text: # -*- coding: utf-8 -*-
  
  # Copyright (c) 2014, Sergio Callegari
  # All rights reserved.
  
  # This file is part of PyDSM.
  
  # PyDSM is free software: you can redistribute it and/or modify it
  # under the terms of the GNU General Public License as published by
  # the Free Software Foundation, either version 3 of the License, or
  # (at your option) any later version.
  
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  # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  # GNU General Public License for more details.
  
  # You should have received a copy of the GNU General Public License
  # along with PyDSM.  If not, see <http://www.gnu.org/licenses/>.
  
  # This file includes code ported from the DELSIG Matlab toolbox
  # (see http://www.mathworks.com/matlabcentral/fileexchange/19)
  # covered by the following copyright and permission notice
  #
  # Copyright (c) 2009 Richard Schreier
  #
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  # met:
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  #   * Redistributions of source code must retain the above copyright
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  #     the documentation and/or other materials provided with the distribution
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  import numpy as np
  
  __all__ = ['partitionABCD']
  
  
  def partitionABCD(ABCD, m=None):
      u"""Partition ABCD ss description in A, B, C, D for an m-input sytem
  
      Parameters
      ----------
      ABCD : matrix like 2D
          input ABCD matrix providing the state space representation
      m : int or None, optional
          number of inputs of state space system. If set to None, a guess
          is made from the ABCD matrix size (defaults to None).
  
      Returns
      -------
      (A, B, C, D) : tuple of 2D ndarrays
          the individual matrices composing ABCD
  
      Notes
      -----
      When the number of inputs is automatically guessed, the minimum
      acceptable value is take, considering the the system must have at least
      an input and an output.
  
      If ABCD is an ndarray, then its content is not copied in A, B, C, D.
      """
      ABCD = np.asarray(ABCD)
      if m is None:
          n = min(ABCD.shape)-1
      else:
          n = ABCD.shape[1]-m
      return (ABCD[:n, :n], ABCD[:n, n:], ABCD[n:, :n], ABCD[n:, n:])
  