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

#*****************************************************************************80
#
## chain_letter_dendrogram() makes a dendrogram from a distance matrix.
#
#  Discussion:
#
#    Students were given 11 chain letters.  Each student was asked to pick
#    a particular chain letter, and then estimate its "distance" from all
#    the chain letters, using a particular scoring system.  
#
#    The results are here assembled into a distance matrix, from which a
#    hierarchical clustering can be made and displayed.
#
#    The distances were based on comparing positions in thw chain letters where
#    a proper name occurred.
#
#    "New England"   0 if the same, 1 otherwise.
#    "R.A.F.Officer" 0 if the same, 1 otherwise.
#    "Gene Welch" 0 if the same, 1 if one name different, 2 if both different.
#    "Saul de Groda" 0 if the same, 1, 2 or 3 if 1, 2 or 3 names different 
#      or added.
#    "Constantine Dias" 0 if the same, 1 or 2 if 1 or 2 names different.
#    "Carlos Daddit" 0 if the same, 1 or 2 if 1 or 2 names different.
#    "Dalan Fairchild" 0 if the same, 1 or 2 if 1 or 2 names different.
#    "a young woman in California" 0 if both letters have or don't have this part, 
#      5 if one does and one doesn't.
#
#    The distance between the two chain letters is the sum of the 8 scores.
#
#    From the distance matrix, we can compute a dendrogram that suggests,
#    based on similarity, a sort of genealogy for the chain letters.
#
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    03 May 2019
#
#  Author:
#
#    John Burkardt
#
  from mpl_toolkits.mplot3d import Axes3D
  from matplotlib import cm
  from scipy import spatial
  from scipy import cluster
  import csv
  import matplotlib
  import matplotlib.pyplot as plt
  import numpy as np
  import platform
  import scipy as sp

  print ( '' )
  print ( 'chain_letter_dendrogram():' )
  print ( '  python version:     ' + platform.python_version ( ) )
  print ( '  numpy version:      ' + np.version.version )
  print ( '  matplotlib version: ' + matplotlib.__version__ )
  print ( '  Construct a dendrogram to analyze the relationship' )
  print ( '  among a set of 11 versions of a chain letter.' )
#
#  Read the distance matrix from a file.
#
  filename = 'chain_letter_data.txt'
  data = np.loadtxt ( filename )

  print ( '' )
  print ( '  Chain letter distance matrix read from "%s"' % ( filename ) )
#
#  Determine pairwise distances.
#
  dv = sp.spatial.distance.pdist ( data )
#
#  Perform hierarchical clustering.
#
  dl = sp.cluster.hierarchy.linkage ( dv, method = 'single' )
#
#  Display the clustering as a dendrogram.
#
  plt.figure ( )
  plt.title ( 'Dendrogram for 11 chain letters A=1 through K=11', fontsize = 16 )

  sp.cluster.hierarchy.dendrogram ( dl, \
    labels = ( 'A','B','C','D','E','F','G','H','I','J','K') )
  filename = 'chain_letter_dendrogram.png'
  plt.savefig ( filename )
  print ( '' )
  print ( '  Graphics saved as "' + filename + '"' )
  plt.show ( block = False )
  plt.close ( )
#
#  Terminate.
#
  print ( '' )
  print ( 'chain_letter_dendrogram():' )
  print ( '  Normal end of execution.' )
  return

def timestamp ( ):

#*****************************************************************************80
#
## timestamp() prints the date as a timestamp.
#
#  Licensing:
#
#    This code is distributed under the MIT license. 
#
#  Modified:
#
#    06 April 2013
#
#  Author:
#
#    John Burkardt
#
  import time

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

  return

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