def main():
trees = dendropy.TreeList.get_from_path(sys.argv[1], sys.argv[2])
for tree in trees:
pdm = treecalc.PatristicDistanceMatrix(tree)
tree.wt_profile = sorted(pdm.distances())
tree.uwt_profile = []
for txidx1 in range(len(tree.taxon_set) - 1):
for txidx2 in range(txidx1 + 1, len(tree.taxon_set)):
tree.uwt_profile.append(
pdm.path_edge_count(tree.taxon_set[txidx1],
tree.taxon_set[txidx2]))
tree.uwt_profile = sorted(tree.uwt_profile)
tree_length = tree.length()
if tree_length > 0:
tree.swt_profile = [
float(x) / tree_length for x in tree.wt_profile
]
else:
tree.swt_profile = [0.0 for x in tree.wt_profile]
for tidx1 in range(len(trees)):
for tidx2 in range(len(trees)):
sys.stdout.write("{}\t{}\t{:.22f}\t{:.22f}\t{:.22f}\n".format(
tidx1, tidx2,
euclidean_distance(trees[tidx1].uwt_profile,
trees[tidx2].uwt_profile),
euclidean_distance(trees[tidx1].wt_profile,
trees[tidx2].wt_profile),
euclidean_distance(trees[tidx1].swt_profile,
trees[tidx2].swt_profile)))
def to_distance_matrix(tree):
"""Create a distance matrix (NumPy array) from clades/branches in tree.
Returns a tuple of (allclades, distance_matrix) where allclades is a list of
clades and distance_matrix is a 2D array.
"""
pdm = treecalc.PatristicDistanceMatrix(tree)
matrixStrainList = tree.taxon_set
return (matrixStrainList, pdm)
def branchOut(self, centerTaxon, subsetSize, **kwargs):
dist = {}
pdm = treecalc.PatristicDistanceMatrix(self.den_tree)
for i, s in enumerate(self.den_tree.taxon_set): # @UnusedVariable
if "filterTaxon" in kwargs:
if not kwargs["filterTaxon"](s):
continue
dist[s.label] = pdm(centerTaxon, s)
incircle = sortByValue(dist)[0:subsetSize]
return [node[0] for node in incircle]
def calculate_pairwise_tree_dists(intree, output):
tree = dendropy.Tree.get_from_path(intree,
"newick",
preserve_underscores=True)
outfile = open("%s" % output, "w")
distances = treecalc.PatristicDistanceMatrix(tree)
for i, t1 in enumerate(tree.taxon_set):
for t2 in tree.taxon_set[i + 1:]:
print >> outfile, "Distance between '%s' and '%s': %s" % (
t1.label, t2.label, distances(t1, t2))
outfile.close()
def testPatDistMatrix(self):
pdm = treecalc.PatristicDistanceMatrix(self.tree)
def _chk_distance(pdm, t1, t2, exp_distance):
tax1 = self.tree.taxon_set.require_taxon(label=t1)
tax2 = self.tree.taxon_set.require_taxon(label=t2)
pd = pdm(tax1, tax2)
self.assertEqual(pd, exp_distance)
_chk_distance(pdm, "a", "b", 2)
_chk_distance(pdm, "a", "c", 4)
_chk_distance(pdm, "b", "c", 4)
_chk_distance(pdm, "a", "d", 6)
_chk_distance(pdm, "f", "d", 4)
_chk_distance(pdm, "c", "d", 6)
#! /usr/bin/env python
# Calculates a patristic distance matrix between all leaves in a tree.
# The name of the tree file (nexus format) should be provided as the first argument.
# The name of the matrix output file should be provided as the second argument.
# Uses Dendropy 3
import dendropy
from dendropy import treecalc
import sys
tree = dendropy.Tree.get_from_path(sys.argv[1],
"nexus",
preserve_underscores=True)
pdm = treecalc.PatristicDistanceMatrix(tree)
matFile = open(sys.argv[2], 'w')
matFile.write("\t")
for t in tree.taxon_set:
matFile.write("%s\t" % t.label, )
matFile.write('\n')
for i, t1 in enumerate(tree.taxon_set):
matFile.write("%s\t" % t1.label, )
matFile.write('\t' * (i + 1), )
for t2 in tree.taxon_set[i + 1:]:
matFile.write("%s\t" % pdm(t1, t2))
matFile.write('\n')