Varying Redshift Binning

Alexia suggested that while I wait for David to get back to me about the problems with the correlation functions on the big data set, that I do some runs on the small data set with slight changes to the redshift bins to see how this effects things in terms of the bouncing.

Here are the runs I have run:

workingDir = 'run2011112_123'
#Angular Correlation
oversample = 5.
corrBins = 10.0 #should be one more than you want it to be
mincorr = 0.3
maxcorr = 3.0

convo = 180./pi

#Auto Correlation
boxside = 2200.0 #in Mpc/h
xicorrmin = 5.0
xicorrmax = 50.0
nxicorr = 40

#make redshift bins
#boxside = 1000.0 #in Mpc/h
rbinmin = 0.0 #in Mpc/h
rbinspace = boxside/10 #in Mpc/h
rbinmax = boxside + 1.0 #in Mpc/h
rbins = makeRbins(rbinmin,rbinmax,rbinspace)
nrbins = int(rbins.size - 1)

rbins
array([ 0., 220., 440., 660., 880., 1100., 1320., 1540., 1760., 1980., 2200.])

Reconstruction Plot

correlation functions:

workingDir = 'run2011113_051'

#Angular Correlation
oversample = 5.
corrBins = 10.0 #should be one more than you want it to be
mincorr = 0.3
maxcorr = 3.0

convo = 180./pi

#Auto Correlation
boxside = 2200.0 #in Mpc/h
xicorrmin = 5.0
xicorrmax = 50.0
nxicorr = 40

#make redshift bins
#boxside = 1000.0 #in Mpc/h
rbinmin = 100 #in Mpc/h
rbinspace = boxside/10 #in Mpc/h
rbinmax = boxside + 1.0 #in Mpc/h
rbins = makeRbins(rbinmin,rbinmax,rbinspace)
nrbins = int(rbins.size - 1)

rbins = array([ 100., 320., 540., 760., 980., 1200., 1420., 1640., 1860., 2080.])

Reconstruction Plot, same binning as above

Correlation Functions

workingDir = 'run2011113_055'

# Create file names
photofile,specfile,corr2dfile,corr3dfile,photo2dfile,photoCDfile,spec2dfile,\
spec3dfile,argumentFile,runConstantsFile,wpsMatrixFile,xiMatrixFile=makeFileNamesData(workingDir)

#Angular Correlation
oversample = 5.
corrBins = 10.0 #should be one more than you want it to be
mincorr = 0.3
maxcorr = 3.0

convo = 180./pi

#Auto Correlation
boxside = 2200.0 #in Mpc/h
xicorrmin = 5.0
xicorrmax = 50.0
nxicorr = 40

#make redshift bins
#boxside = 1000.0 #in Mpc/h
rbinmin = 100 #in Mpc/h
rbinspace = boxside/12 #in Mpc/h
rbinmax = boxside + 1.0 #in Mpc/h
rbins = makeRbins(rbinmin,rbinmax,rbinspace)
nrbins = int(rbins.size - 1)

rbins = array([ 100., 283.33333333, 466.66666667, 650. , 833.33333333, 1016.66666667, 1200., 1383.33333333, 1566.66666667, 1750., 1933.33333333, 2116.66666667])

Reconstruction Plot, same binning as above

workingDir = 'run2011113_110'

#Angular Correlation
oversample = 5.
corrBins = 10.0 #should be one more than you want it to be
mincorr = 0.3
maxcorr = 3.0

convo = 180./pi

#Auto Correlation
boxside = 2200.0 #in Mpc/h
xicorrmin = 5.0
xicorrmax = 50.0
nxicorr = 40

#make redshift bins
#boxside = 1000.0 #in Mpc/h
rbinmin = 100 #in Mpc/h
rbinspace = boxside/8 #in Mpc/h
rbinmax = boxside + 1.0 #in Mpc/h
rbins = makeRbins(rbinmin,rbinmax,rbinspace)
nrbins = int(rbins.size - 1)

rbins
array([ 100., 375., 650., 925., 1200., 1475., 1750., 2025.])


workingDir = 'run2011113_119'#Angular Correlation
oversample = 5.
corrBins = 10.0 #should be one more than you want it to be
mincorr = 0.3
maxcorr = 3.0

convo = 180./pi

#Auto Correlation
boxside = 2200.0 #in Mpc/h
xicorrmin = 5.0
xicorrmax = 50.0
nxicorr = 40

#make redshift bins
#boxside = 1000.0 #in Mpc/h
rbinmin = 400 #in Mpc/h
rbinspace = boxside/10 #in Mpc/h
rbinmax = boxside + 1.0 #in Mpc/h
rbins = makeRbins(rbinmin,rbinmax,rbinspace)
nrbins = int(rbins.size - 1)

rbins
array([ 400., 620., 840., 1060., 1280., 1500., 1720., 1940., 2160.])

Reconstruction

Correlation Functions