def xiwpcorrect(xiNNin, xiangin, splitxi0, splitxi2, wpNNin, wpangin, splitwp, wpstart, wpend):
"""
This function is for new statistic xiwp combining xi and wp.
"""
mywp = wpcorrect(wpNNin, wpangin, splitwp, wpstart, wpend)
myxiell = xicorrect(xiNNin, xiangin, splitxi0, splitxi2)
myxiwp = xiwp.xiwp(myxiell, mywp)
return myxiwp
def xiwpcorrect(xiNNin, xiangin, splitxi0, splitxi2, wpNNin, wpangin, splitwp,
wpstart, wpend):
"""
This function is for new statistic xiwp combining xi and wp.
"""
mywp = wpcorrect(wpNNin, wpangin, splitwp, wpstart, wpend)
myxiell = xicorrect(xiNNin, xiangin, splitxi0, splitxi2)
myxiwp = xiwp.xiwp(myxiell, mywp)
return myxiwp
def getbootcov(
bootfile,
workingdir,
covtag=None,
NSortot=2,
nboot=5000000,
fbaseend="_rmax48deltalog10r",
xiellorwp=0,
rpimax=80.0,
splitwp=7,
wpstart=1,
wpend=19,
nell=3,
binfile=None,
rperpcut=-1.0,
smallRRcut=-1.0,
dfacs=1,
dfacmu=1,
icovfname=None,
smincut=-1.0,
smaxcut=1.0e12,
splitxi0=5,
splitxi2=6,
fbaseendxiell="_rmax48deltalog10r",
fbaseendwp="_xigrid",
):
"""
Get covariance matrix.
fbaseend = '_rmax48deltalog10r' for xiell or '_xigrid' for wp.
Third tier of stuff goes directly to xiellfromDR
expect splitxi0/splitxi2 [those go to xicorrect; values determined in
comparetiledcmockstotruthv0
Added functionality for wp: xiellorwp = 1, splitwp = where to go from ang to NN.
rpimax is for wp, default is 80.
Leaving variable fbaseend for backward compatibility, but if xiellorwp == 2, defaults to
using fbaseendxiell and fbaseendwp
"""
# NNorang = 0 [NN] or 1 [ang] or 2 [optimal unbiased combination, not yet written]
## nevermind, that doesnt get used anywhere!!?? deleted, was the 4th elt in the list.
assert xiellorwp >= 0 and xiellorwp <= 2
nsub, pixelfname, fbaseNNstart, fbaseangstart, fbaseNNtotN, fbaseNNtotS, fbaseangtotN, fbaseangtotS = parsebootinfo(
bootfile, workingdir
)
if nsub is None or pixelfname is None or fbaseNNstart is None or fbaseangstart is None:
print "bad boot file, getbootcov returning None!"
return None
pixlist = getpixlist(pixelfname, nsub)
myfbase_NN = fbaseNNstart
myfbase_ang = fbaseangstart
if xiellorwp == 0 or xiellorwp == 1:
DRfacN_NN, fixRRdownN_NN = ximisc.getDRfactors(fbaseNNtotN + fbaseend)
DRfacS_NN, fixRRdownS_NN = ximisc.getDRfactors(fbaseNNtotS + fbaseend)
DRfacN_ang, fixRRdownN_ang = ximisc.getDRfactors(fbaseangtotN + fbaseend)
DRfacS_ang, fixRRdownS_ang = ximisc.getDRfactors(fbaseangtotS + fbaseend)
else: ##xiwp statistic. xiellorwp == 2
## xiell
DRfacN_NNxiell, fixRRdownN_NNxiell = ximisc.getDRfactors(fbaseNNtotN + fbaseendxiell)
DRfacS_NNxiell, fixRRdownS_NNxiell = ximisc.getDRfactors(fbaseNNtotS + fbaseendxiell)
DRfacN_angxiell, fixRRdownN_angxiell = ximisc.getDRfactors(fbaseangtotN + fbaseendxiell)
DRfacS_angxiell, fixRRdownS_angxiell = ximisc.getDRfactors(fbaseangtotS + fbaseendxiell)
## wp
DRfacN_NNwp, fixRRdownN_NNwp = ximisc.getDRfactors(fbaseNNtotN + fbaseendwp)
DRfacS_NNwp, fixRRdownS_NNwp = ximisc.getDRfactors(fbaseNNtotS + fbaseendwp)
DRfacN_angwp, fixRRdownN_angwp = ximisc.getDRfactors(fbaseangtotN + fbaseendwp)
DRfacS_angwp, fixRRdownS_angwp = ximisc.getDRfactors(fbaseangtotS + fbaseendwp)
if xiellorwp == 0:
splittag = "splits%d_%d" % (splitxi0, splitxi2)
if xiellorwp == 1:
splittag = "splitswp%d_%d_%d" % (splitwp, wpstart, wpend)
if xiellorwp == 2:
splittagxiell = "splits%d_%d" % (splitxi0, splitxi2)
splittagwp = "splitswp%d_%d_%d" % (splitwp, wpstart, wpend)
splittag = splittagxiell + "_" + splittagwp
if binfile is not None:
bintag = binfile.split("/")[-1].split(".")[0]
covoutNN = "covtotv7NN_b%d_N%d_rebin-%s" % (nboot, nsub, bintag)
covoutang = "covtotv7ang_b%d_N%d_rebin-%s" % (nboot, nsub, bintag)
covoutcorr = "covtotv7corr_b%d_N%d_rebin-%s_%s" % (nboot, nsub, bintag, splittag)
else:
covoutNN = "covtotv7NN_b%d_N%d" % (nboot, nsub)
covoutang = "covtotv7ang_b%d_N%d" % (nboot, nsub)
covoutcorr = "covtotv7corr_b%d_N%d_%s" % (nboot, nsub, splittag)
if covtag is not None:
covoutNN = covoutNN + "_%s" % covtag
covoutang = covoutang + "_%s" % covtag
covoutcorr = covoutcorr + "_%s" % covtag
icovoutNN = "i" + covoutNN
icovoutang = "i" + covoutang
icovoutcorr = "i" + covoutcorr
if xiellorwp == 0 or xiellorwp == 1:
DRinfoN_NN = [DRfacN_NN, fixRRdownN_NN]
DRinfoS_NN = [DRfacS_NN, fixRRdownS_NN]
DRinfoN_ang = [DRfacN_ang, fixRRdownN_ang]
DRinfoS_ang = [DRfacS_ang, fixRRdownS_ang]
else:
# xiell
DRinfoN_NNxiell = [DRfacN_NNxiell, fixRRdownN_NNxiell]
DRinfoS_NNxiell = [DRfacS_NNxiell, fixRRdownS_NNxiell]
DRinfoN_angxiell = [DRfacN_angxiell, fixRRdownN_angxiell]
DRinfoS_angxiell = [DRfacS_angxiell, fixRRdownS_angxiell]
# wp
DRinfoN_NNwp = [DRfacN_NNwp, fixRRdownN_NNwp]
DRinfoS_NNwp = [DRfacS_NNwp, fixRRdownS_NNwp]
DRinfoN_angwp = [DRfacN_angwp, fixRRdownN_angwp]
DRinfoS_angwp = [DRfacS_angwp, fixRRdownS_angwp]
for ns in range(nsub):
print ns
fbase_NN = myfbase_NN + ("-%03d" % (ns)) + fbaseend
fbase_ang = myfbase_ang + ("-%03d" % (ns)) + fbaseend
if xiellorwp == 2:
fbase_NNxiell = myfbase_NN + ("-%03d" % (ns)) + fbaseendxiell
fbase_angxiell = myfbase_ang + ("-%03d" % (ns)) + fbaseendxiell
fbase_NNwp = myfbase_NN + ("-%03d" % (ns)) + fbaseendwp
fbase_angwp = myfbase_ang + ("-%03d" % (ns)) + fbaseendwp
xx = np.where(pixlist["PID"] == ns)[0]
assert len(xx) == 1
assert xx[0] == ns
NorSval = pixlist["NorS"][xx[0]]
if NorSval == 0:
if xiellorwp == 0 or xiellorwp == 1:
DRinfo_NN = DRinfoN_NN
DRinfo_ang = DRinfoN_ang
else:
DRinfo_NNxiell = DRinfoN_NNxiell
DRinfo_angxiell = DRinfoN_angxiell
DRinfo_NNwp = DRinfoN_NNwp
DRinfo_angwp = DRinfoN_angwp
else: # south
if xiellorwp == 0 or xiellorwp == 1:
DRinfo_NN = DRinfoS_NN
DRinfo_ang = DRinfoS_ang
else:
DRinfo_NNxiell = DRinfoS_NNxiell
DRinfo_angxiell = DRinfoS_angxiell
DRinfo_NNwp = DRinfoS_NNwp
DRinfo_angwp = DRinfoS_angwp
if xiellorwp == 0:
xiinNN = xiell.xiellfromDR(
fbase_NN, nell, binfile, rperpcut, dfacs, dfacmu, icovfname, smincut, smaxcut, DRinfo_NN, smallRRcut
)
xiinang = xiell.xiellfromDR(
fbase_ang, nell, binfile, rperpcut, dfacs, dfacmu, icovfname, smincut, smaxcut, DRinfo_ang, smallRRcut
)
xicorr = xicorrect(xiinNN, xiinang, splitxi0, splitxi2)
if xiellorwp == 1: ## doing wp
xiinNNtmp = wp.wpfromDR(fbase_NN, DRfacinfo=DRinfo_NN, rpimax=rpimax, icovfname=icovfname)
xiinangtmp = wp.wpfromDR(fbase_ang, DRfacinfo=DRinfo_ang, rpimax=rpimax, icovfname=icovfname)
## these are for later, saving cov of NN and ang separately.
xiinNN = wp.wpfromDR(
fbase_NN, DRfacinfo=DRinfo_NN, rpimax=rpimax, icovfname=icovfname, wpstart=wpstart, wpend=wpend
)
xiinang = wp.wpfromDR(
fbase_ang, DRfacinfo=DRinfo_ang, rpimax=rpimax, icovfname=icovfname, wpstart=wpstart, wpend=wpend
)
##wpstart,end not already applied to this NN and ang!
xicorr = wpcorrect(xiinNNtmp, xiinangtmp, splitwp, wpstart, wpend)
if xiellorwp == 2: ##doing xiwp
xiinNNxiell = xiell.xiellfromDR(
fbase_NNxiell,
nell,
binfile,
rperpcut,
dfacs,
dfacmu,
icovfname,
smincut,
smaxcut,
DRinfo_NNxiell,
smallRRcut,
)
xiinangxiell = xiell.xiellfromDR(
fbase_angxiell,
nell,
binfile,
rperpcut,
dfacs,
dfacmu,
icovfname,
smincut,
smaxcut,
DRinfo_angxiell,
smallRRcut,
)
xiinNNwptmp = wp.wpfromDR(fbase_NNwp, DRfacinfo=DRinfo_NNwp, rpimax=rpimax, icovfname=icovfname)
xiinangwptmp = wp.wpfromDR(fbase_angwp, DRfacinfo=DRinfo_angwp, rpimax=rpimax, icovfname=icovfname)
xiinNNwp = wp.wpfromDR(
fbase_NNwp, DRfacinfo=DRinfo_NNwp, rpimax=rpimax, icovfname=icovfname, wpstart=wpstart, wpend=wpend
)
xiinangwp = wp.wpfromDR(
fbase_angwp, DRfacinfo=DRinfo_angwp, rpimax=rpimax, icovfname=icovfname, wpstart=wpstart, wpend=wpend
)
xiinNN = xiwp.xiwp(xiinNNxiell, xiinNNwp)
xiinang = xiwp.xiwp(xiinangxiell, xiinangwp)
xicorr = xiwpcorrect(
xiinNNxiell, xiinangxiell, splitxi0, splitxi2, xiinNNwptmp, xiinangwptmp, splitwp, wpstart, wpend
)
## tmp! we tested to make sure we recovered the same correlation fxns as with old code. Good!
# tmpfname = "testing/testo%d" % ns
# xiin.printxiellshort(tmpfname)
if ns == 0:
if xiellorwp == 0:
ndata = xiinNN.ndata
ndatacorr = ndata
if xiellorwp == 1:
ndata = len(xiinNN.wp)
ndatacorr = len(xicorr.wp)
if xiellorwp == 2:
ndata = xiinNN.ntot
ndatacorr = ndata
xilistNN = np.zeros([nsub, ndata], dtype="float128")
xilistang = np.zeros([nsub, ndata], dtype="float128")
xilistcorr = np.zeros([nsub, ndatacorr], dtype="float128")
if xiellorwp == 0:
xilistNN[ns, :] = xiinNN.xilong
xilistang[ns, :] = xiinang.xilong
xilistcorr[ns, :] = xicorr.xilong
if xiellorwp == 1:
xilistNN[ns, :] = xiinNN.wp
xilistang[ns, :] = xiinang.wp
xilistcorr[ns, :] = xicorr.wp
if xiellorwp == 2:
xilistNN[ns, :] = xiinNN.xiwp
xilistang[ns, :] = xiinang.xiwp
xilistcorr[ns, :] = xicorr.xiwp
## check means with total counts.
nindx = np.where(pixlist["NorS"] == 0)[0]
sindx = np.where(pixlist["NorS"] == 1)[0]
print "N/S: ", len(nindx), len(sindx)
## now compute mean and bootstrap errors:
if NSortot == 0:
ximeanNN = (xilistNN[nindx, :]).sum(axis=0) / float(len(nindx))
ximeanang = (xilistang[nindx, :]).sum(axis=0) / float(len(nindx))
ximeancorr = (xilistcorr[nindx, :]).sum(axis=0) / float(len(nindx))
ntot = len(nindx)
## restrict xilist to N only
xilistNN = xlistNN[nindx, :]
xilistang = xlistang[nindx, :]
xilistcorr = xlistcorr[nindx, :]
if NSortot == 1:
ximeanNN = (xilistNN[sindx, :]).sum(axis=0) / float(len(sindx))
ximeanang = (xilistang[sindx, :]).sum(axis=0) / float(len(sindx))
ximeancorr = (xilistcorr[sindx, :]).sum(axis=0) / float(len(sindx))
ntot = len(sindx)
## restrict xilist to S only
xilistNN = xlistNN[sindx, :]
xilistang = xlistang[sindx, :]
xilistcorr = xlistcorr[sindx, :]
if NSortot == 2:
ximeanNN = xilistNN.sum(axis=0) / float(nsub)
ximeanang = xilistang.sum(axis=0) / float(nsub)
ximeancorr = xilistcorr.sum(axis=0) / float(nsub)
ntot = nsub
xitotNN = np.zeros(ndata, dtype="float128")
xitotang = np.zeros(ndata, dtype="float128")
xitotcorr = np.zeros(ndatacorr, dtype="float128")
CguessNN = np.zeros([ndata, ndata], dtype="float128")
Cguessang = np.zeros([ndata, ndata], dtype="float128")
Cguesscorr = np.zeros([ndatacorr, ndatacorr], dtype="float128")
for b in range(nboot):
rr = np.random.random_integers(0, ntot - 1, ntot)
xitrialNN = (xilistNN[rr, :]).sum(axis=0) / float(ntot)
xitrialang = (xilistang[rr, :]).sum(axis=0) / float(ntot)
xitrialcorr = (xilistcorr[rr, :]).sum(axis=0) / float(ntot)
xvecNN = np.matrix([xitrialNN - ximeanNN])
xvecang = np.matrix([xitrialang - ximeanang])
xveccorr = np.matrix([xitrialcorr - ximeancorr])
CguessNN += xvecNN.T * xvecNN
Cguessang += xvecang.T * xvecang
Cguesscorr += xveccorr.T * xveccorr
CguessNN = CguessNN / float(nboot - 1)
Cguessang = Cguessang / float(nboot - 1)
Cguesscorr = Cguesscorr / float(nboot - 1)
## put this back in after tests.
#### now let's compute icov for all these.
## eqn 17 of 0608064:
p = len(CguessNN[:, 0])
unbiasicov = float(ntot - p - 2) / float(ntot - 1)
CguessNN = np.matrix(CguessNN, dtype="float64")
invCguessNN = CguessNN.I * unbiasicov
printcov(CguessNN, covoutNN)
printcov(invCguessNN, icovoutNN)
Cguessang = np.matrix(Cguessang, dtype="float64")
invCguessang = Cguessang.I * unbiasicov
printcov(Cguessang, covoutang)
printcov(invCguessang, icovoutang)
Cguesscorr = np.matrix(Cguesscorr, dtype="float64")
invCguesscorr = Cguesscorr.I * unbiasicov
printcov(Cguesscorr, covoutcorr)
printcov(invCguesscorr, icovoutcorr)
return CguessNN, invCguessNN, Cguessang, invCguessang, Cguesscorr, invCguesscorr
def debiasdataandcovxiMwp(
xiNNd,
xiangd,
xiangdhigh,
xiangdlow,
xiNNm,
xiangm,
xi012m,
splitxi0,
splitxi2,
wpNNd,
wpangd,
wpangdhigh,
wpangdlow,
wpNNm,
wpangm,
wp012m,
splitwp,
wpstart,
wpend,
covstatfname,
nell=2,
fname=None,
):
# def debiasdataandcovwp(wpNNd, wpangd, wpangdhigh, wpangdlow, wpNNm, wpangm, wp012m, splitwp, wpstart,wpend,covstatfname,fname=None):
"""
subtract the bias measured from the tiled mocks from the data, return a debiased combination.
print it to a file (fname) to be fed to bethalexie code in long format.
Also take in statistical covariance matrix and add two sources of systematics.
"""
# def xiwpcorrect(xiNNin, xiangin,splitxi0,splitxi2,wpNNin, wpangin, splitwp, wpstart, wpend):
xiwpcorrdtmp = xiwpcorrect(xiNNd, xiangd, splitxi0, splitxi2, wpNNd, wpangd, splitwp, wpstart, wpend)
xiwpcorrm = xiwpcorrect(xiNNm, xiangm, splitxi0, splitxi2, wpNNm, wpangm, splitwp, wpstart, wpend)
xiwpdebiased = copy.deepcopy(xiwpcorrdtmp)
# tmp!
# print xiwpdebiased.xiell
# print xiwpdebiased.wp
mydeltaxi = xi012m.xi - xiwpcorrm.xiell.xi ## subtract xi objects.
mydeltawp = wp012m.wp[wpstart : wpend + 1] - xiwpcorrm.wp.wp
xiwpdebiased.xiell.xi = xiwpdebiased.xiell.xi + mydeltaxi
xiwpdebiased.wp.wp = xiwpdebiased.wp.wp + mydeltawp
xiwpdebiased.xiwp = np.concatenate((xiwpdebiased.xiell.xilong, xiwpdebiased.wp.wp))
xiwpanghigh = xiwp.xiwp(xiangdhigh, wpangdhigh)
xiwpanglow = xiwp.xiwp(xiangdlow, wpangdlow)
## now the cov.
## make sure this is the cov for the corrected statistic with same splits.
if 0 == 0:
# try:
cov = np.loadtxt(covstatfname)
assert len(cov[:, 0]) == xiwpdebiased.ntot
splitz = covstatfname.split("splitswp")[0].split("splits")[1].split("_")
assert len(splitz) >= nell
ilist = []
tmp = 0
for ss in splitz:
tmp += 1
ilist.append(int(ss))
if tmp >= nell:
break
assert ilist[0] == splitxi0
assert ilist[1] == splitxi2
splitzwp = covstatfname.split("splitswp")[1].split("_")
print splitzwp
assert len(splitzwp) >= 3
ilist = []
tmp = 0
for ss in splitzwp:
tmp += 1
ilist.append(int(ss))
if tmp >= 3:
break
assert ilist[0] == splitwp
assert ilist[1] == wpstart
assert ilist[2] == wpend
## new jan 2 2014!!! forgot to take into account the unbiasicov fac. derive if from
## product of cov and icov.
## guess icovfname
tmp = covstatfname.split("/")
tmp[-1] = "i" + tmp[-1]
icovstatfname = "/".join(tmp)
icov = np.loadtxt(icovstatfname)
unbiasicovfac = (ximisc.getmatrixdiag(np.matrix(cov) * np.matrix(icov))).mean()
print "using this unbiasicovfac correction, dividing cov by this", unbiasicovfac
cov = cov / unbiasicovfac
diagstat = np.zeros(xiwpdebiased.ntot)
diagtot = np.zeros(xiwpdebiased.ntot)
diagvar = np.zeros(xiwpdebiased.ntot)
for i in range(len(diagstat)):
diagstat[i] = cov[i, i]
xiangdiffvar = (0.5 * (xiangdhigh.xi.flatten() - xiangdlow.xi.flatten())) ** 2
diagvar[0:splitxi0] = xiangdiffvar[0:splitxi0]
nxi0 = len(xiNNd.xi0)
nxi2 = len(xiNNd.xi2)
diagvar[nxi0 : nxi0 + splitxi2] = xiangdiffvar[nxi0 : nxi0 + splitxi2]
wpangdiffvar = (0.5 * (wpangdhigh.wp - wpangdlow.wp)) ** 2
diagvar[nxi0 + nxi2 : nxi0 + nxi2 + splitwp - wpstart] = wpangdiffvar[wpstart:splitwp]
print "ang high/low variance: ", diagvar / diagstat
diagvar[0 : nxi0 + nxi2] = diagvar[0 : nxi0 + nxi2] + (mydeltaxi.flatten()) ** 2
diagvar[nxi0 + nxi2 :] = diagvar[nxi0 + nxi2 :] + (mydeltawp) ** 2
print "bias variance xi: ", (mydeltaxi.flatten()) ** 2 / diagstat[0 : nxi0 + nxi2]
print "bias variance wp: ", (mydeltawp) ** 2 / diagstat[nxi0 + nxi2 :]
## add it into the covarianace matrix.
for i in range(xiwpdebiased.ntot):
cov[i, i] += diagvar[i]
diagtot[i] = cov[i, i]
print "total sys variance fraction", diagtot / diagstat
## make it a matrix.
cov = np.matrix(cov)
icov = cov.I
fcovout = covstatfname + ".sys"
## print the covariance and icov to new file.
printcov(cov, fcovout)
tmp = fcovout.split("/")
tmp[-1] = "i" + tmp[-1]
ifcovout = "/".join(tmp)
printcov(icov, ifcovout)
xiwpfinal = xiwp.xiwp(xiwpdebiased.xiell, xiwpdebiased.wp, icovfname=ifcovout)
if fname is not None:
ofp = open(fname, "w")
ofp.write("# ellmax = %d\n" % ((nell - 1) * 2))
for i in range(len(xiwpfinal.xiell.svec.flatten())):
ofp.write("%e %e\n" % (xiwpfinal.xiell.svec.flatten()[i], xiwpfinal.xiell.xi.flatten()[i]))
for i in range(len(xiwpfinal.wp.wp)):
ofp.write("%e %e\n" % (xiwpfinal.wp.rsig[i], xiwpfinal.wp.wp[i]))
ofp.close()
return xiwpfinal, cov
else:
print "cov file name does not match input splits, returning None!"
xiwpfinal = xiwp.xiwp(xiwpdebiased.xiell, xiwpdebiased.wp, icovfname=ifcovout)
if fname is not None:
ofp = open(fname, "w")
ofp.write("# ellmax = %d\n" % ((nell - 1) * 2))
for i in range(len(xiwpfinal.xi.svec.flatten())):
ofp.write("%e %e\n" % (xiwpfinal.xiell.svec.flatten()[i], xiwpfinal.xiell.xi.flatten()[i]))
for i in range(len(xiwpfinal.wp.wp)):
ofp.write("%e %e\n" % (xiwpfinal.wp.rsig[i], xiwpfinal.wp.wp[i]))
ofp.close()
return xiwpfinal, None
def getbootcov(bootfile, workingdir, covtag=None, NSortot=2, nboot = 5000000, fbaseend='_rmax48deltalog10r',\
xiellorwp=0,rpimax=80.,splitwp=7,wpstart=1,wpend=19,\
nell=3,binfile=None,rperpcut=-1.,smallRRcut=-1.,\
dfacs=1,dfacmu=1,icovfname=None,smincut=-1.,smaxcut=1.e12,\
splitxi0=5,splitxi2=6,fbaseendxiell='_rmax48deltalog10r',fbaseendwp='_xigrid'):
"""
Get covariance matrix.
fbaseend = '_rmax48deltalog10r' for xiell or '_xigrid' for wp.
Third tier of stuff goes directly to xiellfromDR
expect splitxi0/splitxi2 [those go to xicorrect; values determined in
comparetiledcmockstotruthv0
Added functionality for wp: xiellorwp = 1, splitwp = where to go from ang to NN.
rpimax is for wp, default is 80.
Leaving variable fbaseend for backward compatibility, but if xiellorwp == 2, defaults to
using fbaseendxiell and fbaseendwp
"""
#NNorang = 0 [NN] or 1 [ang] or 2 [optimal unbiased combination, not yet written]
## nevermind, that doesnt get used anywhere!!?? deleted, was the 4th elt in the list.
assert xiellorwp >= 0 and xiellorwp <= 2
nsub, pixelfname, fbaseNNstart, fbaseangstart, \
fbaseNNtotN, fbaseNNtotS, fbaseangtotN, fbaseangtotS = parsebootinfo(bootfile,workingdir)
if nsub is None or pixelfname is None or fbaseNNstart is None or fbaseangstart is None:
print 'bad boot file, getbootcov returning None!'
return None
pixlist = getpixlist(pixelfname, nsub)
myfbase_NN = fbaseNNstart
myfbase_ang = fbaseangstart
if xiellorwp == 0 or xiellorwp == 1:
DRfacN_NN, fixRRdownN_NN = ximisc.getDRfactors(fbaseNNtotN + fbaseend)
DRfacS_NN, fixRRdownS_NN = ximisc.getDRfactors(fbaseNNtotS + fbaseend)
DRfacN_ang, fixRRdownN_ang = ximisc.getDRfactors(fbaseangtotN +
fbaseend)
DRfacS_ang, fixRRdownS_ang = ximisc.getDRfactors(fbaseangtotS +
fbaseend)
else: ##xiwp statistic. xiellorwp == 2
## xiell
DRfacN_NNxiell, fixRRdownN_NNxiell = ximisc.getDRfactors(fbaseNNtotN +
fbaseendxiell)
DRfacS_NNxiell, fixRRdownS_NNxiell = ximisc.getDRfactors(fbaseNNtotS +
fbaseendxiell)
DRfacN_angxiell, fixRRdownN_angxiell = ximisc.getDRfactors(
fbaseangtotN + fbaseendxiell)
DRfacS_angxiell, fixRRdownS_angxiell = ximisc.getDRfactors(
fbaseangtotS + fbaseendxiell)
## wp
DRfacN_NNwp, fixRRdownN_NNwp = ximisc.getDRfactors(fbaseNNtotN +
fbaseendwp)
DRfacS_NNwp, fixRRdownS_NNwp = ximisc.getDRfactors(fbaseNNtotS +
fbaseendwp)
DRfacN_angwp, fixRRdownN_angwp = ximisc.getDRfactors(fbaseangtotN +
fbaseendwp)
DRfacS_angwp, fixRRdownS_angwp = ximisc.getDRfactors(fbaseangtotS +
fbaseendwp)
if xiellorwp == 0:
splittag = 'splits%d_%d' % (splitxi0, splitxi2)
if xiellorwp == 1:
splittag = 'splitswp%d_%d_%d' % (splitwp, wpstart, wpend)
if xiellorwp == 2:
splittagxiell = 'splits%d_%d' % (splitxi0, splitxi2)
splittagwp = 'splitswp%d_%d_%d' % (splitwp, wpstart, wpend)
splittag = splittagxiell + '_' + splittagwp
if binfile is not None:
bintag = binfile.split('/')[-1].split('.')[0]
covoutNN = 'covtotv7NN_b%d_N%d_rebin-%s' % (nboot, nsub, bintag)
covoutang = 'covtotv7ang_b%d_N%d_rebin-%s' % (nboot, nsub, bintag)
covoutcorr = 'covtotv7corr_b%d_N%d_rebin-%s_%s' % (nboot, nsub, bintag,
splittag)
else:
covoutNN = 'covtotv7NN_b%d_N%d' % (nboot, nsub)
covoutang = 'covtotv7ang_b%d_N%d' % (nboot, nsub)
covoutcorr = 'covtotv7corr_b%d_N%d_%s' % (nboot, nsub, splittag)
if covtag is not None:
covoutNN = covoutNN + '_%s' % covtag
covoutang = covoutang + '_%s' % covtag
covoutcorr = covoutcorr + '_%s' % covtag
icovoutNN = 'i' + covoutNN
icovoutang = 'i' + covoutang
icovoutcorr = 'i' + covoutcorr
if xiellorwp == 0 or xiellorwp == 1:
DRinfoN_NN = [DRfacN_NN, fixRRdownN_NN]
DRinfoS_NN = [DRfacS_NN, fixRRdownS_NN]
DRinfoN_ang = [DRfacN_ang, fixRRdownN_ang]
DRinfoS_ang = [DRfacS_ang, fixRRdownS_ang]
else:
#xiell
DRinfoN_NNxiell = [DRfacN_NNxiell, fixRRdownN_NNxiell]
DRinfoS_NNxiell = [DRfacS_NNxiell, fixRRdownS_NNxiell]
DRinfoN_angxiell = [DRfacN_angxiell, fixRRdownN_angxiell]
DRinfoS_angxiell = [DRfacS_angxiell, fixRRdownS_angxiell]
# wp
DRinfoN_NNwp = [DRfacN_NNwp, fixRRdownN_NNwp]
DRinfoS_NNwp = [DRfacS_NNwp, fixRRdownS_NNwp]
DRinfoN_angwp = [DRfacN_angwp, fixRRdownN_angwp]
DRinfoS_angwp = [DRfacS_angwp, fixRRdownS_angwp]
for ns in range(nsub):
print ns
fbase_NN = myfbase_NN + ('-%03d' % (ns)) + fbaseend
fbase_ang = myfbase_ang + ('-%03d' % (ns)) + fbaseend
if xiellorwp == 2:
fbase_NNxiell = myfbase_NN + ('-%03d' % (ns)) + fbaseendxiell
fbase_angxiell = myfbase_ang + ('-%03d' % (ns)) + fbaseendxiell
fbase_NNwp = myfbase_NN + ('-%03d' % (ns)) + fbaseendwp
fbase_angwp = myfbase_ang + ('-%03d' % (ns)) + fbaseendwp
xx = np.where(pixlist['PID'] == ns)[0]
assert len(xx) == 1
assert xx[0] == ns
NorSval = pixlist['NorS'][xx[0]]
if (NorSval == 0):
if xiellorwp == 0 or xiellorwp == 1:
DRinfo_NN = DRinfoN_NN
DRinfo_ang = DRinfoN_ang
else:
DRinfo_NNxiell = DRinfoN_NNxiell
DRinfo_angxiell = DRinfoN_angxiell
DRinfo_NNwp = DRinfoN_NNwp
DRinfo_angwp = DRinfoN_angwp
else: #south
if xiellorwp == 0 or xiellorwp == 1:
DRinfo_NN = DRinfoS_NN
DRinfo_ang = DRinfoS_ang
else:
DRinfo_NNxiell = DRinfoS_NNxiell
DRinfo_angxiell = DRinfoS_angxiell
DRinfo_NNwp = DRinfoS_NNwp
DRinfo_angwp = DRinfoS_angwp
if xiellorwp == 0:
xiinNN = xiell.xiellfromDR(fbase_NN, nell, binfile, rperpcut,
dfacs, dfacmu, icovfname, smincut,
smaxcut, DRinfo_NN, smallRRcut)
xiinang = xiell.xiellfromDR(fbase_ang, nell, binfile, rperpcut,
dfacs, dfacmu, icovfname, smincut,
smaxcut, DRinfo_ang, smallRRcut)
xicorr = xicorrect(xiinNN, xiinang, splitxi0, splitxi2)
if xiellorwp == 1: ## doing wp
xiinNNtmp = wp.wpfromDR(fbase_NN,
DRfacinfo=DRinfo_NN,
rpimax=rpimax,
icovfname=icovfname)
xiinangtmp = wp.wpfromDR(fbase_ang,
DRfacinfo=DRinfo_ang,
rpimax=rpimax,
icovfname=icovfname)
## these are for later, saving cov of NN and ang separately.
xiinNN = wp.wpfromDR(fbase_NN,
DRfacinfo=DRinfo_NN,
rpimax=rpimax,
icovfname=icovfname,
wpstart=wpstart,
wpend=wpend)
xiinang = wp.wpfromDR(fbase_ang,
DRfacinfo=DRinfo_ang,
rpimax=rpimax,
icovfname=icovfname,
wpstart=wpstart,
wpend=wpend)
##wpstart,end not already applied to this NN and ang!
xicorr = wpcorrect(xiinNNtmp, xiinangtmp, splitwp, wpstart, wpend)
if xiellorwp == 2: ##doing xiwp
xiinNNxiell = xiell.xiellfromDR(fbase_NNxiell, nell, binfile,
rperpcut, dfacs, dfacmu, icovfname,
smincut, smaxcut, DRinfo_NNxiell,
smallRRcut)
xiinangxiell = xiell.xiellfromDR(fbase_angxiell, nell, binfile,
rperpcut, dfacs, dfacmu,
icovfname, smincut, smaxcut,
DRinfo_angxiell, smallRRcut)
xiinNNwptmp = wp.wpfromDR(fbase_NNwp,
DRfacinfo=DRinfo_NNwp,
rpimax=rpimax,
icovfname=icovfname)
xiinangwptmp = wp.wpfromDR(fbase_angwp,
DRfacinfo=DRinfo_angwp,
rpimax=rpimax,
icovfname=icovfname)
xiinNNwp = wp.wpfromDR(fbase_NNwp,
DRfacinfo=DRinfo_NNwp,
rpimax=rpimax,
icovfname=icovfname,
wpstart=wpstart,
wpend=wpend)
xiinangwp = wp.wpfromDR(fbase_angwp,
DRfacinfo=DRinfo_angwp,
rpimax=rpimax,
icovfname=icovfname,
wpstart=wpstart,
wpend=wpend)
xiinNN = xiwp.xiwp(xiinNNxiell, xiinNNwp)
xiinang = xiwp.xiwp(xiinangxiell, xiinangwp)
xicorr = xiwpcorrect(xiinNNxiell, xiinangxiell, splitxi0, splitxi2,
xiinNNwptmp, xiinangwptmp, splitwp, wpstart,
wpend)
## tmp! we tested to make sure we recovered the same correlation fxns as with old code. Good!
#tmpfname = "testing/testo%d" % ns
#xiin.printxiellshort(tmpfname)
if (ns == 0):
if (xiellorwp == 0):
ndata = xiinNN.ndata
ndatacorr = ndata
if (xiellorwp == 1):
ndata = len(xiinNN.wp)
ndatacorr = len(xicorr.wp)
if (xiellorwp == 2):
ndata = xiinNN.ntot
ndatacorr = ndata
xilistNN = np.zeros([nsub, ndata], dtype='float128')
xilistang = np.zeros([nsub, ndata], dtype='float128')
xilistcorr = np.zeros([nsub, ndatacorr], dtype='float128')
if (xiellorwp == 0):
xilistNN[ns, :] = xiinNN.xilong
xilistang[ns, :] = xiinang.xilong
xilistcorr[ns, :] = xicorr.xilong
if (xiellorwp == 1):
xilistNN[ns, :] = xiinNN.wp
xilistang[ns, :] = xiinang.wp
xilistcorr[ns, :] = xicorr.wp
if (xiellorwp == 2):
xilistNN[ns, :] = xiinNN.xiwp
xilistang[ns, :] = xiinang.xiwp
xilistcorr[ns, :] = xicorr.xiwp
## check means with total counts.
nindx = np.where(pixlist['NorS'] == 0)[0]
sindx = np.where(pixlist['NorS'] == 1)[0]
print 'N/S: ', len(nindx), len(sindx)
## now compute mean and bootstrap errors:
if (NSortot == 0):
ximeanNN = (xilistNN[nindx, :]).sum(axis=0) / float(len(nindx))
ximeanang = (xilistang[nindx, :]).sum(axis=0) / float(len(nindx))
ximeancorr = (xilistcorr[nindx, :]).sum(axis=0) / float(len(nindx))
ntot = len(nindx)
## restrict xilist to N only
xilistNN = xlistNN[nindx, :]
xilistang = xlistang[nindx, :]
xilistcorr = xlistcorr[nindx, :]
if (NSortot == 1):
ximeanNN = (xilistNN[sindx, :]).sum(axis=0) / float(len(sindx))
ximeanang = (xilistang[sindx, :]).sum(axis=0) / float(len(sindx))
ximeancorr = (xilistcorr[sindx, :]).sum(axis=0) / float(len(sindx))
ntot = len(sindx)
## restrict xilist to S only
xilistNN = xlistNN[sindx, :]
xilistang = xlistang[sindx, :]
xilistcorr = xlistcorr[sindx, :]
if (NSortot == 2):
ximeanNN = xilistNN.sum(axis=0) / float(nsub)
ximeanang = xilistang.sum(axis=0) / float(nsub)
ximeancorr = xilistcorr.sum(axis=0) / float(nsub)
ntot = nsub
xitotNN = np.zeros(ndata, dtype='float128')
xitotang = np.zeros(ndata, dtype='float128')
xitotcorr = np.zeros(ndatacorr, dtype='float128')
CguessNN = np.zeros([ndata, ndata], dtype='float128')
Cguessang = np.zeros([ndata, ndata], dtype='float128')
Cguesscorr = np.zeros([ndatacorr, ndatacorr], dtype='float128')
for b in range(nboot):
rr = np.random.random_integers(0, ntot - 1, ntot)
xitrialNN = (xilistNN[rr, :]).sum(axis=0) / float(ntot)
xitrialang = (xilistang[rr, :]).sum(axis=0) / float(ntot)
xitrialcorr = (xilistcorr[rr, :]).sum(axis=0) / float(ntot)
xvecNN = np.matrix([xitrialNN - ximeanNN])
xvecang = np.matrix([xitrialang - ximeanang])
xveccorr = np.matrix([xitrialcorr - ximeancorr])
CguessNN += (xvecNN.T * xvecNN)
Cguessang += (xvecang.T * xvecang)
Cguesscorr += (xveccorr.T * xveccorr)
CguessNN = CguessNN / float(nboot - 1)
Cguessang = Cguessang / float(nboot - 1)
Cguesscorr = Cguesscorr / float(nboot - 1)
## put this back in after tests.
#### now let's compute icov for all these.
## eqn 17 of 0608064:
p = len(CguessNN[:, 0])
unbiasicov = float(ntot - p - 2) / float(ntot - 1)
CguessNN = np.matrix(CguessNN, dtype='float64')
invCguessNN = CguessNN.I * unbiasicov
printcov(CguessNN, covoutNN)
printcov(invCguessNN, icovoutNN)
Cguessang = np.matrix(Cguessang, dtype='float64')
invCguessang = Cguessang.I * unbiasicov
printcov(Cguessang, covoutang)
printcov(invCguessang, icovoutang)
Cguesscorr = np.matrix(Cguesscorr, dtype='float64')
invCguesscorr = Cguesscorr.I * unbiasicov
printcov(Cguesscorr, covoutcorr)
printcov(invCguesscorr, icovoutcorr)
return CguessNN, invCguessNN, Cguessang, invCguessang, Cguesscorr, invCguesscorr
def debiasdataandcovxiMwp(xiNNd,
xiangd,
xiangdhigh,
xiangdlow,
xiNNm,
xiangm,
xi012m,
splitxi0,
splitxi2,
wpNNd,
wpangd,
wpangdhigh,
wpangdlow,
wpNNm,
wpangm,
wp012m,
splitwp,
wpstart,
wpend,
covstatfname,
nell=2,
fname=None):
#def debiasdataandcovwp(wpNNd, wpangd, wpangdhigh, wpangdlow, wpNNm, wpangm, wp012m, splitwp, wpstart,wpend,covstatfname,fname=None):
"""
subtract the bias measured from the tiled mocks from the data, return a debiased combination.
print it to a file (fname) to be fed to bethalexie code in long format.
Also take in statistical covariance matrix and add two sources of systematics.
"""
#def xiwpcorrect(xiNNin, xiangin,splitxi0,splitxi2,wpNNin, wpangin, splitwp, wpstart, wpend):
xiwpcorrdtmp = xiwpcorrect(xiNNd, xiangd, splitxi0,splitxi2,\
wpNNd, wpangd, splitwp, wpstart, wpend)
xiwpcorrm = xiwpcorrect(xiNNm, xiangm, splitxi0, splitxi2,\
wpNNm,wpangm,splitwp,wpstart,wpend)
xiwpdebiased = copy.deepcopy(xiwpcorrdtmp)
#tmp!
# print xiwpdebiased.xiell
# print xiwpdebiased.wp
mydeltaxi = xi012m.xi - xiwpcorrm.xiell.xi ## subtract xi objects.
mydeltawp = wp012m.wp[wpstart:wpend + 1] - xiwpcorrm.wp.wp
xiwpdebiased.xiell.xi = xiwpdebiased.xiell.xi + mydeltaxi
xiwpdebiased.wp.wp = xiwpdebiased.wp.wp + mydeltawp
xiwpdebiased.xiwp = np.concatenate(
(xiwpdebiased.xiell.xilong, xiwpdebiased.wp.wp))
xiwpanghigh = xiwp.xiwp(xiangdhigh, wpangdhigh)
xiwpanglow = xiwp.xiwp(xiangdlow, wpangdlow)
## now the cov.
## make sure this is the cov for the corrected statistic with same splits.
if (0 == 0):
# try:
cov = np.loadtxt(covstatfname)
assert len(cov[:, 0]) == xiwpdebiased.ntot
splitz = covstatfname.split('splitswp')[0].split('splits')[1].split(
'_')
assert len(splitz) >= nell
ilist = []
tmp = 0
for ss in splitz:
tmp += 1
ilist.append(int(ss))
if tmp >= nell:
break
assert ilist[0] == splitxi0
assert ilist[1] == splitxi2
splitzwp = covstatfname.split('splitswp')[1].split('_')
print splitzwp
assert len(splitzwp) >= 3
ilist = []
tmp = 0
for ss in splitzwp:
tmp += 1
ilist.append(int(ss))
if tmp >= 3:
break
assert ilist[0] == splitwp
assert ilist[1] == wpstart
assert ilist[2] == wpend
## new jan 2 2014!!! forgot to take into account the unbiasicov fac. derive if from
## product of cov and icov.
## guess icovfname
tmp = covstatfname.split('/')
tmp[-1] = 'i' + tmp[-1]
icovstatfname = '/'.join(tmp)
icov = np.loadtxt(icovstatfname)
unbiasicovfac = (ximisc.getmatrixdiag(
np.matrix(cov) * np.matrix(icov))).mean()
print 'using this unbiasicovfac correction, dividing cov by this', unbiasicovfac
cov = cov / unbiasicovfac
diagstat = np.zeros(xiwpdebiased.ntot)
diagtot = np.zeros(xiwpdebiased.ntot)
diagvar = np.zeros(xiwpdebiased.ntot)
for i in range(len(diagstat)):
diagstat[i] = cov[i, i]
xiangdiffvar = (0.5 *
(xiangdhigh.xi.flatten() - xiangdlow.xi.flatten()))**2
diagvar[0:splitxi0] = xiangdiffvar[0:splitxi0]
nxi0 = len(xiNNd.xi0)
nxi2 = len(xiNNd.xi2)
diagvar[nxi0:nxi0 + splitxi2] = xiangdiffvar[nxi0:nxi0 + splitxi2]
wpangdiffvar = (0.5 * (wpangdhigh.wp - wpangdlow.wp))**2
diagvar[nxi0 + nxi2:nxi0 + nxi2 + splitwp -
wpstart] = wpangdiffvar[wpstart:splitwp]
print 'ang high/low variance: ', diagvar / diagstat
diagvar[0:nxi0 +
nxi2] = diagvar[0:nxi0 + nxi2] + (mydeltaxi.flatten())**2
diagvar[nxi0 + nxi2:] = diagvar[nxi0 + nxi2:] + (mydeltawp)**2
print 'bias variance xi: ', (
mydeltaxi.flatten())**2 / diagstat[0:nxi0 + nxi2]
print 'bias variance wp: ', (mydeltawp)**2 / diagstat[nxi0 + nxi2:]
## add it into the covarianace matrix.
for i in range(xiwpdebiased.ntot):
cov[i, i] += diagvar[i]
diagtot[i] = cov[i, i]
print 'total sys variance fraction', diagtot / diagstat
## make it a matrix.
cov = np.matrix(cov)
icov = cov.I
fcovout = covstatfname + '.sys'
## print the covariance and icov to new file.
printcov(cov, fcovout)
tmp = fcovout.split('/')
tmp[-1] = 'i' + tmp[-1]
ifcovout = '/'.join(tmp)
printcov(icov, ifcovout)
xiwpfinal = xiwp.xiwp(xiwpdebiased.xiell,
xiwpdebiased.wp,
icovfname=ifcovout)
if fname is not None:
ofp = open(fname, 'w')
ofp.write("# ellmax = %d\n" % ((nell - 1) * 2))
for i in range(len(xiwpfinal.xiell.svec.flatten())):
ofp.write('%e %e\n' % (xiwpfinal.xiell.svec.flatten()[i],
xiwpfinal.xiell.xi.flatten()[i]))
for i in range(len(xiwpfinal.wp.wp)):
ofp.write('%e %e\n' %
(xiwpfinal.wp.rsig[i], xiwpfinal.wp.wp[i]))
ofp.close()
return xiwpfinal, cov
else:
print 'cov file name does not match input splits, returning None!'
xiwpfinal = xiwp.xiwp(xiwpdebiased.xiell,
xiwpdebiased.wp,
icovfname=ifcovout)
if fname is not None:
ofp = open(fname, 'w')
ofp.write("# ellmax = %d\n" % ((nell - 1) * 2))
for i in range(len(xiwpfinal.xi.svec.flatten())):
ofp.write('%e %e\n' % (xiwpfinal.xiell.svec.flatten()[i],
xiwpfinal.xiell.xi.flatten()[i]))
for i in range(len(xiwpfinal.wp.wp)):
ofp.write('%e %e\n' %
(xiwpfinal.wp.rsig[i], xiwpfinal.wp.wp[i]))
ofp.close()
return xiwpfinal, None
