splat/model.py
def _modelFitEMCEE_lnprior_normal(theta,meansds):
    '''
    compute the log of the probability assuming a normal distribution
    there probably needs to be better error checking here
    '''
    lnp = 0.0
    for i,t in enumerate(theta):
        try:
            lnp-=0.5*(((t-meansds[i][0])/meansds[i][1])**2-numpy.log(meansds[i][1]))
        except:
            pass
    return lnp

    compute the log of the probability assuming a normal distribution
    there probably needs to be better error checking here
    
compute the log of the probability assuming a normal distribution
there probably needs to be better error checking here
compute the log of the probability assuming a normal distribution there probably needs to be better error checking here
def _modelFitEMCEE_lnprior_normal(theta,meansds):
    lnp = 0.0
    for i,t in enumerate(theta):
        try:
            lnp-=0.5*(((t-meansds[i][0])/meansds[i][1])**2-numpy.log(meansds[i][1]))
        except:
            pass
    return lnp
def _modelFitEMCEE_lnprior_normal ( theta , meansds ) : lnp = 0.0 for i , t in enumerate ( theta ) : try : lnp -= 0.5 * ( ( ( t - meansds [ i ] [ 0 ] ) / meansds [ i ] [ 1 ] ) ** 2 - numpy . log ( meansds [ i ] [ 1 ] ) ) except : pass return lnp
compute the log of the probability assuming a normal distribution
there probably needs to be better error checking here
compute the log of the probability assuming a normal distribution there probably needs to be better error checking here
'''
    compute the log of the probability assuming a normal distribution
    there probably needs to be better error checking here
    '''
theta
meansds