718,0.0127137501240334,0.006234771176444316,0.015992226750741637,0.009978341700026167,0.011735812109876372,0.01912307926695178,0.013877593342781476,0.0037190102527374634,0.0225324410607784,0.008660657610260776 +J221210.65-202716.4,3325021301351057099,284056252221573,-0.0341860398629,-0.0012164803948,-0.999414746166,12734297543141,333.0443904,-20.4545666,34.5689,-53.116848,0.040,15.445,15.401,18.410999,17.42,0.075704902,0.075704999,0, 2.1379085460756219e-07,1.5462235836298715e-07,1.3909498851571085e-07,1.707343107281557e-07,1.9008869950264756e-07,4.0638818866787574e-08,4.3981310051622094e-07,8.339654281669123e-08,1.674956071517711e-07,1.295147145384035e-06,4.452933759908409e-07,2.573682023303371e-09,5.143727606944741e-06,0.006067540537462137 +J221100.78-202732.2,3325021301351057191,284056252221230,-0.0373323841145,0.00975354510493,-0.999255303441,12734245659022,332.7532712,-20.4589473,34.419876,-52.859226,0.031,15.871,15.798,18.646,17.635,0.067479499,0.067479998,0, 4.881430334351576e-05,3.824332380264192e-05,3.505125724831111e-05,4.209021011454171e-05,4.626339900964916e-05,1.2688611979112266e-05,8.66418840551758e-05,2.4474040868781703e-05,4.120002133702093e-05,0.00018445484231355646,7.954475083541418e-05,1.908361431294575e-06,0.0004524545448539678,0.004717734248988206 +J221338.59-193704.2,3328019701241016594,284056252286293,0.661770099138,0.281361659122,-0.694907153988,12732061719390,333.4108319,-19.6178467,36.074528,-53.17267,0.025,16.969999,16.544001,19.011999,18.266001,0.036443599,0.036444001,1, 0.048074658876669306,0.045774322043052956,0.04508364235207943,0.04805173259387675,0.0494000273884564,0.035024693733695406,0.0513154967692527,0.04728237800926423,0.04728615733740808,0.048356310182325626,0.04760325586665332,0.0334264259687987,0.043300967478987866,0.002291242512842909 +J221419.92-201529.1,3328019701351000069,284056252235895,0.135640159513,0.0877480720825,-0.986864744012,12732117345172,333.5830269,-20.2580925,35.148247,-53.533855,0.026,14.513,14.269,16.816,16.419001,0.063239299,0.063239001,0, 0.00031402759858336504,0.0002550177101013884,0.0002360093512568626,0.0002799008811797669,0.0003054472842616298,9.548731455910128e-05,0.0005173060082631216,0.0001777118956766532,0.00027377935853766346,0.0009493863101421685,0.00046010684563351477,2.1072435609321296e-05,0.001904946697192326,0.020326202243229865 +J221415.90-202202.4,3328019701351000684,284056252228060,0.0449510007714,0.0280222037883,-0.99859609634,12731936694640,333.5662652,-20.3673573,34.96547,-53.553608,0.024,16.924,16.684,19.097,18.43,0.046004299,0.046004001,0, 0.017798579079111844,0.016155208868020937,0.015558877448438863,0.017352876283866128,0.018290330212879267,0.009677513344112794,0.0220395001801223,0.015014466268402641,0.0169920934097273,0.024872086411806896,0.01927933043069284,0.006514265094773302,0.02738947009692771,0.0019066194529900513 +J221217.50-202631.2,3328019701351002874,284056252222528,-0.0216058732089,-0.00138720130618,-0.999765603487,12734284108085,333.0729381,-20.4420043,34.602776,-53.1383,0.036,14.439,14.414,17.712999,16.636,0.079436697,0.079437003,0, 5.918894044378234e-09,4.094195146398516e-09,3.65221660707901e-09,4.5241326884655774e-09,5.064356063254165e-09,9.687618735589549e-10,1.3116035942831663e-08,2.0391570460901572e-09,4.444118959519939e-09,4.6694951250300224e-08,1.4293293555096537e-08,4.0400857373839284e-11,2.424658221666599e-07,0.015557902515868468 +J221155.07-202217.6,3328019701351002888,284056252227769,0.0487402932397,-0.00143130830627,-0.998810460083,12734150215526,332.9794637,-20.3715716,34.668163,-53.032974,0.033,14.709,14.589,16.83,16.386,0.035179801,0.035179999,0, 0.05302028893124427,0.05080226045839279,0.050184996930543097,0.053121241000144655,0.05440741353179438,0.04013663572067086,0.05549350807028574,0.05303389365594258,0.05232744279277668,0.05129721951158849,0.05204499453625564,0.03962945868892654,0.0449285296700539,0.021175069082168332 +J221305.21-202311.1,3328019701351002979,284056252226700,0.0327598984673,0.0088172950412,-0.999424356498,12734279733459,333.2717398,-20.38642,34.788822,-53.297653,0.026,15.245,15.225,17.952,17.004,0.0620826,0.062082998,0, 0.00048107935288290056,0.00039427692274446937,0.00036586110997724453,0.0004323620420425315,0.00047085910594739524,0.00015256100818903705,0.0007766699869408732,0.0002810244624236008,0.0004228444493808813,0.0013710763539977508,0.0006861764900967017,3.7102838325928355e-05,0.002614697811390259,0.009952925330814896 +J221125.18-202120.2,3328019701351005901,284056252228899,0.0639135840474,-0.00991300526422,-0.997906201053,12734131846375,332.8549472,-20.3556293,34.632103,-52.917175,0.033,15.749,15.692,18.677,17.826,0.076002397,0.076002002,0, 1.6512510646617756e-07,1.1903439844601389e-07,1.0700683128290565e-07,1.314478762785348e-07,1.464149812897147e-07,3.102104477614585e-08,3.416373330442567e-07,6.379587862509296e-08,1.2896735121737017e-07,1.0200662509485314e-06,3.4761650701092235e-07,1.8980028067684814e-09,4.136855099924095e-06,0.0037816461254569496 +J221101.75-201302.8,3328019701351005963,284056252238849,0.195177778128,-0.0501475413727,-0.979484996832,12734137982844,332.7573285,-20.2174457,34.801037,-52.78652,0.026,15.986,15.978,18.641001,18.014,0.066913001,0.066913001,0, 6.459073826321069e-05,5.085973526551405e-05,4.667553014265022e-05,5.5957684550038535e-05,6.14475889175028e-05,1.715294487020213e-05,0.00011348944116300329,3.293113468633538e-05,5.476771370568976e-05,0.00023669335904416875,0.00010368057393582349,2.7239567863963418e-06,0.000564270007625793,0.003048445912305253 +J221116.87-201906.6,3328019701351006334,284056252231543,0.0998660052001,-0.0190525005107,-0.994818467475,12734132044316,332.820306,-20.3185091,34.673462,-52.874626,0.031,16.757,16.559,18.243999,18.554001,0.039997499,0.039997999,0, 0.03520223368493703,0.03293568827312491,0.03216878079209645,0.03491491860840343,0.036252509653252496,0.023026755940306846,0.039699980090658796,0.032879293885155425,0.034278284266312874,0.03971396760080825,0.035852894356314384,0.019681642707240484,0.038075566323899544,0.0016610724209765026 +J221024.01-195344.4,3328019701351016716,284056252264092,0.459658479117,-0.199097255362,-0.865490823454,12734388760267,332.6000748,-19.8956853,35.225,-52.54313,0.024,16.429001,16.167,18.121,18.209999,0.043521099,0.043520998,0, 0.02425513169982362,0.022298713178615617,0.021600517651414776,0.023832809947253973,0.024970694746900574,0.014245429930349331,0.028889930976159196,0.02138960304863254,0.02335823353600646,0.03092328844764174,0.025553239581629444,0.010673482942683521,0.032034954058309265,0.002440534255495187 +J221111.58-195130.5,3328019701351018833,284056252267044,0.521010482975,-0.111347956723,-0.846256290472,12734124091582,332.7982733,-19.8584747,35.382893,-52.70726,0.028,13.954,13.947,17.785999,16.792,0.068537198,0.068536997,0, 2.8226825493270562e-05,2.1904699580813522e-05,2.002828529812229e-05,2.4121883288508943e-05,2.655936180433252e-05,7.051462316515786e-06,5.104795883100433e-05,1.3716701929126562e-05,2.3617153121048214e-05,0.00011294143475073388,4.7321666743169386e-05,9.572986859130822e-07,0.0002924409064932658,0.012860785527656581 +J221152.02-195901.7,3328019701351018849,284056252257293,0.422430122603,-0.0177656997194,-0.906221369992,12734117432392,332.9667901,-19.9838155,35.271904,-52.89781,0.027,14.22,14.041,17.355,16.820999,0.076587602,0.076587997,0, 9.885280867419885e-08,7.0811207504099e-08,6.357358386897821e-08,7.820609723415821e-08,8.718465621018309e-08,1.816202310466649e-08,2.0674820761184475e-07,3.750010489112872e-08,7.674503182004025e-08,6.340903693214405e-07,2.1243946086144007e-07,1.0416515566807148e-09,2.6770710120895873e-06,0.01241565133649731 +J221133.33-195451.8,3328019701351018906,284056252262736,0.481154736991,-0.0579847015866,-0.874715892992,12734119855199,332.8888881,-19.9144052,35.341324,-52.806034,0.028,14.911,14.81,18.030001,17.461,0.076160498,0.076159999,0, 1.4459806673857266e-07,1.0407146480254451e-07,9.352501722404446e-08,1.149284833627884e-07,1.280421223144339e-07,2.7012514805496777e-08,2.999858671842766e-07,5.560837079455289e-08,1.1276514307678848e-07,9.017667722565742e-07,3.059836226600408e-07,1.6260948106628414e-09,3.6946274392480496e-06,0.005782007249224728 +J221136.18-200235.1,3328019701351019210,284056252252434,0.366227145161,-0.0397317032088,-0.929676863166,12734164768709,332.9007547,-20.0430906,35.145752,-52.858227,0.025,16.309999,16.305,18.719,18.544001,0.077462703,0.077463001,0, 4.384970568436605e-08,3.109687129978014e-08,2.7862955862606338e-08,3.435054114351795e-08,3.8343584797282363e-08,7.782971806902478e-09,9.328082879340487e-08,1.6167341169846413e-08,3.3719077562180585e-08,2.986297497096229e-07,9.741165040715162e-08,4.0419593406214815e-10,1.3424747610057483e-06,0.0016795862075143172 +J221211.14-195600.3,3328019701351021254,284056252261340,0.467572527111,0.0175997483777,-0.883779486494,12732032539525,333.0464442,-19.933435,35.391212,-52.952312,0.028,14.116,14.009,16.181,15.814,0.0301445,0.030144,0, 0.07416304753361104,0.07294970938810502,0.0729264005507496,0.07489305029328636,0.07546424537913263,0.06551847173460293,0.07168260805959312,0.07874046460190652,0.07415023217152059,0.06200880788439289,0.07074095713969335,0.07217672241756726,0.05021204161827327,0.0435009632708739 +J221230.16-195918.8,3328019701351021347,284056252256913,0.415638106656,0.0488034426776,-0.908219790732,12732086916118,333.1257042,-19.9885663,35.344677,-53.040657,0.026,15.175,14.988,17.891001,17.472,0.078153998,0.078153998,0, 2.243587424374888e-08,1.5780422536697365e-08,1.4117797049301559e-08,1.7434100371077807e-08,1.9480006189093642e-08,3.874532389045043e-09,4.838433614595542e-08,8.085884395528432e-09,1.7117743752845572e-08,1.604514030837513e-07,5.122568443471115e-08,1.8634788566865685e-10,7.58137613856967e-07,0.005707813612970087 +J221157.18-200840.7,3328019701351021576,284056252244322,0.272167958231,-0.0055886058598,-0.962233531944,12734165088401,332.9882905,-20.1446473,35.03006,-52.9687,0.026,16.334999,16.254999,17.726999,17.83,0.033358399,0.033358,0, 0.060431303832381246,0.058440339991599345,0.05797907690264194,0.0607362279772004,0.061857233979840616,0.048354509784159394,0.061473066183755296,0.0618513712560599,0.05992612065073328,0.05537210081195525,0.05864451742523866,0.049908243102564726,0.04705909544578621,0.003764260159489824 +J221357.33-195652.7,3328019701351023874,284056252260131,0.403561281861,0.21013624611,-0.890494834266,12732099586328,333.4888951,-19.9479875,35.593487,-53.35042,0.025,15.461,15.43,18.504999,17.601999,0.076738,0.076738,0, 8.594555671079738e-08,6.145553263118893e-08,5.51542140886457e-08,6.78757882781514e-08,7.568611038838721e-08,1.5692591023364655e-08,1.8029949719310111e-07,3.243718966605467e-08,6.66113198491967e-08,5.571767736548807e-07,1.8578411039652277e-07,8.838564391065392e-10,2.3788464193291743e-06,0.004902610038091704 +J221404.53-195840.5,3328019701351023906,284056252257775,0.373637826119,0.209040803011,-0.903712740626,12732099477151,333.5189094,-19.9779194,35.561382,-53.386818,0.025,15.654,15.598,18.1,17.875999,0.066261597,0.066261999,0, 8.780327516712254e-05,6.952737327116281e-05,6.390157082418283e-05,7.646732985929374e-05,8.387908376503302e-05,2.388536492475577e-05,0.00015252350686535235,4.5613451883200934e-05,7.483178901906943e-05,0.00031085690799475266,0.0001386001781219315,4.028685173425154e-06,0.0007179420294101175,0.0035702290286809637 +J221359.13-195106.0,3328019701351024098,284056252267598,0.475768131513,0.252288416274,-0.842612152803,12732102212387,333.4963953,-19.8516715,35.74984,-53.325703,0.026,16.191999,15.939,18.576,17.663,0.055101,0.055101,0, 0.0035743057725713904,0.0030781959274073754,0.0029026326498128715,0.003352338088957077,0.003603313010232977,0.0014465162001000616,0.0051254595189570975,0.00249130309540852,0.0032776221378036114,0.007289000315567708,0.004426265947328945,0.0005888745196833949,0.010578189041658375,0.0045665902154589854 +J221329.43-193306.7,3328019701351026849,284056252291819,0.713384398097,0.271648397342,-0.645979758795,12732182999849,333.3726569,-19.551862,36.158363,-53.11682,0.025,16.518999,16.437,19.118,18.163,0.060293399,0.060293,0, 0.000877086498973691,0.0007286730962932251,0.0006789410113896725,0.0007978545219640924,0.0008661010753330123,0.0002965118523205248,0.0013729884958696992,0.000537347801907449,0.0007801576828079098,0.0022863937858149143,0.001202510721211474,8.321085846281181e-05,0.004043965481192471,0.002573683914829814 +J221358.74-193215.9,3328019701351026913,284056252292992,0.682969663741,0.360748112715,-0.635148201275,12732397467871,333.4947776,-19.5377563,36.24451,-53.22063,0.022,16.709999,16.584,19.003,18.642,0.067499101,0.067498997,0, 4.83277209993874e-05,3.785495017743261e-05,3.469360978729893e-05,4.1663265473934066e-05,4.579573430077003e-05,1.2552066910508261e-05,8.581052431100272e-05,2.421488545146507e-05,4.078228723935041e-05,0.00018282422870127116,7.8796093468541e-05,1.8839184056925577e-06,0.0004489260558717601,0.0015070954124709074 +J221413.69-194032.1,3328019701351027035,284056252281688,0.578454078321,0.353269845707,-0.735249138311,12732095628081,333.5570813,-19.6755901,36.05969,-53.321754,0.026,16.587,16.52,18.957001,18.343,0.056288902,0.056288999,0, 0.002694112776492295,0.002302157829787464,0.0021649056013536006,0.0025106387910070185,0.0027049121400348035,0.00104708893939253,0.0039406412659462615,0.0018254328272195486,0.0024546089417711945,0.005801091988789989,0.0034091432911016223,0.00039366916459353664,0.008785404393711588,0.002101421535090325 +J221249.30-194822.9,3328019701351029025,284056252271258,0.565019713004,0.112551873145,-0.817364545212,12732093321131,333.2054474,-19.8063775,35.671833,-53.05225,0.030,14.612,14.57,18.224001,17.334,0.077735797,0.077735998,0, 3.365840361487243e-08,2.3791319950669344e-08,2.1303763602472176e-08,2.6281997787679382e-08,2.9348955303052843e-08,5.908444642713458e-09,7.199486379809564e-08,1.2296672077503227e-08,2.580136803313022e-08,2.3374758883915837e-07,7.559251445649654e-08,2.973920120482693e-10,1.072101591417079e-06,0.006715347029517309 +J221254.76-193316.2,3328019701351029880,284056252291609,0.743393239136,0.165728247636,-0.647997407358,12732188704757,333.2281692,-19.5545081,36.078823,-52.989307,0.026,16.941999,16.799999,19.354,18.701,0.072269797,0.072269998,0, 2.9363923243739088e-06,2.1995823377800367e-06,1.9941285374428243e-06,2.426180645400685e-06,2.6872096884110993e-06,6.3651709244974e-07,5.676280067258131e-06,1.2742452982428172e-06,2.377675586013352e-06,1.4474615877501808e-05,5.478362105128832e-06,5.884556114802555e-08,4.6147235947973606e-05,0.0014123042502315237 +J221125.02-193205.7,3328019701351031647,284056252293260,0.764950493839,-0.11917689268,-0.632967305811,12732177327978,332.8542669,-19.534936,35.915413,-52.650505,0.026,14.607,14.595,17.077999,16.070999,0.046887498,0.046888001,0, 0.01576988425816163,0.014247303164825321,0.013693210633025286,0.015328458757131866,0.016189688961595443,0.008340340120781506,0.01980073688825662,0.013084731214786519,0.01500582280333882,0.02279361381380545,0.01726581757765537,0.005388681570668583,0.02568746357735503,0.03139890761630731 +J221136.68-193442.9,3328019701351031660,284056252289524,0.741635832275,-0.0788788451878,-0.666148947359,12732177675453,332.9028617,-19.5785892,35.87259,-52.7082,0.026,13.864,13.636,16.816,16.344,0.078338198,0.078337997,0, 1.8650565077808635e-08,1.3087953784850212e-08,1.1704104531106911e-08,1.4459936866053381e-08,1.616113894803022e-08,3.1966539661077527e-09,4.037354602438641e-08,6.679533867465318e-09,1.4198490916060287e-08,1.352075707724186e-07,4.2911520457083814e-08,1.505882726562665e-10,6.475740515513129e-07,0.02230906323500926 +J221034.11-194619.1,3328019701351031682,284056252274049,0.563745596724,-0.216514172351,-0.797064937972,12734390771662,332.6421282,-19.7719752,35.4385,-52.54016,0.027,14.237,14.179,17.722,16.98,0.061746601,0.061747,0, 0.0005409992680011655,0.0004445259698487298,0.0004128026160311266,0.00048733473860351127,0.000530413278754951,0.00017362060638088667,0.0008684034552398554,0.0003188661802243514,0.0004765898713656209,0.0015162796707164374,0.0007658665874738221,4.338320232321795e-05,0.0028505886330215126,0.01024453238775473 +J221148.87-194803.4,3328019701351031691,284056252271670,0.579650725743,-0.0320086645742,-0.814236133771,12732043060206,332.9536567,-19.8009681,35.55163,-52.826633,0.032,14.348,14.225,17.41,16.907,0.062687904,0.062688001,0, 0.0003864255756509348,0.00031520094495184243,0.00029207795667746724,0.00034581182002799837,0.0003770064163785047,0.00011989192230550964,0.0006304541514317578,0.00022204244512776254,0.0003382239789216555,0.0011356541309550648,0.000558892279517395,2.7727889474710175e-05,0.0022235704892798742,0.01118751862089865 +J221139.05-193427.7,3328019701351031765,284056252289908,0.745174039507,-0.0718181863438,-0.662991552702,12732177758179,332.9127403,-19.5743637,35.884266,-52.715576,0.026,15.144,15.139,18.136999,17.124001,0.073818699,0.073818997,0, 9.690592285930394e-07,7.147892217438357e-07,6.457664293441117e-07,7.888523214611621e-07,8.757938592471654e-07,1.9803476191540684e-07,1.925985559600194e-06,4.009649481054499e-07,7.734301533089254e-07,5.227875986561327e-06,1.8962515797506348e-06,1.5480492466272766e-08,1.833065192597549e-05,0.008619350735987788 +J221104.31-193528.3,3328019701351031778,284056252288521,0.716074071139,-0.175866472186,-0.675506409002,12732163145623,332.7679898,-19.5912072,35.78352,-52.592503,0.025,15.306,15.124,17.996,17.209,0.075825199,0.075824998,0, 1.9317681688510346e-07,1.3953720746673424e-07,1.2549128403197162e-07,1.540817532711622e-07,1.7157834054208247e-07,3.655439923226675e-08,3.982771351255137e-07,7.507661535822294e-08,1.5116476622606999e-07,1.1790972404156757e-06,4.040083961536275e-07,2.2845895779314546e-09,4.720818244637466e-06,0.007788510648676943 +J221132.06-194009.9,3328019701351032001,284056252282118,0.676971552233,-0.0852133405965,-0.731059644661,12732030808396,332.8836055,-19.6694296,35.721172,-52.721134,0.028,16.245001,15.946,18.004999,18.089001,0.0477219,0.047722001,0, 0.013985578649447534,0.0125788101204104,0.012066101623755121,0.013553385268685787,0.014342224690348137,0.007204020967699165,0.017794003193587423,0.011419422965470518,0.013265135953748706,0.020882502061793016,0.015474832190819156,0.004470524388812757,0.024070130755121724,0.002798959509727458 +J221147.91-193809.9,3328019701351032014,284056252284846,0.705065297267,-0.0417625730889,-0.707911586343,12732037553680,332.9496582,-19.6360929,35.80709,-52.76884,0.028,16.364,15.771,18.347,18.76,0.05483,0.05483,0, 0.003801323168505944,0.0032793989396500364,0.0030942956157342014,0.0035703026875488087,0.0038355304387356235,0.0015525096835625892,0.005426580526042656,0.0026663568637555353,0.003490768330790091,0.007657808995303962,0.0046849550002127085,0.0006432641500130039,0.011008503120452411,0.0013237514693651283 +J221057.31-194241.6,3328019701351032133,284056252278875,0.627315551474,-0.17362662349,-0.759163351654,12734108783366,332.7388036,-19.7115827,35.581505,-52.606293,0.027,16.523001,16.062,18.795,18.584,0.075669497,0.075669996,0, 2.2056090787198772e-07,1.5958475589523065e-07,1.4357162332451428e-07,1.7621207982194842e-07,1.961761635985577e-07,4.19882243601654e-08,4.5341641716936436e-07,8.614224982259682e-08,1.728672876821223e-07,1.3329019126251011e-06,4.587840305236199e-07,2.6708289389642402e-09,5.2798865587287835e-06,0.0016068062676568068 +J221058.27-193209.0,3328019701351032708,284056252293127,0.746396864837,-0.203362020915,-0.633668374318,12731696031148,332.7428196,-19.5358419,35.856503,-52.551754,0.026,16.940001,16.511999,19.188999,18.528999,0.0588562,0.058855999,0, 0.0013560472932723359,0.001138456942951577,0.0010642698399873358,0.0012448726173000318,0.0013477707822719075,0.0004822837470880905,0.0020712037462482283,0.0008622314355286829,0.0012171503072755476,0.003295705090712355,0.0018039891243754177,0.0001510173495694919,0.005501476340110308,0.001707767554355043 +J221019.06-193223.3,3328019701351034347,284056252292778,0.701094264241,-0.320999401688,-0.636730882527,12733639839392,332.5794545,-19.5398071,35.766357,-52.40786,0.024,14.255,14.103,17.472,17.232,0.062660299,0.062660001,0, 0.0003904941164731476,0.0003185925597630413,0.00029524041164328147,0.00034952499057597334,0.00038103505476972836,0.00012128163995849014,0.0006367713555686421,0.00022455830457548137,0.0003418544414241106,0.001145924406373167,0.0005643983277733439,2.811782044417537e-05,0.0022408502839758454,0.00757837538154609 +J221055.36-192551.2,3328019701351044999,284056252301441,0.803608009495,-0.229461762576,-0.54914612499,12731707207601,332.7306833,-19.4308981,36.012856,-52.505966,0.028,14.12,13.946,17.297001,17.030001,0.064996302,0.064995997,0, 0.0001542768449937801,0.00012348969713710353,0.00011382657847176298,0.00013570729503145653,0.00014854329676835298,4.398397622441884e-05,0.0002620876535558667,8.310957814819455e-05,0.000132774806340594,0.0005109138548237081,0.00023586902572202907,8.325051365245442e-06,0.0011108720850531767,0.009646752224739999 +J221053.92-192642.3,3328019701351045010,284056252300376,0.794654358948,-0.23207805325,-0.5609493979,12731693468066,332.7246748,-19.4450881,35.987774,-52.50537,0.027,14.25,14.15,17.745001,16.91,0.077566899,0.077567004,0, 3.976077978108387e-08,2.8163651196035642e-08,2.522901414321769e-08,3.111102258066672e-08,3.473258927257782e-08,7.028980076219582e-09,8.475100106042297e-08,1.4611143679755291e-08,3.05401888227012e-08,2.727089820294691e-07,8.867795787820328e-08,3.6091749467098745e-10,1.234954271119025e-06,0.011147035434395752 +J221101.52-192749.7,3328019701351045039,284056252298877,0.79129860487,-0.2041210229,-0.576351564534,12731693629041,332.7563678,-19.463813,35.975174,-52.539795,0.028,14.753,14.657,17.888,17.436001,0.0687989,0.068798997,0, 2.450389825398248e-05,1.8970345449191408e-05,1.7334976127254656e-05,2.0893266245809363e-05,2.3014233886006774e-05,6.061418262018363e-06,4.4521088720807776e-05,1.1815257652397765e-05,2.0457312007263403e-05,9.945250112239737e-05,4.137460728951803e-05,8.019866233588081e-07,0.00026105821600182113,0.005954392645452534 +J221236.65-192411.1,3328019701351047582,284056252303551,0.841867161111,0.122005839817,-0.525713094846,12732214643277,333.1527422,-19.4031091,36.276123,-52.871693,0.026,14.273,14.118,17.683001,17.055,0.078838103,0.078837998,0, 1.1178955014356094e-08,7.795021000760139e-09,6.962953136169681e-09,8.612845809529537e-09,9.63305052157794e-09,1.8769243002023233e-09,2.4452913152535734e-08,3.935055535004528e-09,8.458649603253373e-09,8.414748077587041e-08,2.62764862460699e-08,8.363435146465233e-11,4.180897834432521e-07,0.009361630620447148 +J221156.26-191756.7,3328019701351048862,284056252311888,0.900374801536,-0.0219518666418,-0.434561080068,12732219329874,332.9844453,-19.2991068,36.34966,-52.68716,0.027,16.215,16.134001,19.159,18.153999,0.078580096,0.07858,0, 1.4582640705594251e-08,1.020208700263061e-08,9.118374212820214e-09,1.1271999091294291e-08,1.2602538195052084e-08,2.474666262080688e-09,3.1726341647437874e-08,5.179340273112286e-09,1.1069161525737386e-08,1.0764665510943952e-07,3.389787822885041e-08,1.1346425906877818e-10,5.248294594898474e-07,0.0026000406168103 +J221416.08-193057.8,3328019701351050374,284056252294734,0.666674012689,0.416315250581,-0.618245398639,12732266843498,333.5670365,-19.5160643,36.316666,-53.27759,0.022,15.445,15.339,18.174999,17.827999,0.077143803,0.077143997,0, 5.941392377565368e-08,4.229383968982618e-08,3.792340468173254e-08,4.671601416412519e-08,5.212174074772295e-08,1.0681891518870611e-08,1.2559095138046333e-07,2.2139812344758174e-08,4.5852061328884406e-08,3.9562992979554373e-07,1.3034762555446945e-07,5.755115243351631e-10,1.737473011607151e-06,0.0037729470153588665 +J221421.41-192756.0,3328019701351050428,284056252298717,0.682526052454,0.447579766168,-0.577780702896,12732276840717,333.5892417,-19.4655629,36.407974,-53.280426,0.023,15.745,15.65,18.632,17.712999,0.076211996,0.076211996,0, 1.3784259363012007e-07,9.914714560243682e-08,8.908813623504753e-08,1.0949193157104555e-07,1.2199559264527817e-07,2.5693302840182096e-08,2.862783203933805e-07,5.291385226151393e-08,1.0743297717056393e-07,8.62834273579098e-07,2.922821910658167e-07,1.536609566806921e-09,3.5490897918739186e-06,0.004309150415680769 +J221315.05-191927.2,3328019701351050788,284056252309933,0.848677542312,0.266164134547,-0.457059167566,12732409388841,333.3127282,-19.3242419,36.483612,-52.98727,0.024,16.577,16.462999,19.157,18.263,0.063661598,0.063662,0, 0.0002667637258689276,0.00021590357840568452,0.0001996200859473401,0.00023704388330771822,0.00025886738513059633,7.988794987205863e-05,0.0004426693931847022,0.00014922373751140606,0.00023187324580849355,0.0008240483027216293,0.0003947606511328255,1.7010272194469477e-05,0.0016848470899666474,0.0022989948994360542 +J221224.13-190158.6,3328019701351055404,284056252333048,0.979092608117,0.0900874521293,-0.182378495713,12732395272937,333.1005741,-19.0329609,36.82417,-52.69972,0.025,13.858,13.758,16.829,16.103001,0.060065601,0.060066,0, 0.0009421647399504089,0.0007840610843442783,0.0007309320510560019,0.0008583256882134242,0.0009313562209362195,0.00032109752463728246,0.0014691070805346084,0.0005806676638330644,0.0008392730845262481,0.002428678219990495,0.001285454461187647,9.172041283565541e-05,0.00425577908757142,0.030158523343797084 +J221258.35-190002.0,3328019701351055620,284056252335539,0.96160257289,0.229545306976,-0.150430860719,12732423850291,333.2431473,-19.00056,36.950974,-52.81517,0.025,16.046,15.499,17.923,17.750999,0.0586375,0.058637999,0, 0.0014437904346124926,0.0012140034114489933,0.0011354635207492334,0.001327199250230747,0.0014363160500310563,0.0005174219333913843,0.002197061951955482,0.0009231199248441436,0.0012976303562349481,0.0034723477088188437,0.001912213418864471,0.00016466050404055543,0.0057469229893313415,0.004123663768269176 +J221247.19-190316.8,3328019701351055697,284056252331322,0.961811847525,0.182822652431,-0.203700387132,12732393014070,333.1966619,-19.054692,36.84191,-52.792492,0.024,16.297001,16.237,18.568001,17.775999,0.038392,0.038392,0, 0.04081540227061113,0.03849071658772003,0.03773663843286638,0.04063182190163017,0.04200408297312813,0.02803294033943812,0.04490061522094408,0.039052958287503305,0.03992988624897334,0.04367395864849781,0.04101520787588477,0.025254446392302073,0.04055288360747739,0.004006201278339481 +J221147.89-190323.8,3328019701351058132,284056252331155,0.976920545886,-0.0579449599524,-0.205593357484,12732198313833,332.9495768,-19.0566259,36.706917,-52.573746,0.026,15.203,14.839,17.298,17.208,0.065505899,0.065505996,0, 0.00012361000659324295,9.85174037070117e-05,9.070246677372998e-05,0.00010830018782225368,0.0001186465888010934,3.458277376923625e-05,0.0002118818096484265,6.562712479428715e-05,0.00010596924277651436,0.0004204714967166719,0.00019140742268999473,6.251545895361256e-06,0.0009364823924818019,0.007797783737179129 +J221150.31-190232.1,3328019701351058170,284056252332272,0.980300321045,-0.0482655835744,-0.191524708594,12732198489922,332.9596255,-19.0422713,36.73446,-52.57775,0.026,15.396,15.318,18.079,17.802,0.0719551,0.071955003,0, 3.6357080115551498e-06,2.7319310438562547e-06,2.4785475576689e-06,3.0130134859820346e-06,3.3355281336717335e-06,7.977493685333875e-07,6.988142109584328e-06,1.5932207180969254e-06,2.9525122379934175e-06,1.759782611093732e-05,6.718621578550831e-06,7.630919228274588e-08,5.5055977282315695e-05,0.003887740768464164 +J221119.83-190831.6,3328019701351058320,284056252324387,0.942684364893,-0.167805450763,-0.288422465978,12732202450872,332.8326584,-19.1421348,36.51276,-52.499104,0.030,15.938,15.815,18.462999,17.968,0.069401696,0.069402002,0, 1.7536771431358445e-05,1.350170363338354e-05,1.2320909552553194e-05,1.4874645561513153e-05,1.6400488219633394e-05,4.240692893324266e-06,3.220433215808827e-05,8.305287145985364e-06,1.4566329348989715e-05,7.355791820649214e-05,3.0106083622749543e-05,5.285343681460351e-07,0.00019934238387931945,0.0032128840412102753 +J221125.30-191134.1,3328019701351058331,284056252320443,0.930599029646,-0.143886445766,-0.336574117762,12732203397772,332.855419,-19.1928323,36.446426,-52.536556,0.030,16.098,15.996,18.052999,17.809,0.042613,0.042613,0, 0.026885339404036156,0.02483016337261228,0.02410392729567179,0.02648594237870644,0.027687648614788743,0.016238040833300542,0.03157320909265325,0.024077968405024037,0.025968657526680223,0.033183440450896856,0.02806120531328292,0.012615312818513125,0.03366295103013817,0.0038564787184705235 +J221139.54-190422.6,3328019701351058353,284056252329902,0.970841604427,-0.0915863289021,-0.221536731652,12732211115634,332.9147626,-19.072946,36.663166,-52.548412,0.026,16.153999,15.852,18.715,17.945,0.075778902,0.075778998,0, 2.0048195651852705e-07,1.448766192591023e-07,1.303050956276659e-07,1.5997614106636764e-07,1.781314399565852e-07,3.799551330979329e-08,4.1302641473230927e-07,7.801453390817609e-08,1.5694548461640363e-07,1.2205051790806569e-06,4.186925727071138e-07,2.3852998781744235e-09,4.872842465003129e-06,0.003298558939807982 +J221147.46-191029.1,3328019701351058598,284056252321944,0.945821444317,-0.0578317881655,-0.319495351684,12732209034088,332.9477528,-19.1747528,36.523163,-52.612434,0.028,16.681999,16.337,19.016001,18.181,0.0573989,0.057399001,0, 0.002029423187584281,0.0017212341321978521,0.0016144822292407982,0.0018793615182765642,0.0020291156271859986,0.0007592826009264263,0.0030241658337756016,0.0013383870542392655,0.001837424827690001,0.004601648963653377,0.0026226380233341697,0.0002642903304911288,0.007261753957905634,0.0025218121303372167 +J221448.42-185410.4,3330018201351000047,284056252343401,0.76827000819,0.637900272503,-0.0533332621937,12731525039076,333.7017711,-18.9029145,37.352226,-53.188507,0.024,13.933,13.77,16.572001,16.134001,0.0573912,0.057390999,0, 0.0020337356190999207,0.001724987230337885,0.0016180327175832514,0.0018834434005743366,0.002033491367539278,0.0007611085114397437,0.0030301795580213757,0.0013414986119809566,0.0018414154060468614,0.004609675996326703,0.0026277999908821364,0.0002650778984677019,0.007272209291245766,0.029005490881066045 +J221444.43-185432.7,3330018201351000072,284056252342899,0.778498156076,0.624819318861,-0.0595099971874,12731524980668,333.685143,-18.9091011,37.333443,-53.175896,0.024,14.434,14.237,17.212999,16.794001,0.071198702,0.071199,0, 5.952880449220613e-06,4.505979738596522e-06,4.095094784059453e-06,4.968119910070349e-06,5.493407145650652e-06,1.3443971158549474e-06,1.1288216587899252e-05,2.6696977738047867e-06,4.8673515569331095e-06,2.7602350334218813e-05,1.0757723748265917e-05,1.3929847998670892e-07,8.264272768293649e-05,0.012829542577679239 +J221458.56-185326.8,3330018201351000183,284056252344386,0.74105614878,0.67017587199,-0.0412320865059,12731528195082,333.7440358,-18.8907997,37.394363,-53.2218,0.024,15.504,15.446,18.330999,17.393999,0.070268102,0.070267998,0, 1.0567915703637754e-05,8.07017538337648e-06,7.349772116056435e-06,8.894366079188642e-06,9.820425707371053e-06,2.4722870068715322e-06,1.9710610963467395e-05,4.874804966636669e-06,8.711878437852808e-06,4.651991518702689e-05,1.8593632139072014e-05,2.8198679187641525e-07,0.00013219761643246778,0.006256214384011547 +J221358.43-185333.6,3330018201351002755,284056252344223,0.884007927837,0.465479232392,-0.0431169077305,12732365455023,333.4934869,-18.8926862,37.254414,-53.000126,0.025,15.864,15.859,18.646999,17.690001,0.0632338,0.063234001,0, 0.0003146498783774019,0.0002555341932233522,0.00023649029342817307,0.0002804666173668706,0.00030606173582325974,9.569553747991055e-05,0.0005182820639605506,0.0001780908442696676,0.0002743325217949058,0.0009510042155287414,0.0004609598094789406,2.112830948308344e-05,0.0019077392668456611,0.0044256272481754165 +J221543.75-204526.9,3341021301351045316,284056252199510,-0.199466855888,-0.263860984007,-0.943710948607,12731899923127,333.9323166,-20.757473,34.519207,-54.00095,0.030,16.103001,16.075001,18.500999,18.195999,0.068483204,0.068483002,0, 2.9041464591411785e-05,2.254759153941423e-05,2.061855828818864e-05,2.4829187904437285e-05,2.733582203234636e-05,7.269255025614077e-06,5.247253092219945e-05,1.4134504394364897e-05,2.4309370877349343e-05,0.00011587037672432659,4.861811985188209e-05,9.919208995314641e-07,0.00029920628816669016,0.002479425082815563 +J221539.40-204536.7,3341021301351045360,284056252199299,-0.205812134503,-0.262232154027,-0.942802027303,12731899839758,333.9141848,-20.7602102,34.505898,-53.98566,0.030,16.136,16.113001,18.542,17.836,0.047815099,0.047814999,0, 0.013795194945004453,0.012401387770577246,0.01189335946776407,0.013364343086013947,0.014145129060366336,0.0070852392651723104,0.017577467644398673,0.011243771801558717,0.0130798120974884,0.020673093711818177,0.015282399807010289,0.004376952873558927,0.02388938493738425,0.003738337790716884 +J221710.89-202655.0,3341021301351061584,284060547157265,-0.00792462448912,-0.0271289484307,-0.999600530454,12853177734721,334.2954154,-20.4486187,35.200497,-54.227726,0.029,14.701,14.614,17.646,17.176001,0.060841899,0.060842,0, 0.000734920498251388,0.0006080697538918162,0.0005658566778349014,0.0006661198295706165,0.0007238177766995633,0.00024366308671057747,0.001161312199318962,0.00044381037591669046,0.0006513748198057159,0.001968337232973794,0.0010196134288431664,6.550504815065694e-05,0.0035611889709615393,0.00810067769149214 +J221655.83-202749.8,3341021301351061590,284060547156371,-0.0147884363977,-0.0408996393561,-0.99905381319,12852994240099,334.2326652,-20.4638571,35.14413,-54.176716,0.030,13.907,13.824,17.791,16.858,0.078358702,0.078359,0, 1.8270865394051303e-08,1.281835352507447e-08,1.1462502798052809e-08,1.4162121058366377e-08,1.5828731780971444e-08,3.129066688614131e-09,3.956759404571312e-08,6.539166957436122e-09,1.3906157882710904e-08,1.326484836525836e-07,4.207262985213022e-08,1.4708451672549845e-10,6.362403656618974e-07,0.011870881919528155 +J221639.82-203006.9,3341021301351061727,284060547154074,-0.0327612556672,-0.0746248076442,-0.996673385925,12852993138820,334.165938,-20.5019422,35.04886,-54.129337,0.031,15.585,15.465,18.212,17.438,0.065468103,0.065467998,0, 0.00012575898831348377,0.00010026465620713225,9.231970044594932e-05,0.00011021806690329168,0.00012073937944077238,3.5237027633871565e-05,0.00021541172807970198,6.684574183135356e-05,0.00010784507700226991,0.00042687254484445066,0.00019453716500681761,6.39355915521236e-06,0.0009489341036709607,0.005940412739256741 +J221518.09-204346.2,3341021301351064324,284056252201640,-0.208335501975,-0.221748915501,-0.952587915676,12731905093761,333.8253972,-20.7295174,34.51203,-53.897213,0.030,14.955,14.945,18.080999,17.247999,0.069260202,0.069260001,0, 1.8965909033311623e-05,1.4620255760359096e-05,1.3345732827429824e-05,1.6105905970952093e-05,1.7754210193289975e-05,4.609707155176556e-06,3.474527455231444e-05,9.018502827972699e-06,1.5771560111319883e-05,7.896277581045576e-05,3.24373908919557e-05,5.822858463359717e-07,0.00021243670809004093,0.007435698758571742 +J221456.75-203524.6,3341021301351064816,284056252211565,-0.126211485132,-0.112418915636,-0.985612828867,12731951177534,333.7364848,-20.5901863,34.69282,-53.77493,0.034,16.447001,16.372999,19.128,18.309999,0.069802299,0.069802001,0, 1.3928216749234376e-05,1.068328915291749e-05,9.740032429565758e-06,1.1771857117689364e-05,1.2987797095458256e-05,3.3170227033527795e-06,2.5761608695472593e-05,6.516702771304952e-06,1.1528981710062653e-05,5.973522148981548e-05,2.418224996351981e-05,3.969176795483094e-07,0.0001654403031932293,0.0021806756554592108 +J221555.58-203030.0,3341021301351065464,284056252217504,-0.049471103167,-0.0726211473411,-0.996131908389,12731923318643,333.981606,-20.5083362,34.94631,-53.967396,0.031,16.729,16.533001,18.819,18.202999,0.035236299,0.035236001,0, 0.05279708362201072,0.05057400677969081,0.049952829890214,0.05289219000818017,0.054182095179340195,0.03989878329578119,0.05530866210641645,0.05277162380387818,0.0520993406691431,0.0511690168044792,0.051845327167858404,0.03933673805276259,0.04485935040971882,0.0024598991681925465 +J221413.61-202754.1,3341021301351066991,284056252220784,-0.0381417275273,-0.0232745562027,-0.99900125308,12731942992017,333.5567161,-20.4650493,34.804607,-53.57589,0.029,14.598,14.587,17.551001,16.544001,0.066910997,0.066910997,0, 6.465424237521405e-05,5.091066498453357e-05,4.672249104615802e-05,5.601365210676469e-05,6.150883573079347e-05,1.717113960313083e-05,0.00011359686282787733,3.2965505685956285e-05,5.482246839608779e-05,0.00023689986263746008,0.00010377690162663149,2.7273807612842633e-06,0.0005647047368947226,0.017418865948105405 +J221424.77-203733.0,3341021301351067423,284056252209024,-0.169057703922,-0.114277535148,-0.978958700715,12731934845749,333.6032354,-20.6258541,34.56998,-53.667484,0.036,16.386,16.284,18.757999,18.584999,0.077046297,0.077045999,0, 6.491785706411785e-08,4.626050263770396e-08,4.1488769781466666e-08,5.1096467271565714e-08,5.700145003496609e-08,1.17135389455009e-08,1.369815062596765e-07,2.4262835905843577e-08,5.014991873066912e-08,4.295652921357311e-07,1.419266589508076e-07,6.376040697389409e-10,1.874046350023007e-06,0.0016050314662926744 +J221552.85-195808.4,3344019701241010174,284056252258451,0.249648569503,0.357649076887,-0.899868173427,12732004904420,333.9702173,-19.9690231,35.807495,-53.785126,0.029,16.382999,16.139999,18.993,18.027,0.0704735,0.070473999,1, 9.335702695506976e-06,7.1154056073004e-06,6.477213036121273e-06,7.842790412002136e-06,8.662156066807512e-06,2.1670985702270862e-06,1.7476042659167465e-05,4.279770512205875e-06,7.682278295226431e-06,4.1567655873067056e-05,1.6522005664565193e-05,2.420347721663795e-07,0.00011947683276104536,0.0030035885612556593 +J221802.14-201515.6,3344019701351005349,284060547169143,0.0116715744384,0.164842798916,-0.986250792646,12853143418275,334.5089313,-20.2543352,35.62498,-54.35594,0.028,14.411,14.266,17.052,16.556,0.057429101,0.057429001,0, 0.0020132556112947043,0.0017071635318228232,0.0016011712257074807,0.001864058285468118,0.0020127107020924723,0.00075243714786475,0.003001620004311312,0.0013267216290642175,0.001822463899083926,0.004571555114590671,0.0026032854831643357,0.0002613376887394172,0.007222556257305285,0.017163549797876362 +J221720.58-201222.6,3344019701351005532,284060547172285,0.0512918817262,0.206142410067,-0.977176775021,12853122374720,334.335752,-20.2062935,35.613914,-54.18662,0.029,15.843,15.746,18.275999,17.514,0.051794101,0.051794,0, 0.007099762350104938,0.006240411425696309,0.005929689924144494,0.006767023216829599,0.007224744306084507,0.0032074804565012634,0.009642931755356066,0.00533171287970669,0.006618011366940861,0.012522144102148546,0.00832238127922147,0.0016009805308571724,0.01627416805596591,0.0054335511234076435 +J221659.23-201522.1,3344019701351005592,284060547169025,0.0534030492673,0.154492948985,-0.986549564413,12853112250113,334.2468085,-20.256153,35.48768,-54.12348,0.028,15.928,15.669,18.219999,17.875999,0.064908601,0.064909004,0, 0.00016027346936735092,0.00012838938093323848,0.00011836803945076862,0.0001410830032979173,0.00015440297816243654,4.5850626433345995e-05,0.00027183194084213564,8.656803529192477e-05,0.00013803222735313625,0.0005282124066548015,0.00024447623736620745,8.751383823605584e-06,0.0011435768844046215,0.0035702290286809637 +J221732.22-201058.5,3344019701351006550,284060547173656,0.0456471006448,0.230716657518,-0.971949672641,12853114562325,334.384291,-20.1829393,35.676704,-54.222282,0.029,16.955999,16.905001,18.843,18.152,0.022397799,0.022398001,0, 0.10933688210095913,0.11288675174893416,0.11494979811290548,0.11135284162880407,0.10877059321371277,0.12451829358282038,0.09260256867657646,0.12423158816905949,0.11163572815483268,0.07474089245309466,0.10140955541810413,0.15001291698273247,0.05499981554590367,0.0026059323485854245 +J221648.32-201402.7,3344019701351007936,284060547170451,0.0683948104824,0.172096728805,-0.982702836993,12853111953818,334.2013367,-20.2340876,35.500103,-54.076008,0.028,14.027,14.017,17.513,16.622999,0.0583717,0.058371998,0, 0.0015567753617914757,0.001311470208526585,0.0012273766458221929,0.00143337807119362,0.0015504495371733886,0.0005631169874902864,0.0023583229506187017,0.0010020721403459197,0.001401428168694975,0.003696664916141024,0.0020508207814284867,0.00018274203751192765,0.006055042082781531,0.015807800616483087 +J221548.77-202123.1,3344019701351007943,284056252228821,0.0374459260378,0.0517495286947,-0.99795780918,12731911530144,333.9532281,-20.3564322,35.176952,-53.894306,0.030,14.173,14.115,17.976,17.219,0.062739797,0.062739998,0, 0.0003788701500390509,0.00030890259511834117,0.0002862051648611256,0.00033891632970716986,0.0003695250910060543,0.00011731116699114962,0.0006187228777351215,0.00021737040428122217,0.00033148208039521644,0.0011165818627080844,0.0005486673512615098,2.7003773738306748e-05,0.002191481293393907,0.007696410136545574 +J221625.51-201353.9,3344019701351008013,284060547170601,0.0855053702943,0.166952401799,-0.982250338348,12731998199269,334.106294,-20.2316598,35.455505,-53.990726,0.030,15.059,14.669,17.309999,17.145,0.076247402,0.076246999,0, 1.332949884756858e-07,9.583222464620212e-08,8.610134574217623e-08,1.0583218923271182e-07,1.1792522939337101e-07,2.4805244367156722e-08,2.7705076379739144e-07,5.10999739183029e-08,1.0384350463915748e-07,8.366259490371546e-07,2.83058751057883e-07,1.476370476269233e-09,3.451117711349356e-06,0.008405845967534123 +J221640.90-202632.0,3344019701351008160,284060547157654,-0.00870432955675,-0.0200724603816,-0.999760636843,12852995616773,334.1704471,-20.4422325,35.147842,-54.11458,0.031,16.033001,15.799,18.454,17.997,0.069151297,0.069150999,0, 2.0194836562856135e-05,1.558430781769085e-05,1.422951675155347e-05,1.7166951361119032e-05,1.8920298536029568e-05,4.9300744695867415e-06,3.692033172369413e-05,9.636433267560691e-06,1.681011772891577e-05,8.354624732328085e-05,3.4428447902034746e-05,6.300944914273494e-07,0.00022339549274339617,0.003108165985840467 +J221602.01-201021.0,3344019701351008268,284056252242159,0.132630624997,0.206371634846,-0.969443069831,12731996645936,334.0084065,-20.1725062,35.500755,-53.88476,0.029,16.212,16.164,18.971001,18.066,0.066377901,0.066377997,0, 8.328974510831584e-05,6.589067233701749e-05,6.054392570892975e-05,7.24724049397053e-05,7.951155531410697e-05,2.2565152814223804e-05,0.00014496523927087445,4.313131897953535e-05,7.092380255494007e-05,0.00029661513836571447,0.0001318498464984079,3.767671604905052e-06,0.0006887662137759997,0.00287311487233342 +J221602.10-202444.3,3344019701351009036,284056252224753,0.00433452145951,0.0067498588213,-0.999967825147,12731911809679,334.0087702,-20.4123304,35.11483,-53.96137,0.032,16.892,16.834999,19.319,18.604,0.066220999,0.066220999,0, 8.951696836512438e-05,7.091058358812956e-05,6.517924655412025e-05,7.798657468524054e-05,8.553943794134605e-05,2.4390449891037346e-05,0.00015538292371943232,4.656140161467961e-05,7.63179345929825e-05,0.00031620592867138427,0.00014115031767826034,4.130346024257222e-06,0.0007287934110940927,0.0015716634219155588 +J221459.58-201030.4,3344019701351011282,284056252241955,0.179380804345,0.163613006012,-0.970079023223,12732146477589,333.7482807,-20.175112,35.36418,-53.65431,0.030,16.796,16.236,18.794001,18.646,0.055571102,0.055571001,0, 0.003204042996481733,0.0027509142179901365,0.002591200579401248,0.0029975699809056903,0.0032249817989774476,0.00127616373648381,0.004630596492034697,0.002208582770969055,0.0029307141901598094,0.006675101271419975,0.004001326064335598,0.0005036308663662392,0.009850106812372443,0.0015004625840686467 +J221501.93-200730.5,3344019701351013404,284056252245924,0.213018372777,0.19814443016,-0.956745503076,12732151810297,333.7580599,-20.1251575,35.44898,-53.646976,0.031,14.102,13.93,16.771,16.405001,0.058634501,0.058634002,0, 0.0014454431140053614,0.0012154277011005407,0.0011368061884808457,0.0013287511240549037,0.001437984672107128,0.0005180869625899571,0.0021994267912410796,0.0009242706889505529,0.0012991474180112418,0.003475652224897008,0.0019142464690260282,0.0001649211008694482,0.005751488547379476,0.020681908289153436 +J221430.33-200013.6,3344019701351013615,284056252255795,0.329871438695,0.234302174588,-0.914487465697,12732158162676,333.6264155,-20.0037953,35.57524,-53.49078,0.028,15.871,15.417,17.51,17.547001,0.048970699,0.048971001,0, 0.011554482298822251,0.010321406851098138,0.009871990289970253,0.011144485332385384,0.011826144364771946,0.005719680243220804,0.014995983475532177,0.009204117021605783,0.010904294252517903,0.018130928034331563,0.012998664556385639,0.003332677757511098,0.02164321945758286,0.005227726005429738 +J221427.49-200156.0,3344019701351013869,284056252253269,0.311100664307,0.215474204558,-0.925628026714,12732158326901,333.6145763,-20.0322477,35.52394,-53.48946,0.028,16.622999,16.27,18.698,18.360001,0.047018901,0.047019001,0, 0.015480234572863329,0.013975748717581903,0.013428065547191592,0.015039896457548243,0.01588976320894955,0.008152990486232668,0.019477758488197033,0.012812043297446864,0.014722784257368273,0.022489597945404765,0.016976587324078862,0.0052344488071507855,0.025434048742538802,0.0020617762270130537 +J221645.10-195445.7,3344019701351016506,284060547190085,0.185569656632,0.449303360978,-0.873893810684,12731969259818,334.1879408,-19.9127115,36.010864,-53.960293,0.030,15.796,15.626,18.452999,17.646999,0.071464904,0.071465001,0, 5.016229129982852e-06,3.787215127026795e-06,3.4397764458270477e-06,4.176087478437139e-06,4.619569020088531e-06,1.12136964100809e-06,9.55770196395246e-06,2.2313264777078777e-06,4.091677769698535e-06,2.36139111924028e-05,9.136471200617406e-06,1.1296455269470002e-07,7.179767097099535e-05,0.00465233329813207 +J221558.63-200327.8,3344019701351016725,284056252251257,0.19597246473,0.295677580869,-0.934970353134,12732007187837,333.9943029,-20.0577314,35.677784,-53.83528,0.028,16.629999,16.375,19.014,18.191999,0.056721799,0.056722,0, 0.002417550006908598,0.002059845142043905,0.0019351020239048195,0.002247457326675402,0.0024234006426747615,0.0009257518816473204,0.003561892378939023,0.0016209416389921724,0.002197295570805795,0.005311252353277123,0.0030841293361731814,0.0003378272260721524,0.008172675571673126,0.002490655571236225 +J221729.39-195455.0,3344019701351018738,284060547189899,0.0995053358337,0.473525299729,-0.87514140495,12853105008755,334.3724939,-19.9152839,36.10322,-54.12514,0.027,14.056,13.985,16.325001,15.944,0.028028199,0.028028,0, 0.08355119926498264,0.08317136309224563,0.08356313527381061,0.08460002195557755,0.08459597962763042,0.07889645724132938,0.07801629586267049,0.09063183336681771,0.08399201903574322,0.06595472621884613,0.07894683246757814,0.08991637508790319,0.05188889197154874,0.03686847334838075 +J221659.66-195139.4,3344019701351019125,284060547193147,0.172459688125,0.501859531652,-0.847581657696,12853098951520,334.2486187,-19.8609673,36.125744,-53.99729,0.030,16.153999,16.073,18.888,18.238001,0.078367896,0.078368001,0, 1.8108142123399285e-08,1.2702814224105812e-08,1.135896248480123e-08,1.4034489763551624e-08,1.5686276088418e-08,3.100101631968846e-09,3.9222197018619464e-08,6.479011619761766e-09,1.3780876273741644e-08,1.3155176684233436e-07,4.171311671899698e-08,1.4558295470422119e-10,6.313832258870668e-07,0.0023646518255639984 +J221709.46-193829.7,3344019701351032068,284056252284404,0.200964543246,0.673039249765,-0.711780458171,12853093279830,334.2894525,-19.6415857,36.499462,-53.96117,0.027,14.259,14.053,17.306,16.872,0.077686198,0.077685997,0, 3.5463940561075795e-08,2.5084980877089967e-08,2.246514478930594e-08,2.7710782647935503e-08,3.09418348260547e-08,6.239982402697429e-09,7.576907838117866e-08,1.29814649966849e-08,2.7203463837119132e-08,2.452752680520797e-07,7.946416230511045e-08,3.1618757672286147e-10,1.1202855318988462e-06,0.011671306960972056 +J221815.16-193544.9,3344019701351032516,284060547208463,0.0120746369429,0.734128025956,-0.678903707936,12853639181050,334.5631715,-19.5958244,36.71938,-54.189034,0.027,16.490999,16.385,18.739,18.212,0.0494349,0.049435001,0, 0.010724737053394485,0.009555509807503125,0.009129606613679914,0.010325211995815174,0.010967938643770537,0.005230704643409953,0.014022558025797497,0.008463416635681072,0.010101717930650289,0.017147258563227092,0.012142643557583427,0.0029749266576198762,0.020744981946700988,0.002435028052814204 +J221618.08-194206.2,3344019701351034642,284056252279690,0.318096560031,0.576398134648,-0.75271493201,12731976797073,334.0753402,-19.7017322,36.289806,-53.790905,0.028,14.285,14.071,17.346001,16.93,0.079287603,0.079287998,0, 6.93016934476355e-09,4.803148299614419e-09,4.286022836647341e-09,5.307430489766225e-09,5.9399753652177064e-09,1.1413104752264045e-09,1.5308566931340127e-08,2.4000835658220264e-09,5.213285932541006e-09,5.40506511120604e-08,1.662503179135421e-08,4.834995118565417e-11,2.777053382707294e-07,0.01088102030744384 +J221601.96-193251.4,3344019701351034758,284056252292158,0.414330125958,0.644362331486,-0.642750132233,12732239493682,334.0081734,-19.5476387,36.499954,-53.680023,0.027,15.689,15.499,17.837999,17.608,0.055362601,0.055362999,0, 0.0033643550869841454,0.002892482631524145,0.0027258610402211723,0.003151060529458888,0.003388722468481832,0.0013495354691562841,0.004845433541495798,0.0023305628073976388,0.0030808012103347416,0.00694282673413573,0.0041857815102009244,0.0005400132992058771,0.010169463986583077,0.0048684368106788395 +J221648.05-193438.1,3344019701351035025,284060547209463,0.276555170447,0.693595443354,-0.665163587892,12732229855719,334.2002092,-19.5772688,36.55489,-53.860485,0.024,16.378,15.932,18.68,18.250999,0.069989301,0.069989003,0, 1.2470470069783267e-05,9.548046019940724e-06,8.701231380627926e-06,1.0521858598006262e-05,1.1612227323576836e-05,2.948378703073506e-06,2.314402767119186e-05,5.801065590026632e-06,1.0305259820311074e-05,5.4052159083336616e-05,2.1768328910954986e-05,3.459964156228534e-07,0.00015126587185492697,0.002330272440900527 +J221524.61-193504.8,3344019701351037393,284056252288973,0.493022303827,0.554169826494,-0.670689802615,12732267228707,333.8525451,-19.5846943,36.35863,-53.554127,0.027,14.662,14.577,15.016,14.974,0.00867,0.00867,0, 0.17681088186193775,0.21578242176269002,0.2271027103298576,0.18228179928045185,0.16153599041899475,0.3802779213659899,0.10728312187258589,0.20468512374697692,0.19322606937906536,0.08880955881137223,0.16260978772333334,0.3941433394604008,0.05835311484856975,0.12998226935129398 +J221445.43-194502.2,3344019701351037744,284056252275821,0.482516054509,0.390570612055,-0.783985238471,12731993266601,333.6892954,-19.7506157,36.00978,-53.464005,0.026,16.437,15.87,18.305,18.422001,0.056032199,0.056031998,0, 0.002868940841095825,0.002455728451135574,0.002310689798759912,0.0026773439107067947,0.0028830742654666246,0.0011248527272412622,0.004178388324670518,0.001955917610383784,0.0026176028944129783,0.006104864018430708,0.0036131732992759798,0.00043034587427193615,0.00915952305104859,0.00192371283535364 +J221528.16-193851.9,3344019701351037937,284056252283874,0.456184365296,0.528293771311,-0.716101610145,12731986547850,333.8673352,-19.647757,36.266262,-53.588223,0.026,16.239,16.221001,18.653999,18.454,0.076013699,0.076013997,0, 1.635667107907367e-07,1.1789842504744672e-07,1.0598330324353755e-07,1.3019373779473838e-07,1.4502012921398858e-07,3.0716720535825234e-08,3.3847518861752414e-07,6.317428988336159e-08,1.277372931362276e-07,1.0110850535402893e-06,3.444557732768081e-07,1.877359779285129e-09,4.103281546060356e-06,0.0018562819570717658 +J221503.26-192544.4,3344019701351040204,284056252301593,0.609527896225,0.573284365621,-0.547558928206,12732286048652,333.7635872,-19.4290001,36.558094,-53.423027,0.024,14.248,14.085,17.424999,16.771,0.079489902,0.079489999,0, 5.559217887073332e-09,3.842044672871938e-09,3.4267933330801415e-09,4.2455403574852305e-09,4.752928121058554e-09,9.073922168765103e-10,1.2336227397702387e-08,1.910787784389483e-09,4.1705524825411656e-09,4.407877951194367e-08,1.346397373650854e-08,3.7573635631945274e-11,2.2993232734802182e-07,0.013193494495922428 +J221449.49-192207.4,3344019701351040419,284056252306479,0.665508636599,0.557591862001,-0.49617493895,12732280767657,333.7062152,-19.3687435,36.622684,-53.351784,0.024,16.025,15.619,18.18,18.120001,0.070665397,0.070665002,0, 8.31873101128319e-06,6.329108071024555e-06,5.7590021382488856e-06,6.976669841665626e-06,7.707812469595624e-06,1.9174292514364112e-06,1.5624084941081253e-05,3.792095480527294e-06,6.8342041999626125e-06,3.742727549266695e-05,1.480114305900594e-05,2.10089152499099e-07,0.00010870622703679595,0.002702171364638781 +J221437.97-192453.2,3344019701351040614,284056252302673,0.672554629263,0.510662811342,-0.535624648209,12732273886551,333.6582404,-19.4148032,36.52453,-53.324684,0.022,16.506001,15.917,18.728001,17.879,0.068375602,0.068375997,0, 3.078886044882361e-05,2.3928822914575734e-05,2.1887272271753892e-05,2.6348646201872562e-05,2.900333329089678e-05,7.739629482458346e-06,5.551821796171898e-05,1.5035505264489738e-05,2.5796364282238758e-05,0.00012209045989853976,5.138558066344442e-05,1.0679704361733441e-06,0.0003134392359856119,0.003557939500483946 +J221638.72-192651.0,3344019701351042887,284060547217001,0.335709416694,0.755227594033,-0.562965779381,12731493568707,334.161335,-19.4475258,36.74119,-53.7825,0.025,14.116,14.018,17.089001,16.502001,0.054687999,0.054687999,0, 0.003924301326502977,0.0033885558452191322,0.0031983383114956372,0.003688509972982577,0.0039613993826874945,0.00161040050179146,0.005589012056940332,0.002761703911744871,0.003606366447651042,0.007855347699947142,0.004824540271675324,0.0006733865399292803,0.011236909250425918,0.018344125820600122 +J221559.40-192629.3,3344019701351042894,284056252300645,0.456236264444,0.693191957593,-0.557972562887,12732242469674,333.9975223,-19.4414966,36.663036,-53.635,0.026,14.14,14.133,17.302,16.219999,0.069531403,0.069531001,0, 1.627558664617183e-05,1.2515141196892814e-05,1.1417140333497653e-05,1.3788639571838164e-05,1.5206349685686636e-05,3.915780564967012e-06,2.9959558372201753e-05,7.677002345645352e-06,1.350326825961645e-05,6.877234143513525e-05,2.8045365166496593e-05,4.814741395117822e-07,0.00018771106733326886,0.026040614482432087 +J221621.59-191829.9,3344019701351042975,284060547225497,0.421681012017,0.791252096428,-0.442837717457,12731502694449,334.0899753,-19.3083171,36.92424,-53.672127,0.025,15.032,15.001,17.540001,17.013,0.045552298,0.045552,0, 0.018892567949828847,0.01718881551066136,0.016571848834623513,0.018447145126139975,0.01942296496495886,0.010418905558971837,0.02322822874184916,0.016070830683643643,0.018066253394021334,0.025953554616179444,0.020355678433520173,0.0071584075121721115,0.028251961304925806,0.009845815673992755 +J221612.70-192222.1,3344019701351043065,284056252306112,0.435407904752,0.748813125639,-0.499698768609,12732236559318,334.052939,-19.3728069,36.801853,-53.661064,0.026,15.639,15.481,18.396,17.469999,0.076146401,0.076145999,0, 1.4641695289581907e-07,1.0539731954034689e-07,9.471963102572456e-08,1.163922548319963e-07,1.296701280718465e-07,2.736770774959752e-08,3.036765734764195e-07,5.6333859947052985e-08,1.142008090322695e-07,9.122492035792407e-07,3.0967268245808964e-07,1.6501883817146893e-09,3.733812912703016e-06,0.005721235755932061 +J221611.23-191443.8,3344019701351043150,284056252316080,0.468650952733,0.794736353817,-0.385694714021,12731502922566,334.0468108,-19.2455166,37.000523,-53.61247,0.025,15.952,15.789,18.533001,17.757999,0.065723501,0.065724,0, 0.00011222958979132542,8.928269558443638e-05,8.215971427449248e-05,9.816188109513344e-05,0.000107578970300006,3.1148575335604964e-05,0.0001931079009733855,5.9217143373018385e-05,9.605284860768092e-05,0.0003861380554217509,0.00017473603742046727,5.5212727245918665e-06,0.0008689314579621784,0.004090415687883347 +J221649.58-192707.1,3344019701351043472,284060547216715,0.300270801356,0.767298826976,-0.566648000062,12731495093934,334.2066034,-19.4519879,36.758385,-53.8242,0.026,16.537001,16.077999,18.767,18.193001,0.057471801,0.057472002,0, 0.001990081546360435,0.0016869952022652696,0.0015820916769429423,0.0018421231416682526,0.0019891964292942503,0.0007426251036279128,0.0029693035638848507,0.00131000079746581,0.0018010194027175991,0.004528419594472491,0.0025755461971695695,0.00025710547047390586,0.007166371579854768,0.0024878373416242485 +J221548.31-192225.0,3344019701351043474,284056252306066,0.508882634815,0.700449003378,-0.500409489968,12732235505319,333.9513209,-19.3736276,36.74599,-53.571106,0.025,16.582001,16.242001,18.753,18.079,0.043149199,0.043148998,0, 0.025316287768675273,0.023318167251095596,0.0226077797486941,0.02490241409629356,0.02606723156912622,0.015040598149866835,0.029979292420948607,0.022468481002434808,0.024410346567213723,0.031847471435171686,0.02656791707379998,0.011440355431553502,0.03270705336991221,0.002830953515036761 +J221731.80-192117.3,3344019701351045590,284060547222783,0.17136608339,0.858100052374,-0.484043350929,12852768449934,334.3825066,-19.3548255,37.00856,-53.94759,0.023,14.059,13.873,17.325001,16.847,0.074181497,0.074181996,0, 7.349796905689278e-07,5.40129741668607e-07,4.875718875486612e-07,5.961617686774229e-07,6.62229781513586e-07,1.481318526666085e-07,1.470366150260422e-06,3.0071139074340726e-07,5.845713420868837e-07,4.052015294700822e-06,1.4549600585367233e-06,1.1125449345142753e-08,1.454126558725068e-05,0.012030184245423889 +J221754.26-191814.6,3344019701351045848,284060547225719,0.0928508166429,0.893661872519,-0.43902982069,12852812657684,334.4760898,-19.3040745,37.140694,-54.013443,0.024,16.045,16.031,18.469999,18.106001,0.066279501,0.06628,0, 8.710284882711151e-05,6.896301652780889e-05,6.338051899811955e-05,7.584738258884484e-05,8.320131449620823e-05,2.3680489511371986e-05,0.0001513505868583847,4.522826529033816e-05,7.422533318068591e-05,0.00030864681734584166,0.0001375526366164307,3.988180119485159e-06,0.0007134144180958717,0.0027454408159461896 +J221658.52-190749.9,3344019701351058868,284060547236302,0.316566583671,0.907130748166,-0.277307417561,12731553632772,334.2438556,-19.1305464,37.290325,-53.74807,0.025,15.759,15.737,18.396999,17.632999,0.059107799,0.059108,0, 0.001259897066985334,0.001055827801825278,0.0009864527096761595,0.0011547993058169891,0.0012508392463085498,0.0004441472148470738,0.001932617214059925,0.0007959615172866528,0.0011290972070026432,0.0030994920928045858,0.0016847643918108653,0.00013648307815804946,0.005225745206118357,0.00472873390657493 +J221552.86-190559.2,3344019701351062201,284056252327731,0.554515677654,0.794473177729,-0.247638311061,12731530579476,333.9702577,-19.0997978,37.189434,-53.494705,0.023,16.434999,16.224001,18.757999,18.52,0.072344899,0.072345003,0, 2.7956703964864823e-06,2.09276892132775e-06,1.8969990754550834e-06,2.3084205525088443e-06,2.5570487684594583e-06,6.044446505894309e-07,5.410860064503842e-06,1.2106483882909915e-06,2.26231335131821e-06,1.3835314204041404e-05,5.226586216527408e-06,5.5478829804955214e-08,4.429182848147971e-05,0.0017249098193484727 diff --git a/papers/171020/Data/zpriors_added.csv b/papers/171020/Data/zpriors_added.csv new file mode 100644 index 0000000..55f3e26 --- /dev/null +++ b/papers/171020/Data/zpriors_added.csv @@ -0,0 +1,114 @@ +J221258.68-204401.8,3325021301351052049,284056252201331,-0.29986786761,-0.0720231680632,-0.951258074992,12734292953977,333.2445393,-20.7338586,34.22261,-53.381756,0.041,16.216999,16.190001,18.431,18.110001,0.055808,0.055808,0, 0.003028431996363633,0.0025960934124943484,0.002444034631493081,0.002829650261845749,0.0030457425614378525,0.0011965240519883115,0.004394144717380802,0.0020757842330807064,0.002766523052650797,0.006378071597664387,0.003798352898542122,0.0004647659042081986,0.00949215130184012 +J221231.15-203946.6,3325021301351054428,284056252206352,-0.238484931538,-0.0289922910515,-0.970713337958,12734295991657,333.1297963,-20.6629661,34.280014,-53.25778,0.041,14.091,14.059,17.531,16.466,0.077429898,0.077430002,0, 4.5338634355400396e-08,3.216682201482422e-08,2.8824081410563447e-08,3.553217883259604e-08,3.966041625090356e-08,8.059201856809263e-09,9.637762730867511e-08,1.6736846657828382e-08,3.4878538371243055e-08,3.079705602179236e-07,1.0057382352486143e-07,4.20331438853676e-10,1.3807826822111328e-06 +J221306.08-203932.5,3325021301351054460,284056252206646,-0.228071487565,-0.0622702903056,-0.971651072919,12734284785041,333.2753548,-20.6590315,34.356857,-53.386066,0.039,14.731,14.698,17.916,17.416,0.061081901,0.061082002,0, 0.0006788260517588788,0.0005606385492331068,0.0005214302320786469,0.0006142867533947117,0.0006677849595478494,0.00022314326179446206,0.001077118807809328,0.00040733060396803934,0.0006007023414729073,0.0018399418564173462,0.0009467657712872504,5.88533606498381e-05,0.0033625034975094527 +J221315.32-202831.7,3325021301351054505,284056252219955,-0.0525689090031,-0.0165508911462,-0.998480133908,12734278487417,333.3138382,-20.475493,34.66801,-53.363113,0.032,14.972,14.876,17.360001,17.120001,0.0485131,0.048512999,0, 0.01241232478833886,0.011115756623628771,0.010643054406402676,0.011993111778118718,0.0127137501240334,0.006234771176444316,0.015992226750741637,0.009978341700026167,0.011735812109876372,0.01912307926695178,0.013877593342781476,0.0037190102527374634,0.0225324410607784 +J221210.65-202716.4,3325021301351057099,284056252221573,-0.0341860398629,-0.0012164803948,-0.999414746166,12734297543141,333.0443904,-20.4545666,34.5689,-53.116848,0.040,15.445,15.401,18.410999,17.42,0.075704902,0.075704999,0, 2.1379085460756219e-07,1.5462235836298715e-07,1.3909498851571085e-07,1.707343107281557e-07,1.9008869950264756e-07,4.0638818866787574e-08,4.3981310051622094e-07,8.339654281669123e-08,1.674956071517711e-07,1.295147145384035e-06,4.452933759908409e-07,2.573682023303371e-09,5.143727606944741e-06 +J221100.78-202732.2,3325021301351057191,284056252221230,-0.0373323841145,0.00975354510493,-0.999255303441,12734245659022,332.7532712,-20.4589473,34.419876,-52.859226,0.031,15.871,15.798,18.646,17.635,0.067479499,0.067479998,0, 4.881430334351576e-05,3.824332380264192e-05,3.505125724831111e-05,4.209021011454171e-05,4.626339900964916e-05,1.2688611979112266e-05,8.66418840551758e-05,2.4474040868781703e-05,4.120002133702093e-05,0.00018445484231355646,7.954475083541418e-05,1.908361431294575e-06,0.0004524545448539678 +J221338.59-193704.2,3328019701241016594,284056252286293,0.661770099138,0.281361659122,-0.694907153988,12732061719390,333.4108319,-19.6178467,36.074528,-53.17267,0.025,16.969999,16.544001,19.011999,18.266001,0.036443599,0.036444001,1, 0.048074658876669306,0.045774322043052956,0.04508364235207943,0.04805173259387675,0.0494000273884564,0.035024693733695406,0.0513154967692527,0.04728237800926423,0.04728615733740808,0.048356310182325626,0.04760325586665332,0.0334264259687987,0.043300967478987866 +J221419.92-201529.1,3328019701351000069,284056252235895,0.135640159513,0.0877480720825,-0.986864744012,12732117345172,333.5830269,-20.2580925,35.148247,-53.533855,0.026,14.513,14.269,16.816,16.419001,0.063239299,0.063239001,0, 0.00031402759858336504,0.0002550177101013884,0.0002360093512568626,0.0002799008811797669,0.0003054472842616298,9.548731455910128e-05,0.0005173060082631216,0.0001777118956766532,0.00027377935853766346,0.0009493863101421685,0.00046010684563351477,2.1072435609321296e-05,0.001904946697192326 +J221415.90-202202.4,3328019701351000684,284056252228060,0.0449510007714,0.0280222037883,-0.99859609634,12731936694640,333.5662652,-20.3673573,34.96547,-53.553608,0.024,16.924,16.684,19.097,18.43,0.046004299,0.046004001,0, 0.017798579079111844,0.016155208868020937,0.015558877448438863,0.017352876283866128,0.018290330212879267,0.009677513344112794,0.0220395001801223,0.015014466268402641,0.0169920934097273,0.024872086411806896,0.01927933043069284,0.006514265094773302,0.02738947009692771 +J221217.50-202631.2,3328019701351002874,284056252222528,-0.0216058732089,-0.00138720130618,-0.999765603487,12734284108085,333.0729381,-20.4420043,34.602776,-53.1383,0.036,14.439,14.414,17.712999,16.636,0.079436697,0.079437003,0, 5.918894044378234e-09,4.094195146398516e-09,3.65221660707901e-09,4.5241326884655774e-09,5.064356063254165e-09,9.687618735589549e-10,1.3116035942831663e-08,2.0391570460901572e-09,4.444118959519939e-09,4.6694951250300224e-08,1.4293293555096537e-08,4.0400857373839284e-11,2.424658221666599e-07 +J221155.07-202217.6,3328019701351002888,284056252227769,0.0487402932397,-0.00143130830627,-0.998810460083,12734150215526,332.9794637,-20.3715716,34.668163,-53.032974,0.033,14.709,14.589,16.83,16.386,0.035179801,0.035179999,0, 0.05302028893124427,0.05080226045839279,0.050184996930543097,0.053121241000144655,0.05440741353179438,0.04013663572067086,0.05549350807028574,0.05303389365594258,0.05232744279277668,0.05129721951158849,0.05204499453625564,0.03962945868892654,0.0449285296700539 +J221305.21-202311.1,3328019701351002979,284056252226700,0.0327598984673,0.0088172950412,-0.999424356498,12734279733459,333.2717398,-20.38642,34.788822,-53.297653,0.026,15.245,15.225,17.952,17.004,0.0620826,0.062082998,0, 0.00048107935288290056,0.00039427692274446937,0.00036586110997724453,0.0004323620420425315,0.00047085910594739524,0.00015256100818903705,0.0007766699869408732,0.0002810244624236008,0.0004228444493808813,0.0013710763539977508,0.0006861764900967017,3.7102838325928355e-05,0.002614697811390259 +J221125.18-202120.2,3328019701351005901,284056252228899,0.0639135840474,-0.00991300526422,-0.997906201053,12734131846375,332.8549472,-20.3556293,34.632103,-52.917175,0.033,15.749,15.692,18.677,17.826,0.076002397,0.076002002,0, 1.6512510646617756e-07,1.1903439844601389e-07,1.0700683128290565e-07,1.314478762785348e-07,1.464149812897147e-07,3.102104477614585e-08,3.416373330442567e-07,6.379587862509296e-08,1.2896735121737017e-07,1.0200662509485314e-06,3.4761650701092235e-07,1.8980028067684814e-09,4.136855099924095e-06 +J221101.75-201302.8,3328019701351005963,284056252238849,0.195177778128,-0.0501475413727,-0.979484996832,12734137982844,332.7573285,-20.2174457,34.801037,-52.78652,0.026,15.986,15.978,18.641001,18.014,0.066913001,0.066913001,0, 6.459073826321069e-05,5.085973526551405e-05,4.667553014265022e-05,5.5957684550038535e-05,6.14475889175028e-05,1.715294487020213e-05,0.00011348944116300329,3.293113468633538e-05,5.476771370568976e-05,0.00023669335904416875,0.00010368057393582349,2.7239567863963418e-06,0.000564270007625793 +J221116.87-201906.6,3328019701351006334,284056252231543,0.0998660052001,-0.0190525005107,-0.994818467475,12734132044316,332.820306,-20.3185091,34.673462,-52.874626,0.031,16.757,16.559,18.243999,18.554001,0.039997499,0.039997999,0, 0.03520223368493703,0.03293568827312491,0.03216878079209645,0.03491491860840343,0.036252509653252496,0.023026755940306846,0.039699980090658796,0.032879293885155425,0.034278284266312874,0.03971396760080825,0.035852894356314384,0.019681642707240484,0.038075566323899544 +J221024.01-195344.4,3328019701351016716,284056252264092,0.459658479117,-0.199097255362,-0.865490823454,12734388760267,332.6000748,-19.8956853,35.225,-52.54313,0.024,16.429001,16.167,18.121,18.209999,0.043521099,0.043520998,0, 0.02425513169982362,0.022298713178615617,0.021600517651414776,0.023832809947253973,0.024970694746900574,0.014245429930349331,0.028889930976159196,0.02138960304863254,0.02335823353600646,0.03092328844764174,0.025553239581629444,0.010673482942683521,0.032034954058309265 +J221111.58-195130.5,3328019701351018833,284056252267044,0.521010482975,-0.111347956723,-0.846256290472,12734124091582,332.7982733,-19.8584747,35.382893,-52.70726,0.028,13.954,13.947,17.785999,16.792,0.068537198,0.068536997,0, 2.8226825493270562e-05,2.1904699580813522e-05,2.002828529812229e-05,2.4121883288508943e-05,2.655936180433252e-05,7.051462316515786e-06,5.104795883100433e-05,1.3716701929126562e-05,2.3617153121048214e-05,0.00011294143475073388,4.7321666743169386e-05,9.572986859130822e-07,0.0002924409064932658 +J221152.02-195901.7,3328019701351018849,284056252257293,0.422430122603,-0.0177656997194,-0.906221369992,12734117432392,332.9667901,-19.9838155,35.271904,-52.89781,0.027,14.22,14.041,17.355,16.820999,0.076587602,0.076587997,0, 9.885280867419885e-08,7.0811207504099e-08,6.357358386897821e-08,7.820609723415821e-08,8.718465621018309e-08,1.816202310466649e-08,2.0674820761184475e-07,3.750010489112872e-08,7.674503182004025e-08,6.340903693214405e-07,2.1243946086144007e-07,1.0416515566807148e-09,2.6770710120895873e-06 +J221133.33-195451.8,3328019701351018906,284056252262736,0.481154736991,-0.0579847015866,-0.874715892992,12734119855199,332.8888881,-19.9144052,35.341324,-52.806034,0.028,14.911,14.81,18.030001,17.461,0.076160498,0.076159999,0, 1.4459806673857266e-07,1.0407146480254451e-07,9.352501722404446e-08,1.149284833627884e-07,1.280421223144339e-07,2.7012514805496777e-08,2.999858671842766e-07,5.560837079455289e-08,1.1276514307678848e-07,9.017667722565742e-07,3.059836226600408e-07,1.6260948106628414e-09,3.6946274392480496e-06 +J221136.18-200235.1,3328019701351019210,284056252252434,0.366227145161,-0.0397317032088,-0.929676863166,12734164768709,332.9007547,-20.0430906,35.145752,-52.858227,0.025,16.309999,16.305,18.719,18.544001,0.077462703,0.077463001,0, 4.384970568436605e-08,3.109687129978014e-08,2.7862955862606338e-08,3.435054114351795e-08,3.8343584797282363e-08,7.782971806902478e-09,9.328082879340487e-08,1.6167341169846413e-08,3.3719077562180585e-08,2.986297497096229e-07,9.741165040715162e-08,4.0419593406214815e-10,1.3424747610057483e-06 +J221211.14-195600.3,3328019701351021254,284056252261340,0.467572527111,0.0175997483777,-0.883779486494,12732032539525,333.0464442,-19.933435,35.391212,-52.952312,0.028,14.116,14.009,16.181,15.814,0.0301445,0.030144,0, 0.07416304753361104,0.07294970938810502,0.0729264005507496,0.07489305029328636,0.07546424537913263,0.06551847173460293,0.07168260805959312,0.07874046460190652,0.07415023217152059,0.06200880788439289,0.07074095713969335,0.07217672241756726,0.05021204161827327 +J221230.16-195918.8,3328019701351021347,284056252256913,0.415638106656,0.0488034426776,-0.908219790732,12732086916118,333.1257042,-19.9885663,35.344677,-53.040657,0.026,15.175,14.988,17.891001,17.472,0.078153998,0.078153998,0, 2.243587424374888e-08,1.5780422536697365e-08,1.4117797049301559e-08,1.7434100371077807e-08,1.9480006189093642e-08,3.874532389045043e-09,4.838433614595542e-08,8.085884395528432e-09,1.7117743752845572e-08,1.604514030837513e-07,5.122568443471115e-08,1.8634788566865685e-10,7.58137613856967e-07 +J221157.18-200840.7,3328019701351021576,284056252244322,0.272167958231,-0.0055886058598,-0.962233531944,12734165088401,332.9882905,-20.1446473,35.03006,-52.9687,0.026,16.334999,16.254999,17.726999,17.83,0.033358399,0.033358,0, 0.060431303832381246,0.058440339991599345,0.05797907690264194,0.0607362279772004,0.061857233979840616,0.048354509784159394,0.061473066183755296,0.0618513712560599,0.05992612065073328,0.05537210081195525,0.05864451742523866,0.049908243102564726,0.04705909544578621 +J221357.33-195652.7,3328019701351023874,284056252260131,0.403561281861,0.21013624611,-0.890494834266,12732099586328,333.4888951,-19.9479875,35.593487,-53.35042,0.025,15.461,15.43,18.504999,17.601999,0.076738,0.076738,0, 8.594555671079738e-08,6.145553263118893e-08,5.51542140886457e-08,6.78757882781514e-08,7.568611038838721e-08,1.5692591023364655e-08,1.8029949719310111e-07,3.243718966605467e-08,6.66113198491967e-08,5.571767736548807e-07,1.8578411039652277e-07,8.838564391065392e-10,2.3788464193291743e-06 +J221404.53-195840.5,3328019701351023906,284056252257775,0.373637826119,0.209040803011,-0.903712740626,12732099477151,333.5189094,-19.9779194,35.561382,-53.386818,0.025,15.654,15.598,18.1,17.875999,0.066261597,0.066261999,0, 8.780327516712254e-05,6.952737327116281e-05,6.390157082418283e-05,7.646732985929374e-05,8.387908376503302e-05,2.388536492475577e-05,0.00015252350686535235,4.5613451883200934e-05,7.483178901906943e-05,0.00031085690799475266,0.0001386001781219315,4.028685173425154e-06,0.0007179420294101175 +J221359.13-195106.0,3328019701351024098,284056252267598,0.475768131513,0.252288416274,-0.842612152803,12732102212387,333.4963953,-19.8516715,35.74984,-53.325703,0.026,16.191999,15.939,18.576,17.663,0.055101,0.055101,0, 0.0035743057725713904,0.0030781959274073754,0.0029026326498128715,0.003352338088957077,0.003603313010232977,0.0014465162001000616,0.0051254595189570975,0.00249130309540852,0.0032776221378036114,0.007289000315567708,0.004426265947328945,0.0005888745196833949,0.010578189041658375 +J221329.43-193306.7,3328019701351026849,284056252291819,0.713384398097,0.271648397342,-0.645979758795,12732182999849,333.3726569,-19.551862,36.158363,-53.11682,0.025,16.518999,16.437,19.118,18.163,0.060293399,0.060293,0, 0.000877086498973691,0.0007286730962932251,0.0006789410113896725,0.0007978545219640924,0.0008661010753330123,0.0002965118523205248,0.0013729884958696992,0.000537347801907449,0.0007801576828079098,0.0022863937858149143,0.001202510721211474,8.321085846281181e-05,0.004043965481192471 +J221358.74-193215.9,3328019701351026913,284056252292992,0.682969663741,0.360748112715,-0.635148201275,12732397467871,333.4947776,-19.5377563,36.24451,-53.22063,0.022,16.709999,16.584,19.003,18.642,0.067499101,0.067498997,0, 4.83277209993874e-05,3.785495017743261e-05,3.469360978729893e-05,4.1663265473934066e-05,4.579573430077003e-05,1.2552066910508261e-05,8.581052431100272e-05,2.421488545146507e-05,4.078228723935041e-05,0.00018282422870127116,7.8796093468541e-05,1.8839184056925577e-06,0.0004489260558717601 +J221413.69-194032.1,3328019701351027035,284056252281688,0.578454078321,0.353269845707,-0.735249138311,12732095628081,333.5570813,-19.6755901,36.05969,-53.321754,0.026,16.587,16.52,18.957001,18.343,0.056288902,0.056288999,0, 0.002694112776492295,0.002302157829787464,0.0021649056013536006,0.0025106387910070185,0.0027049121400348035,0.00104708893939253,0.0039406412659462615,0.0018254328272195486,0.0024546089417711945,0.005801091988789989,0.0034091432911016223,0.00039366916459353664,0.008785404393711588 +J221249.30-194822.9,3328019701351029025,284056252271258,0.565019713004,0.112551873145,-0.817364545212,12732093321131,333.2054474,-19.8063775,35.671833,-53.05225,0.030,14.612,14.57,18.224001,17.334,0.077735797,0.077735998,0, 3.365840361487243e-08,2.3791319950669344e-08,2.1303763602472176e-08,2.6281997787679382e-08,2.9348955303052843e-08,5.908444642713458e-09,7.199486379809564e-08,1.2296672077503227e-08,2.580136803313022e-08,2.3374758883915837e-07,7.559251445649654e-08,2.973920120482693e-10,1.072101591417079e-06 +J221254.76-193316.2,3328019701351029880,284056252291609,0.743393239136,0.165728247636,-0.647997407358,12732188704757,333.2281692,-19.5545081,36.078823,-52.989307,0.026,16.941999,16.799999,19.354,18.701,0.072269797,0.072269998,0, 2.9363923243739088e-06,2.1995823377800367e-06,1.9941285374428243e-06,2.426180645400685e-06,2.6872096884110993e-06,6.3651709244974e-07,5.676280067258131e-06,1.2742452982428172e-06,2.377675586013352e-06,1.4474615877501808e-05,5.478362105128832e-06,5.884556114802555e-08,4.6147235947973606e-05 +J221125.02-193205.7,3328019701351031647,284056252293260,0.764950493839,-0.11917689268,-0.632967305811,12732177327978,332.8542669,-19.534936,35.915413,-52.650505,0.026,14.607,14.595,17.077999,16.070999,0.046887498,0.046888001,0, 0.01576988425816163,0.014247303164825321,0.013693210633025286,0.015328458757131866,0.016189688961595443,0.008340340120781506,0.01980073688825662,0.013084731214786519,0.01500582280333882,0.02279361381380545,0.01726581757765537,0.005388681570668583,0.02568746357735503 +J221136.68-193442.9,3328019701351031660,284056252289524,0.741635832275,-0.0788788451878,-0.666148947359,12732177675453,332.9028617,-19.5785892,35.87259,-52.7082,0.026,13.864,13.636,16.816,16.344,0.078338198,0.078337997,0, 1.8650565077808635e-08,1.3087953784850212e-08,1.1704104531106911e-08,1.4459936866053381e-08,1.616113894803022e-08,3.1966539661077527e-09,4.037354602438641e-08,6.679533867465318e-09,1.4198490916060287e-08,1.352075707724186e-07,4.2911520457083814e-08,1.505882726562665e-10,6.475740515513129e-07 +J221034.11-194619.1,3328019701351031682,284056252274049,0.563745596724,-0.216514172351,-0.797064937972,12734390771662,332.6421282,-19.7719752,35.4385,-52.54016,0.027,14.237,14.179,17.722,16.98,0.061746601,0.061747,0, 0.0005409992680011655,0.0004445259698487298,0.0004128026160311266,0.00048733473860351127,0.000530413278754951,0.00017362060638088667,0.0008684034552398554,0.0003188661802243514,0.0004765898713656209,0.0015162796707164374,0.0007658665874738221,4.338320232321795e-05,0.0028505886330215126 +J221148.87-194803.4,3328019701351031691,284056252271670,0.579650725743,-0.0320086645742,-0.814236133771,12732043060206,332.9536567,-19.8009681,35.55163,-52.826633,0.032,14.348,14.225,17.41,16.907,0.062687904,0.062688001,0, 0.0003864255756509348,0.00031520094495184243,0.00029207795667746724,0.00034581182002799837,0.0003770064163785047,0.00011989192230550964,0.0006304541514317578,0.00022204244512776254,0.0003382239789216555,0.0011356541309550648,0.000558892279517395,2.7727889474710175e-05,0.0022235704892798742 +J221139.05-193427.7,3328019701351031765,284056252289908,0.745174039507,-0.0718181863438,-0.662991552702,12732177758179,332.9127403,-19.5743637,35.884266,-52.715576,0.026,15.144,15.139,18.136999,17.124001,0.073818699,0.073818997,0, 9.690592285930394e-07,7.147892217438357e-07,6.457664293441117e-07,7.888523214611621e-07,8.757938592471654e-07,1.9803476191540684e-07,1.925985559600194e-06,4.009649481054499e-07,7.734301533089254e-07,5.227875986561327e-06,1.8962515797506348e-06,1.5480492466272766e-08,1.833065192597549e-05 +J221104.31-193528.3,3328019701351031778,284056252288521,0.716074071139,-0.175866472186,-0.675506409002,12732163145623,332.7679898,-19.5912072,35.78352,-52.592503,0.025,15.306,15.124,17.996,17.209,0.075825199,0.075824998,0, 1.9317681688510346e-07,1.3953720746673424e-07,1.2549128403197162e-07,1.540817532711622e-07,1.7157834054208247e-07,3.655439923226675e-08,3.982771351255137e-07,7.507661535822294e-08,1.5116476622606999e-07,1.1790972404156757e-06,4.040083961536275e-07,2.2845895779314546e-09,4.720818244637466e-06 +J221132.06-194009.9,3328019701351032001,284056252282118,0.676971552233,-0.0852133405965,-0.731059644661,12732030808396,332.8836055,-19.6694296,35.721172,-52.721134,0.028,16.245001,15.946,18.004999,18.089001,0.0477219,0.047722001,0, 0.013985578649447534,0.0125788101204104,0.012066101623755121,0.013553385268685787,0.014342224690348137,0.007204020967699165,0.017794003193587423,0.011419422965470518,0.013265135953748706,0.020882502061793016,0.015474832190819156,0.004470524388812757,0.024070130755121724 +J221147.91-193809.9,3328019701351032014,284056252284846,0.705065297267,-0.0417625730889,-0.707911586343,12732037553680,332.9496582,-19.6360929,35.80709,-52.76884,0.028,16.364,15.771,18.347,18.76,0.05483,0.05483,0, 0.003801323168505944,0.0032793989396500364,0.0030942956157342014,0.0035703026875488087,0.0038355304387356235,0.0015525096835625892,0.005426580526042656,0.0026663568637555353,0.003490768330790091,0.007657808995303962,0.0046849550002127085,0.0006432641500130039,0.011008503120452411 +J221057.31-194241.6,3328019701351032133,284056252278875,0.627315551474,-0.17362662349,-0.759163351654,12734108783366,332.7388036,-19.7115827,35.581505,-52.606293,0.027,16.523001,16.062,18.795,18.584,0.075669497,0.075669996,0, 2.2056090787198772e-07,1.5958475589523065e-07,1.4357162332451428e-07,1.7621207982194842e-07,1.961761635985577e-07,4.19882243601654e-08,4.5341641716936436e-07,8.614224982259682e-08,1.728672876821223e-07,1.3329019126251011e-06,4.587840305236199e-07,2.6708289389642402e-09,5.2798865587287835e-06 +J221058.27-193209.0,3328019701351032708,284056252293127,0.746396864837,-0.203362020915,-0.633668374318,12731696031148,332.7428196,-19.5358419,35.856503,-52.551754,0.026,16.940001,16.511999,19.188999,18.528999,0.0588562,0.058855999,0, 0.0013560472932723359,0.001138456942951577,0.0010642698399873358,0.0012448726173000318,0.0013477707822719075,0.0004822837470880905,0.0020712037462482283,0.0008622314355286829,0.0012171503072755476,0.003295705090712355,0.0018039891243754177,0.0001510173495694919,0.005501476340110308 +J221019.06-193223.3,3328019701351034347,284056252292778,0.701094264241,-0.320999401688,-0.636730882527,12733639839392,332.5794545,-19.5398071,35.766357,-52.40786,0.024,14.255,14.103,17.472,17.232,0.062660299,0.062660001,0, 0.0003904941164731476,0.0003185925597630413,0.00029524041164328147,0.00034952499057597334,0.00038103505476972836,0.00012128163995849014,0.0006367713555686421,0.00022455830457548137,0.0003418544414241106,0.001145924406373167,0.0005643983277733439,2.811782044417537e-05,0.0022408502839758454 +J221055.36-192551.2,3328019701351044999,284056252301441,0.803608009495,-0.229461762576,-0.54914612499,12731707207601,332.7306833,-19.4308981,36.012856,-52.505966,0.028,14.12,13.946,17.297001,17.030001,0.064996302,0.064995997,0, 0.0001542768449937801,0.00012348969713710353,0.00011382657847176298,0.00013570729503145653,0.00014854329676835298,4.398397622441884e-05,0.0002620876535558667,8.310957814819455e-05,0.000132774806340594,0.0005109138548237081,0.00023586902572202907,8.325051365245442e-06,0.0011108720850531767 +J221053.92-192642.3,3328019701351045010,284056252300376,0.794654358948,-0.23207805325,-0.5609493979,12731693468066,332.7246748,-19.4450881,35.987774,-52.50537,0.027,14.25,14.15,17.745001,16.91,0.077566899,0.077567004,0, 3.976077978108387e-08,2.8163651196035642e-08,2.522901414321769e-08,3.111102258066672e-08,3.473258927257782e-08,7.028980076219582e-09,8.475100106042297e-08,1.4611143679755291e-08,3.05401888227012e-08,2.727089820294691e-07,8.867795787820328e-08,3.6091749467098745e-10,1.234954271119025e-06 +J221101.52-192749.7,3328019701351045039,284056252298877,0.79129860487,-0.2041210229,-0.576351564534,12731693629041,332.7563678,-19.463813,35.975174,-52.539795,0.028,14.753,14.657,17.888,17.436001,0.0687989,0.068798997,0, 2.450389825398248e-05,1.8970345449191408e-05,1.7334976127254656e-05,2.0893266245809363e-05,2.3014233886006774e-05,6.061418262018363e-06,4.4521088720807776e-05,1.1815257652397765e-05,2.0457312007263403e-05,9.945250112239737e-05,4.137460728951803e-05,8.019866233588081e-07,0.00026105821600182113 +J221236.65-192411.1,3328019701351047582,284056252303551,0.841867161111,0.122005839817,-0.525713094846,12732214643277,333.1527422,-19.4031091,36.276123,-52.871693,0.026,14.273,14.118,17.683001,17.055,0.078838103,0.078837998,0, 1.1178955014356094e-08,7.795021000760139e-09,6.962953136169681e-09,8.612845809529537e-09,9.63305052157794e-09,1.8769243002023233e-09,2.4452913152535734e-08,3.935055535004528e-09,8.458649603253373e-09,8.414748077587041e-08,2.62764862460699e-08,8.363435146465233e-11,4.180897834432521e-07 +J221156.26-191756.7,3328019701351048862,284056252311888,0.900374801536,-0.0219518666418,-0.434561080068,12732219329874,332.9844453,-19.2991068,36.34966,-52.68716,0.027,16.215,16.134001,19.159,18.153999,0.078580096,0.07858,0, 1.4582640705594251e-08,1.020208700263061e-08,9.118374212820214e-09,1.1271999091294291e-08,1.2602538195052084e-08,2.474666262080688e-09,3.1726341647437874e-08,5.179340273112286e-09,1.1069161525737386e-08,1.0764665510943952e-07,3.389787822885041e-08,1.1346425906877818e-10,5.248294594898474e-07 +J221416.08-193057.8,3328019701351050374,284056252294734,0.666674012689,0.416315250581,-0.618245398639,12732266843498,333.5670365,-19.5160643,36.316666,-53.27759,0.022,15.445,15.339,18.174999,17.827999,0.077143803,0.077143997,0, 5.941392377565368e-08,4.229383968982618e-08,3.792340468173254e-08,4.671601416412519e-08,5.212174074772295e-08,1.0681891518870611e-08,1.2559095138046333e-07,2.2139812344758174e-08,4.5852061328884406e-08,3.9562992979554373e-07,1.3034762555446945e-07,5.755115243351631e-10,1.737473011607151e-06 +J221421.41-192756.0,3328019701351050428,284056252298717,0.682526052454,0.447579766168,-0.577780702896,12732276840717,333.5892417,-19.4655629,36.407974,-53.280426,0.023,15.745,15.65,18.632,17.712999,0.076211996,0.076211996,0, 1.3784259363012007e-07,9.914714560243682e-08,8.908813623504753e-08,1.0949193157104555e-07,1.2199559264527817e-07,2.5693302840182096e-08,2.862783203933805e-07,5.291385226151393e-08,1.0743297717056393e-07,8.62834273579098e-07,2.922821910658167e-07,1.536609566806921e-09,3.5490897918739186e-06 +J221315.05-191927.2,3328019701351050788,284056252309933,0.848677542312,0.266164134547,-0.457059167566,12732409388841,333.3127282,-19.3242419,36.483612,-52.98727,0.024,16.577,16.462999,19.157,18.263,0.063661598,0.063662,0, 0.0002667637258689276,0.00021590357840568452,0.0001996200859473401,0.00023704388330771822,0.00025886738513059633,7.988794987205863e-05,0.0004426693931847022,0.00014922373751140606,0.00023187324580849355,0.0008240483027216293,0.0003947606511328255,1.7010272194469477e-05,0.0016848470899666474 +J221224.13-190158.6,3328019701351055404,284056252333048,0.979092608117,0.0900874521293,-0.182378495713,12732395272937,333.1005741,-19.0329609,36.82417,-52.69972,0.025,13.858,13.758,16.829,16.103001,0.060065601,0.060066,0, 0.0009421647399504089,0.0007840610843442783,0.0007309320510560019,0.0008583256882134242,0.0009313562209362195,0.00032109752463728246,0.0014691070805346084,0.0005806676638330644,0.0008392730845262481,0.002428678219990495,0.001285454461187647,9.172041283565541e-05,0.00425577908757142 +J221258.35-190002.0,3328019701351055620,284056252335539,0.96160257289,0.229545306976,-0.150430860719,12732423850291,333.2431473,-19.00056,36.950974,-52.81517,0.025,16.046,15.499,17.923,17.750999,0.0586375,0.058637999,0, 0.0014437904346124926,0.0012140034114489933,0.0011354635207492334,0.001327199250230747,0.0014363160500310563,0.0005174219333913843,0.002197061951955482,0.0009231199248441436,0.0012976303562349481,0.0034723477088188437,0.001912213418864471,0.00016466050404055543,0.0057469229893313415 +J221247.19-190316.8,3328019701351055697,284056252331322,0.961811847525,0.182822652431,-0.203700387132,12732393014070,333.1966619,-19.054692,36.84191,-52.792492,0.024,16.297001,16.237,18.568001,17.775999,0.038392,0.038392,0, 0.04081540227061113,0.03849071658772003,0.03773663843286638,0.04063182190163017,0.04200408297312813,0.02803294033943812,0.04490061522094408,0.039052958287503305,0.03992988624897334,0.04367395864849781,0.04101520787588477,0.025254446392302073,0.04055288360747739 +J221147.89-190323.8,3328019701351058132,284056252331155,0.976920545886,-0.0579449599524,-0.205593357484,12732198313833,332.9495768,-19.0566259,36.706917,-52.573746,0.026,15.203,14.839,17.298,17.208,0.065505899,0.065505996,0, 0.00012361000659324295,9.85174037070117e-05,9.070246677372998e-05,0.00010830018782225368,0.0001186465888010934,3.458277376923625e-05,0.0002118818096484265,6.562712479428715e-05,0.00010596924277651436,0.0004204714967166719,0.00019140742268999473,6.251545895361256e-06,0.0009364823924818019 +J221150.31-190232.1,3328019701351058170,284056252332272,0.980300321045,-0.0482655835744,-0.191524708594,12732198489922,332.9596255,-19.0422713,36.73446,-52.57775,0.026,15.396,15.318,18.079,17.802,0.0719551,0.071955003,0, 3.6357080115551498e-06,2.7319310438562547e-06,2.4785475576689e-06,3.0130134859820346e-06,3.3355281336717335e-06,7.977493685333875e-07,6.988142109584328e-06,1.5932207180969254e-06,2.9525122379934175e-06,1.759782611093732e-05,6.718621578550831e-06,7.630919228274588e-08,5.5055977282315695e-05 +J221119.83-190831.6,3328019701351058320,284056252324387,0.942684364893,-0.167805450763,-0.288422465978,12732202450872,332.8326584,-19.1421348,36.51276,-52.499104,0.030,15.938,15.815,18.462999,17.968,0.069401696,0.069402002,0, 1.7536771431358445e-05,1.350170363338354e-05,1.2320909552553194e-05,1.4874645561513153e-05,1.6400488219633394e-05,4.240692893324266e-06,3.220433215808827e-05,8.305287145985364e-06,1.4566329348989715e-05,7.355791820649214e-05,3.0106083622749543e-05,5.285343681460351e-07,0.00019934238387931945 +J221125.30-191134.1,3328019701351058331,284056252320443,0.930599029646,-0.143886445766,-0.336574117762,12732203397772,332.855419,-19.1928323,36.446426,-52.536556,0.030,16.098,15.996,18.052999,17.809,0.042613,0.042613,0, 0.026885339404036156,0.02483016337261228,0.02410392729567179,0.02648594237870644,0.027687648614788743,0.016238040833300542,0.03157320909265325,0.024077968405024037,0.025968657526680223,0.033183440450896856,0.02806120531328292,0.012615312818513125,0.03366295103013817 +J221139.54-190422.6,3328019701351058353,284056252329902,0.970841604427,-0.0915863289021,-0.221536731652,12732211115634,332.9147626,-19.072946,36.663166,-52.548412,0.026,16.153999,15.852,18.715,17.945,0.075778902,0.075778998,0, 2.0048195651852705e-07,1.448766192591023e-07,1.303050956276659e-07,1.5997614106636764e-07,1.781314399565852e-07,3.799551330979329e-08,4.1302641473230927e-07,7.801453390817609e-08,1.5694548461640363e-07,1.2205051790806569e-06,4.186925727071138e-07,2.3852998781744235e-09,4.872842465003129e-06 +J221147.46-191029.1,3328019701351058598,284056252321944,0.945821444317,-0.0578317881655,-0.319495351684,12732209034088,332.9477528,-19.1747528,36.523163,-52.612434,0.028,16.681999,16.337,19.016001,18.181,0.0573989,0.057399001,0, 0.002029423187584281,0.0017212341321978521,0.0016144822292407982,0.0018793615182765642,0.0020291156271859986,0.0007592826009264263,0.0030241658337756016,0.0013383870542392655,0.001837424827690001,0.004601648963653377,0.0026226380233341697,0.0002642903304911288,0.007261753957905634 +J221448.42-185410.4,3330018201351000047,284056252343401,0.76827000819,0.637900272503,-0.0533332621937,12731525039076,333.7017711,-18.9029145,37.352226,-53.188507,0.024,13.933,13.77,16.572001,16.134001,0.0573912,0.057390999,0, 0.0020337356190999207,0.001724987230337885,0.0016180327175832514,0.0018834434005743366,0.002033491367539278,0.0007611085114397437,0.0030301795580213757,0.0013414986119809566,0.0018414154060468614,0.004609675996326703,0.0026277999908821364,0.0002650778984677019,0.007272209291245766 +J221444.43-185432.7,3330018201351000072,284056252342899,0.778498156076,0.624819318861,-0.0595099971874,12731524980668,333.685143,-18.9091011,37.333443,-53.175896,0.024,14.434,14.237,17.212999,16.794001,0.071198702,0.071199,0, 5.952880449220613e-06,4.505979738596522e-06,4.095094784059453e-06,4.968119910070349e-06,5.493407145650652e-06,1.3443971158549474e-06,1.1288216587899252e-05,2.6696977738047867e-06,4.8673515569331095e-06,2.7602350334218813e-05,1.0757723748265917e-05,1.3929847998670892e-07,8.264272768293649e-05 +J221458.56-185326.8,3330018201351000183,284056252344386,0.74105614878,0.67017587199,-0.0412320865059,12731528195082,333.7440358,-18.8907997,37.394363,-53.2218,0.024,15.504,15.446,18.330999,17.393999,0.070268102,0.070267998,0, 1.0567915703637754e-05,8.07017538337648e-06,7.349772116056435e-06,8.894366079188642e-06,9.820425707371053e-06,2.4722870068715322e-06,1.9710610963467395e-05,4.874804966636669e-06,8.711878437852808e-06,4.651991518702689e-05,1.8593632139072014e-05,2.8198679187641525e-07,0.00013219761643246778 +J221358.43-185333.6,3330018201351002755,284056252344223,0.884007927837,0.465479232392,-0.0431169077305,12732365455023,333.4934869,-18.8926862,37.254414,-53.000126,0.025,15.864,15.859,18.646999,17.690001,0.0632338,0.063234001,0, 0.0003146498783774019,0.0002555341932233522,0.00023649029342817307,0.0002804666173668706,0.00030606173582325974,9.569553747991055e-05,0.0005182820639605506,0.0001780908442696676,0.0002743325217949058,0.0009510042155287414,0.0004609598094789406,2.112830948308344e-05,0.0019077392668456611 +J221543.75-204526.9,3341021301351045316,284056252199510,-0.199466855888,-0.263860984007,-0.943710948607,12731899923127,333.9323166,-20.757473,34.519207,-54.00095,0.030,16.103001,16.075001,18.500999,18.195999,0.068483204,0.068483002,0, 2.9041464591411785e-05,2.254759153941423e-05,2.061855828818864e-05,2.4829187904437285e-05,2.733582203234636e-05,7.269255025614077e-06,5.247253092219945e-05,1.4134504394364897e-05,2.4309370877349343e-05,0.00011587037672432659,4.861811985188209e-05,9.919208995314641e-07,0.00029920628816669016 +J221539.40-204536.7,3341021301351045360,284056252199299,-0.205812134503,-0.262232154027,-0.942802027303,12731899839758,333.9141848,-20.7602102,34.505898,-53.98566,0.030,16.136,16.113001,18.542,17.836,0.047815099,0.047814999,0, 0.013795194945004453,0.012401387770577246,0.01189335946776407,0.013364343086013947,0.014145129060366336,0.0070852392651723104,0.017577467644398673,0.011243771801558717,0.0130798120974884,0.020673093711818177,0.015282399807010289,0.004376952873558927,0.02388938493738425 +J221710.89-202655.0,3341021301351061584,284060547157265,-0.00792462448912,-0.0271289484307,-0.999600530454,12853177734721,334.2954154,-20.4486187,35.200497,-54.227726,0.029,14.701,14.614,17.646,17.176001,0.060841899,0.060842,0, 0.000734920498251388,0.0006080697538918162,0.0005658566778349014,0.0006661198295706165,0.0007238177766995633,0.00024366308671057747,0.001161312199318962,0.00044381037591669046,0.0006513748198057159,0.001968337232973794,0.0010196134288431664,6.550504815065694e-05,0.0035611889709615393 +J221655.83-202749.8,3341021301351061590,284060547156371,-0.0147884363977,-0.0408996393561,-0.99905381319,12852994240099,334.2326652,-20.4638571,35.14413,-54.176716,0.030,13.907,13.824,17.791,16.858,0.078358702,0.078359,0, 1.8270865394051303e-08,1.281835352507447e-08,1.1462502798052809e-08,1.4162121058366377e-08,1.5828731780971444e-08,3.129066688614131e-09,3.956759404571312e-08,6.539166957436122e-09,1.3906157882710904e-08,1.326484836525836e-07,4.207262985213022e-08,1.4708451672549845e-10,6.362403656618974e-07 +J221639.82-203006.9,3341021301351061727,284060547154074,-0.0327612556672,-0.0746248076442,-0.996673385925,12852993138820,334.165938,-20.5019422,35.04886,-54.129337,0.031,15.585,15.465,18.212,17.438,0.065468103,0.065467998,0, 0.00012575898831348377,0.00010026465620713225,9.231970044594932e-05,0.00011021806690329168,0.00012073937944077238,3.5237027633871565e-05,0.00021541172807970198,6.684574183135356e-05,0.00010784507700226991,0.00042687254484445066,0.00019453716500681761,6.39355915521236e-06,0.0009489341036709607 +J221518.09-204346.2,3341021301351064324,284056252201640,-0.208335501975,-0.221748915501,-0.952587915676,12731905093761,333.8253972,-20.7295174,34.51203,-53.897213,0.030,14.955,14.945,18.080999,17.247999,0.069260202,0.069260001,0, 1.8965909033311623e-05,1.4620255760359096e-05,1.3345732827429824e-05,1.6105905970952093e-05,1.7754210193289975e-05,4.609707155176556e-06,3.474527455231444e-05,9.018502827972699e-06,1.5771560111319883e-05,7.896277581045576e-05,3.24373908919557e-05,5.822858463359717e-07,0.00021243670809004093 +J221456.75-203524.6,3341021301351064816,284056252211565,-0.126211485132,-0.112418915636,-0.985612828867,12731951177534,333.7364848,-20.5901863,34.69282,-53.77493,0.034,16.447001,16.372999,19.128,18.309999,0.069802299,0.069802001,0, 1.3928216749234376e-05,1.068328915291749e-05,9.740032429565758e-06,1.1771857117689364e-05,1.2987797095458256e-05,3.3170227033527795e-06,2.5761608695472593e-05,6.516702771304952e-06,1.1528981710062653e-05,5.973522148981548e-05,2.418224996351981e-05,3.969176795483094e-07,0.0001654403031932293 +J221555.58-203030.0,3341021301351065464,284056252217504,-0.049471103167,-0.0726211473411,-0.996131908389,12731923318643,333.981606,-20.5083362,34.94631,-53.967396,0.031,16.729,16.533001,18.819,18.202999,0.035236299,0.035236001,0, 0.05279708362201072,0.05057400677969081,0.049952829890214,0.05289219000818017,0.054182095179340195,0.03989878329578119,0.05530866210641645,0.05277162380387818,0.0520993406691431,0.0511690168044792,0.051845327167858404,0.03933673805276259,0.04485935040971882 +J221413.61-202754.1,3341021301351066991,284056252220784,-0.0381417275273,-0.0232745562027,-0.99900125308,12731942992017,333.5567161,-20.4650493,34.804607,-53.57589,0.029,14.598,14.587,17.551001,16.544001,0.066910997,0.066910997,0, 6.465424237521405e-05,5.091066498453357e-05,4.672249104615802e-05,5.601365210676469e-05,6.150883573079347e-05,1.717113960313083e-05,0.00011359686282787733,3.2965505685956285e-05,5.482246839608779e-05,0.00023689986263746008,0.00010377690162663149,2.7273807612842633e-06,0.0005647047368947226 +J221424.77-203733.0,3341021301351067423,284056252209024,-0.169057703922,-0.114277535148,-0.978958700715,12731934845749,333.6032354,-20.6258541,34.56998,-53.667484,0.036,16.386,16.284,18.757999,18.584999,0.077046297,0.077045999,0, 6.491785706411785e-08,4.626050263770396e-08,4.1488769781466666e-08,5.1096467271565714e-08,5.700145003496609e-08,1.17135389455009e-08,1.369815062596765e-07,2.4262835905843577e-08,5.014991873066912e-08,4.295652921357311e-07,1.419266589508076e-07,6.376040697389409e-10,1.874046350023007e-06 +J221552.85-195808.4,3344019701241010174,284056252258451,0.249648569503,0.357649076887,-0.899868173427,12732004904420,333.9702173,-19.9690231,35.807495,-53.785126,0.029,16.382999,16.139999,18.993,18.027,0.0704735,0.070473999,1, 9.335702695506976e-06,7.1154056073004e-06,6.477213036121273e-06,7.842790412002136e-06,8.662156066807512e-06,2.1670985702270862e-06,1.7476042659167465e-05,4.279770512205875e-06,7.682278295226431e-06,4.1567655873067056e-05,1.6522005664565193e-05,2.420347721663795e-07,0.00011947683276104536 +J221802.14-201515.6,3344019701351005349,284060547169143,0.0116715744384,0.164842798916,-0.986250792646,12853143418275,334.5089313,-20.2543352,35.62498,-54.35594,0.028,14.411,14.266,17.052,16.556,0.057429101,0.057429001,0, 0.0020132556112947043,0.0017071635318228232,0.0016011712257074807,0.001864058285468118,0.0020127107020924723,0.00075243714786475,0.003001620004311312,0.0013267216290642175,0.001822463899083926,0.004571555114590671,0.0026032854831643357,0.0002613376887394172,0.007222556257305285 +J221720.58-201222.6,3344019701351005532,284060547172285,0.0512918817262,0.206142410067,-0.977176775021,12853122374720,334.335752,-20.2062935,35.613914,-54.18662,0.029,15.843,15.746,18.275999,17.514,0.051794101,0.051794,0, 0.007099762350104938,0.006240411425696309,0.005929689924144494,0.006767023216829599,0.007224744306084507,0.0032074804565012634,0.009642931755356066,0.00533171287970669,0.006618011366940861,0.012522144102148546,0.00832238127922147,0.0016009805308571724,0.01627416805596591 +J221659.23-201522.1,3344019701351005592,284060547169025,0.0534030492673,0.154492948985,-0.986549564413,12853112250113,334.2468085,-20.256153,35.48768,-54.12348,0.028,15.928,15.669,18.219999,17.875999,0.064908601,0.064909004,0, 0.00016027346936735092,0.00012838938093323848,0.00011836803945076862,0.0001410830032979173,0.00015440297816243654,4.5850626433345995e-05,0.00027183194084213564,8.656803529192477e-05,0.00013803222735313625,0.0005282124066548015,0.00024447623736620745,8.751383823605584e-06,0.0011435768844046215 +J221732.22-201058.5,3344019701351006550,284060547173656,0.0456471006448,0.230716657518,-0.971949672641,12853114562325,334.384291,-20.1829393,35.676704,-54.222282,0.029,16.955999,16.905001,18.843,18.152,0.022397799,0.022398001,0, 0.10933688210095913,0.11288675174893416,0.11494979811290548,0.11135284162880407,0.10877059321371277,0.12451829358282038,0.09260256867657646,0.12423158816905949,0.11163572815483268,0.07474089245309466,0.10140955541810413,0.15001291698273247,0.05499981554590367 +J221648.32-201402.7,3344019701351007936,284060547170451,0.0683948104824,0.172096728805,-0.982702836993,12853111953818,334.2013367,-20.2340876,35.500103,-54.076008,0.028,14.027,14.017,17.513,16.622999,0.0583717,0.058371998,0, 0.0015567753617914757,0.001311470208526585,0.0012273766458221929,0.00143337807119362,0.0015504495371733886,0.0005631169874902864,0.0023583229506187017,0.0010020721403459197,0.001401428168694975,0.003696664916141024,0.0020508207814284867,0.00018274203751192765,0.006055042082781531 +J221548.77-202123.1,3344019701351007943,284056252228821,0.0374459260378,0.0517495286947,-0.99795780918,12731911530144,333.9532281,-20.3564322,35.176952,-53.894306,0.030,14.173,14.115,17.976,17.219,0.062739797,0.062739998,0, 0.0003788701500390509,0.00030890259511834117,0.0002862051648611256,0.00033891632970716986,0.0003695250910060543,0.00011731116699114962,0.0006187228777351215,0.00021737040428122217,0.00033148208039521644,0.0011165818627080844,0.0005486673512615098,2.7003773738306748e-05,0.002191481293393907 +J221625.51-201353.9,3344019701351008013,284060547170601,0.0855053702943,0.166952401799,-0.982250338348,12731998199269,334.106294,-20.2316598,35.455505,-53.990726,0.030,15.059,14.669,17.309999,17.145,0.076247402,0.076246999,0, 1.332949884756858e-07,9.583222464620212e-08,8.610134574217623e-08,1.0583218923271182e-07,1.1792522939337101e-07,2.4805244367156722e-08,2.7705076379739144e-07,5.10999739183029e-08,1.0384350463915748e-07,8.366259490371546e-07,2.83058751057883e-07,1.476370476269233e-09,3.451117711349356e-06 +J221640.90-202632.0,3344019701351008160,284060547157654,-0.00870432955675,-0.0200724603816,-0.999760636843,12852995616773,334.1704471,-20.4422325,35.147842,-54.11458,0.031,16.033001,15.799,18.454,17.997,0.069151297,0.069150999,0, 2.0194836562856135e-05,1.558430781769085e-05,1.422951675155347e-05,1.7166951361119032e-05,1.8920298536029568e-05,4.9300744695867415e-06,3.692033172369413e-05,9.636433267560691e-06,1.681011772891577e-05,8.354624732328085e-05,3.4428447902034746e-05,6.300944914273494e-07,0.00022339549274339617 +J221602.01-201021.0,3344019701351008268,284056252242159,0.132630624997,0.206371634846,-0.969443069831,12731996645936,334.0084065,-20.1725062,35.500755,-53.88476,0.029,16.212,16.164,18.971001,18.066,0.066377901,0.066377997,0, 8.328974510831584e-05,6.589067233701749e-05,6.054392570892975e-05,7.24724049397053e-05,7.951155531410697e-05,2.2565152814223804e-05,0.00014496523927087445,4.313131897953535e-05,7.092380255494007e-05,0.00029661513836571447,0.0001318498464984079,3.767671604905052e-06,0.0006887662137759997 +J221602.10-202444.3,3344019701351009036,284056252224753,0.00433452145951,0.0067498588213,-0.999967825147,12731911809679,334.0087702,-20.4123304,35.11483,-53.96137,0.032,16.892,16.834999,19.319,18.604,0.066220999,0.066220999,0, 8.951696836512438e-05,7.091058358812956e-05,6.517924655412025e-05,7.798657468524054e-05,8.553943794134605e-05,2.4390449891037346e-05,0.00015538292371943232,4.656140161467961e-05,7.63179345929825e-05,0.00031620592867138427,0.00014115031767826034,4.130346024257222e-06,0.0007287934110940927 +J221459.58-201030.4,3344019701351011282,284056252241955,0.179380804345,0.163613006012,-0.970079023223,12732146477589,333.7482807,-20.175112,35.36418,-53.65431,0.030,16.796,16.236,18.794001,18.646,0.055571102,0.055571001,0, 0.003204042996481733,0.0027509142179901365,0.002591200579401248,0.0029975699809056903,0.0032249817989774476,0.00127616373648381,0.004630596492034697,0.002208582770969055,0.0029307141901598094,0.006675101271419975,0.004001326064335598,0.0005036308663662392,0.009850106812372443 +J221501.93-200730.5,3344019701351013404,284056252245924,0.213018372777,0.19814443016,-0.956745503076,12732151810297,333.7580599,-20.1251575,35.44898,-53.646976,0.031,14.102,13.93,16.771,16.405001,0.058634501,0.058634002,0, 0.0014454431140053614,0.0012154277011005407,0.0011368061884808457,0.0013287511240549037,0.001437984672107128,0.0005180869625899571,0.0021994267912410796,0.0009242706889505529,0.0012991474180112418,0.003475652224897008,0.0019142464690260282,0.0001649211008694482,0.005751488547379476 +J221430.33-200013.6,3344019701351013615,284056252255795,0.329871438695,0.234302174588,-0.914487465697,12732158162676,333.6264155,-20.0037953,35.57524,-53.49078,0.028,15.871,15.417,17.51,17.547001,0.048970699,0.048971001,0, 0.011554482298822251,0.010321406851098138,0.009871990289970253,0.011144485332385384,0.011826144364771946,0.005719680243220804,0.014995983475532177,0.009204117021605783,0.010904294252517903,0.018130928034331563,0.012998664556385639,0.003332677757511098,0.02164321945758286 +J221427.49-200156.0,3344019701351013869,284056252253269,0.311100664307,0.215474204558,-0.925628026714,12732158326901,333.6145763,-20.0322477,35.52394,-53.48946,0.028,16.622999,16.27,18.698,18.360001,0.047018901,0.047019001,0, 0.015480234572863329,0.013975748717581903,0.013428065547191592,0.015039896457548243,0.01588976320894955,0.008152990486232668,0.019477758488197033,0.012812043297446864,0.014722784257368273,0.022489597945404765,0.016976587324078862,0.0052344488071507855,0.025434048742538802 +J221645.10-195445.7,3344019701351016506,284060547190085,0.185569656632,0.449303360978,-0.873893810684,12731969259818,334.1879408,-19.9127115,36.010864,-53.960293,0.030,15.796,15.626,18.452999,17.646999,0.071464904,0.071465001,0, 5.016229129982852e-06,3.787215127026795e-06,3.4397764458270477e-06,4.176087478437139e-06,4.619569020088531e-06,1.12136964100809e-06,9.55770196395246e-06,2.2313264777078777e-06,4.091677769698535e-06,2.36139111924028e-05,9.136471200617406e-06,1.1296455269470002e-07,7.179767097099535e-05 +J221558.63-200327.8,3344019701351016725,284056252251257,0.19597246473,0.295677580869,-0.934970353134,12732007187837,333.9943029,-20.0577314,35.677784,-53.83528,0.028,16.629999,16.375,19.014,18.191999,0.056721799,0.056722,0, 0.002417550006908598,0.002059845142043905,0.0019351020239048195,0.002247457326675402,0.0024234006426747615,0.0009257518816473204,0.003561892378939023,0.0016209416389921724,0.002197295570805795,0.005311252353277123,0.0030841293361731814,0.0003378272260721524,0.008172675571673126 +J221729.39-195455.0,3344019701351018738,284060547189899,0.0995053358337,0.473525299729,-0.87514140495,12853105008755,334.3724939,-19.9152839,36.10322,-54.12514,0.027,14.056,13.985,16.325001,15.944,0.028028199,0.028028,0, 0.08355119926498264,0.08317136309224563,0.08356313527381061,0.08460002195557755,0.08459597962763042,0.07889645724132938,0.07801629586267049,0.09063183336681771,0.08399201903574322,0.06595472621884613,0.07894683246757814,0.08991637508790319,0.05188889197154874 +J221659.66-195139.4,3344019701351019125,284060547193147,0.172459688125,0.501859531652,-0.847581657696,12853098951520,334.2486187,-19.8609673,36.125744,-53.99729,0.030,16.153999,16.073,18.888,18.238001,0.078367896,0.078368001,0, 1.8108142123399285e-08,1.2702814224105812e-08,1.135896248480123e-08,1.4034489763551624e-08,1.5686276088418e-08,3.100101631968846e-09,3.9222197018619464e-08,6.479011619761766e-09,1.3780876273741644e-08,1.3155176684233436e-07,4.171311671899698e-08,1.4558295470422119e-10,6.313832258870668e-07 +J221709.46-193829.7,3344019701351032068,284056252284404,0.200964543246,0.673039249765,-0.711780458171,12853093279830,334.2894525,-19.6415857,36.499462,-53.96117,0.027,14.259,14.053,17.306,16.872,0.077686198,0.077685997,0, 3.5463940561075795e-08,2.5084980877089967e-08,2.246514478930594e-08,2.7710782647935503e-08,3.09418348260547e-08,6.239982402697429e-09,7.576907838117866e-08,1.29814649966849e-08,2.7203463837119132e-08,2.452752680520797e-07,7.946416230511045e-08,3.1618757672286147e-10,1.1202855318988462e-06 +J221815.16-193544.9,3344019701351032516,284060547208463,0.0120746369429,0.734128025956,-0.678903707936,12853639181050,334.5631715,-19.5958244,36.71938,-54.189034,0.027,16.490999,16.385,18.739,18.212,0.0494349,0.049435001,0, 0.010724737053394485,0.009555509807503125,0.009129606613679914,0.010325211995815174,0.010967938643770537,0.005230704643409953,0.014022558025797497,0.008463416635681072,0.010101717930650289,0.017147258563227092,0.012142643557583427,0.0029749266576198762,0.020744981946700988 +J221618.08-194206.2,3344019701351034642,284056252279690,0.318096560031,0.576398134648,-0.75271493201,12731976797073,334.0753402,-19.7017322,36.289806,-53.790905,0.028,14.285,14.071,17.346001,16.93,0.079287603,0.079287998,0, 6.93016934476355e-09,4.803148299614419e-09,4.286022836647341e-09,5.307430489766225e-09,5.9399753652177064e-09,1.1413104752264045e-09,1.5308566931340127e-08,2.4000835658220264e-09,5.213285932541006e-09,5.40506511120604e-08,1.662503179135421e-08,4.834995118565417e-11,2.777053382707294e-07 +J221601.96-193251.4,3344019701351034758,284056252292158,0.414330125958,0.644362331486,-0.642750132233,12732239493682,334.0081734,-19.5476387,36.499954,-53.680023,0.027,15.689,15.499,17.837999,17.608,0.055362601,0.055362999,0, 0.0033643550869841454,0.002892482631524145,0.0027258610402211723,0.003151060529458888,0.003388722468481832,0.0013495354691562841,0.004845433541495798,0.0023305628073976388,0.0030808012103347416,0.00694282673413573,0.0041857815102009244,0.0005400132992058771,0.010169463986583077 +J221648.05-193438.1,3344019701351035025,284060547209463,0.276555170447,0.693595443354,-0.665163587892,12732229855719,334.2002092,-19.5772688,36.55489,-53.860485,0.024,16.378,15.932,18.68,18.250999,0.069989301,0.069989003,0, 1.2470470069783267e-05,9.548046019940724e-06,8.701231380627926e-06,1.0521858598006262e-05,1.1612227323576836e-05,2.948378703073506e-06,2.314402767119186e-05,5.801065590026632e-06,1.0305259820311074e-05,5.4052159083336616e-05,2.1768328910954986e-05,3.459964156228534e-07,0.00015126587185492697 +J221524.61-193504.8,3344019701351037393,284056252288973,0.493022303827,0.554169826494,-0.670689802615,12732267228707,333.8525451,-19.5846943,36.35863,-53.554127,0.027,14.662,14.577,15.016,14.974,0.00867,0.00867,0, 0.17681088186193775,0.21578242176269002,0.2271027103298576,0.18228179928045185,0.16153599041899475,0.3802779213659899,0.10728312187258589,0.20468512374697692,0.19322606937906536,0.08880955881137223,0.16260978772333334,0.3941433394604008,0.05835311484856975 +J221445.43-194502.2,3344019701351037744,284056252275821,0.482516054509,0.390570612055,-0.783985238471,12731993266601,333.6892954,-19.7506157,36.00978,-53.464005,0.026,16.437,15.87,18.305,18.422001,0.056032199,0.056031998,0, 0.002868940841095825,0.002455728451135574,0.002310689798759912,0.0026773439107067947,0.0028830742654666246,0.0011248527272412622,0.004178388324670518,0.001955917610383784,0.0026176028944129783,0.006104864018430708,0.0036131732992759798,0.00043034587427193615,0.00915952305104859 +J221528.16-193851.9,3344019701351037937,284056252283874,0.456184365296,0.528293771311,-0.716101610145,12731986547850,333.8673352,-19.647757,36.266262,-53.588223,0.026,16.239,16.221001,18.653999,18.454,0.076013699,0.076013997,0, 1.635667107907367e-07,1.1789842504744672e-07,1.0598330324353755e-07,1.3019373779473838e-07,1.4502012921398858e-07,3.0716720535825234e-08,3.3847518861752414e-07,6.317428988336159e-08,1.277372931362276e-07,1.0110850535402893e-06,3.444557732768081e-07,1.877359779285129e-09,4.103281546060356e-06 +J221503.26-192544.4,3344019701351040204,284056252301593,0.609527896225,0.573284365621,-0.547558928206,12732286048652,333.7635872,-19.4290001,36.558094,-53.423027,0.024,14.248,14.085,17.424999,16.771,0.079489902,0.079489999,0, 5.559217887073332e-09,3.842044672871938e-09,3.4267933330801415e-09,4.2455403574852305e-09,4.752928121058554e-09,9.073922168765103e-10,1.2336227397702387e-08,1.910787784389483e-09,4.1705524825411656e-09,4.407877951194367e-08,1.346397373650854e-08,3.7573635631945274e-11,2.2993232734802182e-07 +J221449.49-192207.4,3344019701351040419,284056252306479,0.665508636599,0.557591862001,-0.49617493895,12732280767657,333.7062152,-19.3687435,36.622684,-53.351784,0.024,16.025,15.619,18.18,18.120001,0.070665397,0.070665002,0, 8.31873101128319e-06,6.329108071024555e-06,5.7590021382488856e-06,6.976669841665626e-06,7.707812469595624e-06,1.9174292514364112e-06,1.5624084941081253e-05,3.792095480527294e-06,6.8342041999626125e-06,3.742727549266695e-05,1.480114305900594e-05,2.10089152499099e-07,0.00010870622703679595 +J221437.97-192453.2,3344019701351040614,284056252302673,0.672554629263,0.510662811342,-0.535624648209,12732273886551,333.6582404,-19.4148032,36.52453,-53.324684,0.022,16.506001,15.917,18.728001,17.879,0.068375602,0.068375997,0, 3.078886044882361e-05,2.3928822914575734e-05,2.1887272271753892e-05,2.6348646201872562e-05,2.900333329089678e-05,7.739629482458346e-06,5.551821796171898e-05,1.5035505264489738e-05,2.5796364282238758e-05,0.00012209045989853976,5.138558066344442e-05,1.0679704361733441e-06,0.0003134392359856119 +J221638.72-192651.0,3344019701351042887,284060547217001,0.335709416694,0.755227594033,-0.562965779381,12731493568707,334.161335,-19.4475258,36.74119,-53.7825,0.025,14.116,14.018,17.089001,16.502001,0.054687999,0.054687999,0, 0.003924301326502977,0.0033885558452191322,0.0031983383114956372,0.003688509972982577,0.0039613993826874945,0.00161040050179146,0.005589012056940332,0.002761703911744871,0.003606366447651042,0.007855347699947142,0.004824540271675324,0.0006733865399292803,0.011236909250425918 +J221559.40-192629.3,3344019701351042894,284056252300645,0.456236264444,0.693191957593,-0.557972562887,12732242469674,333.9975223,-19.4414966,36.663036,-53.635,0.026,14.14,14.133,17.302,16.219999,0.069531403,0.069531001,0, 1.627558664617183e-05,1.2515141196892814e-05,1.1417140333497653e-05,1.3788639571838164e-05,1.5206349685686636e-05,3.915780564967012e-06,2.9959558372201753e-05,7.677002345645352e-06,1.350326825961645e-05,6.877234143513525e-05,2.8045365166496593e-05,4.814741395117822e-07,0.00018771106733326886 +J221621.59-191829.9,3344019701351042975,284060547225497,0.421681012017,0.791252096428,-0.442837717457,12731502694449,334.0899753,-19.3083171,36.92424,-53.672127,0.025,15.032,15.001,17.540001,17.013,0.045552298,0.045552,0, 0.018892567949828847,0.01718881551066136,0.016571848834623513,0.018447145126139975,0.01942296496495886,0.010418905558971837,0.02322822874184916,0.016070830683643643,0.018066253394021334,0.025953554616179444,0.020355678433520173,0.0071584075121721115,0.028251961304925806 +J221612.70-192222.1,3344019701351043065,284056252306112,0.435407904752,0.748813125639,-0.499698768609,12732236559318,334.052939,-19.3728069,36.801853,-53.661064,0.026,15.639,15.481,18.396,17.469999,0.076146401,0.076145999,0, 1.4641695289581907e-07,1.0539731954034689e-07,9.471963102572456e-08,1.163922548319963e-07,1.296701280718465e-07,2.736770774959752e-08,3.036765734764195e-07,5.6333859947052985e-08,1.142008090322695e-07,9.122492035792407e-07,3.0967268245808964e-07,1.6501883817146893e-09,3.733812912703016e-06 +J221611.23-191443.8,3344019701351043150,284056252316080,0.468650952733,0.794736353817,-0.385694714021,12731502922566,334.0468108,-19.2455166,37.000523,-53.61247,0.025,15.952,15.789,18.533001,17.757999,0.065723501,0.065724,0, 0.00011222958979132542,8.928269558443638e-05,8.215971427449248e-05,9.816188109513344e-05,0.000107578970300006,3.1148575335604964e-05,0.0001931079009733855,5.9217143373018385e-05,9.605284860768092e-05,0.0003861380554217509,0.00017473603742046727,5.5212727245918665e-06,0.0008689314579621784 +J221649.58-192707.1,3344019701351043472,284060547216715,0.300270801356,0.767298826976,-0.566648000062,12731495093934,334.2066034,-19.4519879,36.758385,-53.8242,0.026,16.537001,16.077999,18.767,18.193001,0.057471801,0.057472002,0, 0.001990081546360435,0.0016869952022652696,0.0015820916769429423,0.0018421231416682526,0.0019891964292942503,0.0007426251036279128,0.0029693035638848507,0.00131000079746581,0.0018010194027175991,0.004528419594472491,0.0025755461971695695,0.00025710547047390586,0.007166371579854768 +J221548.31-192225.0,3344019701351043474,284056252306066,0.508882634815,0.700449003378,-0.500409489968,12732235505319,333.9513209,-19.3736276,36.74599,-53.571106,0.025,16.582001,16.242001,18.753,18.079,0.043149199,0.043148998,0, 0.025316287768675273,0.023318167251095596,0.0226077797486941,0.02490241409629356,0.02606723156912622,0.015040598149866835,0.029979292420948607,0.022468481002434808,0.024410346567213723,0.031847471435171686,0.02656791707379998,0.011440355431553502,0.03270705336991221 +J221731.80-192117.3,3344019701351045590,284060547222783,0.17136608339,0.858100052374,-0.484043350929,12852768449934,334.3825066,-19.3548255,37.00856,-53.94759,0.023,14.059,13.873,17.325001,16.847,0.074181497,0.074181996,0, 7.349796905689278e-07,5.40129741668607e-07,4.875718875486612e-07,5.961617686774229e-07,6.62229781513586e-07,1.481318526666085e-07,1.470366150260422e-06,3.0071139074340726e-07,5.845713420868837e-07,4.052015294700822e-06,1.4549600585367233e-06,1.1125449345142753e-08,1.454126558725068e-05 +J221754.26-191814.6,3344019701351045848,284060547225719,0.0928508166429,0.893661872519,-0.43902982069,12852812657684,334.4760898,-19.3040745,37.140694,-54.013443,0.024,16.045,16.031,18.469999,18.106001,0.066279501,0.06628,0, 8.710284882711151e-05,6.896301652780889e-05,6.338051899811955e-05,7.584738258884484e-05,8.320131449620823e-05,2.3680489511371986e-05,0.0001513505868583847,4.522826529033816e-05,7.422533318068591e-05,0.00030864681734584166,0.0001375526366164307,3.988180119485159e-06,0.0007134144180958717 +J221658.52-190749.9,3344019701351058868,284060547236302,0.316566583671,0.907130748166,-0.277307417561,12731553632772,334.2438556,-19.1305464,37.290325,-53.74807,0.025,15.759,15.737,18.396999,17.632999,0.059107799,0.059108,0, 0.001259897066985334,0.001055827801825278,0.0009864527096761595,0.0011547993058169891,0.0012508392463085498,0.0004441472148470738,0.001932617214059925,0.0007959615172866528,0.0011290972070026432,0.0030994920928045858,0.0016847643918108653,0.00013648307815804946,0.005225745206118357 +J221552.86-190559.2,3344019701351062201,284056252327731,0.554515677654,0.794473177729,-0.247638311061,12731530579476,333.9702577,-19.0997978,37.189434,-53.494705,0.023,16.434999,16.224001,18.757999,18.52,0.072344899,0.072345003,0, 2.7956703964864823e-06,2.09276892132775e-06,1.8969990754550834e-06,2.3084205525088443e-06,2.5570487684594583e-06,6.044446505894309e-07,5.410860064503842e-06,1.2106483882909915e-06,2.26231335131821e-06,1.3835314204041404e-05,5.226586216527408e-06,5.5478829804955214e-08,4.429182848147971e-05 diff --git a/papers/171020/README.txt b/papers/171020/README.txt new file mode 100644 index 0000000..31e0e17 --- /dev/null +++ b/papers/171020/README.txt @@ -0,0 +1,55 @@ + +Find "clancy_testing" under the 171020 paper page. Use that. Ignore the rest below. + + +Look in +/Users/cjames/CRAFT/Localisation/FromPravir/RecentResults +for Pravir's original data + +Here, read in everything using "mag_prior.py" + +Need to check confidence limits, they are inconsistent + + +"mod" files have been modified by me for easy reading + +Using R-MAG_CANDIDATES.csv for r-band magnitudes +cand_positional_likelihood for likelihoods EXCEPY they seem to be 0.000... + + + +######## Steps ########### + +171020.py + - This file produces a prior on p(z), saves it to + z_priors_bestfit.npy and zvalues_for_priors.npy + +use_z_priors.py + - This step adds priors on redshift according to estimates of p(z|DM), + contained in z_priors_bestfit.npy and zvalues_for_priors.npy. + - It reads in all FRBs with 'reasonable' localisations, contained in + 'second_cut.csv', and spits out a file with this prior at the end + - The output should be added to "zpriors_added.csv" + - It does NOT account for the greater number of galaxies at large z. + +paper_mag_prior.py + - This functon adds the magnitude-based priors from Driver et al + as per the PATH methodology. + - It reads in "zpriors_added.csv", and spits out an array with + the magnitude from Driver et al at the end + +mag_prior.py + - This reads in the file modR-MAG_CANDIDATES.csv, and reads in + the positional likelihoods from 'mod_cand_pos_likelihood.csv' + + + +####### Other Scripts ##### + +match_galaxies.py + - Used to match galaxies from Liz's paper with galaxies I found from + the catalogue. + +plot_prior.py + - This is used to produce to prior p(z) figure for the paper + - Outputs paper_plot_pz_bestfit_only.pdf diff --git a/papers/171020/errors_misc_functions.py b/papers/171020/errors_misc_functions.py new file mode 100644 index 0000000..a92d31b --- /dev/null +++ b/papers/171020/errors_misc_functions.py @@ -0,0 +1,1578 @@ +import numpy as np +import matplotlib.pyplot as plt +import zdm + +import pickle + +def get_sc_grid(grid,nsnr,snrs): + + # calculate vector of grid thresholds + Emax=grid.Emax + Emin=grid.Emin + gamma=grid.gamma + ndm=grid.dmvals.size + # holds cumulative and differential source counts + cpsnrs=np.zeros([nsnr]) + psnrs=np.zeros([nsnr-1]) + + # holds DM-dependent source counts + dmcpsnrs=np.zeros([nsnr,ndm]) + dmpsnrs=np.zeros([nsnr-1,ndm]) + + backup1=np.copy(grid.thresholds) + Emin=grid.Emin + Emax=grid.Emax + gamma=grid.gamma + + # modifies grid to simplify beamshape + grid.beam_b=np.array([grid.beam_b[-1]]) + grid.beam_o=np.array([grid.beam_o[-1]]) + grid.b_fractions=None + + for i,s in enumerate(snrs): + + grid.thresholds=backup1*s + grid.calc_pdv(Emin,Emax,gamma) + grid.calc_rates() + rates=grid.rates + dmcpsnrs[i,:]=np.sum(rates,axis=0) + cpsnrs[i]=np.sum(dmcpsnrs[i,:]) + + # the last one contains cumulative values + for i,s in enumerate(snrs): + if i==0: + continue + psnrs[i-1]=cpsnrs[i-1]-cpsnrs[i] + dmpsnrs[i-1,:]=dmcpsnrs[i-1,:]-dmcpsnrs[i,:] + return psnrs,dmpsnrs + +def error_get_source_counts(grid,errorsets,Emin,plot=None,Slabel=None,load=False,tag=None): + """ + Calculates the source-counts function for a given grid + It does this in terms of p(SNR)dSNR + """ + # this is closely related to the likelihood for observing a given psnr! + + + + nsnr=71 + snrmin=0.001 + snrmax=1000. + + snrs=np.logspace(0,2,nsnr) # histogram array of values for s=SNR/SNR_th + + if load: + with open('Pickle/error_snrs'+tag+'.pkl', 'rb') as infile: + psnrs=pickle.load(infile) + Epsnrs=pickle.load(infile) + err_psnrs=pickle.load(infile) + + else: + psnrs,dmpsnrs=get_sc_grid(grid,nsnr,snrs) + Epsnrs,Edmpsnrs=get_sc_grid(Emin,nsnr,snrs) + + err_psnrs=[] + err_dmpsnrs=[] + for eset in errorsets: + err_psnr,err_dmpsnr=get_sc_grid(eset,nsnr,snrs) + err_psnrs.append(err_psnr) + err_dmpsnrs.append(err_dmpsnr) + with open('Pickle/error_snrs'+tag+'.pkl', 'wb') as output: + pickle.dump(psnrs, output, pickle.HIGHEST_PROTOCOL) + pickle.dump(Epsnrs, output, pickle.HIGHEST_PROTOCOL) + pickle.dump(err_psnrs, output, pickle.HIGHEST_PROTOCOL) + + mod=1.5 + snrs=snrs[:-1] + imid=int((nsnr+1)/2) + xmid=snrs[imid] + ymid=psnrs[imid] + slopes=np.linspace(1.3,1.7,5) + ys=[] + for i,s in enumerate(slopes): + ys.append(ymid*xmid**s*snrs**-s) + + # scales other values to pass through xmin,ymid + Epsnrs *= ymid/Epsnrs[imid] + + for i,err in enumerate(err_psnrs): + err *= ymid/err[imid] + + + if plot is not None: + # use the following point to scale standard plots to a left-hand min of 1,1 + fixpoint=ys[0][0]*snrs[0]**mod + plt.figure() + plt.xscale('log') + plt.yscale('log') + plt.ylim(1,3) + plt.xlim(0.5,100) + + plt.xlabel('$s=\\frac{\\rm SNR}{\\rm SNR_{\\rm th}}$') + plt.ylabel('$p_s(s) s^{1.5} d\\,\\log(s)$ [a.u.]') + + + plt.plot(snrs,psnrs*snrs**mod/fixpoint,label='Best fit ('+Slabel+')',color='orange',linewidth=3,zorder=3) # this is in relative units + plt.plot(snrs,Epsnrs*snrs**mod/fixpoint,label='Emin',color='red',linestyle=':',linewidth=2,zorder=5) # this is in relative units + + for i,err in enumerate(err_psnrs): + #err *= ymid/err[imid] + if i==0: + plt.plot(snrs,err*snrs**mod/fixpoint,label='90% C.L.',color='grey',linewidth=1,zorder=2) # this is in relative units + else: + plt.plot(snrs,err*snrs**mod/fixpoint,color='grey',linewidth=1,zorder=2) + + temp=[1.02,1.3,1.6,2.05,2.5] + for i,s in enumerate(slopes): + plt.plot(snrs,ys[i]*snrs**mod/fixpoint,linestyle='--',zorder=0,color='black')#,label='slope='+str(s)[0:3]) + plt.text(0.65,temp[i],'$s^{-'+str(s)[0:3]+'}$',zorder=0)#,rotation=30-i*15) + + + #for i,g in enumerate(errorsets): + # psnrs,dmpsnrs=get_sc_grid(grid,nsnr,snrs) + + ax=plt.gca() + #labels = [item.get_text() for item in ax.get_yticklabels()] + #print("Labels are ",labels) + #labels[0] = '1' + #labels[1] = '2' + #labels[2] = '3' + #ax.set_yticklabels(labels) + ax.set_yticks([1,2,3]) + ax.set_yticklabels(['1','2','3']) + plt.legend(fontsize=12)#,loc=[6,8]) + plt.tight_layout() + plt.savefig(plot) + plt.close() + #return snrs,psnrs,dmpsnrs + + +def get_p_zgdm(DMs,grid,dmvals,zvals): + """ Calcuates the probability of redshift given a DM + We already have our grid of observed DM values. + Just take slices! + dmvals are the dmvals of the grid + zvals are the zvals of the grid + DMs are the list of DMs in the survey + grid is the full zdm grid + """ + + + priors=np.zeros([DMs.size,zvals.size]) + for i,dm in enumerate(DMs): + DM2=np.where(dmvals > dm)[0][0] + DM1=DM2-1 + kDM=(dm-dmvals[DM1])/(dmvals[DM2]-dmvals[DM1]) + priors[i,:]=kDM*grid[:,DM2]+(1.-kDM)*grid[:,DM1] + priors[i,:] /= np.sum(priors[i,:]) + return priors + +def error_get_zgdm_priors(survey,grid,errorgrids,mingrid,dirname,basename,dmvals,zvals,zlim=None): + """ Plots priors as a function of redshift for each FRB in the survey + Likely outdated, should use the likelihoods function. + Does this for a list of error grids also + + # now trying to add this for a given SNR... + """ + DMs=survey.DMEGs + priors=get_p_zgdm(DMs,grid,dmvals,zvals) + epriors=[] + for i,e in enumerate(errorgrids): + eprior=get_p_zgdm(DMs,errorgrids[i],dmvals,zvals) + epriors.append(eprior) + + minpriors=get_p_zgdm(DMs,mingrid,dmvals,zvals) + + plt.figure() + plt.xlabel('$z$') + plt.ylabel('$p(z|{\\rm DM})$') + for i,dm in enumerate(survey.DMs): + if i<10: + style="-" + else: + style=":" + + plt.plot(zvals,priors[i,:],label=str(dm),linestyle=style) + if zlim is not None: + plt.xlim(0,zlim) + plt.legend(fontsize=8,ncol=2) + plt.tight_layout() + plt.savefig(dirname+basename) + plt.close() + + + for i,dm in enumerate(survey.DMs): + plt.figure() + plt.xlabel('$z$') + plt.ylabel('$p(z|{\\rm DM})$ [a.u.]') + + plt.plot(zvals,priors[i,:],label='Standard ' +str(dm),linestyle='-') + plt.plot(zvals,minpriors[i,:],label='with Emin included',linestyle=':') + + for k,ep in enumerate(epriors): + if k==0: + plt.plot(zvals,ep[i,:],linestyle=style,color='grey',label='90\% parameter errors') + else: + plt.plot(zvals,ep[i,:],linestyle=style,color='grey') + + if zlim is not None: + plt.xlim(0,zlim) + else: + xmax=(int((dm-100)/100))/10.-0.05 + plt.xlim(0,xmax) + #plt.ylim(0,0.02) + + plt.legend(fontsize=8,ncol=2) + plt.tight_layout() + plt.savefig(dirname+str(int(dm))+'_'+basename) + plt.close() + + ### does cdf + plt.figure() + plt.xlabel('$z$') + plt.ylabel('$p(z|{\\rm DM})$ [a.u.]') + cs=np.cumsum(priors[i,:]) + cs /= cs[-1] + + cm=np.cumsum(minpriors[i,:]) + cm /= cm[-1] + + plt.plot(zvals,cs,label='Standard ' +str(dm),linestyle='-') + plt.plot(zvals,cm,label='with Emin included',linestyle=':') + + for k,ep in enumerate(epriors): + + ct=np.cumsum(ep[i,:]) + ct /= ct[-1] + if k==0: + plt.plot(zvals,ct,linestyle=style,color='grey',label='90\% parameter errors') + else: + plt.plot(zvals,ct,linestyle=style,color='grey') + + if zlim is not None: + plt.xlim(0,zlim) + else: + xmax=(int((dm-100)/100))/10.-0.05 + plt.xlim(0,xmax) + #plt.ylim(0,0.02) + plt.ylim(0,1) + plt.legend(fontsize=8,ncol=2) + plt.tight_layout() + plt.savefig(dirname+'cdf_'+str(int(dm))+'_'+basename) + plt.close() + + + +def error_get_zgdmsnr_priors(survey,grid,errorgrids,mingrid,dirname,basename,dmvals,zvals,z_evaluate=None, dm_evaluate=None): + """ Plots priors as a function of redshift for each FRB in the survey + Likely outdated, should use the likelihoods function. + Does this for a list of error grids also + + # now trying to add this for a given SNR... + """ + DMs=survey.DMEGs + priors=get_p_zgdmsnr(grid,survey) # best fit + + + + if z_evaluate is not None: + for z in z_evaluate: + dz=zvals[1]-zvals[0] + iz1s=int(z/dz) + iz2s=iz1s+1 + kz2s=z/dz-iz1s + kz1s=1.-kz2s + if dm_evaluate is not None: + DMlist=dm_evaluate + else: + DMlist=survey.DMEGs + + for i,dm in enumerate(DMlist): + # norm such that int priors dz=1 => sum priors * dz = 1 => dz * sum priors=1 + likelihoods=kz1s*priors[iz1s,i]+kz2s*priors[iz2s,i] + #norm=np.sum(likelihoods) + #print("For DMEG ",dm," redshift ",z,",relative priors are ",likelihoods) + print(z,likelihoods) + + exit() + epriors=[] + for i,e in enumerate(errorgrids): # for possible errors + eprior=get_p_zgdmsnr(e,survey) # returns 2D array in NFRB, z space + epriors.append(eprior) # now makes it 3D, for all errorgrids + + minpriors=get_p_zgdmsnr(mingrid,survey) # for Emin + + plt.figure() + plt.xlabel('$z$') + plt.ylabel('$p(z|{\\rm DM})$') + for i,dm in enumerate(survey.DMs): + if i<10: + style="-" + else: + style=":" + + plt.plot(zvals,priors[:,i],label=str(dm),linestyle=style) + + + plt.xlim(0,0.5) + plt.legend(fontsize=8,ncol=2) + plt.tight_layout() + plt.savefig(dirname+basename) + plt.close() + + + for i,dm in enumerate(survey.DMs): + plt.figure() + plt.xlabel('$z$') + plt.ylabel('$p(z|{\\rm DM, SNR, w})$') + + + #plt.plot(zvals,minpriors[:,i],label='90% U.L. on $E_{\\rm min}$',linestyle=':',color='orange',linewidth=3) + + for k,ep in enumerate(epriors): + if k==0: + plt.plot(zvals,ep[:,i],linestyle='--',color='grey',label='90% parameter errors') + else: + plt.plot(zvals,ep[:,i],linestyle='--',color='grey') + plt.plot(zvals,priors[:,i],label='Best fit',linestyle='-',color='blue',linewidth=3) + xmax=(int(dm/100)/10.)-0.05 + #plt.ylim(0,0.1) + #plt.ylim(0,0.01) + #plt.xlim(0,xmax) + plt.xlim(0,0.08) + plt.ylim(0,80) + plt.legend(fontsize=12,ncol=1) + plt.tight_layout() + plt.savefig(dirname+str(int(dm))+'_'+basename) + plt.close() + + # we now print out the relative likelihoods at the redshifts of the FRBs in question + zhg=0.00867 + dz=zvals[1]-zvals[0] + ihg1=int(zhg/dz) + ihg2=ihg1+1 + k2=(zhg-ihg1*dz)/dz + k1=1.-k2 + + plist=[] + pbest=priors[ihg1,i]*k1+priors[ihg2,i]*k2 + plist.append(pbest) + for k,ep in enumerate(epriors): + p=ep[ihg1,i]*k1+ep[ihg2,i]*k2 + plist.append(p) + pmin=minpriors[ihg1,i]*k1+minpriors[ihg2,i]*k2 + plist.append(pmin) + + zh2=0.024 + dz=zvals[1]-zvals[0] + ihg1=int(zh2/dz) + ihg2=ihg1+1 + k2=(zh2-ihg1*dz)/dz + k1=1.-k2 + + plist2=[] + pbest=priors[ihg1,i]*k1+priors[ihg2,i]*k2 + plist2.append(pbest) + for k,ep in enumerate(epriors): + p=ep[ihg1,i]*k1+ep[ihg2,i]*k2 + plist2.append(p) + pmin=minpriors[ihg1,i]*k1+minpriors[ihg2,i]*k2 + plist2.append(pmin) + + plist1=np.array(plist) + plist2=np.array(plist2) + #print(plist1) + #print(plist2) + print(plist1/plist2) + + +def get_p_zgdmsnr(grid,survey,doplot=False,norm=True,psnr=False,Pn=True,dolist=False): + """ For each FRB, returns an array showing p(z|dm,snr) + """ + rates=grid.rates + dmvals=grid.dmvals + zvals=grid.zvals + DMobs=survey.DMEGs + + # start by collapsing over z + pdm=np.sum(rates,axis=0) + + ddm=dmvals[1]-dmvals[0] + kdms=DMobs/ddm + idms1=kdms.astype('int') + idms2=idms1+1 + dkdms=kdms-idms1 + pvals=pdm[idms1]*(1.-dkdms) + pdm[idms2]*dkdms + + # gets total pvalue summed over DM space for that DM - for all SNR + # we may very well not need nor want this + + + # routine taken from calc_likelihoods_1d + # NOTE: to break this into a p(SNR|b) p(b) term, we first take + # the relative likelihood of the threshold b value compared + # to the entire lot, and then we calculate the local + # psnr for that beam only. But this requires a much more + # refined view of 'b', rather than the crude standatd + # parameterisation + + # calculate vector of grid thresholds + Emax=grid.Emax + Emin=grid.Emin + gamma=grid.gamma + psnr=np.zeros([survey.Ss.size]) + + # get vector of thresholds as function of z and threshold/weight list + # note that the dimensions are, nthresh (weights), z, nDM + Eths = grid.thresholds[:,:,idms1]*(1.-dkdms)+ grid.thresholds[:,:,idms2]*dkdms + + from matplotlib import pyplot as plt + + ##### IGNORE THIS, PVALS NOW CONTAINS CORRECT NORMALISATION ###### + # we have previously calculated p(DM), normalised by the global sum over all DM (i.e. given 1 FRB detection) + # what we need to do now is calculate this normalised by p(DM), + # i.e. psnr is the probability of snr given DM, and hence the total is + # p(snr,DM)/p(DM) * p(DM)/b(burst) + # get a vector of rates as a function of z + #rs = rates[:,idms1[j]]*(1.-dkdms[j])+ rates[:,idms2[j]]*dkdms[j] + rs = rates[:,idms1]*(1.-dkdms)+ rates[:,idms2]*dkdms + + #norms=np.sum(rs,axis=0)/global_norm + norms=pvals + + zpsnr=np.zeros(Eths.shape[1:]) + beam_norm=np.sum(survey.beam_o) + #in theory, we might want to normalise by the sum of the omeba_b weights, although it does not matter here + + for i,b in enumerate(survey.beam_b): + #iterate over the grid of weights + bEths=Eths/b #this is the only bit that depends on j, but OK also! + #now wbEths is the same 2D grid + #wbEths=bEths #this is the only bit that depends on j, but OK also! + bEobs=bEths*survey.Ss #should correctky multiply the last dimensions + for j,w in enumerate(grid.eff_weights): + temp=(zdm.array_diff_power_law(bEobs[j,:,:],Emin,Emax,gamma).T*grid.FtoE).T + zpsnr += temp*survey.beam_o[i]*w #weights this be beam solid angle and efficiency + + # we have now effectively calculated the local probabilities in the source-counts histogram for a given DM + # we have to weight this by the sfr_smear factors, and the volumetric probabilities + # this are the grid smearing factors incorporating pcosmic and the host contributions + sg = grid.sfr_smear[:,idms1]*(1.-dkdms)+ grid.sfr_smear[:,idms2]*dkdms + sgV = (sg.T*grid.dV.T).T + wzpsnr = zpsnr * sgV + + #THIS HAS NOT YET BEEN NORMALISED!!!!!!!! + # at this point, wzpsnr should look exactly like the grid.rates, albeit + # A: differential, and + # B: slightly modified according to observed and not threshold fluence + + # normalises for total probability of DM occurring in the first place. + # We need to do this. This effectively cancels however the Emin-Emax factor. + # sums down the z-axis + #psnr=np.sum(wzpsnr,axis=0) + #wzpsnr /= norms #normalises according to the per-DM probability + # normalise such that integral dz=1 + wzpsnr /= np.sum(wzpsnr,axis=0) * (zvals[1]-zvals[0]) + return wzpsnr + + + + + +def compare_z_fits(surveys,bestrates,errorrates,n0rates,Eminrates,zvals,dmvals,outdir='ErrorPlots',ks=False,ylim=None,obs=True): + """ compiles a histogram of DM and compares fit expectations to it """ + + ### compiles a list of DMs ### + + # if survey is multiple surveys, add all DMs together + + for i,s in enumerate(surveys): + if s.Zs is not None: + zs=s.Zs + NZ=len(zs) + which=i + # width of histogram bins in Z + HIST_WIDTH=0.1 + + ######## Best ###### + bestrate=bestrates[which] + + ### now compiles expectations from parameter sets + + + #rates=g.rates + pz=np.sum(bestrate,axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + best_pz = pz + + ######## Emin ###### + Eminrate=Eminrates[which] + + #rates=g.rates + pz=np.sum(Eminrate,axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + Emin_pz = pz + + ######## n0 ######## + n0rate=n0rates[which] + #rates=g.rates + pz=np.sum(n0rate,axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + n0_pz = pz + + + error_pz=[] + for i,rateset in enumerate(errorrates): + n0rate=rateset[which] + #rates=g.rates + pz=np.sum(n0rate,axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + + error_pz.append(pz) + + nbins=21 + bins=np.linspace(0,(nbins-1)*HIST_WIDTH,nbins) + + h,b=np.histogram(zs,bins=bins) + print("About to histogram ",zs,bins,h) + bcs=bins[:-1]+HIST_WIDTH/2. # because I know the spacing is 100 + + + clist=plt.rcParams['axes.prop_cycle'].by_key()['color'] + + plt.figure() + + plt.xlabel('$z$') + plt.ylabel('$N_{\\rm FRB}$') + #plt.xlim(0,2600) + if obs: + plt.bar(bcs,h,width=HIST_WIDTH,label='observed',color=clist[0]) + plt.xlim(0,1.5) + if ylim is not None: + plt.ylim(0,ylim) + else: + plt.ylim(0,4) + for i,pz in enumerate(error_pz): + plt.plot(zvals,pz,linewidth=1,linestyle='-.',color='gray') + plt.plot(zvals,best_pz,linewidth=3,linestyle='-',label='best fit',color=clist[1]) + plt.plot(zvals,n0_pz,linewidth=3,linestyle='--',label='no evolution',color=clist[2]) + plt.plot(zvals,Emin_pz,linewidth=3,linestyle=':',label='$E_{\\rm min}$',color=clist[3]) + plt.plot(-zvals,error_pz[0],linewidth=1,linestyle='-.',color='gray',label='90% C.L.') + plt.legend() + + plt.tight_layout() + plt.savefig(outdir+'/Zerr.pdf') + + plt.close() + + + make_zcdf_plot(zvals,[best_pz,n0_pz,Emin_pz],zs,['best fit','no evolution','$E_{\\rm min}$'],sys=error_pz,outdir=outdir) + if ks: + NMC=100000 + ks_test(zvals,best_pz,zs,NMC,label=' (best fit)',tag='best_z',outdir=outdir,xmax=0.6) + ks_test(zvals,n0_pz,zs,NMC,label=' (no evolution)',tag='n0_z',outdir=outdir,xmax=0.6) + return + +def compare_z_fits2(survey,bestrate,errorrates,n0rate,Eminrate,zvals,dmvals,outdir='ErrorPlots',xmax=None,ymax=None): + """ compiles a histogram of DM and compares fit expectations to it """ + + ### compiles a list of DMs ### + + # if survey is multiple surveys, add all DMs together + + survey=survey[0] + # width of histogram bins in Z + HIST_WIDTH=0.1 + NZ=survey.NFRB + + ### now compiles expectations from parameter sets + + #rates=g.rates + pz=np.sum(bestrate[0],axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + best_pz = pz + + cum=np.cumsum(best_pz) + cum=cum/cum[-1] + iz=np.where(zvals < 0.1)[0][-1] + print(cum[iz]," frbs from z < 0.1 (best)") + + ######## Emin ###### + + + #rates=g.rates + pz=np.sum(Eminrate[0],axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + Emin_pz = pz + + cum=np.cumsum(Emin_pz) + cum=cum/cum[-1] + iz=np.where(zvals < 0.1)[0][-1] + print(cum[iz]," frbs from z < 0.1 (Emin)") + + ######## n0 ######## + #rates=g.rates + pz=np.sum(n0rate[0],axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + n0_pz = pz + + cum=np.cumsum(n0_pz) + cum=cum/cum[-1] + iz=np.where(zvals < 0.1)[0][-1] + print(cum[iz]," frbs from z < 0.1 (n0)") + + error_pz=[] + for i,rateset in enumerate(errorrates): + n0rate=rateset[0] + #rates=g.rates + pz=np.sum(n0rate,axis=1) + + # normalise to observed values + norm=NZ/np.sum(pz) + pz *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(zvals[1]-zvals[0]) + pz *= rel_bin_size + + error_pz.append(pz) + + cum=np.cumsum(pz) + cum=cum/cum[-1] + iz=np.where(zvals < 0.1)[0][-1] + print(cum[iz]," frbs from z < 0.1 (error ",i,")") + + nbins=21 + bins=np.linspace(0,(nbins-1)*HIST_WIDTH,nbins) + + + + + clist=plt.rcParams['axes.prop_cycle'].by_key()['color'] + + plt.figure() + + plt.xlabel('$z$') + plt.ylabel('$N_{\\rm FRB}$') + #plt.xlim(0,2600) + if survey.Zs is not None: + zs=survey.Zs + h,b=np.histogram(zs,bins=bins) + bcs=bins[:-1]+HIST_WIDTH/2. # because I know the spacing is 100 + plt.bar(bcs,h,width=HIST_WIDTH,label='observed',color=clist[0]) + + plt.xlim(0,xmax) + plt.ylim(0,ymax) + + for i,pz in enumerate(error_pz): + plt.plot(zvals,pz,linewidth=1,linestyle='-.',color='gray') + plt.plot(zvals,best_pz,linewidth=3,linestyle='-',label='best fit',color=clist[1]) + plt.plot(zvals,n0_pz,linewidth=3,linestyle='--',label='no evolution',color=clist[2]) + plt.plot(zvals,Emin_pz,linewidth=3,linestyle=':',label='$E_{\\rm min}$',color=clist[3]) + plt.plot(-zvals,error_pz[0],linewidth=1,linestyle='-.',color='gray',label='90% C.L.') + plt.legend() + + plt.tight_layout() + plt.savefig(outdir+'/Zerr.pdf') + + plt.close() + + return + +def compare_dm_fits(surveys,bestrates,errorrates,n0rates,Eminrates,zvals,dmvals,outdir='ErrorPlots',ks=False): + """ compiles a histogram of DM and compares fit expectations to it """ + + ### compiles a list of DMs ### + print("Comparing DM fits") + # if survey is multiple surveys, add all DMs together + dms=[] + ns=[] + for i,s in enumerate(surveys): + dms.append(s.DMEGs) + ns.append(len(dms[i])) + + dms=np.concatenate(dms) + + ### now compiles expectations from parameter sets + HIST_WIDTH=100 + for i,rates in enumerate(bestrates): + #rates=g.rates + pdm=np.sum(rates,axis=0) + + # normalise to observed values + norm=ns[i]/np.sum(pdm) + pdm *= norm + + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(dmvals[1]-dmvals[0]) + + pdm *= rel_bin_size + if i==0: + best_pdm = pdm + else: + best_pdm += pdm + + ###### for n0 ######### + for i,rates in enumerate(n0rates): + #rates=g.rates + pdm=np.sum(rates,axis=0) + + # normalise to observed values + norm=ns[i]/np.sum(pdm) + pdm *= norm + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(dmvals[1]-dmvals[0]) + + pdm *= rel_bin_size + if i==0: + n0_pdm = pdm + else: + n0_pdm += pdm + + ###### for Emin ######### + for i,rates in enumerate(Eminrates): + #rates=g.rates + pdm=np.sum(rates,axis=0) + + # normalise to observed values + norm=ns[i]/np.sum(pdm) + pdm *= norm + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(dmvals[1]-dmvals[0]) + + pdm *= rel_bin_size + if i==0: + Emin_pdm = pdm + else: + Emin_pdm += pdm + + temp=np.zeros(best_pdm.shape) + error_pdms=[] + for i,rateset in enumerate(errorrates): + # create a new version + gs_apdm=np.copy(temp) + for j,g in enumerate(rateset): + pdm=np.sum(g,axis=0) + # normalise to observed values + # (grids will be in the same order) + norm=ns[j]/np.sum(pdm) + pdm *= norm + # correct for bin width effect + rel_bin_size=HIST_WIDTH/(dmvals[1]-dmvals[0]) + + pdm *= rel_bin_size + gs_apdm += pdm + error_pdms.append(gs_apdm) + + bins=np.linspace(0,2600,27) + h,b=np.histogram(dms,bins=bins) + bcs=bins[:-1]+50 # because I know the spacing is 100 + + clist=plt.rcParams['axes.prop_cycle'].by_key()['color'] + + plt.figure() + + plt.xlabel('${\\rm DM}_{\\rm EG}$') + plt.ylabel('$N_{\\rm FRB}$') + plt.xlim(0,2600) + plt.bar(bcs,h,width=100,color=clist[0]) + + for i,apdm in enumerate(error_pdms): + plt.plot(dmvals,apdm,linewidth=1,linestyle='-',color='gray') + plt.plot(dmvals,best_pdm,linewidth=3,linestyle='-',color=clist[1],label='best fit') + plt.plot(dmvals,n0_pdm,linewidth=3,linestyle='--',color=clist[2],label='no evolution') + plt.plot(dmvals,Emin_pdm,linewidth=3,linestyle=':',label='$E_{\\rm min}$',color=clist[3]) + plt.plot(-dmvals,apdm,linewidth=1,linestyle='-',color='gray',label='90% C.L.') + + plt.legend() + plt.tight_layout() + plt.savefig(outdir+'/DMerr.pdf') + + plt.close() + + + make_cdf_plot(dmvals,[best_pdm,n0_pdm,Emin_pdm],dms,['best fit','no evolution','$E_{\\rm min}$'],outdir=outdir) + + if ks: + NMC=10000 # was 100k for DM. Strange + ks_test(dmvals,best_pdm,dms,NMC,label=' (best fit)',tag='best_dm',outdir=outdir,xmax=0.4) + ks_test(dmvals,n0_pdm,dms,NMC,label=' (no evolution)',tag='n0_dm',outdir=outdir,xmax=0.4) + + +def make_cumulative_hist(x,dms): + # sorts through the DMs to make the cumulative histogram + #ordered_dms=np.sort(dms) + + y=np.zeros([x.size]) + for i,x in enumerate(x): + temp=np.where(dms < x)[0] + y[i]=len(temp) + y /= y[-1] + return y + +def make_zcdf_plot(x,theories,obs,labels,sys=None,outdir='ErrorPlots'): + # get cumulatvie theory + cobs=make_cumulative_hist(x,obs) + + clist=plt.rcParams['axes.prop_cycle'].by_key()['color'] + + nx=x.size + # number of FRBs + nobs=obs.size + + #### plots cumultive hist ### + plt.figure() + + plt.xlabel('z') + plt.ylabel('CDF') + plt.plot(x,cobs,label='observed') + for i,theory in enumerate(theories): + ctheory=np.cumsum(theory) + ctheory /= ctheory[-1] + plt.plot(x,ctheory,label=labels[i]) + + if sys is not None: + for i,theory in enumerate(sys): + ctheory=np.cumsum(theory) + ctheory /= ctheory[-1] + if i==0: + plt.plot(x,ctheory,color='gray', label='90\%') + else: + plt.plot(x,ctheory,color='gray') + + plt.xlim(0,1.5) + plt.ylim(0,1) + + plt.legend() + plt.tight_layout() + plt.savefig(outdir+'/all_z_cdfs.pdf') + plt.close() + +def make_cdf_plot(x,theories,obs,labels,outdir='ErrorPlots'): + # get cumulatvie theory + cobs=make_cumulative_hist(x,obs) + + + nx=x.size + # number of FRBs + nobs=obs.size + + #### plots cumultive hist ### + plt.figure() + + plt.xlabel('DM [pc cm$^{-3}$]') + plt.ylabel('CDF') + plt.plot(x,cobs,label='observed') + for i,theory in enumerate(theories): + ctheory=np.cumsum(theory) + ctheory /= ctheory[-1] + plt.plot(x,ctheory,label=labels[i]) + + plt.xlim(0,3000) + plt.ylim(0,1) + + plt.legend(loc='lower right') + plt.tight_layout() + plt.savefig(outdir+'/all_dm_cdfs.pdf') + plt.close() + +def ks_test(x,theory,obs,nMC,label='',tag='',outdir='ErrorPlots',xmax=0.5): + # get cumulatvie theory + cobs=make_cumulative_hist(x,obs) + + ctheory=np.cumsum(theory) + ctheory /= ctheory[-1] + + nx=x.size + # number of FRBs + nobs=obs.size + + # get ks stat + ks_stat=np.max(np.abs(ctheory-cobs)) + print("ks stat is ",ks_stat,", now simulating its significance...") + + #### plots cumultive hist ### + plt.figure() + + plt.xlabel('DM [pc cm$^{-3}$]') + plt.ylabel('CDF') + plt.plot(x,cobs,label='Observed') + plt.plot(x,ctheory,label='Predicted') + #plt.xlim(0,3000) + #plt.ylim(0,1) + + ks_stats=np.zeros(nMC) + nctheory=ctheory.size + for i in np.arange(nMC): + + rs=np.random.rand(nobs) + dmlist=[] + for j,r in enumerate(rs): + idm0=np.array(np.where(ctheory < r)[0]) + + if idm0.size==0: + idm0=nctheory-2 + else: + idm0=idm0[-1] + if idm0 > nctheory-2: + idm0=nctheory-2 + idm1=idm0+1 + k=(ctheory[idm1]-r)/(ctheory[idm1]-ctheory[idm0]) + dmsim=k*x[idm0] + (1-k)*x[idm1] + dmlist.append(dmsim) + + dmlist=np.sort(dmlist) + cmc_obs=make_cumulative_hist(x,dmlist) + ks_stats[i]=np.max(np.abs(ctheory-cmc_obs)) + #if i==0: + # plt.plot(x,cmc_obs,label='simulated') + + plt.legend() + plt.tight_layout() + plt.savefig(outdir+'/cdf'+tag+'.pdf') + plt.close() + + ### plots the ks distribution and gets a p-value ### + mean=np.mean(ks_stats) + std_dev=(np.sum((ks_stats-mean)**2)/nMC)**0.5 + nsigma=3 + #bins=np.linspace(mean-std_dev*nsigma,mean+std_dev*nsigma,101) + bins=np.linspace(0,xmax,51) + h,b=np.histogram(ks_stats,bins=bins) + db=(bins[1]-bins[0]) + plotb=bins[0:-1]+db/2 + + nless=np.where(ks_stats < ks_stat)[0] + p_value=1.-len(nless)/nMC + print("p-value of this ks test is ",p_value) + + p_value=np.round(p_value,decimals=2) + + plt.figure() + plt.xlabel('ks statistic') + plt.ylabel('p(ks) [a.u.]') + plt.bar(plotb,h,width=db,label='simulated\n'+label) + plt.plot([ks_stat,ks_stat],[0,np.max(h)],label='observed\n(p='+str(p_value)+')',color='red') + plt.legend() + #plt.tight_layout() + plt.savefig(outdir+'/pvalue_ks_stat'+tag+'.pdf') + plt.close() + +def err_get_source_counts(grids,plot=None): + """ + Calculates the source-counts function for a given grid + It does this in terms of p(SNR)dSNR + """ + print("Getting cumulative source counts") + # this is closely related to the likelihood for observing a given psnr! + # just use one of them for getting Emax etc + # calculate vector of grid thresholds + grid=grids[0] + Emax=grid.Emax + Emin=grid.Emin + gamma=grid.gamma + ndm=grid.dmvals.size + + nsnr=51 + #snrmin=0.001 + #snrmax=1000. + + snrs=np.logspace(0,2,nsnr) # histogram array of values for s=SNR/SNR_th + + + for i,grid in enumerate(grids): + # holds cumulative and differential source counts + cpsnrs=np.zeros([nsnr]) + psnrs=np.zeros([nsnr-1]) + + # holds DM-dependent source counts + dmcpsnrs=np.zeros([nsnr,ndm]) + dmpsnrs=np.zeros([nsnr-1,ndm]) + backup1=np.copy(grid.thresholds) + + # modifies grid to simplify beamshape. I have no idea why it is doing this... + # maybe just to make it faster? + #grid.beam_b=np.array([grid.beam_b[-1]]) # sets value of beam to max b + #grid.beam_o=np.array([grid.beam_o[-1]]) # sets to corresponding value of above + #grid.b_fractions=None # just resets this + + # it seems we are calculating the cumulative rate as a function of s + for i,s in enumerate(snrs): + + grid.thresholds=backup1*s #increases thresholds by a constant factor of s + grid.calc_pdv(Emin,Emax,gamma) + grid.calc_rates() + rates=grid.rates + dmcpsnrs[i,:]=np.sum(rates,axis=0) # rate as function of dm + cpsnrs[i]=np.sum(dmcpsnrs[i,:]) # total rate + + # the last one contains cumulative values + for i,s in enumerate(snrs): + if i==0: + continue + psnrs[i-1]=cpsnrs[i-1]-cpsnrs[i] + dmpsnrs[i-1,:]=dmcpsnrs[i-1,:]-dmcpsnrs[i,:] + + # adjusts values by 1.5 + mod=1.5 + snrs=snrs[:-1] + imid=int((nsnr+1)/2) + xmid=snrs[imid] + ymid=psnrs[imid] + slopes=np.linspace(1.3,1.7,5) + ys=[] + for i,s in enumerate(slopes): + ys.append(ymid*xmid**s*snrs**-s) + + if plot is not None: + plt.figure() + plt.xscale('log') + plt.yscale('log') + plt.xlabel('$s=\\frac{\\rm SNR}{\\rm SNR_{\\rm th}}$') + plt.ylabel('$p(s) s^{1.5} d\\,\\log(s)$') + plt.plot(snrs,psnrs*snrs**mod,label='Prediction',color='black',linewidth=2) # this is in relative units + for i,s in enumerate(slopes): + plt.plot(snrs,ys[i]*snrs**mod,label='slope='+str(s)[0:3]) + plt.legend() + plt.tight_layout() + plt.savefig(plot) + plt.close() + return snrs,psnrs,dmpsnrs + + + +def error_plot_psnrs(gridsets,labels,surveys,psets,plot=None): + """ for each gridset in gridets, we have surveys grids """ + + import os + if plot is not None: + basedir=os.path.dirname(plot) + + ##### calculates observed SNR histogram for each gridset #### + + sobs=[] + for i,s in enumerate(surveys): + sobs.append(s.Ss) + sobs=np.concatenate(sobs) + Nobs=sobs.size + sbins=np.linspace(0,3,31) + lsobs=np.log10(sobs) + shist,sbins=np.histogram(lsobs,bins=sbins) + + dbins=sbins[1]-sbins[0] + sx=sbins[0:-1]+dbins/2. + + + plt.figure() + plt.xlabel('$\\log_{10} (s={\\rm SNR}/{\\rm SNR}_{\\rm th})$') + plt.ylabel('N(s)') + plt.bar(sx,shist,width=dbins,alpha=0.5,label='observed') + + + xmaxs=[10,10,1000] + ymins=[0.01,0.01,0.0001] + ymaxs=[0.2,0.2,0.2] + + #### gets expectations for each gridset#### + #NS=401 + #smin=0 + #smax=4 + NS=76 + smin=0 + smax=3 + slist=np.logspace(smin,smax,NS) + lslist=np.log10(slist) + dlogs=(smax-smin)/NS*np.log(10) # width in natural log space + + csets=[] + for i,gridset in enumerate(gridsets): + pset=psets[i] + for j,grid in enumerate(gridset): + + title=basedir+'/'+labels[i]+'_'+surveys[j].name+'.pdf' + print("Doing grid ",j," from gridset ",i,"...") + if surveys[j].Zs is not None: + slist,psnrs=calc_psnr_2D(grid,surveys[j],pset,slist=slist,doplot=title,xlim=[1,xmaxs[j]],ylim=[ymins[j],ymaxs[j]]) + else: + slist,psnrs=calc_psnr_1D(grid,surveys[j],pset,slist=slist,doplot=title,xlim=[1,xmaxs[j]],ylim=[ymins[j],ymaxs[j]]) + print("... done") + + + NFRBs=psnrs.shape[0] + #normalises each FRB so the sum is equal to 1 + # accounts for log-spacing and d log width + wpsnrs = psnrs*slist + norm=np.sum(wpsnrs,axis=1)/dlogs + + psnrs = (psnrs.T/norm).T + + # adds to cumulative count over all observations + if j==0: + cpsnrs=np.sum(psnrs,axis=0) + else: + cpsnrs += np.sum(psnrs,axis=0) + + + # we now how have the cumulative sum of psnr over all FRBs in all surveys + # we can then compare this to the observed histogram to see how we go + csets.append(cpsnrs) + + # we have already normalised each FRB so that the log sum should come to NFRB + # however, we should still re-normalise to the bin width of the histogram + # this means multiplyng by slist and dlogs of the original histogram + + # now convert to log-space + # first multiply by the slist itself + cpsnrs *= slist*dbins*np.log(10) # to convert the bin width into a natural log width + yvals=cpsnrs*Nobs + plt.plot(np.log10(slist),cpsnrs*Nobs,label=labels[i]) + + + plt.legend() + plt.tight_layout() + plt.savefig(plot) + +def calc_psnr_1D(grid,survey,pset,slist,doplot=None,xlim=[1,100],ylim=[0.01,1]): + """ Calculates psnr as a function of snr + """ + rates=grid.rates + dmvals=grid.dmvals + zvals=grid.zvals + DMobs=survey.DMEGs + #DMobs=np.sort(DMobs) + DMobs=np.sort(survey.DMEGs) + idmsort=np.argsort(survey.DMEGs) + + # start by collapsing over z + pdm=np.sum(rates,axis=0) + + ddm=dmvals[1]-dmvals[0] + kdms=DMobs/ddm + idms1=kdms.astype('int') + idms2=idms1+1 + dkdms=kdms-idms1 + pvals=pdm[idms1]*(1.-dkdms) + pdm[idms2]*dkdms + + global_norm=np.sum(pdm) + log_global_norm=np.log10(global_norm) + + + llsum=np.sum(np.log10(pvals))-log_global_norm*DMobs.size + + NS=slist.size + psnrs=np.zeros([survey.Ss.size,NS]) # generates plot for each individual FRB + for k,s in enumerate(slist): + # NOTE: to break this into a p(SNR|b) p(b) term, we first take + # the relative likelihood of the threshold b value compared + # to the entire lot, and then we calculate the local + # psnr for that beam only. But this requires a much more + # refined view of 'b', rather than the crude standatd + # parameterisation + + # calculate vector of grid thresholds + Emax=grid.Emax + Emin=grid.Emin + gamma=grid.gamma + psnr=np.zeros([survey.Ss.size]) + + # get vector of thresholds as function of z and threshold/weight list + # note that the dimensions are, nthresh (weights), z, DM + Eths = grid.thresholds[:,:,idms1]*(1.-dkdms)+ grid.thresholds[:,:,idms2]*dkdms + + ##### IGNORE THIS, PVALS NOW CONTAINS CORRECT NORMALISATION ###### + # we have previously calculated p(DM), normalised by the global sum over all DM (i.e. given 1 FRB detection) + # what we need to do now is calculate this normalised by p(DM), + # i.e. psnr is the probability of snr given DM, and hence the total is + # p(snr,DM)/p(DM) * p(DM)/b(burst) + # get a vector of rates as a function of z + #rs = rates[:,idms1[j]]*(1.-dkdms[j])+ rates[:,idms2[j]]*dkdms[j] + rs = rates[:,idms1]*(1.-dkdms)+ rates[:,idms2]*dkdms + #norms=np.sum(rs,axis=0)/global_norm + norms=pvals + + zpsnr=np.zeros(Eths.shape[1:]) + beam_norm=np.sum(survey.beam_o) + #in theory, we might want to normalise by the sum of the omeba_b weights, although it does not matter here + + + for i,b in enumerate(survey.beam_b): + #iterate over the grid of weights + bEths=Eths/b #this is the only bit that depends on j, but OK also! + #now wbEths is the same 2D grid + #wbEths=bEths #this is the only bit that depends on j, but OK also! + + #bEobs=bEths*survey.Ss #should correctky multiply the last dimensions + # we simply now replace survey.Ss with the value of s + bEobs=bEths*s + + for j,w in enumerate(grid.eff_weights): + temp=(zdm.array_diff_power_law(bEobs[j,:,:],Emin,Emax,gamma).T*grid.FtoE).T + zpsnr += temp*survey.beam_o[i]*w #weights this be beam solid angle and efficiency + + + # we have now effectively calculated the local probabilities in the source-counts histogram for a given DM + # we have to weight this by the sfr_smear factors, and the volumetric probabilities + # this are the grid smearing factors incorporating pcosmic and the host contributions + sg = grid.sfr_smear[:,idms1]*(1.-dkdms)+ grid.sfr_smear[:,idms2]*dkdms + sgV = (sg.T*grid.dV.T).T + wzpsnr = zpsnr * sgV + + + #THIS HAS NOT YET BEEN NORMALISED!!!!!!!! + # at this point, wzpsnr should look exactly like the grid.rates, albeit + # A: differential, and + # B: slightly modified according to observed and not threshold fluence + + # normalises for total probability of DM occurring in the first place. + # We need to do this. This effectively cancels however the Emin-Emax factor. + # sums down the z-axis + psnr=np.sum(wzpsnr,axis=0) + psnr /= norms #normalises according to the per-DM probability + + + psnrs[:,k]=psnr + + + # checks to ensure all frbs have a chance of being detected + bad=np.array(np.where(psnr == 0.)) + if bad.size > 0: + snrll = float('NaN') # none of this is possible! [somehow...] + else: + snrll = np.sum(np.log10(psnr)) + + llsum += snrll + + if doplot is not None: + plt.figure() + ax=plt.gca() + ax.set_aspect('auto') + + dlogs=np.log(slist[1])-np.log(slist[0]) + wpsnrs = psnrs*(slist) + norm=np.sum(wpsnrs,axis=1) + plotpsnrs = (psnrs.T/norm).T #correctly normalised now + #print("For plot ",doplot," norms were ",norm) + #now multiply by slist to some power + plotpsnrs*=slist + + xmin=xlim[0] + xmax=xlim[1] + ymin=ylim[0] + ymax=ylim[1] + + plt.ylim(ymin,ymax) + plt.xlim(xmin,xmax) + plt.yscale('log') + plt.xscale('log') + + linestyles=['-',':','--','-.'] + markerstyles=['o','^','x'] + ylist=[] + DMs=survey.DMs[idmsort] + Ss=survey.Ss[idmsort] + for j,DM in enumerate(DMs): + ls=linestyles[int(j/10)] + ms=markerstyles[int(j/10)] + plt.plot(slist,plotpsnrs[j],linestyle=ls,zorder=1) + # adds in observed plot + sobs=Ss[j] + + i2=np.where(slist>sobs)[0][0] + i1=i2-1 + k=(sobs-slist[i1])/(slist[i2]-slist[i1]) + y=(k*plotpsnrs[j,i2]+(1.-k)*plotpsnrs[j,i1]) + plt.scatter(Ss[j],y,color=plt.gca().lines[-1].get_color(),s=70,marker=ms,zorder=2) + plt.plot(slist,-plotpsnrs[j],label=str(int(round(DM,0))),linestyle=ls,zorder=1,marker=ms,color=plt.gca().lines[-1].get_color()) + + #plt.plot(DMobs,pvals,'ro') + plt.xlabel('$s$') + plt.ylabel('$s \\, p_s(s)$') + + if xmax == 10: + from matplotlib.ticker import ScalarFormatter + ax=plt.gca() + for axis in [ax.xaxis, ax.yaxis]: + axis.set_major_formatter(ScalarFormatter()) + + import matplotlib.ticker as ticker + + ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda y, _: '{:g}'.format(y))) + ax.xaxis.set_minor_formatter(ticker.FuncFormatter(lambda y, _: '{:g}'.format(y))) + + plt.legend(loc='upper right',ncol=4,fontsize=10) + plt.tight_layout() + ax.set_aspect('auto') + plt.savefig(doplot) + plt.close() + return slist,psnrs + + +def calc_psnr_2D(grid,survey,pset,slist,doplot=None,xlim=[1,100],ylim=[0.01,1]): + """ Calculates 2D likelihoods using observed DM,z values """ + + ######## Calculates p(DM,z | FRB) ######## + # i.e. the probability of a given z,DM assuming + # an FRB has been observed. The normalisation + # below is proportional to the total rate (ish) + + rates=grid.rates + zvals=grid.zvals + dmvals=grid.dmvals + + #DMobs=survey.DMEGs + DMobs=np.sort(survey.DMEGs) + idmsort=np.argsort(survey.DMEGs) + Zobs=survey.Zs[idmsort] + + #Zobs=survey.Zs + + + + #if survey.meta["TOBS"] is not None: + # TotalRate=np.sum(rates)*survey.meta["TOBS"] + # this is in units of number per MPc^3 at Emin + + # normalise to total probability of 1 + norm=np.sum(rates) # gets multiplied by event size later + + + # get indices in dm space + ddm=dmvals[1]-dmvals[0] + kdms=DMobs/ddm + idms1=kdms.astype('int') + idms2=idms1+1 + dkdms=kdms-idms1 + + # get indices in z space + dz=zvals[1]-zvals[0] + kzs=Zobs/dz + izs1=kzs.astype('int') + izs2=izs1+1 + dkzs=kzs-izs1 + + pvals = rates[izs1,idms1]*(1.-dkdms)*(1-dkzs) + pvals += rates[izs2,idms1]*(1.-dkdms)*dkzs + pvals += rates[izs1,idms2]*dkdms*(1-dkzs) + pvals += rates[izs2,idms2]*dkdms*dkzs + + bad=np.array(np.where(pvals <= 0.)) + if bad.size > 0: + pvals[bad]=1e-20 # hopefully small but not infinitely so + llsum=np.sum(np.log10(pvals))#-norm + llsum -= np.log10(norm)*Zobs.size # once per event + + + + ###### Calculates p(E | z,DM) ######## + # i.e. the probability of observing an FRB + # with energy E given redshift and DM + # this calculation ignores beam values + # this is the derivative of the cumulative distribution + # function from Eth to Emax + # this does NOT account for the probability of + # observing something at a relative sensitivty of b, i.e. assumes you do NOT know localisation in your beam... + # to do that, one would calculate this for the exact value of b for that event. The detection + # probability has already been integrated over the full beam pattern, so it would be trivial to + # calculate this in one go. Or in other words, one could simple add in survey.Bs, representing + # the local sensitivity to the event [keeping in mind that Eths has already been calculated + # taking into account the burst width and DM, albeit for a mean FRB] + # Note this would be even simpler than the procedure described here - we just + # use b! Huzzah! (for the beam) + # IF: + # - we want to make FRB width analogous to beam, THEN + # - we need an analogous 'beam' (i.e. width) distribution to integrate over, + # which gives the normalisation + + NS=slist.size + psnrs=np.zeros([survey.Ss.size,NS]) # generates plot for each individual FRB + for k,s in enumerate(slist): + # NOTE: to break this into a p(SNR|b) p(b) term, we first take + # the relative likelihood of the threshold b value compare + # to the entire lot, and then we calculate the local + # psnr for that beam only. But this requires a much more + # refined view of 'b', rather than the crude standatd + # parameterisation + + # calculate vector of grid thresholds + Emax=grid.Emax + Emin=grid.Emin + gamma=grid.gamma + #Eths has dimensions of width likelihoods and nobs + # i.e. later, the loop over j,w uses the first index + Eths = grid.thresholds[:,izs1,idms1]*(1.-dkdms)*(1-dkzs) + Eths += grid.thresholds[:,izs2,idms1]*(1.-dkdms)*dkzs + Eths += grid.thresholds[:,izs1,idms2]*dkdms*(1-dkzs) + Eths += grid.thresholds[:,izs2,idms2]*dkdms*dkzs + + FtoE = grid.FtoE[izs1]*(1.-dkzs) + FtoE += grid.FtoE[izs2]*dkzs + + beam_norm=np.sum(survey.beam_o) + + # now do this in one go + # We integrate p(snr|b,w) p(b,w) db dw. I have no idea how this could be multidimensional + psnr=np.zeros(Eths.shape[1:]) + for i,b in enumerate(survey.beam_b): + bEths=Eths/b # array of shape NFRB, 1/b + #bEobs=bEths*survey.Ss + bEobs=bEths*s + for j,w in enumerate(grid.eff_weights): + temp=zdm.array_diff_power_law(bEobs[j,:],Emin,Emax,gamma) * FtoE #one dim in beamshape, one dim in FRB + + psnr += temp.T*survey.beam_o[i]*w #multiplies by beam factors and weight + + # at this stage, we have the amplitude from diff power law summed over beam and weight + + # we only alculate the following sg and V factors to get units to be + # comparable to the 1D case - otherwise it is superfluous + sg = grid.sfr_smear[izs1,idms1]*(1.-dkdms)*(1-dkzs) + sg += grid.sfr_smear[izs2,idms1]*(1.-dkdms)*dkzs + sg += grid.sfr_smear[izs1,idms2]*dkdms*(1-dkzs) + sg += grid.sfr_smear[izs2,idms2]*dkdms*dkzs + dV = grid.dV[izs1]*(1-dkzs) + grid.dV[izs2]*dkzs + # at this stage, sg and dV account for the DM distribution and SFR; + # dV is the volume elements + # we just multiply these together + sgV = sg*dV + wzpsnr = psnr.T*sgV + + # this step weights psnr by the volumetric values + + ######## NORMALISATION DISCUSSION ###### + # we want to calculate p(snr) dpsnr + # this must be \int p(snr | w,b) p(w,b) dw,b + # \int p(snr | detection) p(det|w,b) p(w,b) dw,b + # to make it an indpeendent factor, and not double-count it, means calculating + # \int p(snr | detection) dsnr p(det|w,b) p(w,b) dw,b / \int p(det|w,b) p(w,b) dw,b + # array_diff_power_law simply calculates p(snr), which is the probability amplitude + # -(gamma*Eth**(gamma-1)) / (Emin**gamma-Emax**gamma ) + # this includes the probability; hence need to account for this + + # it is essential that this normalisation occurs for a normalised pvals + # this normalisation essentially undoes the absolute calculation of the rate, i.e. we are normalising by the total distribution + # hence we *really* ought to be adding the normalisation to this... + # the idea here is that p(snr,det)/p(det) * p(det)/pnorm. Hence pvals - which contains + # the normalisation - should be the un-normalised values. + + wzpsnr /= pvals + + psnrs[:,k]=wzpsnr + + # checks to ensure all frbs have a chance of being detected + bad=np.array(np.where(wzpsnr == 0.)) + if bad.size > 0: + snrll = float('NaN') # none of this is possible! [somehow...] + else: + snrll = np.sum(np.log10(wzpsnr)) + + llsum += snrll + if doplot is not None: + plt.figure() + ax=plt.gca() + ax.set_aspect('auto') + + dlogs=np.log(slist[1])-np.log(slist[0]) + wpsnrs = psnrs*(slist) + norm=np.sum(wpsnrs,axis=1) + plotpsnrs = (psnrs.T/norm).T #correctly normalised now + print("For plot ",doplot," norms were ",norm) + #now multiply by slist to some power + plotpsnrs*=slist + + xmin=xlim[0] + xmax=xlim[1] + ymin=ylim[0] + ymax=ylim[1] + + plt.ylim(ymin,ymax) + plt.xlim(xmin,xmax) + plt.yscale('log') + plt.xscale('log') + + linestyles=['-',':','--','-.'] + markerstyles=['o','^','x'] + ylist=[] + DMs=survey.DMs[idmsort] + Ss=survey.Ss[idmsort] + for j,DM in enumerate(DMs): + ls=linestyles[int(j/10)] + ms=markerstyles[int(j/10)] + plt.plot(slist,plotpsnrs[j],linestyle=ls,zorder=1) + # adds in observed plot + sobs=Ss[j] + i2=np.where(slist>sobs)[0][0] + i1=i2-1 + k=(sobs-slist[i1])/(slist[i2]-slist[i1]) + y=(k*plotpsnrs[j,i2]+(1.-k)*plotpsnrs[j,i1]) + plt.scatter(Ss[j],y,color=plt.gca().lines[-1].get_color(),s=70,marker=ms,zorder=2) + plt.plot(slist,-plotpsnrs[j],label=str(int(round(DM,0))),linestyle=ls,zorder=1,marker=ms,color=plt.gca().lines[-1].get_color()) + + #plt.plot(DMobs,pvals,'ro') + plt.xlabel('$s$') + plt.ylabel('$s \\, p_s(s)$') + from matplotlib.ticker import ScalarFormatter + ax=plt.gca() + for axis in [ax.xaxis, ax.yaxis]: + axis.set_major_formatter(ScalarFormatter()) + + import matplotlib.ticker as ticker + + ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda y, _: '{:g}'.format(y))) + ax.xaxis.set_minor_formatter(ticker.FuncFormatter(lambda y, _: '{:g}'.format(y))) + + plt.legend(loc='upper right',ncol=4,fontsize=10) + plt.tight_layout() + ax.set_aspect('auto') + plt.savefig(doplot) + plt.close() + return slist,psnrs + diff --git a/papers/171020/galaxy_redshifts.dat b/papers/171020/galaxy_redshifts.dat new file mode 100644 index 0000000..36ea2cd --- /dev/null +++ b/papers/171020/galaxy_redshifts.dat @@ -0,0 +1,26 @@ +WISEA J221142.57-194059.0 0.065725 +WISEA J221145.17-195400.1 0.066143 +WISEA J221149.26-194704.0 0.067867 +WISEA J221151.41-193329.2 0.052109 +WISEA J221152.88-194139.0 0.070138 +WISEA J221211.44-195557.8 0.053017 +WISEA J221220.88-195835.8 0.066299 +WISEA J221232.13-194917.7 0.059029 +WISEA J221232.55-201832.1 0.077918 +WISEA J221233.47-193736.7 0.08324 +WISEA J221241.55-192515.5 0.05253 +WISEA J221246.95-201055.2 0.068124 +WISEA J221254.49-192811.7 0.09038 +WISEA J221258.42-191529.1 0.06812 +WISEA J221304.50-191722.7 0.09329 +WISEA J221312.66-194532.5 0.061579 +WISEA J221319.95-200202.0 0.061942 +WISEA J221322.11-193412.1 0.078301 +WISEA J221355.53-202723.5 0.09126 +WISEA J221412.12-191801.2 0.051404 +WISEA J221437.05-191904.9 0.060737 +WISEA J221501.14-192536.9 0.066579 +ESO 601-G036 0.008672 +WISEA J221655.11-193432.7 0.06319 +2MASX J22165626-1955155 0.08260 +2MASX J22172928-1954557 0.051379 diff --git a/papers/171020/mag_prior.py b/papers/171020/mag_prior.py new file mode 100644 index 0000000..fb21dd0 --- /dev/null +++ b/papers/171020/mag_prior.py @@ -0,0 +1,234 @@ +import numpy as np +from scipy import interpolate + + +def main(): + + file1 = open('modR-MAG_CANDIDATES.csv', 'r') + Lines = file1.readlines() + rmags=[] + ras=[] + decs=[] + for line in Lines: + words=line.split() + rmag=words[3] + gal=words[1] + rmags.append(float(rmag)) + ra=float(words[6]) + dec=float(words[7]) + ras.append(ra) + decs.append(dec) + decs=np.array(decs) + ras=np.array(ras) + rmags=np.array(rmags) + + + #rmags=np.array([19.2233, 20.3648, 20.0347, 17.5727, 20.6629, 18.2049, 18.3037, 18.5523, 0, 19.7793, 18.6564, 18.6821, 19.7689, 17.7779, 17.8776, 19.8369, 15.1775, 17.2688, 20.6667, 17.519, 19.7686, 18.4509, 18.8426]) + #rmags[8]=np.max(rmags) + + priors=[] + for rmag in rmags: + prior=driver_sigma(rmag) + priors.append(prior) + priors=np.array(priors) + priors=1./priors + norm=np.sum(priors) + priors = priors/norm + + ##### reads in positional likelihoods ####### + file2 = open('mod_cand_pos_likelihood.csv', 'r') + Lines2 = file2.readlines() + poss=[] + for line in Lines2: + words=line.split(',') + pos=words[3] + poss.append(float(pos)) + + + #### reads in redshifts #### + file1 = open('galaxy_redshifts.dat', 'r') + Lines = file1.readlines() + zgals=[] + zs=[] + for line in Lines: + words=line.split() + + gal=words[0]+" "+words[1] + z=words[2] + zgals.append(gal) + zs.append(float(z)) + zs=np.array(zs) + + #poss=get_z_priors(zs) + + ##### performs normalisation of probabilities ###### + poss=np.array(poss) + OK = np.where(poss>0)[0] + norm=np.sum(poss[OK]) + poss = poss/norm + + # previous norm is over all candidates + norm=np.sum(priors[OK]) + priors = priors/norm + + # product + pprod = poss*priors + norm=np.sum(pprod[OK]) + pprod = pprod/norm + + # re-reads data + file2 = open('mod_cand_pos_likelihood.csv', 'r') + Lines2 = file2.readlines() + + ###### prints out combined value for latex table ##### + print("Name & $\alpha$ [deg] & $\delta$ [deg] & $p(\alpha,\delta)$ & $m_r$ & $p(m_r)$ & $z$ & $p(z)$) \\") + for i,line in enumerate(Lines2): + #if poss[i]==0: + # continue + words=line.split(',') + + number = "{:.2f}" + string=words[0] + string = string +" " + words[1] + string = string + " & {:.2f}".format(float(words[2])) + string = string + " & {:.2f}".format(float(words[3])) + string = string + " & {:.3f}".format(poss[i]) + string = string + " & {:.3f}".format(rmags[i]) + string = string + " & {:.3f}".format(priors[i]) + string = string + " & {:.2f}".format(pprod[i]) + string = string + " & {:.2f}".format(zs[i]) + + print(string) + + +def get_z_priors(redshifts): + + ########## gets FRB data ########## + #Place your redshift values here + #dummy_redshifts=[0.01,0.06,0.007,0.001] + NZ=len(redshifts) + + + ############ reads in p(z,snr) prior data ########## + # first priors for best-fit population + best_prior=np.load("z_priors_bestfit.npy") + + # now priors for systematics + systematic_priors=[] + NSYS=12 + for i in np.arange(NSYS): + prior=np.load("z_priors_systematic_"+str(i)+".npy") + systematic_priors.append(prior) + + # now redshift values at which each prior is calculated + zvals=np.load("zvalues_for_priors.npy") + dz=zvals[1]-zvals[0] + ######## evaluates priors ####### + + prior_list = np.zeros([NZ]) + sys_prior_list = np.zeros([NZ,NSYS]) + + for i,z in enumerate(redshifts): + if z>zvals[-1]: + raise ValueError("Warning - priors not evaluated beyond ",zvals[-1]) + + if z rmag + using Driver et al. 2016 number counts. + Spline parameters (globals) are for rmag vs sigma + Args: + rmag (float or np.ndarray): r band magnitude of galaxy + Returns: + float or np.ndarray: Galaxy number density + """ + return 10**driver_spl(rmag) + +main() diff --git a/papers/171020/match_galaxies.py b/papers/171020/match_galaxies.py new file mode 100644 index 0000000..6d58702 --- /dev/null +++ b/papers/171020/match_galaxies.py @@ -0,0 +1,39 @@ +# searches for a match to 171020 host galaxies +import numpy as np + +def main(): + + file1 = open('object_search_2.csv', 'r') + Lines = file1.readlines() + gals=[] + zs=[] + for i,line in enumerate(Lines): + if i==0: + continue + words=line.split(',') + gal=words[1] + z=words[6] + zs.append(float(z)) + gals.append(gal) + + zs=np.array(zs) + + file2 = open('modR-MAG_CANDIDATES.csv', 'r') + Lines = file2.readlines() + gals2 = [] + for line in Lines: + words=line.split() + pre=words[0] + gal=words[1] + string=pre+" "+gal + gals2.append(string) + + for i2,g2 in enumerate(gals2): + OK=False + for i1,g1 in enumerate(gals): + if g1==g2: + OK=True + print(g1,zs[i1]) + if not OK: + print("No matching galaxy found for ",g2) +main() diff --git a/papers/171020/paper_mag_prior.py b/papers/171020/paper_mag_prior.py new file mode 100644 index 0000000..004a1ca --- /dev/null +++ b/papers/171020/paper_mag_prior.py @@ -0,0 +1,187 @@ +import numpy as np +from scipy import interpolate + + +def main(): + + file1 = open('zpriors_added.csv', 'r') + Lines = file1.readlines() + rmags=[] + ras=[] + decs=[] + zs=[] + for line in Lines: + words=line.split(',') + rmag=words[15] + gal=words[0] + rmags.append(float(rmag)) + ra=float(words[7]) + dec=float(words[8]) + z=float(words[17]) + ras.append(ra) + decs.append(dec) + zs.append(z) + + + decs=np.array(decs) + ras=np.array(ras) + rmags=np.array(rmags) + zs=np.array(zs) + + # calculates adjusted rmags based on redshifts + #dl = (0.01/zs)**2 #luminosity change to redshift of 0.01 + #dmag = np.log10(dl)*2.5 + #magprime=rmags+dmag + #for i,r in enumerate(rmags): + # print(i,r,zs[i],dmag[i],magprime[i]) + + #rmags=np.array([19.2233, 20.3648, 20.0347, 17.5727, 20.6629, 18.2049, 18.3037, 18.5523, 0, 19.7793, 18.6564, 18.6821, 19.7689, 17.7779, 17.8776, 19.8369, 15.1775, 17.2688, 20.6667, 17.519, 19.7686, 18.4509, 18.8426]) + #rmags[8]=np.max(rmags) + + priors=[] + for rmag in rmags: + prior=driver_sigma(rmag) + priors.append(prior) + priors=np.array(priors) + priors=1./priors + norm=np.sum(priors) + priors = priors/norm + + + + ### output + for i,line in enumerate(Lines): + string=line[:-1]+"," + str(priors[i]) + print(string) + +def get_z_priors(redshifts): + + ########## gets FRB data ########## + #Place your redshift values here + #dummy_redshifts=[0.01,0.06,0.007,0.001] + NZ=len(redshifts) + + + ############ reads in p(z,snr) prior data ########## + # first priors for best-fit population + best_prior=np.load("z_priors_bestfit.npy") + + # now priors for systematics + systematic_priors=[] + NSYS=12 + for i in np.arange(NSYS): + prior=np.load("z_priors_systematic_"+str(i)+".npy") + systematic_priors.append(prior) + + # now redshift values at which each prior is calculated + zvals=np.load("zvalues_for_priors.npy") + dz=zvals[1]-zvals[0] + ######## evaluates priors ####### + + prior_list = np.zeros([NZ]) + sys_prior_list = np.zeros([NZ,NSYS]) + + for i,z in enumerate(redshifts): + if z>zvals[-1]: + raise ValueError("Warning - priors not evaluated beyond ",zvals[-1]) + + if z rmag + using Driver et al. 2016 number counts. + Spline parameters (globals) are for rmag vs sigma + Args: + rmag (float or np.ndarray): r band magnitude of galaxy + Returns: + float or np.ndarray: Galaxy number density + """ + return 10**driver_spl(rmag) + +main() diff --git a/papers/171020/plot_prior.py b/papers/171020/plot_prior.py new file mode 100644 index 0000000..eb34658 --- /dev/null +++ b/papers/171020/plot_prior.py @@ -0,0 +1,44 @@ +import matplotlib.pyplot as plt +import numpy as np +import matplotlib + + +matplotlib.rcParams['image.interpolation'] = None + +defaultsize=16 +font = {'family' : 'normal', + 'weight' : 'normal', + 'size' : defaultsize} +matplotlib.rc('font', **font) + +def main(): + + ############ reads in p(z,snr) prior data ########## + # first priors for best-fit population + best_prior=np.load("z_priors_bestfit.npy") + + # now priors for systematics + systematic_priors=[] + NSYS=12 + #for i in np.arange(NSYS): + # prior=np.load("z_priors_systematic_"+str(i)+".npy") + # systematic_priors.append(prior) + + # now redshift values at which each prior is calculated + zvals=np.load("zvalues_for_priors.npy") + dz=zvals[1]-zvals[0] + + plt.figure() + plt.plot(zvals,best_prior,linewidth=3,color='blue') + plt.plot([0.00867,0.00867],[0,50],linestyle="--",linewidth=3,color='red') + plt.xlabel('$z$') + plt.ylabel('$p(z|{\\rm DM},{\\rm SNR},w)$') + plt.xlim(0.,0.08) + plt.ylim(0,50) + plt.tight_layout() + plt.savefig('paper_plot_pz_bestfit_only.pdf') + plt.savefig('paper_plot_pz_bestfit_only.png') + plt.close() + + +main() diff --git a/papers/171020/rmag_HG171020 _field - Sheet1.xlsx b/papers/171020/rmag_HG171020 _field - Sheet1.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..fda9969346d0dfdfbad5aa96d5474750841d9687 GIT binary patch literal 9860 zcmaKS1yo$ivi3l52(H21g1c)VxVyW%yZhko?(Q1g21~HuZb5@fu*bdUt@|hEzVq){ z(=)qfeKlRxwX17&eezQ8AkYB-01Uv$#aXE+Q>AL|22XE zkl%jz^Qunhvm9VV2>s~;x8qgV6vQN!s5_k3%G?2cZcWY*2bsd-l<_*{dJ)Ibyf3v`Uh4Ukr)lkbr0f zEC_@_4&0kTfhINCB}k|*JVa@6@qvFz%wK_2!q+Z**ksqtUPpV0eh)UpNecCmsRk{T zJ!(XIGt?_djHh9wVJbJGB92WPbNNL#ykknso5%|hdoKZNDsVc#v&LAl!_@gIS8RN+ zb%M7K_u^id)wjKO8+v&jTK*5eKoEb&=yrOSGrtQ`jEJ&;2*#GT2-BZ2r z^kq-b7p~FhNulDTqw6}S2L8eNf#Ss0L&coEZ&;^nivFXB38U|%v$fMj3YBMQ@3~akhga1jefRNp z3Pv?c4alCa{i|YJd=%?g^1zfr38{Jv8!vjwh@T%)&t_?j|FmXu^&R>Bn!_ z(pdMiu)=f<&ja7euZQ+UUFnhTX=ulmU~6Lk{OW9ym6Y>~7v_!LmJE!btAj)*vy|^P zqdu_Q!~6%lb04A+;1>#I8izedjT*Z5YAniz&D8vGn&KHk)SY6pfrrZ9=AhfAUk2d5 zh?fV70ZTgbfn@M14l*|qoWVx~mzGO=f~xKL!PnDYi_|~}h5T`m+yJ4w8a5D(Ay< z+`T1rCVj#FbR5`(7Qx1wN zhG?%`g9UfBNLx#ow+eVX+A4iEgvp1_9RxP96I4mcV{BWz&SyFA z_Gx4ui=cE}MOSx?yzn(0Fa#wJg=-q3p^8 zYA#W_*#=?PMk!z!AN7Tjr^V){J=)qxctEYP`6av2)vyM=2&%PHn6X# zY<>iH35U7Xqn`&=X6Zvtj|wm8^K_wMTsYl^>xtsKEF+*_K6DzgjGmV}M?*+|x@y^| z=o||Q03c8W0MPz&m9x2tjS0iQN2Y&@vs3LA+Z9e^pPy$YI7;V^{vFxY!RX6t3jk0zJed_CPU771m)wgIYjD zt13%2s`i82Acg}Qm=!#@O1~T>vRpcDf;ZF1x=N8$*@Y&AqmWmwJ(Au;CyUD6yus4X zaVt^2!&TT3Fb7GDrk1bpM9jL}_A3{?atNBD3b()Hw%6PGV7FQcEbVC9bYE!`PO#~M zyKPhZRpr^k!%t^bBk#511;Ia<7@j3U+ik*h5cMV zl2zA+QB))7o7L9y2g2)3@ovBMerPt|xuCl*O06Lrd2CS+C2ml($I5XgWBj-MW{pr? zVqEXHcTd}wg$jecC58}=CRf2Z+KbPaJIIK8`4wG7RPa!bS=JQkLh+O?VTh8vz^2}% zOHBB6gEvc;THqU18mE@0Fd3;G#Z1FnBqdWv72=?004S@xiWi2(3QAfv|UJ2m2bev8>fGvz~@md$+qGRCvbe6{?ZiPCX zvpsyTZ3muf)@pjLBgq>Z4~M za+f~_eSfd%&A2TgP`Mh=Gk!#ek{)*i9CcYAQ%+tXti8Dd*IU{0fZF6$iP~CxMryVv zkfqb1wn$sv7=Q*tQ@vWOJT{2{Q49V_(E&8Mdf7vtQa4(d*lP2ve9U_vYCX%%v zV8{T2=%R$9U?t@Tc4&mctZ8$)a7aGUt@7|$O6GK7hnKpdAMyvUErP7uO`~hv#pd4K zT4;S350DSL3s@gSpauOXbLyO0HtQbD(ms_=qvH*>WOnY~X5tYsL6l0lU(h+`Mo_O= zx4P7^uCTInM3Z9~mnd*XL-P@na_ADb3D0#_$igO6fw=6ORlJy+Q@%y9g%Rju*3hO8 zL%6aNNq9*bKYV4+rWHu>8zr4$OS9~dk6!!mfCHwb&2i;7&vCz2QTY`^KgaS?eQS!c zbd<@7g7mGGU6C;$NnW-D=3}h1a#|gIg2L=aQQx>G_se^C_l9=c;OS4scIu(3v1+Mjw1iBmC z9>0#dC65}VlOM7iEfXR?O40;C()a1Zvs6IFqZ2HYfv`&)&B2FhC6Zs&NY>?b+)NbZ z9cv$|CtK+7O>&&kqg7-Q|9$mSc@< z1r>&kVA_7gX$VPl0}%_BW(6>FF>v|}te!BEG*U!U*8C)tG{Ul271_BMpK-rqua6qG zi9{cy;@l<{k0bTLyReKwh$kJ5=Z`_Kin$*}+5Jo?{**xq<_N(xGZLS-KMKPYhb0}axQz7nabcx#wYQ?>X6DSQ3gpBbH0 zOcCUNXsn^DVnG1~+PKVvf8A-r@D4HZ)M)sP&_b%`3`lqsZ$gWo6?G|6Sz=CR{OnMa z&xVK0If4(_bJC&8%Y6`dz(JTN{lusoi`OU5=LjKkGm7#i5dTB43ln-2Puk;H)*bn+ za)mPF=wj_zZ?$%ck-TvqKF44O$&jP3~8Ra=xH!hIme0KZCk@Ea6 zH2mE`vA9@JxACOdi{df*`h;A80Y1Ahe+cmUdifo9@8C|bF1$-0gZTGiU$VM>LH)V6 zw4=Yk#=Mo{MFyGU7;$VQtxjXDklh zJ~6EM632;`n}Czh1Qh~|l;QB>&pYK~rV43^l#~eUJkZow4R>;_QuUR<^YP8z?ut1# zuG~6*uHr&g4se}-Qf{Y}Ay>}tx%f_r`+0Wg=Twhe$!nSqe)K+NoL{C-hA;m!cW#d# zH%}&?Fa8()iH^j(Y224v-t=z%iHqaw@ul4dk0;KK9A4k9nDu=#YiZ8R7+r#WY^z(B zCw?9sa~{!!6CZ9*cS{*}AB{3kAMBoN-K^FH-yGen9szHhXCKzro9BZ=(3Shk_^z3D zFH3fgPF8Mpan3Mf3GXS$`TFVM`sMI|CPp-|Aw<5t)D`=N+FLkB#YiU~U z(gNu2#rbItxFObfJ2v~{VQa@kmtI#!mtxlPn@sYT+X(%QOT^EwU+*nmZmukjmM@#X z-?W16`plp9j`2DiIdb1-Ke3;rHA1(i7rg*gSZr3)T4)oK<&?19ly;lt{$~I8<`#G z)Wz?9x{W`sA+0=MuRLG+zP{G+Rqfl%-rEOXG&SS|%GioT-0Z$iqGi!x)_- z_U3-@ogq0b0t6ayxJRP`b%S3JsGnlqbI=~UI=r0rXXX0PTBf3=Ha)E@UG^t;+0!ws zj>7YKXFlckwLTGHr`JgKseR!Euz7<_we>xYJ_JOXic`!Mp zu6eLI#lCqkI_11sR8A^bv!IsTkdEmaYI#cAJZgJN+X}N0xXhiF=wKE5+t7sA8vc~F zU|D`DBxS>K-5$bU50~2VT&P!--+WGINM4JHnxA|R+r30tFT0S5T4E&)*ZvC?zK0so zXF?TW2@OtZ%ydUe+FB3sXWrgO_j^IS5^%x2s< z3;S_3RDFtIT^f7fsT*ySRUe>3;@US%_I=CO!Oi+-ccYo#tIIuMPtW6*=it}H*8|O1 zDNJr_$!}=8ifmz8JgZGD8Ziq=7a5jay%CO0-w*JVmSaA(4i_v1eQFIDYEt(KD3n@w zVXCdiE*hK{4x9i7nuyZgesduQ2ZQy`lmyUN+xL@S7scoo?Zl&o7El%-;~UuwZX+f+ zU&pmfw`Wml)C<{K!FPhxcd8pfoP>ir>o%j{t~D(7+1*<7xu;@+E6_%0EUJ#zi705E zJzd$DZB9H6W?79)`U?I=45`Ij{hJ0R_B*a5NFTZ)mJCNYa|<_O`23J>yM}YrzezFF zj|gv%(VpPcPc7&g%60HF)R|QUo5>y3sT#^jV!Pg7E*X`I84YbCJUMJu$^>6E&Sw@ao^QF`5e6`Wr$;OkE?LZU2EWT8bpU7`xE{%r#jhaI;< zq@57#t(&^Lt=iT?2E5?zylG0zXJ(jdE}ZZh;Sm1dNwR}2?(mRdX83y*RH{I4W7wX& zUUI-pDk^(g8guecjKo<+z)Y%B;2{^9DTi2nkZlMjudGA2e2o1F$?94NoNqo0Ot$YL zLObw>ilLq?zz6lpg^=bxB>Q~F9Mp~-ah@h7E$##y8W`KAI0+@Jmx!XXK~^>AdD<2_ zkNV(CvaTa`_>|y2R!OymMc!6Z7@X_hFM~eC01;80GZ$d#q_>Zyg5Gk@N>ERe%K34i z4!x9Qm4Y*?Ya-4zgtb956DPT92RmVr-K{>*@C3oIlrVK1r-P{kkejKQdb|lcG`Bhe z7wOPS$r>@b1M;U!V-OK>0!|K$?NgjV6@tL@uz7f(kxp6)nec;$i>9dr?bC}`l8Wb4 z63h`$XBH$ps5SglQIk5V`d#E`zz870iy=xzpii^|%cA>t5R&+nX$Vl>OCX|pmOrV6 zcZT0c!>sNaVb)*Ms{o|4g^sRg9btX2e3zJwh^)DmF(Z{Adz*0m!OGP?M<|$@Tu?)VGS_(yfy~%Q6mN-BMHncb&9fg#kEOLEu!|v@*l^3_-KJBjh)L9#xJ46M3smb z)nF#Fh0d<8J_O)7*`I$x%;B;Y(XT;HyCeF>Ox#MFk3SUc-oFnM6_z|Cvkog5_!F2l zj@qAS3r*OB7uZcgu|>IJ_o-x9FK%g5(YO%1e#T3Cf!PL=4xlat2v{En7MC_*AA%=C zfM?&7t&@=73+3DAVSE@X%Ti#40;KG*w^!TM>Fit6F-c}42*M)U!o3SI=Ekm@Wq9sz zWg>Oepn|2Xvt|5|qTZ!gFG`Z`GtJ382ZJtINItIJ!8LqoVB^vF!8KC=0b-K{7pbSC zkW$1M8&6^&o*Yl2mubuwG_Pp#xUWx=0Uu&fqJL^xZan27r8 z6h$r>RdSaD<{eq$Vyb8sIn~hn#e$r;Oi0&rv>7&ANi1blc?m)CYAcgf?5>O8$oWBl zvRH&-gLo~CMZA^oY`pSA0)VgL95s%Avo*$Byk)HoW&*c)8gq(RqKpZ>WdVG zcm|KtOsG98hjaw%k<)>4%vfzJN%VQf{s~}`x+B)=sdW35N#CSb1VL<69)wgu2G(Rr zDWlF`tRFbUm-wq}>{Q>`$q*>)nw)IUTnn>ac5b)wA*84dD7MstB*PAq2b#tV8XVJ9 zxhIP7N-)6iBpMRyk}MNcP*!QuJW}aY$386`<5O)3x2fC77ZYDPNy8;?Ly09QWC)I$ zBw8x0*}OSNGj9ewnU*G`BPX6BFEYR_CcPEXUaN5COo$v0LmQ*8$t0LfLzd9wR~`Eo z;!fzK^LW@Rm9)H*NqRL8>_*`X&G?8XatYfZO3+n2iP|EqT6r4ExYq|(j%IH#^MpfN zx#J1AJXtqeEo)17iKnWCBu2hBGlVkXzq* zsMDTatpX- zgbQVbCcWB|x~zCl4=Z@doa0p~c%*_gEv4;=^HpWMv2?Bu_|=EpP(Cw-UTT6${)_T^<0wwWiImn zYxYKQ7ntau@HonnohSh5> z&gaNu6Ql0(aq9gUG^0fd8W&zO153Apez*ylf%3%@@{v_haNkfrQ((%JUx)l2@g~C@=A( zIvu3gZlXKzMK*uul|9HVt|?GeZTBWExg(EtOqcqycdLKJI2FcvXr!{B)4>1PgPa}X zbcf8!TSh~L#n&!GxlyoNO2M>}uXvU-IRXyF`eP){u~^7mBBRPLM?hAhTp50#nF;~j zUaGiaCslg20a_G1uqx;ne_k8eq?h$wi3k2sq0tF!q7RqZz>2WXB(%c18d3fz-1#Vz z!6gE98of&UyGg?D`y5Gb+jH${b^U3UMM&k#BsZXwfkoo)Cvm(R?w|yG$D=wZT#OTP z{ThuKMupIXOihT3Y>@;?^5hRo700x&!FXii+EXx;l&xF(-sVRe9X(BZ%6hqzK#Op)I#H7q%Y)JYMmoqlRnHi+j zgx|k&bVZS0?rmBf^5=02o3;Y37Q-WbBvZjTT)^^-5S_zF=*V6NehsHV)wwu;nJ96ok(|t@sJ`IV{;qqY$Fwu zv{DEo!Tr?H+eMC~Q|<_zAy74G=Z@tQOomLYym@Qw7wLh-iBuS?J}Z9NbxW@0@+9J+ zP}z}J)JyG=fo*c85%sYnufboK%KQhaC7h?G+`+`Q|XKS|>p=`O9EQClLl^NBIcI7>! zWXFa?RKaxzejc0*A@!7g6yBJ}~am+Hbt4|o^B^H6fH z-7bZ7rD?*7z9Lb1wDxchS{->T75#-11!1tC3W`Rl#4gEZ-_It5h&h?hp1;@y5 znftknu=x!lo;&-OYTY3#xE?rK({+4$rrkjAI>-<(Y(>jiR9Q-J*&}cL(bDHsPJ?Iu zQpet7(2AD*`9{hD^BuAByjG#ufdL+>6{3XWQtC%8vXu4LNQ|%~;AM`k$5)@F9T_^g zy-i|TwF=$Si?L*yVTf;}nd9&<-z|kL$o<@#cRA{hvB0GvrApxP=k8> zx_-EHy2*9|ScW?y=*t@mKbLYR2nd%s&fkxX#V(amvL(f;r^SfdUEQ*L*wz7^SYz*_)7Tlz?A;# z!07qFo)Jq(DEq?v3;TItL+tIZ2}+`Rnhgg}TOWsVto&Xco=(DsuNpn88gE8iwV%#j z0e_xrm>4AiuHSkxoREOGQ;mQ3Wt_|n98HXsoEN;zi!~EZ4iOTHYMd zk3g`=a9kv%mFN+l+tcI6Y!E?3=ggzvl+oSs$-3+T(+P_Kb6|1dhFK}{K8ME{5+w&K zLCI2dnn!v{KJ0xvo|6DWjm0ylo$;Ehv}rV~1h=u`T1O=m4rKIv@opCAD0!4Rbwjrl zf1utw#;a^X(C2qvIlC*u#~!3*mQrH7=74XrMvT!tcVz*m^!z+~)tM<)Mu}0m)=;$* zI~hMA#X+~5vnTN6K{Rm8c4cy@BN&vKbG`P}#kIRKSop4q3=091xS-nso{uX53Sk8mF`n(gcQuVNRU#O6>TL2 ze9< zqAKwr9(*sdNgICy8j;4nNLE1fT~#$W&8@ccQK&$i>dS!PMSQ5*6nXv!LF|A$zOcj^v8* zJtt{f0sAOyOjvA`9X_lV2E0~KSYI}^!D#+;T!wg73a5I_ypyL&DTY0UhY*Ph2V^)z z!nYEAEcyUrMXd?1N;z=W=6OzJS}+__K*$_Iyi@Oel}SbrB!=z|K`Gqtky10KR44pk zCS0few#3Cik^(4mWSyeoSM7Vjs_Wx!j#<-RHYVz^jAOc5mNbPE#X1A`h+c_QM!!z# zx$dGS+s&-cnT(urT-SA3+3y8f{Hi7HL#&L*U6_=hDhT zYBh~MyD`7-{WYNA5J5Zb&08t@aujL^emv~V3d%bhS@h5yc8hn`rKqMIZnQeoo(2{H zoW2q3N_n;u<}~fj7w9=wWbXUmARe_$@OTMv$t692-8RyD-OV>_(4D1Rob)BXtOAir zdl$2zc9_`saFzxGREjHMhQ8&+*z3ZI-4EL@3^@> zFDfd6o2*ncLnvn*Zu%YKhp{I-A%!>nVHKn>gwGQ(~@89=Cm? z0DQW8j-*6h6h&>hbCAjk%W`$Wi~9!oWWiY zX~~#E%M}xLNE9Pz8tI?`+{qmhzeJUzL@FGT4T`p}ZrW*cmhC|`Y(#}cfRGYfzRg4b~S9?DaW+CxoeeYlf=JVG_z7qf^u*brRSURmDC^W>hUf*5w-B$(F~ zPwHMjFH$;JGU}zjwxwxMG5w-!>C@<VSW{ppzSx}Qe0ce0hUJ4AH|7{8XZ@$l)_>b#1 z_Rrsee-dJTM`6E3%74*g{to|>Qt~?-i zeh1@0{2Tlaq=vui`LoCWyB_hk3g&GRfA9GJUBRD?oZl4`!vDvL|7z>}UCWzvals[-1]: + raise ValueError("Warning - priors not evaluated beyond ",zvals[-1]) + + if z Date: Wed, 23 Oct 2024 16:46:52 +0800 Subject: [PATCH 2/3] A few further comments --- papers/171020/{ => Data}/galaxy_redshifts.dat | 0 papers/171020/README.txt | 37 ++++++++++--------- papers/171020/mag_prior.py | 13 +++++-- 3 files changed, 28 insertions(+), 22 deletions(-) rename papers/171020/{ => Data}/galaxy_redshifts.dat (100%) diff --git a/papers/171020/galaxy_redshifts.dat b/papers/171020/Data/galaxy_redshifts.dat similarity index 100% rename from papers/171020/galaxy_redshifts.dat rename to papers/171020/Data/galaxy_redshifts.dat diff --git a/papers/171020/README.txt b/papers/171020/README.txt index 31e0e17..f16d370 100644 --- a/papers/171020/README.txt +++ b/papers/171020/README.txt @@ -1,28 +1,26 @@ +################ ABOUT ############### +This directory contains files that were used to produce the +z-priors for the paper: +https://ui.adsabs.harvard.edu/abs/2023PASA...40...29L/abstract -Find "clancy_testing" under the 171020 paper page. Use that. Ignore the rest below. +These calculations were done before zDM was put on github, and +effectively lived in a local "branch". +These files will almost certainly not run as-is, and no effort +has been made to allow them to run on any modern version of +zDM. -Look in -/Users/cjames/CRAFT/Localisation/FromPravir/RecentResults -for Pravir's original data - -Here, read in everything using "mag_prior.py" - -Need to check confidence limits, they are inconsistent - - -"mod" files have been modified by me for easy reading - -Using R-MAG_CANDIDATES.csv for r-band magnitudes -cand_positional_likelihood for likelihoods EXCEPY they seem to be 0.000... - +They are simply included for posterity, and scientific +reproducability. ######## Steps ########### 171020.py - - This file produces a prior on p(z), saves it to + - This file produces a prior on p(z) given observed + properties of FRB20171020A, saves it to z_priors_bestfit.npy and zvalues_for_priors.npy + (likely in the "Data" folder) use_z_priors.py - This step adds priors on redshift according to estimates of p(z|DM), @@ -31,16 +29,19 @@ use_z_priors.py 'second_cut.csv', and spits out a file with this prior at the end - The output should be added to "zpriors_added.csv" - It does NOT account for the greater number of galaxies at large z. + In other words, this is NOT a prior on a particular galaxy at a + particular z, but a prior on all galaxies in a given z bin paper_mag_prior.py - This functon adds the magnitude-based priors from Driver et al as per the PATH methodology. - It reads in "zpriors_added.csv", and spits out an array with the magnitude from Driver et al at the end + This gets printed to the screen. Save it as a "Data/z_mag_priors.csv" mag_prior.py - - This reads in the file modR-MAG_CANDIDATES.csv, and reads in - the positional likelihoods from 'mod_cand_pos_likelihood.csv' + - This reads in data from all the above and other sources, + and prints out a table for insertion into the paper diff --git a/papers/171020/mag_prior.py b/papers/171020/mag_prior.py index fb21dd0..24f82a5 100644 --- a/papers/171020/mag_prior.py +++ b/papers/171020/mag_prior.py @@ -1,10 +1,15 @@ +""" +This script reads in all the data, +and prints out a table for the paper +""" + import numpy as np from scipy import interpolate def main(): - file1 = open('modR-MAG_CANDIDATES.csv', 'r') + file1 = open('Data/modR-MAG_CANDIDATES.csv', 'r') Lines = file1.readlines() rmags=[] ras=[] @@ -36,7 +41,7 @@ def main(): priors = priors/norm ##### reads in positional likelihoods ####### - file2 = open('mod_cand_pos_likelihood.csv', 'r') + file2 = open('Data/mod_cand_pos_likelihood.csv', 'r') Lines2 = file2.readlines() poss=[] for line in Lines2: @@ -46,7 +51,7 @@ def main(): #### reads in redshifts #### - file1 = open('galaxy_redshifts.dat', 'r') + file1 = open('Data/galaxy_redshifts.dat', 'r') Lines = file1.readlines() zgals=[] zs=[] @@ -77,7 +82,7 @@ def main(): pprod = pprod/norm # re-reads data - file2 = open('mod_cand_pos_likelihood.csv', 'r') + file2 = open('Data/mod_cand_pos_likelihood.csv', 'r') Lines2 = file2.readlines() ###### prints out combined value for latex table ##### From 1a12ff20f08a71b7c3d64e3ad73c67e2f6e85a04 Mon Sep 17 00:00:00 2001 From: Clancy James Date: Thu, 24 Oct 2024 07:29:34 +0800 Subject: [PATCH 3/3] Finished adding files --- papers/171020/Data/171020.dat | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 papers/171020/Data/171020.dat diff --git a/papers/171020/Data/171020.dat b/papers/171020/Data/171020.dat new file mode 100644 index 0000000..9916638 --- /dev/null +++ b/papers/171020/Data/171020.dat @@ -0,0 +1,14 @@ +FBAR 1196 #MHz +BW 336 #MHz +FRES 1 #MHz +TRES 1.265 #ms +DIAM 12 +NBEAMS 36 +BEAM lat50_log #prefix of beam file +NFRB 1 #Number of FRBs +NORM_FRB 1 #number corresponding to Tobs +TOBS 63.73 +THRESH 22 #Jy ms to a 1 ms burst +SNRTHRESH 9.5 # signal-to-noise threshold: scales jy ms to snr +KEY ID TIME DM X X X X X WIDTH SNR X X FIELD ANT X DMG X XWIDTH +FRB 171020 10:28:35.59870 114.1 200 22:15 -19:40 29.3 -51.3 1.7 19.5 32.7 95 G35-50 0 0.63 38.4 25.8 0.58