def do_distance_analysis(X):
# get the matrix of squared distances
labels = list('0123')
# reconstruct the matrix of Euclidean distances from a tree
D_sqrt = np.array([[np.linalg.norm(y - x) for x in X] for y in X])
sqrt_tree = NeighborJoining.make_tree(D_sqrt, labels)
sqrt_tree_string = NewickIO.get_newick_string(sqrt_tree)
sqrt_feltree = NewickIO.parse(sqrt_tree_string, FelTree.NewickTree)
D_sqrt_reconstructed = np.array(sqrt_feltree.get_distance_matrix(labels))
# reconstruct the matrix of squared Euclidean distances from a tree
D = D_sqrt**2
tree = NeighborJoining.make_tree(D, labels)
tree_string = NewickIO.get_newick_string(tree)
feltree = NewickIO.parse(tree_string, FelTree.NewickTree)
D_reconstructed = np.array(feltree.get_distance_matrix(labels))
# start writing
out = StringIO()
# matrix of Euclidean distances and its reconstruction from a tree
print >> out, 'matrix of Euclidean distances between tetrahedron vertices:'
print >> out, D_sqrt
print >> out, 'neighbor joining tree constructed from D = non-squared Euclidean distances (unusual):'
print >> out, sqrt_tree_string
print >> out, 'distance matrix implied by this tree:'
print >> out, D_sqrt_reconstructed
# matrix of squared Euclidean distances and its reconstruction from a tree
print >> out, 'matrix of squared distances between tetrahedron vertices:'
print >> out, D
print >> out, 'neighbor joining tree constructed from D = squared Euclidean distances (normal):'
print >> out, tree_string
print >> out, 'distance matrix implied by this tree:'
print >> out, D_reconstructed
return out.getvalue().strip()
def do_distance_analysis(X):
# get the matrix of squared distances
labels = list("0123")
# reconstruct the matrix of Euclidean distances from a tree
D_sqrt = np.array([[np.linalg.norm(y - x) for x in X] for y in X])
sqrt_tree = NeighborJoining.make_tree(D_sqrt, labels)
sqrt_tree_string = NewickIO.get_newick_string(sqrt_tree)
sqrt_feltree = NewickIO.parse(sqrt_tree_string, FelTree.NewickTree)
D_sqrt_reconstructed = np.array(sqrt_feltree.get_distance_matrix(labels))
# reconstruct the matrix of squared Euclidean distances from a tree
D = D_sqrt ** 2
tree = NeighborJoining.make_tree(D, labels)
tree_string = NewickIO.get_newick_string(tree)
feltree = NewickIO.parse(tree_string, FelTree.NewickTree)
D_reconstructed = np.array(feltree.get_distance_matrix(labels))
# start writing
out = StringIO()
# matrix of Euclidean distances and its reconstruction from a tree
print >> out, "matrix of Euclidean distances between tetrahedron vertices:"
print >> out, D_sqrt
print >> out, "neighbor joining tree constructed from D = non-squared Euclidean distances (unusual):"
print >> out, sqrt_tree_string
print >> out, "distance matrix implied by this tree:"
print >> out, D_sqrt_reconstructed
# matrix of squared Euclidean distances and its reconstruction from a tree
print >> out, "matrix of squared distances between tetrahedron vertices:"
print >> out, D
print >> out, "neighbor joining tree constructed from D = squared Euclidean distances (normal):"
print >> out, tree_string
print >> out, "distance matrix implied by this tree:"
print >> out, D_reconstructed
return out.getvalue().strip()
def get_response_content(fs):
# read the matrix
D = fs.matrix
if len(D) < 3:
raise HandlingError('the matrix should have at least three rows')
# read the ordered labels
ordered_labels = Util.get_stripped_lines(fs.labels.splitlines())
if len(ordered_labels) != len(D):
msg_a = 'the number of ordered labels should be the same '
msg_b = 'as the number of rows in the matrix'
raise HandlingError(msg_a + msg_b)
# get the newick tree
tree = NeighborJoining.make_tree(D.tolist(), ordered_labels)
# return the response
return NewickIO.get_newick_string(tree) + '\n'
def get_response_content(fs):
# read the matrix
D = fs.matrix
if len(D) < 3:
raise HandlingError('the matrix should have at least three rows')
# read the ordered labels
ordered_labels = Util.get_stripped_lines(fs.labels.splitlines())
if len(ordered_labels) != len(D):
msg_a = 'the number of ordered labels should be the same '
msg_b = 'as the number of rows in the matrix'
raise HandlingError(msg_a + msg_b)
# get the newick tree
tree = NeighborJoining.make_tree(D.tolist(), ordered_labels)
# return the response
return NewickIO.get_newick_string(tree) + '\n'
