# | \-B
# /--------|
# | | /-C
# | \--------|
# | | /-D
#---------| \--------|
# | \-E
# |
# | /-F
# \--------|
# | /-G
# \--------|
# \-H
# Now we can ask the numerical profile associated to each node
A = t.search_nodes(name='A')[0]
print "A associated profile:\n", A.profile
# [-1.23 -0.81 1.79 0.78 -0.42 -0.69 0.58]
#
# Or we can ask for the mean numerical profile of an internal
# partition, which is computed as the average of all vectors under the
# the given node.
cluster = t.get_common_ancestor("E", "A")
print "Internal cluster mean profile:\n", cluster.profile
#[-1.574 -0.686 1.048 -0.012 -0.118 0.614 0.728]
#
# We can also obtain the std. deviation vector of the mean profile
print "Internal cluster std deviation profile:\n", cluster.deviation
#[ 0.36565558 0.41301816 0.40676283 0.56211743 0.50704635 0.94949671
# 0.26753691]
# If would need to re-link the tree to a different matrix or use
# | \-B
# /--------|
# | | /-C
# | \--------|
# | | /-D
# ---------| \--------|
# | \-E
# |
# | /-F
# \--------|
# | /-G
# \--------|
# \-H
# Now we can ask the numerical profile associated to each node
A = t.search_nodes(name="A")[0]
print "A associated profile:\n", A.profile
# [-1.23 -0.81 1.79 0.78 -0.42 -0.69 0.58]
#
# Or we can ask for the mean numerical profile of an internal
# partition, which is computed as the average of all vectors under the
# the given node.
cluster = t.get_common_ancestor("E", "A")
print "Internal cluster mean profile:\n", cluster.profile
# [-1.574 -0.686 1.048 -0.012 -0.118 0.614 0.728]
#
# We can also obtain the std. deviation vector of the mean profile
print "Internal cluster std deviation profile:\n", cluster.deviation
# [ 0.36565558 0.41301816 0.40676283 0.56211743 0.50704635 0.94949671
# 0.26753691]
# If would need to re-link the tree to a different matrix or use
