class_docstr: OpenSimplex n-dimensional gradient noise functions.
class_docstr_tok: ["OpenSimplex","n-dimensional","gradient","noise","functions","."]
docstr: Initiate the class and generate permutation arrays from a seed number.
docstr_tok: ["Initiate","the","class","and","generate","permutation","arrays","from","a","seed","number","."]
code: def __init__(self, seed=DEFAULT_SEED):
      """
      Initiate the class and generate permutation arrays from a seed number.
      """
      # Initializes the class using a permutation array generated from a 64-bit seed.
      # Generates a proper permutation (i.e. doesn't merely perform N
      # successive pair swaps on a base array)
      perm = self._perm = [
          0
      ] * 256  # Have to zero fill so we can properly loop over it later
      perm_grad_index_3D = self._perm_grad_index_3D = [0] * 256
      source = [i for i in range(0, 256)]
      seed = overflow(seed * 6364136223846793005 + 1442695040888963407)
      seed = overflow(seed * 6364136223846793005 + 1442695040888963407)
      seed = overflow(seed * 6364136223846793005 + 1442695040888963407)
      for i in range(255, -1, -1):
          seed = overflow(
              seed * 6364136223846793005 + 1442695040888963407
          )
          r = int((seed + 31) % (i + 1))
          if r < 0:
              r += i + 1
          perm[i] = source[r]
          perm_grad_index_3D[i] = int(
              (perm[i] % (len(GRADIENTS_3D) / 3)) * 3
          )
          source[r] = source[i]
  
file_path: imgaug/imgaug/external/opensimplex.py
imports: import sys
  from ctypes import c_long
  from math import floor
label_desc: Use image node_id 1 to create a new OpenSimplex object from inherited base classes: object with example: obj = OpenSimplex(seed)