`scipy.optimize._numdiff:_adjust_scheme_to_bounds`

Signature

   def     _adjust_scheme_to_bounds (    x0    ,    h    ,    num_steps    ,    scheme    ,    lb    ,    ub      )

Adjust final difference scheme to the presence of bounds.

x0 : ndarray, shape (n,): Point at which we wish to estimate derivative.
h : ndarray, shape (n,): Desired absolute finite difference steps.
num_steps : int: Number of h steps in one direction required to implement finite difference scheme. For example, 2 means that we need to evaluate f(x0 + 2 * h) or f(x0 - 2 * h)
scheme : {'1-sided', '2-sided'}: Whether steps in one or both directions are required. In other words '1-sided' applies to forward and backward schemes, '2-sided' applies to center schemes.
lb : ndarray, shape (n,): Lower bounds on independent variables.
ub : ndarray, shape (n,): Upper bounds on independent variables.

h_adjusted : ndarray, shape (n,): Adjusted absolute step sizes. Step size decreases only if a sign flip or switching to one-sided scheme doesn't allow to take a full step.
use_one_sided : ndarray of bool, shape (n,): Whether to switch to one-sided scheme. Informative only for scheme='2-sided'.