-2LogLF function with Planck map values

gistfile1.txt

# The -2 log-likelihood
#
# -2lnL \propto m^T C^-1 m + ln det C + N ln (2pi)
#
# First term, m^T C^-1 m is the "model fit term"
# Second term, lndetC is the "complexity penalty"
# Third term, N ln 2pi, a constant
#
# m = tempval
# C = Sij + N_ij

tempp = patch # shape (768,)
# e.g. values: 
#        72.88291049,  76.82649994,  11.31249934, -11.75291294,
#       -23.46053075,  22.16308705,  21.45570391, -21.91874249,
#        62.40692039,  91.91584206
noisepatch # corresponding noise values, shape (768,)
# e.g. values:
#         3.02168953, -12.46713406,   7.32537   ,   1.7936974 ,
#         2.54476212,  -4.8399158 ,   1.17796342,  -4.80521029,
#        -2.31289673,   5.95576501

Npix2pi = (len(patch))*2*math.pi  # LF constant

def LogLikehood_wNoise(param, sig):
    # param is our parameter, C_3
    Sij = param[:, None, None] * correctmatrix[None, :, :]
    #newSij = (1e12)*Sij   # multiply S_ij by 1e12
    Nij = sig * id_mat[None, :, :]
    #newNij = (1e12)*Nij
    # Format 7/4pi * param * P_3(M) where param is the parameter we vary, C_l
    # Sij.shape = (20, 3072, 3072)
    Cij = Sij + Nij
    #invCij = np.linalg.inv(Cij)
    logdetC = np.linalg.slogdet(Cij)  # returns sign and determinant; use logdetC[1]
    # model_fit_terms = m^T C^-1 m
    model_fit_terms = np.array([np.dot(tempp.T , np.linalg.solve(Cij[i], tempp) ) for i in range(Cij.shape[0]) ]) 
    return model_fit_terms + logdetC[1] + Npix2pi