def createSampleData(self):
# hardcoded data for now
tree = ClusterTree('(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);')
leaves = tree.get_leaf_names()
idx_dict = {'A':0,'B':1,'C':2,'D':3}
idx_labels = sorted(idx_dict, key=idx_dict.get)
dmat = np.zeros((4,4))
for l1,l2 in combinations(leaves,2):
d = tree.get_distance(l1,l2)
dmat[idx_dict[l1],idx_dict[l2]] = dmat[idx_dict[l2],idx_dict[l1]] = d
self.X = dmat
def newick(self, user_input_file):
tree = ClusterTree(user_input_file)
leaves = tree.get_leaf_names()
ts = TreeStyle()
ts.show_leaf_name = True
ts.show_branch_length = True
ts.show_branch_support = True
leaf_dict = {}
# Convert leaves (a list) into a dictionary
for i in range(len(leaves)):
leaf_dict[leaves[i]] = i
i = i + 1
# Cast dictionary attributes as list and create index labels
k = list(leaf_dict.keys())
v = list(leaf_dict.values())
w = list(leaf_dict.items())
leaf_labels = [k[v.index(j)] for j in range(0, len(w))]
# Create a numpy array of zeros based on the number of taxa in the tree
dmat = np.zeros((len(leaves), len(leaves)))
print('Converting input tree:')
# Compute distance matrix from newick tree (this is not yet a linked distance matrix)
for l1, l2 in tqdm(combinations(leaves, 2)):
d = tree.get_distance(l1, l2)
dmat[leaf_dict[l1], leaf_dict[l2]] = dmat[leaf_dict[l2],
leaf_dict[l1]] = d
# Convert dmat into a linkage distance matrix for scipy
schlink = sch.linkage(scipy.spatial.distance.squareform(dmat),
method='average',
metric='euclidean')
return dmat, schlink
def newick_to_linkage(filePath):
""" converts newick tree to scipy linkage matrix """
tree = ClusterTree(filePath)
leaves = tree.get_leaf_names()
ts = TreeStyle()
ts.show_leaf_name = True
ts.show_branch_length = True
ts.show_branch_support = True
idx_dict = {}
idx = 0
for leaf in leaves:
idx_dict[leaf] = idx
idx += 1
idx_labels = [idx_dict.keys()[idx_dict.values().index(i)] for i in range(len(idx_dict))]
dmat = np.zeros((len(leaves), len(leaves))) # FIXME need to understand
for leaf1, leaf2 in combinations(leaves, 2):
d = tree.get_distance(leaf1, leaf2)
dmat[idx_dict[leaf1], idx_dict[leaf2]] = dmat[idx_dict[leaf2], idx_dict[leaf1]] = d
schlink = sch.linkage(scipy.spatial.distance.squareform(dmat),method='average',metric='euclidean')
