text: # MIT License
  # 
  # Copyright (c) 2016 Anders Steen Christensen
  # 
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  # 
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  # 
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  import numpy as np
  
  from .fdistance import fmanhattan_distance, fl2_distance
  from .fdistance import fp_distance_integer, fp_distance_double
  
  def manhattan_distance(A, B):
      """ Calculates the Manhattan distances, D,  between two
          Numpy arrays of representations.
      
              :math:`D_{ij} = \\|A_i - B_j\\|_1`
  
          Where :math:`A_{i}` and :math:`B_{j}` are representation vectors.
          D is calculated using an OpenMP parallel Fortran routine.
  
          :param A: 2D array of descriptors - shape (N, representation size).
          :type A: numpy array
          :param B: 2D array of descriptors - shape (M, representation size).
          :type B: numpy array
  
          :return: The Manhattan-distance matrix.
          :rtype: numpy array
      """
  
      if len(A.shape) != 2 or len(B.shape) != 2:
          raise ValueError('expected matrices of dimension=2')
  
      if B.shape[1] != A.shape[1]:
          raise ValueError('expected matrices containing vectors of same size')
  
      na = A.shape[0]
      nb = B.shape[0]
  
      D = np.empty((na, nb), order='F')
  
      fmanhattan_distance(A.T, B.T, D)
  
      return D
  
  def l2_distance(A, B):
      """ Calculates the L2 distances, D, between two
          Numpy arrays of representations.
  
              :math:`D_{ij} = \\|A_i - B_j\\|_2`
  
          Where :math:`A_{i}` and :math:`B_{j}` are representation vectors.
          D is calculated using an OpenMP parallel Fortran routine.
  
          :param A: 2D array of descriptors - shape (N, representation size).
          :type A: numpy array
          :param B: 2D array of descriptors - shape (M, representation size).
          :type B: numpy array
      
          :return: The L2-distance matrix.
          :rtype: numpy array
      """
  
      if len(A.shape) != 2 or len(B.shape) != 2:
          raise ValueError('expected matrices of dimension=2')
  
      if B.shape[1] != A.shape[1]:
          raise ValueError('expected matrices containing vectors of same size')
  
      na = A.shape[0]
      nb = B.shape[0]
  
      D = np.empty((na, nb), order='F')
  
      fl2_distance(A.T, B.T, D)
  
      return D
  
  def p_distance(A, B, p = 2):
      """ Calculates the p-norm distances between two
          Numpy arrays of representations.
          The value of the keyword argument ``p =`` sets the norm order. 
          E.g. ``p = 1.0`` and ``p = 2.0`` with yield the Manhattan and L2 distances, respectively.
      
              .. math:: D_{ij} = \|A_i - B_j\|_p
  
          Where :math:`A_{i}` and :math:`B_{j}` are representation vectors.
          D is calculated using an OpenMP parallel Fortran routine.
  
          :param A: 2D array of descriptors - shape (N, representation size).
          :type A: numpy array
          :param B: 2D array of descriptors - shape (M, representation size).
          :type B: numpy array
          :param p: The norm order
          :type p: float
  
          :return: The distance matrix.
          :rtype: numpy array
      """
  
      if len(A.shape) != 2 or len(B.shape) != 2:
          raise ValueError('expected matrices of dimension=2')
  
      if B.shape[1] != A.shape[1]:
          raise ValueError('expected matrices containing vectors of same size')
  
      na = A.shape[0]
      nb = B.shape[0]
  
      D = np.empty((na, nb), order='F')
  
  
      if (type(p) == type(1)):
          if (p == 2):
              fl2_distance(A, B, D)
          else:
              fp_distance_integer(A.T, B.T, D, p)
  
      elif (type(p) == type(1.0)):
          if p.is_integer():
              p = int(p)
              if (p == 2):
                  fl2_distance(A, B, D)
              else:
                  fp_distance_integer(A.T, B.T, D, p)
  
          else:
              fp_distance_double(A.T, B.T, D, p)
      else:
          raise ValueError('expected exponent of integer or float type')
  
      return D
  