def __init__(self,fbase,wdir,nbar2d=None,nbar3d=None,tasklist = [0,1,3],icovlist=[None,None,None]):
"""
tasklist matches whichtask convention in catalog.callxi
for each task, you can specify an icov matrix.
"""
self.fbase = fbase
self.wdir = wdir
if 0 in tasklist:
cut=0.533655
## remove the hard-coding here soon!
try:
indx = np.where(np.array(tasklist) == 0)[0]
icovfname = icovlist[indx]
except:
icovfname = None
self.xiell = xiell.xiellfromDR(wdir+"outputdr12-xiell/"+fbase,nell=2,binfile=wdir+"xibinfiles/bin1fineMU.txt",rperpcut=cut,icovfname=icovfname)
if 1 in tasklist:
try:
indx = np.where(np.array(tasklist) == 1)[0]
icovfname = icovlist[indx]
except:
icovfname = None
self.wp = wp.wpfromDR(fbase=wdir+"outputdr12-xigrid/"+fbase,rpimax=80.0)
if 3 in tasklist:
try:
indx = np.where(np.array(tasklist) == 3)[0]
icovfname = icovlist[indx]
except:
icovfname = None
if nbar2d is None or nbar3d is None:
print 'cannot compute wpcross without number densities!'
else:
self.nbar2d = nbar2d
self.nbar3d = nbar3d
self.wpcross = wp.wpcrossHogg(wdir+"outputdr12-wpcross/"+fbase,nbar2d=nbar2d,nbar3d=nbar3d)
def __init__(self,
fbase,
wdir,
nbar2d=None,
nbar3d=None,
tasklist=[0, 1, 3],
icovlist=[None, None, None]):
"""
tasklist matches whichtask convention in catalog.callxi
for each task, you can specify an icov matrix.
"""
self.fbase = fbase
self.wdir = wdir
if 0 in tasklist:
cut = 0.533655
## remove the hard-coding here soon!
try:
indx = np.where(np.array(tasklist) == 0)[0]
icovfname = icovlist[indx]
except:
icovfname = None
self.xiell = xiell.xiellfromDR(wdir + "outputdr12-xiell/" + fbase,
nell=2,
binfile=wdir +
"xibinfiles/bin1fineMU.txt",
rperpcut=cut,
icovfname=icovfname)
if 1 in tasklist:
try:
indx = np.where(np.array(tasklist) == 1)[0]
icovfname = icovlist[indx]
except:
icovfname = None
self.wp = wp.wpfromDR(fbase=wdir + "outputdr12-xigrid/" + fbase,
rpimax=80.0)
if 3 in tasklist:
try:
indx = np.where(np.array(tasklist) == 3)[0]
icovfname = icovlist[indx]
except:
icovfname = None
if nbar2d is None or nbar3d is None:
print 'cannot compute wpcross without number densities!'
else:
self.nbar2d = nbar2d
self.nbar3d = nbar3d
self.wpcross = wp.wpcrossHogg(wdir + "outputdr12-wpcross/" +
fbase,
nbar2d=nbar2d,
nbar3d=nbar3d)
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 getbootcov(bootfile, basedir, outdirbase = 'outputdr12', covoutfname=None, NSortot=2, nboot = 5000000, \
rpimax=80.,wpstart=1,wpend=19,\
nell=3,rperpcut=-1.,smallRRcut=-1.,\
dfacs=1,dfacmu=1,icovfname=None,smincut=-1.,smaxcut=1.e12,\
binfname_xiell='xibinfiles/bin1fineMU.txt',\
nbar2d=[-1.,-1.],nbar3d=[-1.,-1],\
whichtask=4):
## resurrect these later.
# splitxi0=5,splitxi2=6,splitwp=7):
"""
Get covariance matrix.
We're going to do all tasks at once by default (4).
whichtask = 0: xiell
whichtask = 1: wp (compute xi(rp,rpi))
whichtask = 2: wtheta
whichtask = 3: Hogg spec-im cross-correlation.
whichtask = 4: combine xiell and wp in usual way.
Third tier of stuff goes directly to xiellfromDR
rpimax is for wp, default is 80.
nbar2d,nbar3d needs to be computed separately for N and S.
"""
nsub, nsubdir, pixelfname, fbase, fbasetotN, fbasetotS = parsebootinfo(bootfile=basedir+bootfile)
NSlist = [0,1]
NStaglist = ['N','S']
for xx in [nsub, nsubdir, pixelfname, fbase, fbasetotN, fbasetotS]:
if xx is None:
print 'bad bootfile!'
return None
b = boot.bootpix()
b.readregions(basedir + pixelfname)
assert b.nsub == nsub
## this list will be filled
DRinfolist = [-1,-1,-1,-1]
taglist= ['-xiell','-xigrid','-wtheta','-wpcross']
## get global DR factors for taglist.
for ii in range(len(taglist)-1):
tag = taglist[ii]
tmp = np.zeros([2,2]) # first index is N or S. DRfac, fixRR stored for each.
for NS, NStag, ff in zip(NSlist, NStaglist,[fbasetotN,fbasetotS]):
try:
#if 0==0:
tmp[NS,0], tmp[NS,1] = ximisc.getDRfactors(basedir + '/'+outdirbase + tag +'/'+ff)
except:
tmp[NS,:] = -1.
DRinfolist[ii] = tmp.copy()
## now get DR info for wpcross.
### nevermind! we reduce this to two ratios.
## DRinfolist[3] = np.zeros([2,4,2])
DRinfolist[3] = np.zeros([2,2])
tag = taglist[3]
for NS, NStag, ff in zip(NSlist, NStaglist,[fbasetotN,fbasetotS]):
try:
normfac = ximisc.getDRnormswpcross(basedir + '/'+outdirbase + tag +'/'+ff)
DRinfolist[3][NS][0] = normfac[0,0]/normfac[2,0]
DRinfolist[3][NS][1] = normfac[0,1]/normfac[1,1]
except:
DRinfolist[3][NS][:] = -1.
tasklist = np.zeros(4,dtype='int')
if whichtask == 4:
tasklist = np.array([1,1,0,1],dtype='int')
else:
tasklist[whichtask] = 1
if tasklist[3] > 0:
assert (nbar2d[:] > 0).all()
assert (nbar3d[:] > 0).all()
assert (DRinfolist[3][:,:].flatten() > 0).all()
for ns in range(nsub):
xx = np.where(b.pixlist['PID'] == ns)[0]
assert len(xx) == 1
assert xx[0] == ns
NorSval = b.pixlist['NorS'][xx[0]]
for tt in range(len(tasklist)):
if tasklist[tt] == 0: continue
tag = taglist[tt]
ff = basedir+'/'+outdirbase + tag +'/' + nsubdir + '/' + fbase + '.%04d.Np' % (ns)
if tt == 0: #xiell
xitmp = xiell.xiellfromDR(ff,binfile=binfname_xiell,rperpcut=rperpcut,nell=nell,smallRRcut=smallRRcut,dfacs=dfacs,dfacmu=dfacmu,smincut=smincut,smaxcut=smaxcut,DRfacinfo=DRinfolist[tt][NorSval])
dvec = xitmp.xilong
if tt == 1: #wp
wptmp = wp.wpfromDR(ff,DRfacinfo=DRinfolist[tt][NorSval],rpimax=rpimax)
dvec = wptmp.wp
if tt == 2: #wtheta
wttmp = wtheta.wthetafromDR(ff,DRfacinfo=DRinfolist[tt][NorSval])
dvec = wttmp.wtheta
if tt == 3: #wpcross
wpcrosstmp = wp.wpcrossHogg(ff,DRfacinfo=DRinfolist[tt][NorSval],nbar2d=nbar2d[NorSval],nbar3d=nbar3d[NorSval])
dvec = wpcrosstmp.wp
if whichtask == 4:
dvec = xiwpvec(xitmp,wptmp,wpcrosstmp,wpstart,wpend)
if ns == 0: ## allocate!
ndata = len(dvec)
dveclist = np.zeros([nsub,ndata],dtype='float128')
dveclist[ns,:] = dvec[:]
## check means with total counts.
nindx = np.where(b.pixlist['NorS'] == 0)[0]
sindx = np.where(b.pixlist['NorS'] == 1)[0]
nsindx = np.where((b.pixlist['NorS'] == 0) | (b.pixlist['NorS'] == 1))[0]
print 'N/S: ',len(nindx), len(sindx), len(nsindx)
assert len(nsindx) == nsub
assert (nsindx == np.arange(0,nsub,1,dtype='int')).all()
myindx= nsindx
## assume we want nsindx for this, but can restore N/S option later if I want.
dmean = (dveclist[myindx,:]).sum(axis=0)/float(len(myindx))
ntot = len(myindx)
ntotflt = float(ntot)
print 'hi beth'
print dmean
Cmat = np.zeros([ndata,ndata],dtype='float128')
for b in range(nboot):
rr = np.random.random_integers(0,ntot-1,ntot)
dtrial = (dveclist[rr,:]).sum(axis=0)/ntotflt
xvec = np.matrix([dtrial-dmean])
Cmat += (xvec.T*xvec)
Cmat = Cmat/float(nboot-1)
Cmat = np.matrix(Cmat,dtype='float64')
iCmat = Cmat.I ##
print 'not assuming any bootstrap unbias factor for now!'
if covoutfname is not None:
printcov(Cmat,covoutfname)
printcov(iCmat,covoutfname+'.inv')
printmean(dmean,covoutfname+'.mean')
return Cmat, iCmat, dmean
def getbootcov(bootfile, workingdir, covtag, NNorang=0, NSortot=2, nboot = 5000000, fbaseend='_rmax48deltalog10r',\
nell=3,binfile=None,rperpcut=-1.,smallRRcut=-1.,
dfacs=1,dfacmu=1,icovfname=None,smincut=-1.,smaxcut=1.e12):
"""
Get covariance matrix.
fbaseend = '_rmax48deltalog10r' for xiell or '_xigrid' for wp.
NNorang = 0 [NN] or 1 [ang] or 2 [optimal unbiased combination, not yet written]
Third tier of stuff goes directly to xiellfromDR
"""
print 'this is not up to date! i am using bootcov.py for this job'
print 'BETH, you should edit/delete/merge for consistency and reduce defns of same functions in two places!!'
return 0
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)
if NNorang == 0:
myfbase = fbaseNNstart
DRfacN, fixRRdownN = ximisc.getDRfactors(fbaseNNtotN + fbaseend)
DRfacS, fixRRdownS = ximisc.getDRfactors(fbaseNNtotS + fbaseend)
elif NNorang == 1:
myfbase = fbaseangstart
DRfacN, fixRRdownN = ximisc.getDRfactors(fbaseangtotN + fbaseend)
DRfacS, fixRRdownS = ximisc.getDRfactors(fbaseangtotS + fbaseend)
else:
print 'NNorang = ', NNorang, 'not supported.'
return None
DRinfoN = [DRfacN, fixRRdownN]
DRinfoS = [DRfacS, fixRRdownS]
for ns in range(nsub):
fbase = myfbase + ('-%03d' % (ns)) + fbaseend
xx = np.where(pixlist['PID'] == ns)[0]
assert len(xx) == 1
assert xx[0] == ns
NorSval = pixlist['NorS'][xx[0]]
if (NorSval == 0):
DRinfo = DRinfoN
else:
DRinfo = DRinfoS
xiin = xiell.xiellfromDR(fbase, nell, binfile, rperpcut, dfacs, dfacmu,
icovfname, smincut, smaxcut, DRinfo,
smallRRcut)
## 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):
ndata = xiin.ndata
xilist = np.zeros([nsub, ndata], dtype='float128')
xilist[ns, :] = xiin.xilong
## 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):
ximean = (xilist[nindx, :]).sum(axis=0) / float(len(nindx))
ntot = len(nindx)
## restrict xilist to N only
xilist = xlist[nindx, :]
if (NSortot == 1):
ximean = (xilist[sindx, :]).sum(axis=0) / float(len(sindx))
ntot = len(sindx)
xilist = xlist[sindx, :]
if (NSortot == 2):
ximean = xilist.sum(axis=0) / float(nsub)
ntot = nsub
xitot = np.zeros(ndata, dtype='float128')
Cguess = np.zeros([ndata, ndata], dtype='float128')
for b in range(nboot):
rr = np.random.random_integers(0, ntot - 1, ntot)
xitrial = (xilist[rr, :]).sum(axis=0) / float(ntot)
xvec = np.matrix([xitrial - ximean])
Cguess += (xvec.T * xvec)
Cguess = Cguess / float(nboot - 1)
#### now let's compute icov for all these.
## eqn 17 of 0608064:
p = len(Cguess[:, 0])
unbiasicov = float(ntot - p - 2) / float(ntot - 1)
Cguess = np.matrix(Cguess, dtype='float64')
invCguess = Cguess.I * unbiasicov
# printcov(Cguess,"cov.tmp")
# printcov(invCguess,"icov.tmp")
return Cguess, invCguess
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 getbootcov(bootfile, workingdir, covtag, NNorang=0, NSortot=2, nboot = 5000000, fbaseend='_rmax48deltalog10r',\
nell=3,binfile=None,rperpcut=-1.,smallRRcut=-1.,
dfacs=1,dfacmu=1,icovfname=None,smincut=-1.,smaxcut=1.e12):
"""
Get covariance matrix.
fbaseend = '_rmax48deltalog10r' for xiell or '_xigrid' for wp.
NNorang = 0 [NN] or 1 [ang] or 2 [optimal unbiased combination, not yet written]
Third tier of stuff goes directly to xiellfromDR
"""
print 'this is not up to date! i am using bootcov.py for this job'
print 'BETH, you should edit/delete/merge for consistency and reduce defns of same functions in two places!!'
return 0
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)
if NNorang == 0:
myfbase = fbaseNNstart
DRfacN, fixRRdownN = ximisc.getDRfactors(fbaseNNtotN+fbaseend)
DRfacS, fixRRdownS = ximisc.getDRfactors(fbaseNNtotS+fbaseend)
elif NNorang == 1:
myfbase = fbaseangstart
DRfacN, fixRRdownN = ximisc.getDRfactors(fbaseangtotN+fbaseend)
DRfacS, fixRRdownS = ximisc.getDRfactors(fbaseangtotS+fbaseend)
else:
print 'NNorang = ',NNorang,'not supported.'
return None
DRinfoN = [DRfacN, fixRRdownN]
DRinfoS = [DRfacS, fixRRdownS]
for ns in range(nsub):
fbase = myfbase + ('-%03d' % (ns))+fbaseend
xx = np.where(pixlist['PID'] == ns)[0]
assert len(xx) == 1
assert xx[0] == ns
NorSval = pixlist['NorS'][xx[0]]
if(NorSval == 0):
DRinfo = DRinfoN
else:
DRinfo = DRinfoS
xiin = xiell.xiellfromDR(fbase,nell,binfile,rperpcut,dfacs,dfacmu,icovfname,smincut,smaxcut,DRinfo,smallRRcut)
## 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):
ndata = xiin.ndata
xilist = np.zeros([nsub,ndata],dtype='float128')
xilist[ns,:] = xiin.xilong
## 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):
ximean = (xilist[nindx,:]).sum(axis=0)/float(len(nindx))
ntot = len(nindx)
## restrict xilist to N only
xilist = xlist[nindx,:]
if(NSortot == 1):
ximean = (xilist[sindx,:]).sum(axis=0)/float(len(sindx))
ntot = len(sindx)
xilist = xlist[sindx,:]
if(NSortot == 2):
ximean = xilist.sum(axis=0)/float(nsub)
ntot = nsub
xitot = np.zeros(ndata,dtype='float128')
Cguess = np.zeros([ndata,ndata],dtype='float128')
for b in range(nboot):
rr = np.random.random_integers(0,ntot-1,ntot)
xitrial = (xilist[rr,:]).sum(axis=0)/float(ntot)
xvec = np.matrix([xitrial-ximean])
Cguess += (xvec.T*xvec)
Cguess = Cguess/float(nboot-1)
#### now let's compute icov for all these.
## eqn 17 of 0608064:
p = len(Cguess[:,0])
unbiasicov = float(ntot - p - 2)/float(ntot-1)
Cguess = np.matrix(Cguess,dtype='float64')
invCguess = Cguess.I*unbiasicov
# printcov(Cguess,"cov.tmp")
# printcov(invCguess,"icov.tmp")
return Cguess, invCguess
def getbootcov(bootfile, basedir, outdirbase = 'outputdr12', covoutfname=None, NSortot=2, nboot = 5000000, \
rpimax=80.,wpstart=1,wpend=19,\
nell=3,rperpcut=-1.,smallRRcut=-1.,\
dfacs=1,dfacmu=1,icovfname=None,smincut=-1.,smaxcut=1.e12,\
binfname_xiell='xibinfiles/bin1fineMU.txt',\
nbar2d=[-1.,-1.],nbar3d=[-1.,-1],\
whichtask=4):
## resurrect these later.
# splitxi0=5,splitxi2=6,splitwp=7):
"""
Get covariance matrix.
We're going to do all tasks at once by default (4).
whichtask = 0: xiell
whichtask = 1: wp (compute xi(rp,rpi))
whichtask = 2: wtheta
whichtask = 3: Hogg spec-im cross-correlation.
whichtask = 4: combine xiell and wp in usual way.
Third tier of stuff goes directly to xiellfromDR
rpimax is for wp, default is 80.
nbar2d,nbar3d needs to be computed separately for N and S.
"""
nsub, nsubdir, pixelfname, fbase, fbasetotN, fbasetotS = parsebootinfo(
bootfile=basedir + bootfile)
NSlist = [0, 1]
NStaglist = ['N', 'S']
for xx in [nsub, nsubdir, pixelfname, fbase, fbasetotN, fbasetotS]:
if xx is None:
print 'bad bootfile!'
return None
b = boot.bootpix()
b.readregions(basedir + pixelfname)
assert b.nsub == nsub
## this list will be filled
DRinfolist = [-1, -1, -1, -1]
taglist = ['-xiell', '-xigrid', '-wtheta', '-wpcross']
## get global DR factors for taglist.
for ii in range(len(taglist) - 1):
tag = taglist[ii]
tmp = np.zeros(
[2, 2]) # first index is N or S. DRfac, fixRR stored for each.
for NS, NStag, ff in zip(NSlist, NStaglist, [fbasetotN, fbasetotS]):
try:
#if 0==0:
tmp[NS, 0], tmp[NS, 1] = ximisc.getDRfactors(basedir + '/' +
outdirbase + tag +
'/' + ff)
except:
tmp[NS, :] = -1.
DRinfolist[ii] = tmp.copy()
## now get DR info for wpcross.
### nevermind! we reduce this to two ratios.
## DRinfolist[3] = np.zeros([2,4,2])
DRinfolist[3] = np.zeros([2, 2])
tag = taglist[3]
for NS, NStag, ff in zip(NSlist, NStaglist, [fbasetotN, fbasetotS]):
try:
normfac = ximisc.getDRnormswpcross(basedir + '/' + outdirbase +
tag + '/' + ff)
DRinfolist[3][NS][0] = normfac[0, 0] / normfac[2, 0]
DRinfolist[3][NS][1] = normfac[0, 1] / normfac[1, 1]
except:
DRinfolist[3][NS][:] = -1.
tasklist = np.zeros(4, dtype='int')
if whichtask == 4:
tasklist = np.array([1, 1, 0, 1], dtype='int')
else:
tasklist[whichtask] = 1
if tasklist[3] > 0:
assert (nbar2d[:] > 0).all()
assert (nbar3d[:] > 0).all()
assert (DRinfolist[3][:, :].flatten() > 0).all()
for ns in range(nsub):
xx = np.where(b.pixlist['PID'] == ns)[0]
assert len(xx) == 1
assert xx[0] == ns
NorSval = b.pixlist['NorS'][xx[0]]
for tt in range(len(tasklist)):
if tasklist[tt] == 0: continue
tag = taglist[tt]
ff = basedir + '/' + outdirbase + tag + '/' + nsubdir + '/' + fbase + '.%04d.Np' % (
ns)
if tt == 0: #xiell
xitmp = xiell.xiellfromDR(ff,
binfile=binfname_xiell,
rperpcut=rperpcut,
nell=nell,
smallRRcut=smallRRcut,
dfacs=dfacs,
dfacmu=dfacmu,
smincut=smincut,
smaxcut=smaxcut,
DRfacinfo=DRinfolist[tt][NorSval])
dvec = xitmp.xilong
if tt == 1: #wp
wptmp = wp.wpfromDR(ff,
DRfacinfo=DRinfolist[tt][NorSval],
rpimax=rpimax)
dvec = wptmp.wp
if tt == 2: #wtheta
wttmp = wtheta.wthetafromDR(ff,
DRfacinfo=DRinfolist[tt][NorSval])
dvec = wttmp.wtheta
if tt == 3: #wpcross
wpcrosstmp = wp.wpcrossHogg(ff,
DRfacinfo=DRinfolist[tt][NorSval],
nbar2d=nbar2d[NorSval],
nbar3d=nbar3d[NorSval])
dvec = wpcrosstmp.wp
if whichtask == 4:
dvec = xiwpvec(xitmp, wptmp, wpcrosstmp, wpstart, wpend)
if ns == 0: ## allocate!
ndata = len(dvec)
dveclist = np.zeros([nsub, ndata], dtype='float128')
dveclist[ns, :] = dvec[:]
## check means with total counts.
nindx = np.where(b.pixlist['NorS'] == 0)[0]
sindx = np.where(b.pixlist['NorS'] == 1)[0]
nsindx = np.where((b.pixlist['NorS'] == 0) | (b.pixlist['NorS'] == 1))[0]
print 'N/S: ', len(nindx), len(sindx), len(nsindx)
assert len(nsindx) == nsub
assert (nsindx == np.arange(0, nsub, 1, dtype='int')).all()
myindx = nsindx
## assume we want nsindx for this, but can restore N/S option later if I want.
dmean = (dveclist[myindx, :]).sum(axis=0) / float(len(myindx))
ntot = len(myindx)
ntotflt = float(ntot)
print 'hi beth'
print dmean
Cmat = np.zeros([ndata, ndata], dtype='float128')
for b in range(nboot):
rr = np.random.random_integers(0, ntot - 1, ntot)
dtrial = (dveclist[rr, :]).sum(axis=0) / ntotflt
xvec = np.matrix([dtrial - dmean])
Cmat += (xvec.T * xvec)
Cmat = Cmat / float(nboot - 1)
Cmat = np.matrix(Cmat, dtype='float64')
iCmat = Cmat.I ##
print 'not assuming any bootstrap unbias factor for now!'
if covoutfname is not None:
printcov(Cmat, covoutfname)
printcov(iCmat, covoutfname + '.inv')
printmean(dmean, covoutfname + '.mean')
return Cmat, iCmat, dmean
