def __init__(self, name, z, DV, DVErr, fidtheory, maxchi2=1e30):
BaseLikelihood.__init__(self, name)
self.z = z
rd = fidtheory.rd
DV /= rd
DVErr /= rd
self.maxchi2 = maxchi2
print(name, "measurement in ", fidtheory.rd_approx, ":", DV, "+-",
DVErr)
self.setData(DV, DVErr)
def __init__(self,
name,
filename,
chi2col,
fid_theory,
z,
skiprows=0,
aperp_col=0,
apar_col=1):
BaseLikelihood.__init__(self, name)
print()
print("Loading ", filename)
data = sp.loadtxt(filename, skiprows=skiprows)
# first let's autolearn the binning
aperp = set()
aparl = set()
for line in data:
aperp.add(line[aperp_col])
aparl.add(line[apar_col])
aperp = sorted(list(aperp))
aparl = sorted(list(aparl))
logltab = sp.zeros((len(aperp), len(aparl)))
print("Aperp min,max,step,N:", aperp[0], aperp[-1],
aperp[1] - aperp[0], len(aperp))
print("Aparl min,max,step,N:", aparl[0], aparl[-1],
aparl[1] - aparl[0], len(aparl))
self.aperp = sp.array(aperp)
self.aparl = sp.array(aparl)
# now fill in the table
for line in data:
ii = aperp.index(line[aperp_col])
jj = aparl.index(line[apar_col])
if chi2col > 0:
chi2 = line[chi2col]
logltab[ii, jj] = -chi2 / 2.0
else:
# col is probability, add 1e-50 to avoid taking log of zero.
logltab[ii, jj] = sp.log(line[chi2col * -1] + 1e-50)
logltab = logltab - logltab.max()
self.loglint = RectBivariateSpline(self.aperp,
self.aparl,
logltab,
kx=1,
ky=1)
print("Loading done")
print("rd = ", fid_theory.rd, " Mpc")
self.fidDaOverrd = fid_theory.DaOverrd(z)
self.fidHIOverrd = fid_theory.HIOverrd(z)
print("Fiducials at z=", z, ":", self.fidDaOverrd, self.fidHIOverrd)
self.z = z
def __init__(self, name, mean, cov, kill_Da=False, kill_rd=False):
print("Initializing CMB likelihood:", name)
cov = sp.array(cov)
if kill_Da:
cov[2, :] = 0.0
cov[:, 2] = 0.0
cov[2, 2] = 1e10
name += "_noDa"
if kill_rd:
cov[0:2, 0:2] = 0.0
cov[0, 0] = 1e10
cov[1, 1] = 1e10
name += "_noRd"
BaseLikelihood.__init__(self, name)
self.mean = sp.array(mean)
self.cov = cov
self.icov = la.inv(cov)
def __init__(self, name, filename, fid_theory, z):
BaseLikelihood.__init__(self, name)
print()
print("Loading ", filename)
data = sp.loadtxt(filename)
self.chi2i = interp1d(data[:, 0], data[:, 1])
self.fidDVOverrd = fid_theory.DVOverrd(z)
# find minimum
alphamin = minimize(self.chi2i, 1.0, bounds=[
[0.9, 1.1]], method='SLSQP').x[0]
rms = quad(lambda x: sp.exp(-self.chi2i(alphamin+x)/2)*x*x, -0.1, 0.1)[0]
rms /= quad(lambda x: sp.exp(-self.chi2i(alphamin+x)/2), -0.1, 0.1)[0]
DVf = self.fidDVOverrd*fid_theory.rd
print(name, "measurement in", fid_theory.rd_approx,
" : DV=", DVf*alphamin, "+-", sp.sqrt(rms)*DVf)
print("with rd=", fid_theory.rd, "DV_fid=", DVf, "alphamin=", alphamin)
self.z = z
def __init__(self):
BaseLikelihood.__init__(self, "testlike")
self.x = 0
self.y = 0
def setTheory(self, theory):
BaseLikelihood.setTheory(self, theory)
for l in self.llist_:
l.setTheory(theory)
assert (self.theory_ is l.theory_)
def __init__(self, llist=[]):
BaseLikelihood.__init__(self, "Composite")
self.llist_ = llist
def __init__(self):
BaseLikelihood.__init__(self,"testlike")
self.x=0
self.y=0