splat/model.py
def _modelFitEMCEE_lnprior_limits(theta,limits):
    '''
    compute the log of the probability assuming a uniform distribution
    with hard limits; if outside limits, probability returns -infinity
    '''
    for i,t in enumerate(theta):
        try:
            if t < numpy.min(limits[i]) or t > numpy.max(limits[i]):
                return -1.e30
        except:
            pass
    return 0.0

    compute the log of the probability assuming a uniform distribution
    with hard limits; if outside limits, probability returns -infinity
    
compute the log of the probability assuming a uniform distribution
with hard limits; if outside limits, probability returns -infinity
compute the log of the probability assuming a uniform distribution with hard limits ; if outside limits probability returns - infinity
def _modelFitEMCEE_lnprior_limits(theta,limits):
    for i,t in enumerate(theta):
        try:
            if t < numpy.min(limits[i]) or t > numpy.max(limits[i]):
                return -1.e30
        except:
            pass
    return 0.0
def _modelFitEMCEE_lnprior_limits ( theta , limits ) : for i , t in enumerate ( theta ) : try : if t < numpy . min ( limits [ i ] ) or t > numpy . max ( limits [ i ] ) : return - 1.e30 except : pass return 0.0
compute the log of the probability assuming a uniform distribution
with hard limits; if outside limits, probability returns -infinity
compute the log of the probability assuming a uniform distribution with hard limits ; if outside limits probability returns - infinity
'''
    compute the log of the probability assuming a uniform distribution
    with hard limits; if outside limits, probability returns -infinity
    '''
theta
limits