raputil.py 文件源码

def nlfunc_(r_,sc_,grid,gg_,return_gradient=True):
    'returns xhat_nl = rhat_nl * interp( rhat_nl / sc,grid,gg) and optionally the gradient of xhat_nl wrt rhat_nl'
    g_ = r_ * interp1d_(r_/sc_,grid,gg_)
    if return_gradient:
        #I had some code that computed the gradient, but it was far more complicated and no faster than just computing the empirical gradient
        # technically, this computes a subgradient
        dr_ = sc_ * (grid[1]-grid[0]) * 1e-3
        dgdr_ = (nlfunc_(r_+.5*dr_,sc_,grid,gg_,False) - nlfunc_(r_-.5*dr_,sc_,grid,gg_,False)) / dr_
        return (g_,dgdr_)
    else:
        return g_