def add_boolean(self, node, trait, boolean_trait_name, regex):
if node.annotations.get_value(trait) is not None:
if regex.match(str(node.annotations.get_value(trait))):
safeNodeAnnotator.annotate(node, boolean_trait_name, True)
else:
safeNodeAnnotator.annotate(node, boolean_trait_name, False)
def annotate_tips(self, annotations):
for node in self.tree.leaf_node_iter():
for ak, av in annotations.get(node.taxon.label, {}).items():
if av and (type(av) is str
and len(av) > 0) or type(av) is not str:
safeNodeAnnotator.annotate(node, ak,
check_str_for_bool(av))
def reconstruct_ancestors(self,
node,
parent_states,
acctran,
name,
maxtran_value=None):
node_states = node.annotations.get_value(name) if isinstance(
node.annotations.get_value(name),
list) else [node.annotations.get_value(name)]
children_at_maxtran_value = 0
if maxtran_value is not None and node.num_child_nodes() > 2:
for child in node.child_node_iter():
if child.annotations.get_value(name) == maxtran_value:
children_at_maxtran_value += 1
if node.is_leaf() and len(
node_states) == 1 and node_states[0] is not None:
assigned_states = node_states
elif children_at_maxtran_value > 1:
assigned_states = [maxtran_value]
else:
assigned_states = list(
set(node_states).intersection(parent_states)
) if len(
set(node_states).intersection(parent_states)) > 0 else list(
set(node_states).union(parent_states))
if len(assigned_states) > 1:
if acctran:
assigned_states = [
state for state in assigned_states
if state not in parent_states
]
else:
# can we delay
child_states = []
for child in node.child_node_iter():
child_states += child.annotations.get_value(
name) if isinstance(
child.annotations.get_value(name),
list) else [child.annotations.get_value(name)]
assigned_states = [
state for state in assigned_states
if state in parent_states and state in child_states
] if len(
set(parent_states).intersection(child_states)) > 0 else [
state for state in assigned_states
if state in child_states
]
safeNodeAnnotator.annotate(
node, name, assigned_states[0]
if len(assigned_states) == 1 else assigned_states)
for child in node.child_node_iter():
self.reconstruct_ancestors(child, assigned_states, acctran, name,
maxtran_value)
def action(n):
"""
partially loading this action with the trait_name and value to add to each node
this will be called below on each node if it passes the predicate.
:param n:
:return:
"""
if n.is_leaf():
safeNodeAnnotator.annotate(n, trait_name, value)
def phylotype_nodes(self, node, phylotype):
safeNodeAnnotator.annotate(node, "phylotype", "\"" + phylotype + "\"")
i = 1
for child in node.child_node_iter():
suffix = ""
if child.taxon is None:
if child.edge.length > self.threshold:
suffix = "." + str(i)
i += 1
self.phylotype_nodes(child, phylotype + suffix)
def annotate_node(self, tip_label, annotations):
node = self.tree.find_node_with_taxon(
lambda taxon: True
if self.taxon_parser(taxon.label) == tip_label else False)
if node is None:
warnings.warn("Taxon: %s not found in tree" % tip_label,
UserWarning)
else:
for a in annotations:
if annotations[a] and (type(annotations[a]) is str
and len(annotations[a]) > 0) or type(
annotations[a]) is not str:
safeNodeAnnotator.annotate(
node, a, check_str_for_bool(annotations[a]))
def annotate_mrca(self, trait_name, value):
taxon_set = [
tip.taxon for tip in self.tree.leaf_node_iter(
lambda node: node.annotations.get_value(trait_name) == value)
]
mrca = self.tree.mrca(taxa=taxon_set)
current_annotation = mrca.annotations.get_value("%s-mrca" % trait_name)
if current_annotation is not None:
print("common mrca concatenating %s and %s" %
(current_annotation, value))
safeNodeAnnotator.annotate(mrca, "%s-mrca" % trait_name,
current_annotation + "-" + value)
else:
safeNodeAnnotator.annotate(mrca, "%s-mrca" % trait_name, value)
return mrca
def fitch_parsimony(self, node, name, polytomy_behavior=None):
if len(node.child_nodes()) == 0:
tip_annotation = node.annotations.get_value(
name) if node.annotations.get_value(name) is not None else []
return tip_annotation if isinstance(tip_annotation,
list) else [tip_annotation]
union = set()
intersection = set()
all_states = []
i = 0
for child in node.child_node_iter():
child_states = self.fitch_parsimony(child, name)
union = union.union(child_states)
intersection = set(
child_states) if i == 0 else intersection.intersection(
child_states)
all_states.extend(child_states)
i += 1
value = list(intersection) if len(intersection) > 0 else list(union)
if self.majority_rule and len(intersection) == 0:
if node.num_child_nodes() > 2:
unique_states = list(union)
state_counts = [0 for state in unique_states]
for child_state in all_states:
state_counts[unique_states.index(child_state)] += 1
cutoff = node.num_child_nodes() / 2
majority = [
state for state in unique_states
if state_counts[unique_states.index(state)] > cutoff
]
value = majority if len(majority) > 0 else value
safeNodeAnnotator.annotate(node, "children_" + name,
unique_states)
safeNodeAnnotator.annotate(node,
"children_" + name + "_counts",
state_counts)
elif self.polytomy_tie_breaker is not None and len(
intersection) == 0 and node.num_child_nodes() > 2:
# print("polytomy states :")
# print(list(union))
if any(
[state in self.polytomy_tie_breaker for state in list(union)]):
sorted_states = sorted(
list(union),
key=lambda state: self.polytomy_tie_breaker.index(state)
if state in self.polytomy_tie_breaker else math.inf)
# print("sorted States: ")
# print(sorted_states)
value = [sorted_states[0]] # highest rated state in union
safeNodeAnnotator.annotate(node, name,
value[0] if len(value) == 1 else value)
return value
def annotate_nodes(self, tree, trait_name, trait_value):
for node in tree.postorder_node_iter():
safeNodeAnnotator.annotate(node, trait_name, trait_value)
