def annotate_tree_depths(tree: CassiopeiaTree) -> None:
"""Annotates tree depth at every node.
Adds two attributes to the tree: how far away each node is from the root of
the tree and how many triplets are rooted at that node. Modifies the tree
in place.
Args:
tree: An ete3 Tree
Returns:
A dictionary mapping depth to the list of nodes at that depth.
"""
depth_to_nodes = defaultdict(list)
for n in tree.depth_first_traverse_nodes(source=tree.root,
postorder=False):
if tree.is_root(n):
tree.set_attribute(n, "depth", 0)
else:
tree.set_attribute(n, "depth",
tree.get_attribute(tree.parent(n), "depth") + 1)
depth_to_nodes[tree.get_attribute(n, "depth")].append(n)
number_of_leaves = 0
correction = 0
for child in tree.children(n):
number_of_leaves += len(tree.leaves_in_subtree(child))
correction += nCr(len(tree.leaves_in_subtree(child)), 3)
tree.set_attribute(n, "number_of_triplets",
nCr(number_of_leaves, 3) - correction)
return depth_to_nodes
def fitch_hartigan_bottom_up(
cassiopeia_tree: CassiopeiaTree,
meta_item: str,
add_key: str = "S1",
copy: bool = False,
) -> Optional[CassiopeiaTree]:
"""Performs Fitch-Hartigan bottom-up ancestral reconstruction.
Performs the bottom-up phase of the Fitch-Hartigan small parsimony
algorithm. A new attribute called "S1" will be added to each node
storing the optimal set of ancestral states inferred from this bottom-up
algorithm. If copy is False, the tree will be modified in place.
Args:
cassiopeia_tree: CassiopeiaTree object with cell meta data.
meta_item: A column in the CassiopeiaTree cell meta corresponding to a
categorical variable.
add_key: Key to add for bottom-up reconstruction
copy: Modify the tree in place or not.
Returns:
A new CassiopeiaTree if the copy is set to True, else None.
Raises:
CassiopeiaError if the tree does not have the specified meta data
or the meta data is not categorical.
"""
if meta_item not in cassiopeia_tree.cell_meta.columns:
raise CassiopeiaError(
"Meta item does not exist in the cassiopeia tree")
meta = cassiopeia_tree.cell_meta[meta_item]
if is_numeric_dtype(meta):
raise CassiopeiaError("Meta item is not a categorical variable.")
if not is_categorical_dtype(meta):
meta = meta.astype("category")
cassiopeia_tree = cassiopeia_tree.copy() if copy else cassiopeia_tree
for node in cassiopeia_tree.depth_first_traverse_nodes():
if cassiopeia_tree.is_leaf(node):
cassiopeia_tree.set_attribute(node, add_key, [meta.loc[node]])
else:
children = cassiopeia_tree.children(node)
if len(children) == 1:
child_assignment = cassiopeia_tree.get_attribute(
children[0], add_key)
cassiopeia_tree.set_attribute(node, add_key,
[child_assignment])
all_labels = np.concatenate([
cassiopeia_tree.get_attribute(child, add_key)
for child in children
])
states, frequencies = np.unique(all_labels, return_counts=True)
S1 = states[np.where(frequencies == np.max(frequencies))]
cassiopeia_tree.set_attribute(node, add_key, S1)
return cassiopeia_tree if copy else None
def place_tree(
tree: CassiopeiaTree,
depth_key: Optional[str] = None,
orient: Union[Literal["down", "up", "left", "right"], float] = "down",
depth_scale: float = 1.0,
width_scale: float = 1.0,
extend_branches: bool = True,
angled_branches: bool = True,
polar_interpolation_threshold: float = 5.0,
polar_interpolation_step: float = 1.0,
add_root: bool = False,
) -> Tuple[Dict[str, Tuple[float, float]], Dict[Tuple[str, str], Tuple[
List[float], List[float]]], ]:
"""Given a tree, computes the coordinates of the nodes and branches.
This function computes the x and y coordinates of all nodes and branches (as
lines) to be used for visualization. Several options are provided to
modify how the elements are placed. This function returns two dictionaries,
where the first has nodes as keys and an (x, y) tuple as the values.
Similarly, the second dictionary has (parent, child) tuples as keys denoting
branches in the tree and a tuple (xs, ys) of two lists containing the x and
y coordinates to draw the branch.
This function also provides functionality to place the tree in polar
coordinates as a circular plot when the `orient` is a number. In this case,
the returned dictionaries have (thetas, radii) as its elements, which are
the angles and radii in polar coordinates respectively.
Note:
This function only *places* (i.e. computes the x and y coordinates) a
tree on a coordinate system and does no real plotting.
Args:
tree: The CassiopeiaTree to place on the coordinate grid.
depth_key: The node attribute to use as the depth of the nodes. If
not provided, the distances from the root is used by calling
`tree.get_distances`.
orient: The orientation of the tree. Valid arguments are `left`, `right`,
`up`, `down` to display a rectangular plot (indicating the direction
of going from root -> leaves) or any number, in which case the
tree is placed in polar coordinates with the provided number used
as an angle offset.
depth_scale: Scale the depth of the tree by this amount. This option
has no effect in polar coordinates.
width_scale: Scale the width of the tree by this amount. This option
has no effect in polar coordinates.
extend_branches: Extend branch lengths such that the distance from the
root to every node is the same. If `depth_key` is also provided, then
only the leaf branches are extended to the deepest leaf.
angled_branches: Display branches as angled, instead of as just a
line from the parent to a child.
polar_interpolation_threshold: When displaying in polar coordinates,
many plotting frameworks (such as Plotly) just draws a straight line
from one point to another, instead of scaling the radius appropriately.
This effect is most noticeable when two points are connected that
have a large angle between them. When the angle between two connected
points in polar coordinates exceed this amount (in degrees), this function
adds additional points that smoothly interpolate between the two
endpoints.
polar_interpolation_step: Interpolation step. See above.
add_root: Add a root node so that only one branch connects to the
start of the tree. This node will have the name `synthetic_root`.
Returns:
Two dictionaries, where the first contains the node coordinates and
the second contains the branch coordinates.
"""
root = tree.root
nodes = tree.nodes.copy()
edges = tree.edges.copy()
depths = None
if depth_key:
depths = {
node: tree.get_attribute(node, depth_key)
for node in tree.nodes
}
else:
depths = tree.get_distances(root)
placement_depths = {}
positions = {}
leaf_i = 0
leaves = set()
for node in tree.depth_first_traverse_nodes(postorder=False):
if tree.is_leaf(node):
positions[node] = leaf_i
leaf_i += 1
leaves.add(node)
placement_depths[node] = depths[node]
if extend_branches:
placement_depths[node] = (max(depths.values())
if depth_key else 0)
# Place nodes by depth
for node in sorted(depths, key=lambda k: depths[k], reverse=True):
# Leaves have already been placed
if node in leaves:
continue
# Find all immediate children and place at center.
min_position = np.inf
max_position = -np.inf
min_depth = np.inf
for child in tree.children(node):
min_position = min(min_position, positions[child])
max_position = max(max_position, positions[child])
min_depth = min(min_depth, placement_depths[child])
positions[node] = (min_position + max_position) / 2
placement_depths[node] = (min_depth - 1 if extend_branches
and not depth_key else depths[node])
# Add synthetic root
if add_root:
root_name = "synthetic_root"
positions[root_name] = 0
placement_depths[root_name] = min(placement_depths.values()) - 1
nodes.append(root_name)
edges.append((root_name, root))
polar = isinstance(orient, (float, int))
polar_depth_offset = -min(placement_depths.values())
polar_angle_scale = 360 / (len(leaves) + 1)
# Define some helper functions to modify coordinate system.
def reorient(pos, depth):
pos *= width_scale
depth *= depth_scale
if orient == "down":
return (pos, -depth)
elif orient == "right":
return (depth, pos)
elif orient == "left":
return (-depth, pos)
# default: up
return (pos, depth)
def polarize(pos, depth):
# angle, radius
return (
(pos + 1) * polar_angle_scale + orient,
depth + polar_depth_offset,
)
node_coords = {}
for node in nodes:
pos = positions[node]
depth = placement_depths[node]
coords = polarize(pos, depth) if polar else reorient(pos, depth)
node_coords[node] = coords
branch_coords = {}
for parent, child in edges:
parent_pos, parent_depth = positions[parent], placement_depths[parent]
child_pos, child_depth = positions[child], placement_depths[child]
middle_x = (child_pos if angled_branches else
(parent_pos + child_pos) / 2)
middle_y = (parent_depth if angled_branches else
(parent_depth + child_depth) / 2)
_xs = [parent_pos, middle_x, child_pos]
_ys = [parent_depth, middle_y, child_depth]
xs = []
ys = []
for _x, _y in zip(_xs, _ys):
if polar:
x, y = polarize(_x, _y)
if xs:
# Interpolate to prevent weird angles if the angle exceeds threshold.
prev_x = xs[-1]
prev_y = ys[-1]
if abs(x - prev_x) > polar_interpolation_threshold:
num = int(abs(x - prev_x) / polar_interpolation_step)
for inter_x, inter_y in zip(
np.linspace(prev_x, x, num)[1:-1],
np.linspace(prev_y, y, num)[1:-1],
):
xs.append(inter_x)
ys.append(inter_y)
else:
x, y = reorient(_x, _y)
xs.append(x)
ys.append(y)
branch_coords[(parent, child)] = (xs, ys)
return node_coords, branch_coords
def compute_expansion_pvalues(
tree: CassiopeiaTree,
min_clade_size: int = 10,
min_depth: int = 1,
copy: bool = False,
) -> Union[CassiopeiaTree, None]:
"""Call expansion pvalues on a tree.
Uses the methodology described in Yang, Jones et al, BioRxiv (2021) to
assess the expansion probability of a given subclade of a phylogeny.
Mathematical treatment of the coalescent probability is described in
Griffiths and Tavare, Stochastic Models (1998).
The probability computed corresponds to the probability that, under a simple
neutral coalescent model, a given subclade contains the observed number of
cells; in other words, a one-sided p-value. Often, if the probability is
less than some threshold (e.g., 0.05), this might indicate that there exists
some subclade under this node that to which this expansion probability can
be attributed (i.e. the null hypothesis that the subclade is undergoing
neutral drift can be rejected).
This function will add an attribute "expansion_pvalue" to the tree, and
return None unless :param:`copy` is set to True.
On a typical balanced tree, this function will perform in O(n log n) time,
but can be up to O(n^3) on highly unbalanced trees. A future endeavor may
be to impelement the function in O(n) time.
Args:
tree: CassiopeiaTree
min_clade_size: Minimum number of leaves in a subtree to be considered.
min_depth: Minimum depth of clade to be considered. Depth is measured
in number of nodes from the root, not branch lengths.
copy: Return copy.
Returns:
If copy is set to False, returns the tree with attributes added
in place. Else, returns a new CassiopeiaTree.
"""
tree = tree.copy() if copy else tree
# instantiate attributes
_depths = {}
for node in tree.depth_first_traverse_nodes(postorder=False):
tree.set_attribute(node, "expansion_pvalue", 1.0)
if tree.is_root(node):
_depths[node] = 0
else:
_depths[node] = _depths[tree.parent(node)] + 1
for node in tree.depth_first_traverse_nodes(postorder=False):
n = len(tree.leaves_in_subtree(node))
k = len(tree.children(node))
for c in tree.children(node):
if len(tree.leaves_in_subtree(c)) < min_clade_size:
continue
depth = _depths[c]
if depth < min_depth:
continue
b = len(tree.leaves_in_subtree(c))
# this value below is a simplification of the quantity:
# sum[simple_coalescent_probability(n, b2, k) for \
# b2 in range(b, n - k + 2)]
p = nCk(n - b, k - 1) / nCk(n - 1, k - 1)
tree.set_attribute(c, "expansion_pvalue", p)
return tree if copy else None
def log_likelihood_of_character(
tree: CassiopeiaTree,
character: int,
use_internal_character_states: bool,
mutation_probability_function_of_time: Callable[[float], float],
missing_probability_function_of_time: Callable[[float], float],
stochastic_missing_probability: float,
implicit_root_branch_length: float,
) -> float:
"""Calculates the log likelihood of a given character on the tree.
Calculates the log likelihood of a tree given the states at a given
character in the leaves using Felsenstein's Pruning Algorithm, which sets
up a recursive relation between the likelihoods of states at nodes for this
character. The likelihood L(s, n) at a given state s at a given node n is:
L(s, n) = Π_{n'}(Σ_{s'}(P(s'|s) * L(s', n')))
for all n' that are children of n, and s' in the state space, with
P(s'|s) being the transition probability from s to s'. That is,
the likelihood at a given state at a given node is the product of
the likelihoods of the states at this character at the children scaled by
the probability of the current state transitioning to those states. This
includes the missing state, as specified by `tree.missing_state_indicator`.
We assume here that mutations are irreversible. Once a character mutates to
a certain state that character cannot mutate again, with the exception of
the fact that any non-missing state can mutate to a missing state.
`mutation_probability_function_of_time` is expected to be a function that
determine the probability of a mutation occuring given an amount of time.
To determine the probability of acquiring a given (non-missing) state once
a mutation occurs, the priors of the tree are used. Likewise,
`missing_probability_function_of_time` determines the the probability of a
missing data event occuring given an amount of time.
The user can choose to use the character states annotated at internal
nodes. If these are not used, then the likelihood is marginalized over
all possible internal state characters. If the actual internal states
are not provided, then the root is assumed to have the unmutated state
at each character. Additionally, it is assumed that there is a single
branch leading from the root that represents the roots' lifetime. If
this branch does not exist and `use_internal_character_states` is set
to False, then this branch is added with branch length equal to the
average branch length of this tree.
Args:
tree: The tree on which to calculate the likelihood
character: The index of the character to calculate the likelihood of
use_internal_character_states: Indicates if internal node
character states should be assumed to be specified exactly
mutation_probability_function_of_time: The function defining the
probability of a lineage acquiring a mutation within a given time
missing_probability_function_of_time: The function defining the
probability of a lineage acquiring heritable missing data within a
given time
stochastic_missing_probability: The probability that a cell/character
pair acquires stochastic missing data at the end of the lineage
implicit_root_branch_length: The length of the implicit root branch.
Used if the implicit root needs to be added
Returns:
The log likelihood of the tree on one character
"""
# This dictionary uses a nested dictionary structure. Each node is mapped
# to a dictionary storing the likelihood for each possible state
# (states that have non-0 likelihood)
likelihoods_at_nodes = {}
# Perform a DFS to propagate the likelihood from the leaves
for n in tree.depth_first_traverse_nodes(postorder=True):
state_at_n = tree.get_character_states(n)
# If states are observed, their likelihoods are set to 1
if tree.is_leaf(n):
likelihoods_at_nodes[n] = {state_at_n[character]: 0}
continue
possible_states = []
# If internal character states are to be used, then the likelihood
# for all other states are ignored. Otherwise, marginalize over
# only states that do not break irreversibility, as all states that
# do have likelihood of 0
if use_internal_character_states:
possible_states = [state_at_n[character]]
else:
child_possible_states = []
for c in [
set(likelihoods_at_nodes[child])
for child in tree.children(n)
]:
if tree.missing_state_indicator not in c and "&" not in c:
child_possible_states.append(c)
# "&" stands in for any non-missing state (including uncut), and
# is a possible state when all children are missing, as any
# state could have occurred at the parent if all missing data
# events occurred independently. Used to avoid marginalizing
# over the entire state space.
if child_possible_states == []:
possible_states = [
"&",
tree.missing_state_indicator,
]
else:
possible_states = list(
set.intersection(*child_possible_states))
if 0 not in possible_states:
possible_states.append(0)
# This stores the likelihood of each possible state at the current node
likelihoods_per_state_at_n = {}
# We calculate the likelihood of the states at the current node
# according to the recurrence relation. For each state, we marginalize
# over the likelihoods of the states that it could transition to in the
# daughter nodes
for s in possible_states:
likelihood_for_s = 0
for child in tree.children(n):
likelihoods_for_s_marginalize_over_s_ = []
for s_ in likelihoods_at_nodes[child]:
likelihood_s_ = (log_transition_probability(
tree,
character,
s,
s_,
tree.get_branch_length(n, child),
mutation_probability_function_of_time,
missing_probability_function_of_time,
) + likelihoods_at_nodes[child][s_])
# Here we take into account the probability of
# stochastic missing data
if tree.is_leaf(child):
if (s_ == tree.missing_state_indicator
and s != tree.missing_state_indicator):
likelihood_s_ = np.log(
np.exp(likelihood_s_) +
(1 - missing_probability_function_of_time(
tree.get_branch_length(n, child))) *
stochastic_missing_probability)
if s_ != tree.missing_state_indicator:
likelihood_s_ += np.log(
1 - stochastic_missing_probability)
likelihoods_for_s_marginalize_over_s_.append(likelihood_s_)
likelihood_for_s += scipy.special.logsumexp(
np.array(likelihoods_for_s_marginalize_over_s_))
likelihoods_per_state_at_n[s] = likelihood_for_s
likelihoods_at_nodes[n] = likelihoods_per_state_at_n
# If we are not to use the internal state annotations explicitly,
# then we assume an implicit root where each state is the uncut state (0)
# Thus, we marginalize over the transition from 0 in the implicit root
# to all non-0 states in its child
if not use_internal_character_states:
# If the implicit root does not exist in the tree, then we impose it,
# with the length of the branch being specified as
# `implicit_root_branch_length`. Otherwise, we just use the existing
# root with a singleton child as the implicit root
if len(tree.children(tree.root)) != 1:
likelihood_contribution_from_each_root_state = [
log_transition_probability(
tree,
character,
0,
s_,
implicit_root_branch_length,
mutation_probability_function_of_time,
missing_probability_function_of_time,
) + likelihoods_at_nodes[tree.root][s_]
for s_ in likelihoods_at_nodes[tree.root]
]
likelihood_at_implicit_root = scipy.special.logsumexp(
likelihood_contribution_from_each_root_state)
return likelihood_at_implicit_root
else:
# Here we account for the edge case in which all of the leaves are
# missing, in which case the root will have "&" in place of 0. The
# likelihood at "&" will have the same likelihood as 0 based on the
# transition rules regarding "&". As "&" is a placeholder when the
# state is unknown, this can be thought of realizing "&" as 0.
if 0 not in likelihoods_at_nodes[tree.root]:
return likelihoods_at_nodes[tree.root]["&"]
else:
# Otherwise, we return the likelihood of the 0 state at the
# existing implicit root
return likelihoods_at_nodes[tree.root][0]
# If we use the internal state annotations explicitly, then we return
# the likelihood of the state annotated at this character at the root
else:
return list(likelihoods_at_nodes[tree.root].values())[0]
def percolate(
self,
character_matrix: pd.DataFrame,
samples: List[str],
priors: Optional[Dict[int, Dict[int, float]]] = None,
weights: Optional[Dict[int, Dict[int, float]]] = None,
missing_state_indicator: int = -1,
) -> Tuple[List[str], List[str]]:
"""The function used by the percolation algorithm to partition the
set of samples in two.
First, a pairwise similarity graph is generated with samples as nodes
such that edges between a pair of nodes is some provided function on
the number of character/state mutations shared. Then, the algorithm
removes the minimum edge (in the case of ties all are removed) until
the graph is split into multiple connected components. If there are more
than two connected components, the procedure joins them until two remain.
This is done by inferring the mutations of the LCA of each sample set
obeying Camin-Sokal Parsimony, and then clustering the groups of samples
based on their LCAs. The provided solver is used to cluster the groups
into two clusters.
Args:
character_matrix: Character matrix
samples: A list of samples to partition
priors: A dictionary storing the probability of each character
mutating to a particular state.
weights: Weighting of each (character, state) pair. Typically a
transformation of the priors.
missing_state_indicator: Character representing missing data.
Returns:
A tuple of lists, representing the left and right partition groups
"""
sample_indices = solver_utilities.convert_sample_names_to_indices(
character_matrix.index, samples)
unique_character_array = character_matrix.to_numpy()
G = nx.Graph()
G.add_nodes_from(sample_indices)
# Add edge weights into the similarity graph
edge_weight_buckets = defaultdict(list)
for i, j in itertools.combinations(sample_indices, 2):
similarity = self.similarity_function(
unique_character_array[i, :],
unique_character_array[j, :],
missing_state_indicator,
weights,
)
if similarity > self.threshold:
edge_weight_buckets[similarity].append((i, j))
G.add_edge(i, j)
if len(G.edges) == 0:
return samples, []
connected_components = list(nx.connected_components(G))
sorted_edge_weights = sorted(edge_weight_buckets, reverse=True)
# Percolate the similarity graph by continuously removing the minimum
# edge until at least two components exists
while len(connected_components) <= 1:
min_weight = sorted_edge_weights.pop()
for edge in edge_weight_buckets[min_weight]:
G.remove_edge(edge[0], edge[1])
connected_components = list(nx.connected_components(G))
# If the number of connected components > 2, merge components by
# joining the most similar LCAs of each component until
# only 2 remain
partition_sides = []
if len(connected_components) > 2:
for c in range(len(connected_components)):
connected_components[c] = list(connected_components[c])
lcas = {}
component_to_nodes = {}
# Find the LCA of the nodes in each connected component
for ind in range(len(connected_components)):
component_identifier = "component" + str(ind)
component_to_nodes[
component_identifier] = connected_components[ind]
character_vectors = [
list(i) for i in list(unique_character_array[
connected_components[ind], :])
]
lcas[component_identifier] = data_utilities.get_lca_characters(
character_vectors, missing_state_indicator)
# Build a tree on the LCA characters to cluster the components
lca_tree = CassiopeiaTree(
pd.DataFrame.from_dict(lcas, orient="index"),
missing_state_indicator=missing_state_indicator,
priors=priors,
)
self.joining_solver.solve(lca_tree,
collapse_mutationless_edges=False)
grouped_components = []
# Take the split at the root as the clusters of components
# in the split, ignoring unifurcations
current_node = lca_tree.root
while len(grouped_components) == 0:
successors = lca_tree.children(current_node)
if len(successors) == 1:
current_node = successors[0]
else:
for i in successors:
grouped_components.append(
lca_tree.leaves_in_subtree(i))
# For each component in each cluster, take the nodes in that
# component to form the final split
for cluster in grouped_components:
sample_index_group = []
for component in cluster:
sample_index_group.extend(component_to_nodes[component])
partition_sides.append(sample_index_group)
else:
for c in range(len(connected_components)):
partition_sides.append(list(connected_components[c]))
# Convert from component indices back to the sample names in the
# original character matrix
sample_names = list(character_matrix.index)
partition_named = []
for sample_index_group in partition_sides:
sample_name_group = []
for sample_index in sample_index_group:
sample_name_group.append(sample_names[sample_index])
partition_named.append(sample_name_group)
return partition_named
def sample_triplet_at_depth(
tree: CassiopeiaTree,
depth: int,
depth_to_nodes: Optional[Dict[int, List[str]]] = None,
) -> Tuple[List[int], str]:
"""Samples a triplet at a given depth.
Samples a triplet of leaves such that the depth of the LCA of the triplet
is at the specified depth.
Args:
tree: CassiopeiaTree
depth: Depth at which to sample the triplet
depth_to_nodes: An optional dictionary that maps a depth to the nodes
that appear at that depth. This speeds up the function considerably.
Returns:
A list of three leaves corresponding to the triplet name of the outgroup
of the triplet.
"""
if depth_to_nodes is None:
candidate_nodes = tree.filter_nodes(
lambda x: tree.get_attribute(x, "depth") == depth)
else:
candidate_nodes = depth_to_nodes[depth]
total_triplets = sum(
[tree.get_attribute(v, "number_of_triplets") for v in candidate_nodes])
# sample a node from this depth with probability proportional to the number
# of triplets underneath it
probs = [
tree.get_attribute(v, "number_of_triplets") / total_triplets
for v in candidate_nodes
]
node = np.random.choice(candidate_nodes, size=1, replace=False, p=probs)[0]
# Generate the probabilities to sample each combination of 3 daughter clades
# to sample from, proportional to the number of triplets in each daughter
# clade. Choices include all ways to choose 3 different daughter clades
# or 2 from one daughter clade and one from another
probs = []
combos = []
denom = 0
for (i, j, k) in itertools.combinations_with_replacement(
list(tree.children(node)), 3):
if i == j and j == k:
continue
combos.append((i, j, k))
size_of_i = len(tree.leaves_in_subtree(i))
size_of_j = len(tree.leaves_in_subtree(j))
size_of_k = len(tree.leaves_in_subtree(k))
val = 0
if i == j:
val = nCr(size_of_i, 2) * size_of_k
elif j == k:
val = nCr(size_of_j, 2) * size_of_i
elif i == k:
val = nCr(size_of_k, 2) * size_of_j
else:
val = size_of_i * size_of_j * size_of_k
probs.append(val)
denom += val
probs = [val / denom for val in probs]
# choose daughter clades
ind = np.random.choice(range(len(combos)), size=1, replace=False,
p=probs)[0]
(i, j, k) = combos[ind]
if i == j:
in_group = np.random.choice(tree.leaves_in_subtree(i),
2,
replace=False)
out_group = np.random.choice(tree.leaves_in_subtree(k))
elif j == k:
in_group = np.random.choice(tree.leaves_in_subtree(j),
2,
replace=False)
out_group = np.random.choice(tree.leaves_in_subtree(i))
elif i == k:
in_group = np.random.choice(tree.leaves_in_subtree(k), 2, replace=True)
out_group = np.random.choice(tree.leaves_in_subtree(j))
else:
return (
(
str(np.random.choice(tree.leaves_in_subtree(i))),
str(np.random.choice(tree.leaves_in_subtree(j))),
str(np.random.choice(tree.leaves_in_subtree(k))),
),
"None",
)
return (str(in_group[0]), str(in_group[1]), str(out_group)), out_group