def wtfromDR(fbase,fend='.ang'): """ Quick hack, fill in options later. """ fDD = fbase+'.DRopt1'+fend fDR = fbase+'.DRopt2'+fend fRR = fbase+'.DRopt3'+fend ximisc.getDRfactors(fbase,fend) t, DDg = np.loadtxt(fDD,skiprows=2,unpack=True) tchk, DRg = np.loadtxt(fDR,skiprows=2,unpack=True) assert (t==tchk).all() tchk, RRg = np.loadtxt(fRR,skiprows=2,unpack=True) assert (t==tchk).all() DRfac, fixRR = ximisc.getDRfactors(fbase,fend) wt = (DDg-DRg*DRfac)/RRg/DRfac**2/fixRR**2 + 1. return wtheta(wtfname=fbase,twlist = [t,wt])
def xiellfromDR(fbase,nell=3,binfile=None,rperpcut=-1.,
dfacs=1,dfacmu=1,icovfname=None,smincut=-1.,smaxcut=1.e12,\
DRfacinfo=None,smallRRcut=-1.,periodicopt=0,printmaskopt=0,xiopt=0):
"""
This function should supercede older codes with names like rebinxismugeneric*py
input the base for the DR files, as well as optional bin files and small scale
cutoff for masking (rperpcut). If you don't want to use a binfile to specify
the downsampling, use dfacs and dfacmu inputs.
smincut/smaxcut will only be used if binfile is None.
DRfacinfo is used to pass [DRfac, fixRR] (dont get it from file headers in this case)
That feature is necessary for computing covariance matrices from bootstrap, where that factor
should be fixed by total N and/or S, it does not vary from bootstrap region to region.
smallRRcut allows you to replace randoms(mu) with the average over mu where the total number of
randoms in the bin is smaller than smallRRcut, just to keep Poisson noise down.
If periodicopt == 1, assume the input is file containing [rg, mug, Vg, Ng] as currently output
by correlationfxnMASTERv4.
xiopt introduced on May 13 2014 to input xi(s,mu) and simply rebin (for comparison with Hong).
"""
if periodicopt == 1: # periodic box.
rg, mug, Vg, Ng = np.loadtxt(fbase,unpack=True)
Vzerochk = Vg.min()*0.1
fDR = fbase ## just for the return xiell purposes.
elif xiopt == 1:
rg, mug, xig = np.loadtxt(fbase,unpack=True,usecols=[0,1,2]) ## Hong format.
fDR = fbase
if binfile is None:
ikeep = np.where((rg >= smincut) & (rg <= smaxcut))[0]
rg = rg[ikeep]
mug = mug[ikeep]
xig = xig[ikeep]
else: # DD,RR counts.
fDD = fbase+'.DRopt1'
fDR = fbase+'.DRopt2'
fRR = fbase+'.DRopt3'
rg, mug, DDg = np.loadtxt(fDD,skiprows=3,unpack=True)
rg, mug, DRg = np.loadtxt(fDR,skiprows=3,unpack=True)
rg, mug, RRg = np.loadtxt(fRR,skiprows=3,unpack=True)
if binfile is None:
ikeep = np.where((rg >= smincut) & (rg <= smaxcut))[0]
rg = rg[ikeep]
mug = mug[ikeep]
DDg = DDg[ikeep]
DRg = DRg[ikeep]
RRg = RRg[ikeep]
if DRfacinfo is None:
DRfac, fixRR = ximisc.getDRfactors(fbase)
else:
DRfac = DRfacinfo[0]
fixRR = DRfacinfo[1]
nmu = len(np.where(rg == rg[0])[0])
dmu = 1./float(nmu)
nr = len(np.where(mug == mug[0])[0])
if nmu*nr != len(rg):
return None
## is s (called r here) log or linear binning?
s1d = rg.reshape([nr,nmu])[:,0]
mu1d = rg.reshape([nr,nmu])[0,:]
## compute linear spacings.
dmutmp = (mu1d[1:]-mu1d[:-1]).mean()
ds = (s1d[1:]-s1d[:-1]).mean()
dlogs = (np.log(s1d[1:]/s1d[:-1])).mean()
if (np.fabs(mu1d[1:]-mu1d[:-1] - dmutmp) > 0.0001*dmutmp).any():
## not linear binning!
## this shouldn't be the case for a s,mu grid.
assert 0==1
assert np.fabs(dmutmp - dmu) < 1.0e-4
if (np.fabs(s1d[1:]-s1d[:-1] - ds) < 0.0001*ds).all():
logsopt = 0
rglowedge = rg.copy()
rglowedge = rglowedge-0.5*ds
## added specifically for xiopt
rghighedge = rg.copy()
rghighedge = rghighedge+0.5*ds
if (np.fabs(np.log(s1d[1:])-np.log(s1d[:-1]) - dlogs) < 0.0001*dlogs).all():
logsopt = 1
rglowedge = rg.copy()
rglowedge = rglowedge*np.exp(-0.5*dlogs)
## added specifically for xiopt
rghighedge = rg.copy()
rghighedge = rghighedge*np.exp(0.5*dlogs)
assert logsopt == 0 or logsopt == 1
if xiopt == 1:
Vg = rghighedge**3 - rglowedge**3 ## just need something proportional to volume.
Vzerochk = Vg.min()*0.1
## cut at edge of bin.
xx = np.where(rglowedge*(1-(mug+0.5*dmu)**2)**0.5 < rperpcut)[0]
if(rperpcut < 0.): assert len(xx) == 0
if len(xx) > 0:
if periodicopt == 1:
Vg[xx] = 0.
Ng[xx] = 0.
elif xiopt == 1:
Vg[xx] = 0.
xig[xx] = 0.
else:
DDg[xx] = 0.
DRg[xx] = 0.
RRg[xx] = 0.
mymask = np.zeros(len(rg),dtype='int')
mymask[xx] = 1
## tmp! print a mask file.
if(printmaskopt == 1):
print 'yoyoyo opening masktmp.dat'
ofpmask = open('masktmp.dat','w')
for i in range(len(mymask)):
ofpmask.write('%d\n' % (mymask[i]))
ofpmask.close()
if(printmaskopt == 2):
#### nevermind, let's print below the boundaries of each s,mu bin.
# print 'yoyoyo opening masktmp2.dat'
# ofpmask = open('masktmp2.dat','w')
mumaxlist = np.zeros(len(s1d)) + 1.
for qi in range(len(s1d)):
rgval = s1d[qi]
qq = np.where((rg == rgval) & (mymask == 1))[0]
ww = np.where((rg == rgval))[0]
assert len(ww) > 0
if(len(qq) == 0):
pass
# ofpmask.write('%e %e %e\n' % (rgval,rglowedge[ww[0]],1.0))
else:
# ofpmask.write('%e %e %e\n' % (rgval,rglowedge[ww[0]],mug[qq].max()))
mumaxlist[qi] = mug[qq].min()-0.5*dmu
### testing.
# print s1d[qi], mumaxlist[qi]
#ofpmask.write('%d\n' % (mymask[i]))
# ofpmask.close()
if binfile is not None:
## specifies how many rbins to join together for first bin, next bin, etc.
rjoin, mujoin = np.loadtxt(binfile,unpack=True,dtype='int')
nrcut = len(rjoin)
### use later.
#bintag = binfile.split('.')[0]
else:
# dfacs = dfacsin
# dfacmu = dfacmuin
nrcut = len(s1d)/dfacs
dmudown = dfacmu*dmu
if printmaskopt == 2:
ofpmask = open('masktmp2.dat','w')
## check mask if I'm going to cut more.
ristart = 0
badlist = np.zeros(nrcut,dtype='int')
for i in range(nrcut):
if binfile is not None:
dfacs = rjoin[i]
if (mymask[ristart*nmu:(ristart+dfacs)*nmu] == 1).all():
badlist[i] = 1
ristart = ristart + dfacs
nrcutkeep = nrcut-len(np.where(badlist == 1)[0])
xi = np.zeros([nell,nrcutkeep],dtype=float)
svec = np.zeros([nell,nrcutkeep],dtype=float)
rcen = np.zeros(nrcut,dtype=float)
ristart = 0
xiindx = 0
for i in range(nrcut):
if binfile is not None:
dfacs = rjoin[i]
dfacmu = mujoin[i]
dmudown = dfacmu*dmu
riend = ristart + dfacs - 1
if logsopt == 0:
rcen[i] = 0.5*(s1d[ristart] + s1d[riend])
if logsopt == 1:
rcen[i] = np.exp(0.5*np.log(s1d[ristart]*s1d[riend]))
for ishort in range(dfacs):
i1 = (ristart+ishort)*nmu
i2 = (ristart+ishort+1)*nmu
if periodicopt == 1:
if(ishort == 0):
mymu = ximisc.downsample1d(mug[i1:i2],dfacmu)
myVg = ximisc.downsample1dsum(Vg[i1:i2],dfacmu)
myNg = ximisc.downsample1dsum(Ng[i1:i2],dfacmu)
else:
myVg = myVg + ximisc.downsample1dsum(Vg[i1:i2],dfacmu)
myNg = myNg + ximisc.downsample1dsum(Ng[i1:i2],dfacmu)
elif xiopt == 1:
if(ishort == 0):
mymu = ximisc.downsample1d(mug[i1:i2],dfacmu)
myxig = ximisc.downsample1dvweight(xig[i1:i2],Vg[i1:i2],dfacmu) #perform volume weighted sum.
myVg = ximisc.downsample1dsum(Vg[i1:i2],dfacmu)
else:
myxig = myxig + ximisc.downsample1dvweight(xig[i1:i2],Vg[i1:i2],dfacmu) # perform volume weighted sum, divide out at the end.
myVg = myVg + ximisc.downsample1dsum(Vg[i1:i2],dfacmu)
else:
if(ishort == 0):
mymu = ximisc.downsample1d(mug[i1:i2],dfacmu)
mydd = ximisc.downsample1dsum(DDg[i1:i2],dfacmu)
mydr = ximisc.downsample1dsum(DRg[i1:i2],dfacmu)
myrr = ximisc.downsample1dsum(RRg[i1:i2],dfacmu)
else:
mydd = mydd + ximisc.downsample1dsum(DDg[i1:i2],dfacmu)
mydr = mydr + ximisc.downsample1dsum(DRg[i1:i2],dfacmu)
myrr = myrr + ximisc.downsample1dsum(RRg[i1:i2],dfacmu)
if periodicopt == 1:
yy = np.where(myVg < Vzerochk)[0]
xitmp = myNg/myVg-1.
xitmp[yy] = 0.
elif xiopt == 1:
yy = np.where(myVg < Vzerochk)[0]
xitmp = myxig/myVg
xitmp[yy] = 0.
## for now, nothing to 0 out (?)
else: ##DR stuff
zz = np.where(myrr < smallRRcut)[0]
if(len(zz) > 0):
print 'using smallRRcut! here are details',i,rcen[i],smallRRcut
myrr = myrr.mean()
yy = np.where(myrr < 0.01)[0]
xitmp = (mydd-mydr*DRfac)/myrr/DRfac**2/fixRR**2+1.
xitmp[yy] = 0. ## fix 0'd out regions
if(len(yy) > 0 and 0==1):
print 'ack why zerod out regions?'
print len(yy)
print i, dfacs, dfacmu
print myrr
print mymu
for ell in np.arange(0,nell*2,2):
if(badlist[i] == 0):
xi[ell/2,xiindx] = (xitmp*ximisc.legendre(ell,mymu)).sum()*dmudown*(2.*ell+1.)
svec[ell/2,xiindx] = rcen[i]
if printmaskopt == 2:
ofpmask.write('%e %e %e %e %e ' % (rcen[i], s1d[ristart]*np.exp(-0.5*dlogs), s1d[riend]*np.exp(0.5*dlogs), mumaxlist[ristart],mumaxlist[riend]))
for ell in np.arange(0,nell*2,2):
if badlist[i] == 0:
ofpmask.write('%e ' % (xi[ell/2,xiindx]))
else:
ofpmask.write('%e ' % (0.0))
ofpmask.write('\n')
ristart = ristart + dfacs
if(badlist[i] == 0):
xiindx += 1
return xiell(xiellfname=fDR,sxilist=[svec, xi],icovfname=icovfname)
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 xiellfromDR(fbase,nell=3,binfile=None,rperpcut=-1.,
dfacs=1,dfacmu=1,icovfname=None,smincut=-1.,smaxcut=1.e12,\
DRfacinfo=None,smallRRcut=-1.,periodicopt=0,printmaskopt=0,xiopt=0):
"""
This function should supercede older codes with names like rebinxismugeneric*py
input the base for the DR files, as well as optional bin files and small scale
cutoff for masking (rperpcut). If you don't want to use a binfile to specify
the downsampling, use dfacs and dfacmu inputs.
smincut/smaxcut will only be used if binfile is None.
DRfacinfo is used to pass [DRfac, fixRR] (dont get it from file headers in this case)
That feature is necessary for computing covariance matrices from bootstrap, where that factor
should be fixed by total N and/or S, it does not vary from bootstrap region to region.
smallRRcut allows you to replace randoms(mu) with the average over mu where the total number of
randoms in the bin is smaller than smallRRcut, just to keep Poisson noise down.
If periodicopt == 1, assume the input is file containing [rg, mug, Vg, Ng] as currently output
by correlationfxnMASTERv4.
xiopt introduced on May 13 2014 to input xi(s,mu) and simply rebin (for comparison with Hong).
"""
if periodicopt == 1: # periodic box.
rg, mug, Vg, Ng = np.loadtxt(fbase, unpack=True)
Vzerochk = Vg.min() * 0.1
fDR = fbase ## just for the return xiell purposes.
elif xiopt == 1:
rg, mug, xig = np.loadtxt(fbase, unpack=True,
usecols=[0, 1, 2]) ## Hong format.
fDR = fbase
if binfile is None:
ikeep = np.where((rg >= smincut) & (rg <= smaxcut))[0]
rg = rg[ikeep]
mug = mug[ikeep]
xig = xig[ikeep]
else: # DD,RR counts.
fDD = fbase + '.DRopt1'
fDR = fbase + '.DRopt2'
fRR = fbase + '.DRopt3'
rg, mug, DDg = np.loadtxt(fDD, skiprows=3, unpack=True)
rg, mug, DRg = np.loadtxt(fDR, skiprows=3, unpack=True)
rg, mug, RRg = np.loadtxt(fRR, skiprows=3, unpack=True)
if binfile is None:
ikeep = np.where((rg >= smincut) & (rg <= smaxcut))[0]
rg = rg[ikeep]
mug = mug[ikeep]
DDg = DDg[ikeep]
DRg = DRg[ikeep]
RRg = RRg[ikeep]
if DRfacinfo is None:
DRfac, fixRR = ximisc.getDRfactors(fbase)
else:
DRfac = DRfacinfo[0]
fixRR = DRfacinfo[1]
nmu = len(np.where(rg == rg[0])[0])
dmu = 1. / float(nmu)
nr = len(np.where(mug == mug[0])[0])
if nmu * nr != len(rg):
return None
## is s (called r here) log or linear binning?
s1d = rg.reshape([nr, nmu])[:, 0]
mu1d = rg.reshape([nr, nmu])[0, :]
## compute linear spacings.
dmutmp = (mu1d[1:] - mu1d[:-1]).mean()
ds = (s1d[1:] - s1d[:-1]).mean()
dlogs = (np.log(s1d[1:] / s1d[:-1])).mean()
if (np.fabs(mu1d[1:] - mu1d[:-1] - dmutmp) > 0.0001 * dmutmp).any():
## not linear binning!
## this shouldn't be the case for a s,mu grid.
assert 0 == 1
assert np.fabs(dmutmp - dmu) < 1.0e-4
if (np.fabs(s1d[1:] - s1d[:-1] - ds) < 0.0001 * ds).all():
logsopt = 0
rglowedge = rg.copy()
rglowedge = rglowedge - 0.5 * ds
## added specifically for xiopt
rghighedge = rg.copy()
rghighedge = rghighedge + 0.5 * ds
if (np.fabs(np.log(s1d[1:]) - np.log(s1d[:-1]) - dlogs) <
0.0001 * dlogs).all():
logsopt = 1
rglowedge = rg.copy()
rglowedge = rglowedge * np.exp(-0.5 * dlogs)
## added specifically for xiopt
rghighedge = rg.copy()
rghighedge = rghighedge * np.exp(0.5 * dlogs)
assert logsopt == 0 or logsopt == 1
if xiopt == 1:
Vg = rghighedge**3 - rglowedge**3 ## just need something proportional to volume.
Vzerochk = Vg.min() * 0.1
## cut at edge of bin.
xx = np.where(rglowedge * (1 - (mug + 0.5 * dmu)**2)**0.5 < rperpcut)[0]
if (rperpcut < 0.): assert len(xx) == 0
if len(xx) > 0:
if periodicopt == 1:
Vg[xx] = 0.
Ng[xx] = 0.
elif xiopt == 1:
Vg[xx] = 0.
xig[xx] = 0.
else:
DDg[xx] = 0.
DRg[xx] = 0.
RRg[xx] = 0.
mymask = np.zeros(len(rg), dtype='int')
mymask[xx] = 1
## tmp! print a mask file.
if (printmaskopt == 1):
print 'yoyoyo opening masktmp.dat'
ofpmask = open('masktmp.dat', 'w')
for i in range(len(mymask)):
ofpmask.write('%d\n' % (mymask[i]))
ofpmask.close()
if (printmaskopt == 2):
#### nevermind, let's print below the boundaries of each s,mu bin.
# print 'yoyoyo opening masktmp2.dat'
# ofpmask = open('masktmp2.dat','w')
mumaxlist = np.zeros(len(s1d)) + 1.
for qi in range(len(s1d)):
rgval = s1d[qi]
qq = np.where((rg == rgval) & (mymask == 1))[0]
ww = np.where((rg == rgval))[0]
assert len(ww) > 0
if (len(qq) == 0):
pass
# ofpmask.write('%e %e %e\n' % (rgval,rglowedge[ww[0]],1.0))
else:
# ofpmask.write('%e %e %e\n' % (rgval,rglowedge[ww[0]],mug[qq].max()))
mumaxlist[qi] = mug[qq].min() - 0.5 * dmu
### testing.
# print s1d[qi], mumaxlist[qi]
#ofpmask.write('%d\n' % (mymask[i]))
# ofpmask.close()
if binfile is not None:
## specifies how many rbins to join together for first bin, next bin, etc.
rjoin, mujoin = np.loadtxt(binfile, unpack=True, dtype='int')
nrcut = len(rjoin)
### use later.
#bintag = binfile.split('.')[0]
else:
# dfacs = dfacsin
# dfacmu = dfacmuin
nrcut = len(s1d) / dfacs
dmudown = dfacmu * dmu
if printmaskopt == 2:
ofpmask = open('masktmp2.dat', 'w')
## check mask if I'm going to cut more.
ristart = 0
badlist = np.zeros(nrcut, dtype='int')
for i in range(nrcut):
if binfile is not None:
dfacs = rjoin[i]
if (mymask[ristart * nmu:(ristart + dfacs) * nmu] == 1).all():
badlist[i] = 1
ristart = ristart + dfacs
nrcutkeep = nrcut - len(np.where(badlist == 1)[0])
xi = np.zeros([nell, nrcutkeep], dtype=float)
svec = np.zeros([nell, nrcutkeep], dtype=float)
rcen = np.zeros(nrcut, dtype=float)
ristart = 0
xiindx = 0
for i in range(nrcut):
if binfile is not None:
dfacs = rjoin[i]
dfacmu = mujoin[i]
dmudown = dfacmu * dmu
riend = ristart + dfacs - 1
if logsopt == 0:
rcen[i] = 0.5 * (s1d[ristart] + s1d[riend])
if logsopt == 1:
rcen[i] = np.exp(0.5 * np.log(s1d[ristart] * s1d[riend]))
for ishort in range(dfacs):
i1 = (ristart + ishort) * nmu
i2 = (ristart + ishort + 1) * nmu
if periodicopt == 1:
if (ishort == 0):
mymu = ximisc.downsample1d(mug[i1:i2], dfacmu)
myVg = ximisc.downsample1dsum(Vg[i1:i2], dfacmu)
myNg = ximisc.downsample1dsum(Ng[i1:i2], dfacmu)
else:
myVg = myVg + ximisc.downsample1dsum(Vg[i1:i2], dfacmu)
myNg = myNg + ximisc.downsample1dsum(Ng[i1:i2], dfacmu)
elif xiopt == 1:
if (ishort == 0):
mymu = ximisc.downsample1d(mug[i1:i2], dfacmu)
myxig = ximisc.downsample1dvweight(
xig[i1:i2], Vg[i1:i2],
dfacmu) #perform volume weighted sum.
myVg = ximisc.downsample1dsum(Vg[i1:i2], dfacmu)
else:
myxig = myxig + ximisc.downsample1dvweight(
xig[i1:i2], Vg[i1:i2], dfacmu
) # perform volume weighted sum, divide out at the end.
myVg = myVg + ximisc.downsample1dsum(Vg[i1:i2], dfacmu)
else:
if (ishort == 0):
mymu = ximisc.downsample1d(mug[i1:i2], dfacmu)
mydd = ximisc.downsample1dsum(DDg[i1:i2], dfacmu)
mydr = ximisc.downsample1dsum(DRg[i1:i2], dfacmu)
myrr = ximisc.downsample1dsum(RRg[i1:i2], dfacmu)
else:
mydd = mydd + ximisc.downsample1dsum(DDg[i1:i2], dfacmu)
mydr = mydr + ximisc.downsample1dsum(DRg[i1:i2], dfacmu)
myrr = myrr + ximisc.downsample1dsum(RRg[i1:i2], dfacmu)
if periodicopt == 1:
yy = np.where(myVg < Vzerochk)[0]
xitmp = myNg / myVg - 1.
xitmp[yy] = 0.
elif xiopt == 1:
yy = np.where(myVg < Vzerochk)[0]
xitmp = myxig / myVg
xitmp[yy] = 0.
## for now, nothing to 0 out (?)
else: ##DR stuff
zz = np.where(myrr < smallRRcut)[0]
if (len(zz) > 0):
print 'using smallRRcut! here are details', i, rcen[
i], smallRRcut
myrr = myrr.mean()
yy = np.where(myrr < 0.01)[0]
xitmp = (mydd - mydr * DRfac) / myrr / DRfac**2 / fixRR**2 + 1.
xitmp[yy] = 0. ## fix 0'd out regions
if (len(yy) > 0 and 0 == 1):
print 'ack why zerod out regions?'
print len(yy)
print i, dfacs, dfacmu
print myrr
print mymu
for ell in np.arange(0, nell * 2, 2):
if (badlist[i] == 0):
xi[ell / 2, xiindx] = (xitmp * ximisc.legendre(
ell, mymu)).sum() * dmudown * (2. * ell + 1.)
svec[ell / 2, xiindx] = rcen[i]
if printmaskopt == 2:
ofpmask.write(
'%e %e %e %e %e ' %
(rcen[i], s1d[ristart] * np.exp(-0.5 * dlogs), s1d[riend] *
np.exp(0.5 * dlogs), mumaxlist[ristart], mumaxlist[riend]))
for ell in np.arange(0, nell * 2, 2):
if badlist[i] == 0:
ofpmask.write('%e ' % (xi[ell / 2, xiindx]))
else:
ofpmask.write('%e ' % (0.0))
ofpmask.write('\n')
ristart = ristart + dfacs
if (badlist[i] == 0):
xiindx += 1
return xiell(xiellfname=fDR, sxilist=[svec, xi], icovfname=icovfname)
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 wpfromDR(fbase,dfacr=1,periodicopt=0,DRfacinfo=None,rpimax=80.,testing=0,icovfname=None,wpstart=-1,wpend=-1,xiopt=0):
"""
This function computes wp from DRopt[1-3] files.
DRfacinfo is used to pass [DRfac, fixRR] (dont get it from file headers in this case)
That feature is necessary for computing covariance matrices from bootstrap, where that factor
should be fixed by total N and/or S, it does not vary from bootstrap region to region.
xiopt added to input Hong's xi values rather than DR counts.
"""
if(dfacr != 1):
print 'dfacr not 1 is not coded up yet!'
sys.exit(1)
if(periodicopt == 1): # periodic box.
fDR = fbase
rpg, rpig, xi = np.loadtxt(fbase,unpack=True)
nrpibins = len(np.where(rpg == rpg[0])[0])
nrpbins = len(np.where(rpig == rpig[0])[0])
if nrpibins*nrpbins != len(xi):
return None
elif xiopt == 1:
rpg, rpig, xi, xigerr = np.loadtxt(fbase,unpack=True)
nrpibins = len(np.where(rpg == rpg[0])[0])
nrpbins = len(np.where(rpig == rpig[0])[0])
if nrpibins*nrpbins != len(xi):
return None
fDR = fbase
else:
fDD = fbase+'.DRopt1'
fDR = fbase+'.DRopt2'
fRR = fbase+'.DRopt3'
rpg, rpig, DDg = np.loadtxt(fDD,skiprows=3,unpack=True)
rpg, rpig, DRg = np.loadtxt(fDR,skiprows=3,unpack=True)
rpg, rpig, RRg = np.loadtxt(fRR,skiprows=3,unpack=True)
if DRfacinfo is None:
DRfac, fixRR = ximisc.getDRfactors(fbase)
else:
DRfac = DRfacinfo[0]
fixRR = DRfacinfo[1]
nrpibins = len(np.where(rpg == rpg[0])[0])
nrpbins = len(np.where(rpig == rpig[0])[0])
if nrpibins*nrpbins != len(DDg):
return None
xi = ((DDg-DRg*DRfac)/RRg/DRfac**2/fixRR**2 + 1.)
rpi1d = rpig.reshape(nrpbins,nrpibins)[0]
## check that it's linearly spaced, get drpi
drpi = rpi1d[1]-rpi1d[0]
chk = rpi1d[1:] - rpi1d[:-1] - drpi
aaa = np.where(np.fabs(chk) > 1.0e-5)[0]
if(len(aaa) > 0):
print 'error!'
sys.exit(1)
assert np.fabs(rpi1d[0]-drpi*0.5) < 1e-3
rp1d = rpg.reshape(nrpbins,nrpibins)[:,0]
mywp = np.zeros(len(rp1d),dtype='float')
wpi = 0
for rpval in rp1d:
if(testing==1):
print 'inside testing'
xx = np.where(rpg == rpval)[0]
assert len(xx) == nrpibins
xicurr = xi[xx]
assert (rpig[xx] == rpi1d).all()
assert np.fabs(rpi1d[0]-drpi*0.5) < 1e-3
## ok, we're sure rpigrid is sane.
xx = np.where((rpg == rpval) & (rpig < rpimax))[0]
mywp[wpi] = (xi[xx]).sum()*drpi*2.
wpi += 1
return wp(wpfname=fDR,icovfname=icovfname,rpwplist=[rp1d,mywp],wpstart=wpstart,wpend=wpend)
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
