def simulate_branch_path(tree, node):
"""
Simulate the nucleotide history on the path between a node and its parent.
This simulated path is conditional on known values at each node.
Purines are red; pyrimidines are blue.
A and T are brighter; G and C are darker.
@param tree: a SpatialTree with simulated nucleotides at each node
@param node: the node that defines the branch on which to simulate a history
"""
nucleotide_to_color = {
'A': 'FF4444',
'G': 'FF8888',
'T': '4444FF',
'C': '8888FF'
}
node.branch_color = nucleotide_to_color[node.state]
rate_matrix = RateMatrix.get_jukes_cantor_rate_matrix()
initial_state = node.parent.state
terminal_state = node.state
states = 'ACGT'
events = None
while events is None:
events = PathSampler.get_nielsen_sample(initial_state, terminal_state,
states, node.blen, rate_matrix)
parent = node.parent
last_t = 0
for t, state in events:
new = SpatialTree.SpatialTreeNode()
new.name = node.name
new.state = state
new.branch_color = nucleotide_to_color[parent.state]
tree.insert_node(new, parent, node, (t - last_t) / float(node.blen))
last_t = t
parent = new
def simulate_branch_path(tree, node):
"""
Simulate the nucleotide history on the path between a node and its parent.
This simulated path is conditional on known values at each node.
Purines are red; pyrimidines are blue.
A and T are brighter; G and C are darker.
@param tree: a SpatialTree with simulated nucleotides at each node
@param node: the node that defines the branch on which to simulate a history
"""
nucleotide_to_color = {
'A':'FF4444', 'G':'FF8888', 'T':'4444FF', 'C':'8888FF'}
node.branch_color = nucleotide_to_color[node.state]
rate_matrix = RateMatrix.get_jukes_cantor_rate_matrix()
initial_state = node.parent.state
terminal_state = node.state
states = 'ACGT'
events = None
while events is None:
events = PathSampler.get_nielsen_sample(
initial_state, terminal_state, states, node.blen, rate_matrix)
parent = node.parent
last_t = 0
for t, state in events:
new = SpatialTree.SpatialTreeNode()
new.name = node.name
new.state = state
new.branch_color = nucleotide_to_color[parent.state]
tree.insert_node(new, parent, node, (t - last_t) / float(node.blen))
last_t = t
parent = new
def simulate_branch_path(tree, node, rate_matrix_object):
# purines are red; pyrimidines are blue
# A and T are brighter, G and C are darker
nt_to_color = {'A':'ff4444', 'G':'ffaaaa', 'T':'4444ff', 'C':'aaaaff'}
node.branch_color = nt_to_color[node.state]
rate_matrix = rate_matrix_object.dictionary_rate_matrix
initial_state = node.parent.state
terminal_state = node.state
states = 'ACGT'
events = None
while events is None:
events = PathSampler.get_nielsen_sample(
initial_state, terminal_state, states, node.blen, rate_matrix)
parent = node.parent
last_t = 0
for t, state in events:
new = SpatialTree.SpatialTreeNode()
new.name = node.name
new.state = state
new.branch_color = nt_to_color[parent.state]
tree.insert_node(new, parent, node, (t - last_t) / float(node.blen))
last_t = t
parent = new
