def topologyDistanceMatrix(self, metric='sd'):
"""Returns a DistanceMatrix object showing topology distances.
Uses the :meth:`Tree.Tree.topologyDistance` method to compare
trees. That method returns distances between pairs of trees,
and this method simply collates those distances into a
:class:`DistanceMatrix.DistanceMatrix` object, which is
returned.
See :meth:`Tree.Tree.topologyDistance` for an explanation of the
different metrics. The metrics are given in
``var.topologyDistanceMetrics``"""
from distancematrix import DistanceMatrix
d = DistanceMatrix()
d.names = []
for t in self.trees:
d.names.append(t.name)
d.dim = len(d.names)
# print d.names
# sys.exit()
d.matrix = []
for i in range(d.dim):
d.matrix.append([0.0] * d.dim)
for i in range(d.dim):
t1 = self.trees[i]
for j in range(i + 1, d.dim):
t2 = self.trees[j]
theDist = t1.topologyDistance(t2, metric=metric)
d.matrix[i][j] = theDist
d.matrix[j][i] = theDist
return d
def topologyDistanceMatrix(self, metric='sd'):
"""Returns a DistanceMatrix object showing topology distances.
Uses the :meth:`Tree.Tree.topologyDistance` method to compare
trees. That method returns distances between pairs of trees,
and this method simply collates those distances into a
:class:`DistanceMatrix.DistanceMatrix` object, which is
returned.
See :meth:`Tree.Tree.topologyDistance` for an explanation of the
different metrics. The metrics are given in
``var.topologyDistanceMetrics``"""
from distancematrix import DistanceMatrix
d = DistanceMatrix()
d.names = []
for t in self.trees:
d.names.append(t.name)
d.dim = len(d.names)
# print d.names
# sys.exit()
d.matrix = []
for i in range(d.dim):
d.matrix.append([0.0] * d.dim)
for i in range(d.dim):
t1 = self.trees[i]
for j in range(i + 1, d.dim):
t2 = self.trees[j]
theDist = t1.topologyDistance(t2, metric=metric)
d.matrix[i][j] = theDist
d.matrix[j][i] = theDist
return d
def patristicDistanceMatrix(self):
"""Matrix of distances along tree path.
This method sums the branch lengths between each pair of taxa, and
puts the result in a DistanceMatrix object, which is returned.
Self.taxNames is required.
"""
gm = ['Tree.patristicDistanceMatrix()']
if not self.taxNames:
gm.append("No taxNames.")
raise P4Error(gm)
# The tree will be rearranged, so make a copy to play with, so
# self is undisturbed.
t = self.dupe()
d = DistanceMatrix()
d.names = self.taxNames
d.dim = len(d.names)
# print d.names
d.matrix = []
for i in range(d.dim):
d.matrix.append([0.0] * d.dim)
for i in range(d.dim):
n1 = t.node(d.names[i])
t.reRoot(n1, moveInternalName=False)
for j in range(i + 1, d.dim):
n2 = t.node(d.names[j])
# sum up distances between n1 and n2
sum = n2.br.len
n2 = n2.parent
while n2 != n1:
sum = sum + n2.br.len
n2 = n2.parent
# print "Dist from %s to %s is %f" % (d.names[i], d.names[j],
# sum)
d.matrix[i][j] = sum
d.matrix[j][i] = sum
return d
