def wrfdist_task(newick_string_a, newick_string_b, normalise):
"""
Celery-distributed version of tree_distance.rfdist
Parameters: two valid newick strings and a boolean
"""
try:
tree_a = Tree(newick_string_a)
tree_b = Tree(newick_string_b)
return treedist.wrfdist(tree_a, tree_b, normalise)
except Exception as exc:
wrfdist_task.retry(exc=exc, countdown=1, max_retries=5)
def get_tree_collection_strings(self, scale=1):
""" Function to get input strings for tree_collection
tree_collection needs distvar, genome_map and labels -
these are returned in the order above
"""
# aliases
num_matrices = len(self.distances)
label_set = reduce(lambda x, y: x.union(y), (set(l) for l in self.headers))
labels_len = len(label_set)
# labels string can be built straight away
labels_string = '{0}\n{1}\n'.format(labels_len, ' '.join(label_set))
# distvar and genome_map need to be built up
distvar_list = [str(num_matrices)]
genome_map_list = ['{0} {1}'.format(num_matrices, labels_len)]
# build up lists to turn into strings
for i in range(num_matrices):
labels = self.headers[i]
dim = len(labels)
dmatrix = np.array(self.distances[i])
vmatrix = np.array(self.variances[i])
matrix = np.zeros(dmatrix.shape)
matrix[np.triu_indices(len(dmatrix), 1)] = dmatrix[np.triu_indices(len(dmatrix), 1)]
matrix[np.tril_indices(len(vmatrix), -1)] = vmatrix[np.tril_indices(len(vmatrix), -1)]
if scale:
matrix[np.triu_indices(dim, 1)] *= scale
matrix[np.tril_indices(dim, -1)] *= scale * scale
if isinstance(matrix, np.ndarray):
matrix_string = '\n'.join([' '.join(str(x) for x in row)
for row in matrix]) + '\n'
else:
matrix_string = matrix
distvar_list.append('{0} {0} {1}\n{2}'.format(dim, i + 1,
matrix_string))
genome_map_entry = ' '.join((str(labels.index(lab) + 1)
if lab in labels else '-1')
for lab in label_set)
genome_map_list.append(genome_map_entry)
distvar_string = '\n'.join(distvar_list)
genome_map_string = '\n'.join(genome_map_list)
guide_tree = Tree(self.mrp_tree)
for e in guide_tree.postorder_edge_iter():
if e.length is None:
if e.head_node == guide_tree.seed_node:
e.length = 0.0
else:
e.length = np.random.uniform()
if not guide_tree.is_rooted:
guide_tree.reroot_at_midpoint()
if not guide_tree.is_rooted:
raise Exception('Couldn\'t root the guide tree')
tree_string = guide_tree.scale(scale).newick
return distvar_string, genome_map_string, labels_string, tree_string
def get_tree_collection_strings(self, scale=1):
""" Function to get input strings for tree_collection
tree_collection needs distvar, genome_map and labels -
these are returned in the order above
"""
# aliases
num_matrices = len(self.distances)
label_set = reduce(lambda x, y: x.union(y),
(set(l) for l in self.headers))
labels_len = len(label_set)
# labels string can be built straight away
labels_string = '{0}\n{1}\n'.format(labels_len, ' '.join(label_set))
# distvar and genome_map need to be built up
distvar_list = [str(num_matrices)]
genome_map_list = ['{0} {1}'.format(num_matrices, labels_len)]
# build up lists to turn into strings
for i in range(num_matrices):
labels = self.headers[i]
dim = len(labels)
dmatrix = np.array(self.distances[i])
vmatrix = np.array(self.variances[i])
matrix = np.zeros(dmatrix.shape)
matrix[np.triu_indices(len(dmatrix), 1)] = dmatrix[np.triu_indices(
len(dmatrix), 1)]
matrix[np.tril_indices(len(vmatrix),
-1)] = vmatrix[np.tril_indices(
len(vmatrix), -1)]
if scale:
matrix[np.triu_indices(dim, 1)] *= scale
matrix[np.tril_indices(dim, -1)] *= scale * scale
if isinstance(matrix, np.ndarray):
matrix_string = '\n'.join(
[' '.join(str(x) for x in row) for row in matrix]) + '\n'
else:
matrix_string = matrix
distvar_list.append('{0} {0} {1}\n{2}'.format(
dim, i + 1, matrix_string))
genome_map_entry = ' '.join(
(str(labels.index(lab) + 1) if lab in labels else '-1')
for lab in label_set)
genome_map_list.append(genome_map_entry)
distvar_string = '\n'.join(distvar_list)
genome_map_string = '\n'.join(genome_map_list)
guide_tree = Tree(self.mrp_tree)
for e in guide_tree.postorder_edge_iter():
if e.length is None:
if e.head_node == guide_tree.seed_node:
e.length = 0.0
else:
e.length = np.random.uniform()
if not guide_tree.is_rooted:
guide_tree.reroot_at_midpoint()
if not guide_tree.is_rooted:
raise Exception('Couldn\'t root the guide tree')
tree_string = guide_tree.scale(scale).newick
return distvar_string, genome_map_string, labels_string, tree_string
def minsq_task(dv, gm, lab, tree, niters=10):
tree, sse = tree_collection.compute(dv, gm, lab, tree, niters, False, True)
tree = Tree(tree)
tree.deroot()
return dict(tree=tree.newick, sse=sse)
def minsq_task(dv, gm, lab, tree, niters=10):
tree, sse = tree_collection.compute(dv, gm, lab, tree, niters, False, True)
tree = Tree(tree)
tree.deroot()
return dict(tree=tree.newick, sse=sse)