#! /usr/bin/env python3
#
def neighbor_risk_test ( ):

#*****************************************************************************80
#
## neighbor_risk_test() tests neighbor_risk().
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    19 March 2025
#
#  Author:
#
#    John Burkardt
#
  import graphviz
  import numpy as np
  import platform

  print ( '' )
  print ( 'neighbor_risk_test():' )
  print ( '  python version: ' + platform.python_version ( ) )
  print ( '  numpy version:  ' + np.version.version )
  print ( '  neighbor_risk() records connections between territories' )
  print ( '  in the game of RISK.' )
  print ( '  Plot a web of these connections as a graph.' )
#
#  Retrieve the adjacency matrix.
#
  A = risk_adjacency_matrix ( )

  n = A.shape[0]
#
#  List pairs (s,t) of connected territories.
#
  nz = np.sum ( A ) // 2 
  s = np.zeros ( nz, dtype = int )
  t = np.zeros ( nz, dtype = int )

  k = 0
  for i in range ( 0, n ):
    for j in range ( i + 1, n ):
      if ( A[i,j] == 1 ):
        s[k] = i
        t[k] = j
        k = k + 1

  plot = graphviz.Graph ( comment = 'RISK', format = 'png', engine = 'neato' )
#
#  Set up node labels.
#
  labels = risk_label ( )
#
#  Specify the nodes, giving each an internal code, and a label.
#
  for i in range ( 0, n ):
    risk_code = str ( i )
    risk_id = labels[i]
    plot.node ( risk_code, risk_id, fontsize = "6" ) 
    print ( risk_code, risk_id )
#
#  Connect nodes.
#
  for k in range ( 0, nz ):
    i = s[k]
    j = t[k]
    plot.edge ( str(i), str(j) )

  plot.layout = 'circo'

  print ( plot.source )
#
#  Save graph to a file, and optionally display an image to the screen.
#
  plot.render ( 'neighbor_risk', view = False )

  filename = 'neighbor_risk.png'
  print ( '' )
  print ( '  Graphics saved as "' + filename + '"' )
#
#  Terminate.
#
  print ( '' )
  print ( 'neighbor_risk_test():' )
  print ( '  Normal end of execution.' )

  return

def risk_adjacency_matrix ( ):

#*****************************************************************************80
#
## risk_adjacency_matrix() returns the RISK adjacency matrix.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    02 January 2020
#
#  Author:
#
#    John Burkardt
#
#  Output:
#
#    real A(42,42), the adjacency matrix.
#
  import numpy as np

  A = np.array ( [ \
    [ 0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0], \
    [ 1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,1,1,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,1,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,1,0,0,0,0,0,0], \
    [ 1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,1,1,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1], \
    [ 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0] ], \
    dtype = np.int32 )

  return A

def risk_label ( ):

#*****************************************************************************80
#
## risk_label() returns the labels for the RISK matrix.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    02 January 2020
#
#  Author:
#
#    John Burkardt
#
#  Output:
#
#    string label(42), the labels.
#
  import numpy as np

  label = np.array ( [ \
    "Alaska", \
    "Northwest Territory", \
    "Alberta", \
    "Ontario", \
    "Greenland", \
    "Eastern Canada", \
    "Western US", \
    "Eastern US", \
    "Central America", \
    "Venezuela", \
    "Peru", \
    "Brazil", \
    "Argentina", \
    "North Africa", \
    "Egypt", \
    "East Africa", \
    "Central Africa", \
    "South Africa", \
    "Madagascar", \
    "Iceland", \
    "Great Britain", \
    "Scandinavia", \
    "Northern Europe", \
    "Russia", \
    "Western Europe", \
    "Southern Europe", \
    "Middle East", \
    "Afghanistan", \
    "Ural", \
    "Siberia", \
    "Yakutsk", \
    "Irkutsk", \
    "Mongolia", \
    "Kamchatka", \
    "Japan", \
    "China", \
    "India", \
    "Siam", \
    "Indonesia", \
    "New Guinea", \
    "Western Australia", \
    "Eastern Australia" ] )

  return label

def timestamp ( ):

#*****************************************************************************80
#
## timestamp() prints the date as a timestamp.
#
#  Licensing:
#
#    This code is distributed under the MIT license. 
#
#  Modified:
#
#    21 August 2019
#
#  Author:
#
#    John Burkardt
#
  import time

  t = time.time ( )
  print ( time.ctime ( t ) )

  return

if ( __name__ == '__main__' ):
  timestamp ( )
  neighbor_risk_test ( )
  timestamp ( )