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 * np.interp(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