"""Nelder-Mead in numpy (no scipy on this PC), for fitting part parameters to the picture."""
import numpy as np


def nelder_mead(f, x0, step, iters=400, tol=1e-5, log=None):
    n = len(x0)
    pts = [np.array(x0, float)]
    for i in range(n):
        p = np.array(x0, float)
        p[i] += step[i]
        pts.append(p)
    vals = [f(p) for p in pts]
    for it in range(iters):
        order = np.argsort(vals)
        pts = [pts[i] for i in order]
        vals = [vals[i] for i in order]
        if log and it % 20 == 0:
            log(it, vals[0])
        if abs(vals[-1] - vals[0]) < tol:
            break
        c = np.mean(pts[:-1], 0)
        xr = c + (c - pts[-1])
        fr = f(xr)
        if fr < vals[0]:
            xe = c + 2 * (c - pts[-1])
            fe = f(xe)
            if fe < fr:
                pts[-1], vals[-1] = xe, fe
            else:
                pts[-1], vals[-1] = xr, fr
        elif fr < vals[-2]:
            pts[-1], vals[-1] = xr, fr
        else:
            xc = c + 0.5 * (pts[-1] - c) if fr >= vals[-1] else c + 0.5 * (xr - c)
            fc = f(xc)
            if fc < min(fr, vals[-1]):
                pts[-1], vals[-1] = xc, fc
            else:
                for i in range(1, len(pts)):
                    pts[i] = pts[0] + 0.5 * (pts[i] - pts[0])
                    vals[i] = f(pts[i])
    i = int(np.argmin(vals))
    return pts[i], vals[i]