def test_computeMinimalDistance(self):
distanceDictionary = {
"0 1": 1,
"0 2": 2,
"0 3": 3,
"1 2": 2,
"1 3": 3,
"1 4": 3
}
upgma = UpgmaWpgma(distanceDictionary, 4)
expectedValue = ["0 1", 1]
self.assertEqual(expectedValue, upgma.compute_minimal_distance())
def upgmaWpgma(upgmaWpgma, sequences, outputFile, fileFormat):
"""Executes the a phylogenetic clustering with a upgm or wpgm weighting.
sequences: All defined input sequences as a list.
outputFile: The name of the output file
fileFormat: The file format of the output file"""
#create
print "The following sequences are given:"
for i in sequences:
print i
print "Computing clustering..."
data = mah().createDataForUpgmaWpgma(sequences)
if upgmaWpgma:
upgma = UpgmaWpgma(data[0], len(data[1]))
upgma.compute_clustering()
if not fileFormat:
outputFile += ".graphML"
io().writeGraphMLFile(upgma.mapping, outputFile)
print "Clustering written as graphML file: ", os.path.abspath(outputFile)
else:
outputFile += ".newickTree"
cluster = upgma.get_newick_tree(with_edge_weights=True)
io().writeNewickTree(cluster, outputFile)
print "Computed upgma cluster: ", cluster
print "The clustering was also written to: ", os.path.abspath(outputFile)
else:
wpgma = UpgmaWpgma(data[0], len(data[1]), False, data[2])
wpgma.compute_clustering()
if not fileFormat:
outputFile += ".graphML"
io().writeGraphMLFile(wpgma.mapping, outputFile)
print "Clustering written as graphML file: ", os.path.abspath(outputFile)
else:
outputFile += ".newickTree"
cluster = wpgma.get_newick_tree(with_edge_weights=True)
io().writeNewickTree(cluster, outputFile)
print "Computed wpgma cluster: ", cluster
print "The clustering was also written to: ", os.path.abspath(outputFile)
def test_getNewickTree(self):
mapping = {'1 3': 5, '4 6': 7, '5 7': 8, '0 2': 6}
distanceDictionary = {}
upgma = UpgmaWpgma(distanceDictionary, 5)
upgma.mapping = mapping
upgma.get_newick_tree()
def test_computeClustering(self):
distanceDictionary = {"0 1": 1, "0 2": 2, "0 3": 3, "1 2": 2, "1 3": 3, "2 3": 3}
upgma = UpgmaWpgma(distanceDictionary, 4)
expectedValue = {"0 1": 4, "2 4": 5, "3 5": 6}
upgma.compute_clustering()
print upgma.get_newick_tree()
self.assertEqual(expectedValue, upgma.mapping)
print upgma.get_newick_tree(with_edge_weights=True)
distanceDictionary = {"0 1": 6, "0 2": 10, "0 3": 10, "0 4": 10, "1 2": 10, "1 3": 10, "1 4": 10, "2 3": 2,
"2 4": 6, "3 4": 6}
upgma2 = UpgmaWpgma(distanceDictionary, 5)
expectedValue = {"2 3": 5, "0 1": 7, "4 5": 6, "6 7": 8}
upgma2.compute_clustering()
print upgma2.get_newick_tree(with_edge_weights=False)
self.assertEqual(expectedValue, upgma2.mapping)
print upgma2.get_newick_tree(with_edge_weights=True)
def test_computeMinimalDistance(self):
distanceDictionary = {"0 1": 1, "0 2": 2, "0 3": 3, "1 2": 2, "1 3": 3, "1 4": 3}
upgma = UpgmaWpgma(distanceDictionary, 4)
expectedValue = ["0 1", 1]
self.assertEqual(expectedValue, upgma.compute_minimal_distance())
def test_getNewickTree(self):
mapping = {'1 3': 5, '4 6': 7, '5 7': 8, '0 2': 6}
distanceDictionary = {}
upgma = UpgmaWpgma(distanceDictionary, 5)
upgma.mapping = mapping
upgma.get_newick_tree()
def test_computeClustering(self):
distanceDictionary = {
"0 1": 1,
"0 2": 2,
"0 3": 3,
"1 2": 2,
"1 3": 3,
"2 3": 3
}
upgma = UpgmaWpgma(distanceDictionary, 4)
expectedValue = {"0 1": 4, "2 4": 5, "3 5": 6}
upgma.compute_clustering()
print upgma.get_newick_tree()
self.assertEqual(expectedValue, upgma.mapping)
print upgma.get_newick_tree(with_edge_weights=True)
distanceDictionary = {
"0 1": 6,
"0 2": 10,
"0 3": 10,
"0 4": 10,
"1 2": 10,
"1 3": 10,
"1 4": 10,
"2 3": 2,
"2 4": 6,
"3 4": 6
}
upgma2 = UpgmaWpgma(distanceDictionary, 5)
expectedValue = {"2 3": 5, "0 1": 7, "4 5": 6, "6 7": 8}
upgma2.compute_clustering()
print upgma2.get_newick_tree(with_edge_weights=False)
self.assertEqual(expectedValue, upgma2.mapping)
print upgma2.get_newick_tree(with_edge_weights=True)
def buildTree(self):
"""This function computes the phylogenetic tree with UPGMA and stores it in the Newick-Tree format."""
upgma = UpgmaWpgma(self.distanceDictionary, len(self.sequences))
upgma.compute_clustering()
self.newickTree = upgma.get_newick_tree()