#from: https://github.com/iskandr/parakeet/blob/master/benchmarks/nd_local_maxima.py
#setup: import numpy as np ; shape = (3,4,3,2) ; x = np.arange(72, dtype=np.float64).reshape(*shape)
#run: local_maxima(x)

#pythran export local_maxima(float [][][][])
import numpy as np

def wrap(pos, offset, bound):
    return ( pos + offset ) % bound

def clamp(pos, offset, bound):
    return min(bound-1,max(0,pos+offset))

def reflect(pos, offset, bound):
    idx = pos+offset
    return min(2*(bound-1)-idx,max(idx,-idx))


def local_maxima(data, mode=wrap):
  wsize = data.shape
  result = np.ones(data.shape, bool)
  for pos in np.ndindex(data.shape):
    myval = data[pos]
    for offset in np.ndindex(wsize):
      neighbor_idx = tuple(mode(p, o-w//2, w) for (p, o, w) in zip(pos, offset, wsize))
      result[pos] &= (data[neighbor_idx] <= myval)
  return result