# Fisher = otherFisher+calcFisher(paramList,ellrange,fidCls,dCls,fnTT,fnEE,fnKK,fsky,verbose=True)
# # Get prior sigmas and add to Fisher
# priorList = Config.get("fisher","priorList").split(',')
# for prior,param in zip(priorList,paramList):
# try:
# priorSigma = float(prior)
# except ValueError:
# continue
# ind = paramList.index(param)
# Fisher[ind,ind] += 1./priorSigma**2.
# # get index of mnu and print marginalized constraint
# indMnu = paramList.index('mnu')
# mnu = np.sqrt(np.linalg.inv(Fisher)[indMnu,indMnu])*1000.
# printC("Sum of neutrino masses 1-sigma: "+ str(mnu) + " meV",color="green",bold=True)
# mnus.append(mnu)
# CLKK S/N ============================================
# Calculate Clkk S/N
Clkk = fidCls[:, 4]
frange = np.array(range(len(Clkk)))
snrange = np.arange(kellmin, kellmax)
LF = LensForecast()
LF.loadKK(frange, Clkk, ls, Nls)
sn, errs = LF.sn(snrange, fsky, "kk")
printC("Lensing autopower S/N: " + str(sn), color="green", bold=True)
sns.append(sn)
lc2.addNz("s",zedges,dndzs,bias=None,magbias=None,numzIntegral=300)
lc2.addStepNz("g",0.4,0.7,bias=2,magbias=None,numzIntegral=300)
lc2.generateCls(ellrange,autoOnly=False,zmin=0.)
clkk2 = lc2.getCl("cmb","cmb")
clss2 = lc2.getCl("s","s")
clsk2 = lc2.getCl("cmb","s")
clsg2 = lc2.getCl("s","g")
clgk2 = lc2.getCl("cmb","g")
clgg2 = lc2.getCl("g","g")
from orphics.theory.gaussianCov import LensForecast
LF = LensForecast()
LF.loadSS(ellrange,clsg,30.,shapeNoise=0.3)
pl.add(ellrange,LF.Nls['ss'](ellrange),ls="--")
lcdm = ellrange
cond = np.logical_and(lcdm>100,lcdm<5000)
csscdm = clss[cond]
cssfdm = clss2[cond]
nlss = interp1d(ellrange,LF.Nls['ss'](ellrange))(lcdm[cond])
ells = lcdm[cond]
fsky = 0.4
print(("S/N: ",np.sqrt(np.sum((np.sqrt(fsky*(2.*ells+1.)/2.)*(csscdm-cssfdm)/(csscdm+nlss))**2.))))
lcdm,ckkcdm = np.loadtxt(cdmfile,unpack=True)
lfdm,ckkfdm = np.loadtxt(fdmfile,unpack=True)
# myNls.updateNoise(beamY,noiseTY,noisePY,tellminY,tellmaxY, \
# pellminY,pellmaxY,beamY=beamX,noiseTY=noiseTX, \
# noisePY=noisePX,tellminY=tellminX,tellmaxY=tellmaxX, \
# pellminY=pellminX,pellmaxY=pellmaxX,lkneesX=lkneeY,alphasX=alphaY, \
# lkneesY=lkneeX,alphasY=alphaX)
ls, Nls = myNls.getNl(polComb=polComb, halo=halo)
nlfunc = interp1d(ls, Nls, bounds_error=False, fill_value=np.inf)
nlnow = nlfunc(kellrange)
Nlmvinv += (1. / nlnow)
pl.add(ls, 4. * Nls / 2. / np.pi, label=polComb, ls="--")
LF = LensForecast()
LF.loadKK(kfrange, Clkk, ls, Nls) #kellrange,nlnow)
sn, errs = LF.sn(kellrange, fsky, "kk")
print((polComb, sn))
pl.add(kfrange, 4. * Clkk / 2. / np.pi)
Nlmv = 1. / Nlmvinv
pl.add(kellrange, 4. * Nlmv / 2. / np.pi, label="mv", color='black')
pl.legendOn(loc='lower right', labsize=12)
pl._ax.set_xlim(kellrange.min(), kellrange.max())
pl.done("output/projnl.png")
print((ls.shape))
print((Nlmv.shape))
np.savetxt(expX + expY + "_nlmv.txt",
ellrange = np.arange(100, maximum_ell, 40)
M500 = 10**14.7 #5.e14
c500 = 1.18
# zlens = 0.5
delta = 500
lc.addNz("gal", z_edges, nz, bias=None, magbias=None, numzIntegral=300) # hack
lc.generateCls(ellrange, autoOnly=True, zmin=0.)
Clss = lc.getCl("gal", "gal")
Clkk = lc.getCl("cmb", "cmb")
ls, Nlkk = np.loadtxt("../SZ_filter/data/LA_all_Nl.txt",
unpack=True,
delimiter=",")
LF = LensForecast()
LF.loadKK(ellrange, Clkk, ls, Nlkk)
sns = []
snsCMB = []
winsGal = []
winsCMB = []
stdsGal = []
stdsCMB = []
import cPickle as pickle
mexpgrid, zgrid, errgrid = pickle.load(open("../SZ_filter/data/owl2.pkl",
'rb'))
sngrid = 1. / errgrid