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 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 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 calculate_parsimony(
tree: CassiopeiaTree,
infer_ancestral_characters: bool = False,
treat_missing_as_mutation: bool = False,
) -> int:
"""
Calculates the number of mutations that have occurred on a tree.
Calculates the parsimony, defined as the number of character/state
mutations that occur on edges of the tree, from the character state
annotations at the nodes. A mutation is said to have occurred on an
edge if a state is present at a character at the child node and this
state is not in the parent node.
If `infer_ancestral_characters` is set to True, then the internal
nodes' character states are inferred by Camin-Sokal Parsimony from the
current character states at the leaves. Use
`tree.set_character_states_at_leaves` to use a different layer to infer
ancestral states. Otherwise, the current annotations at the internal
states are used. If `treat_missing_as_mutations` is set to True, then
transitions from a non-missing state to a missing state are counted in
the parsimony calculation. Otherwise, they are not included.
Args:
tree: The tree to calculate parsimony over
infer_ancestral_characters: Whether to infer the ancestral
characters states of the tree
treat_missing_as_mutations: Whether to treat missing states as
mutations
Returns:
The number of mutations that have occurred on the tree
Raises:
TreeMetricError if the tree has not been initialized or if
a node does not have character states initialized
"""
if infer_ancestral_characters:
tree.reconstruct_ancestral_characters()
parsimony = 0
if tree.get_character_states(tree.root) == []:
raise TreeMetricError(
f"Character states empty at internal node. Annotate"
" character states or infer ancestral characters by"
" setting infer_ancestral_characters=True.")
for u, v in tree.depth_first_traverse_edges():
if tree.get_character_states(v) == []:
if tree.is_leaf(v):
raise TreeMetricError(
"Character states have not been initialized at leaves."
" Use set_character_states_at_leaves or populate_tree"
" with the character matrix that specifies the leaf"
" character states.")
else:
raise TreeMetricError(
f"Character states empty at internal node. Annotate"
" character states or infer ancestral characters by"
" setting infer_ancestral_characters=True.")
parsimony += len(
tree.get_mutations_along_edge(u, v, treat_missing_as_mutation))
return parsimony
def plot_plotly(
tree: CassiopeiaTree,
depth_key: Optional[str] = None,
meta_data: Optional[List[str]] = None,
allele_table: Optional[pd.DataFrame] = None,
indel_colors: Optional[pd.DataFrame] = None,
indel_priors: Optional[pd.DataFrame] = None,
orient: Union[Literal["up", "down", "left", "right"], float] = 90.0,
extend_branches: bool = True,
angled_branches: bool = True,
add_root: bool = False,
width: float = 500.0,
height: float = 500.0,
colorstrip_width: Optional[float] = None,
colorstrip_spacing: Optional[float] = None,
clade_colors: Optional[Dict[str, Tuple[float, float, float]]] = None,
internal_node_kwargs: Optional[Dict] = None,
leaf_kwargs: Optional[Dict] = None,
branch_kwargs: Optional[Dict] = None,
colorstrip_kwargs: Optional[Dict] = None,
continuous_cmap: Union[str, mpl.colors.Colormap] = "viridis",
vmin: Optional[float] = None,
vmax: Optional[float] = None,
categorical_cmap: Union[str, mpl.colors.Colormap] = "tab10",
value_mapping: Optional[Dict[str, int]] = None,
figure: Optional[go.Figure] = None,
random_state: Optional[np.random.RandomState] = None,
) -> go.Figure:
"""Generate a static plot of a tree using Plotly.
Args:
tree: The CassiopeiaTree to plot.
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`.
meta_data: Meta data to plot alongside the tree, which must be columns
in the CassiopeiaTree.cell_meta variable.
allele_table: Allele table to plot alongside the tree.
indel_colors: Color mapping to use for plotting the alleles for each
cell. Only necessary if `allele_table` is specified.
indel_priors: Prior probabilities for each indel. Only useful if an
allele table is to be plotted and `indel_colors` is None.
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. Defaults to 90.
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.
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`.
width: Width of the figure.
height: Height of the figure.
colorstrip_width: Width of the colorstrip. Width is defined as the
length in the direction of the leaves. Defaults to 5% of the tree
depth.
colorstrip_spacing: Space between consecutive colorstrips. Defaults to
half of `colorstrip_width`.
clade_colors: Dictionary containing internal node-color mappings. These
colors will be used to color all the paths from this node to the
leaves the provided color.
internal_node_kwargs: Keyword arguments to pass to `plt.scatter` when
plotting internal nodes.
leaf_kwargs: Keyword arguments to pass to `plt.scatter` when
plotting leaf nodes.
branch_kwargs: Keyword arguments to pass to `plt.plot` when plotting
branches.
colorstrip_kwargs: Keyword arguments to pass to `plt.fill` when plotting
colorstrips.
continuous_cmap: Colormap to use for continuous variables. Defaults to
`viridis`.
vmin: Value representing the lower limit of the color scale. Only applied
to continuous variables.
vmax: Value representing the upper limit of the color scale. Only applied
to continuous variables.
categorical_cmap: Colormap to use for categorical variables. Defaults to
`tab10`.
value_mapping: An optional dictionary containing string values to their
integer mappings. These mappings are used to assign colors by
calling the `cmap` with the designated integer mapping. By default,
the values are assigned pseudo-randomly (whatever order the set()
operation returns). Only applied for categorical variables.
figure: Plotly figure to plot the tree.
random_state: A random state for reproducibility
Returns:
The Plotly figure.
"""
# Warn user if there are many leaves
if len(tree.leaves) > 2000:
warnings.warn(
"Tree has greater than 2000 leaves. This may take a while.",
PlottingWarning,
)
is_polar = isinstance(orient, (float, int))
(
node_coords,
branch_coords,
node_colors,
branch_colors,
colorstrips,
) = place_tree_and_annotations(
tree,
depth_key,
meta_data,
allele_table,
indel_colors,
indel_priors,
orient,
extend_branches,
angled_branches,
add_root,
colorstrip_width,
colorstrip_spacing,
clade_colors,
continuous_cmap,
vmin,
vmax,
categorical_cmap,
value_mapping,
random_state,
)
figure = figure if figure is not None else go.Figure()
# Plot all nodes
_leaf_kwargs = dict(
x=[],
y=[],
text=[],
marker_size=3,
marker_color="black",
mode="markers",
showlegend=False,
hoverinfo="text",
)
# NOTE: setting marker_size=0 has no effect for some reason?
_node_kwargs = dict(
x=[],
y=[],
text=[],
marker_size=0.1,
marker_color="black",
mode="markers",
showlegend=False,
hoverinfo="text",
)
_leaf_kwargs.update(leaf_kwargs or {})
_node_kwargs.update(internal_node_kwargs or {})
for node, (x, y) in node_coords.items():
if node in node_colors:
continue
text = f"<b>NODE</b><br>{node}"
if is_polar:
x, y = utilities.polar_to_cartesian(x, y)
if tree.is_leaf(node):
_leaf_kwargs["x"].append(x)
_leaf_kwargs["y"].append(y)
_leaf_kwargs["text"].append(text)
else:
_node_kwargs["x"].append(x)
_node_kwargs["y"].append(y)
_node_kwargs["text"].append(text)
figure.add_trace(go.Scatter(**_leaf_kwargs))
figure.add_trace(go.Scatter(**_node_kwargs))
_leaf_colors = []
_node_colors = []
_leaf_kwargs.update({"x": [], "y": [], "text": []})
_node_kwargs.update({"x": [], "y": [], "text": []})
for node, color in node_colors.items():
x, y = node_coords[node]
text = f"<b>NODE</b><br>{node}"
if is_polar:
x, y = utilities.polar_to_cartesian(x, y)
if tree.is_leaf(node):
_leaf_kwargs["x"].append(x)
_leaf_kwargs["y"].append(y)
_leaf_kwargs["text"].append(text)
_leaf_colors.append(color)
else:
_node_kwargs["x"].append(x)
_node_kwargs["y"].append(y)
_node_kwargs["text"].append(text)
_node_colors.append(color)
_leaf_kwargs["marker_color"] = _leaf_colors
_node_kwargs["marker_color"] = _node_colors
figure.add_trace(go.Scatter(**_leaf_kwargs))
figure.add_trace(go.Scatter(**_node_kwargs))
# Plot all branches
_branch_kwargs = dict(
x=[],
y=[],
text=[],
line_color="black",
line_width=1,
mode="lines",
showlegend=False,
hoverinfo="text",
)
_branch_kwargs.update(branch_kwargs or {})
for branch, (xs, ys) in branch_coords.items():
if branch in branch_colors:
continue
_branch_kwargs["x"], _branch_kwargs["y"] = xs, ys
text = f"<b>BRANCH</b><br>{branch[0]}<br>{branch[1]}"
if is_polar:
(
_branch_kwargs["x"],
_branch_kwargs["y"],
) = utilities.polars_to_cartesians(xs, ys)
_branch_kwargs["text"] = [text] * len(xs)
figure.add_trace(go.Scatter(**_branch_kwargs))
for branch, color in branch_colors.items():
xs, ys = branch_coords[branch]
_branch_kwargs["x"], _branch_kwargs["y"] = xs, ys
_branch_kwargs["line_color"] = color
text = f"<b>BRANCH</b><br>{branch[0]}<br>{branch[1]}"
if is_polar:
(
_branch_kwargs["x"],
_branch_kwargs["y"],
) = utilities.polars_to_cartesians(xs, ys)
_branch_kwargs["text"] = [text] * len(xs)
figure.add_trace(go.Scatter(**_branch_kwargs))
# Colorstrips
_colorstrip_kwargs = dict(
x=[],
y=[],
text=[],
line_width=0,
fill="toself",
mode="lines",
showlegend=False,
hoverinfo="text",
hoveron="fills",
)
_colorstrip_kwargs.update(colorstrip_kwargs or {})
for colorstrip in colorstrips:
for xs, ys, c, text in colorstrip.values():
_colorstrip_kwargs["x"], _colorstrip_kwargs["y"] = xs, ys
_colorstrip_kwargs["fillcolor"] = mpl.colors.to_hex(c)
if is_polar:
(
_colorstrip_kwargs["x"],
_colorstrip_kwargs["y"],
) = utilities.polars_to_cartesians(xs, ys)
_colorstrip_kwargs["text"] = text.replace("\n", "<br>")
figure.add_trace(go.Scatter(**_colorstrip_kwargs))
figure.update_layout(
width=width,
height=height,
xaxis=dict(showgrid=False, visible=False),
yaxis=dict(showgrid=False, visible=False),
margin=dict(l=0, r=0, t=0, b=0),
)
return figure
def plot_matplotlib(
tree: CassiopeiaTree,
depth_key: Optional[str] = None,
meta_data: Optional[List[str]] = None,
allele_table: Optional[pd.DataFrame] = None,
indel_colors: Optional[pd.DataFrame] = None,
indel_priors: Optional[pd.DataFrame] = None,
orient: Union[Literal["up", "down", "left", "right"], float] = 90.0,
extend_branches: bool = True,
angled_branches: bool = True,
add_root: bool = False,
figsize: Tuple[float, float] = (7.0, 7.0),
colorstrip_width: Optional[float] = None,
colorstrip_spacing: Optional[float] = None,
clade_colors: Optional[Dict[str, Tuple[float, float, float]]] = None,
internal_node_kwargs: Optional[Dict] = None,
leaf_kwargs: Optional[Dict] = None,
branch_kwargs: Optional[Dict] = None,
colorstrip_kwargs: Optional[Dict] = None,
continuous_cmap: Union[str, mpl.colors.Colormap] = "viridis",
vmin: Optional[float] = None,
vmax: Optional[float] = None,
categorical_cmap: Union[str, mpl.colors.Colormap] = "tab10",
value_mapping: Optional[Dict[str, int]] = None,
ax: Optional[plt.Axes] = None,
random_state: Optional[np.random.RandomState] = None,
) -> Tuple[plt.Figure, plt.Axes]:
"""Generate a static plot of a tree using Matplotlib.
Args:
tree: The CassiopeiaTree to plot.
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`.
meta_data: Meta data to plot alongside the tree, which must be columns
in the CassiopeiaTree.cell_meta variable.
allele_table: Allele table to plot alongside the tree.
indel_colors: Color mapping to use for plotting the alleles for each
cell. Only necessary if `allele_table` is specified.
indel_priors: Prior probabilities for each indel. Only useful if an
allele table is to be plotted and `indel_colors` is None.
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. Defaults to 90.
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.
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`.
figsize: Size of the plot. Defaults to (7., 7.,)
colorstrip_width: Width of the colorstrip. Width is defined as the
length in the direction of the leaves. Defaults to 5% of the tree
depth.
colorstrip_spacing: Space between consecutive colorstrips. Defaults to
half of `colorstrip_width`.
clade_colors: Dictionary containing internal node-color mappings. These
colors will be used to color all the paths from this node to the
leaves the provided color.
internal_node_kwargs: Keyword arguments to pass to `plt.scatter` when
plotting internal nodes.
leaf_kwargs: Keyword arguments to pass to `plt.scatter` when
plotting leaf nodes.
branch_kwargs: Keyword arguments to pass to `plt.plot` when plotting
branches.
colorstrip_kwargs: Keyword arguments to pass to `plt.fill` when plotting
colorstrips.
continuous_cmap: Colormap to use for continuous variables. Defaults to
`viridis`.
vmin: Value representing the lower limit of the color scale. Only applied
to continuous variables.
vmax: Value representing the upper limit of the color scale. Only applied
to continuous variables.
categorical_cmap: Colormap to use for categorical variables. Defaults to
`tab10`.
value_mapping: An optional dictionary containing string values to their
integer mappings. These mappings are used to assign colors by
calling the `cmap` with the designated integer mapping. By default,
the values are assigned pseudo-randomly (whatever order the set()
operation returns). Only applied for categorical variables.
ax: Matplotlib axis to place the tree. If not provided, a new figure is
initialized.
random_state: A random state for reproducibility
Returns:
If `ax` is provided, `ax` is returned. Otherwise, a tuple of (fig, ax)
of the newly initialized figure and axis.
"""
is_polar = isinstance(orient, (float, int))
(
node_coords,
branch_coords,
node_colors,
branch_colors,
colorstrips,
) = place_tree_and_annotations(
tree,
depth_key,
meta_data,
allele_table,
indel_colors,
indel_priors,
orient,
extend_branches,
angled_branches,
add_root,
colorstrip_width,
colorstrip_spacing,
clade_colors,
continuous_cmap,
vmin,
vmax,
categorical_cmap,
value_mapping,
random_state,
)
fig = None
if ax is None:
fig, ax = plt.subplots(figsize=figsize, tight_layout=True)
ax.set_axis_off()
# Plot all nodes
_leaf_kwargs = dict(x=[], y=[], s=5, c="black")
_node_kwargs = dict(x=[], y=[], s=0, c="black")
_leaf_kwargs.update(leaf_kwargs or {})
_node_kwargs.update(internal_node_kwargs or {})
for node, (x, y) in node_coords.items():
if node in node_colors:
continue
if is_polar:
x, y = utilities.polar_to_cartesian(x, y)
if tree.is_leaf(node):
_leaf_kwargs["x"].append(x)
_leaf_kwargs["y"].append(y)
else:
_node_kwargs["x"].append(x)
_node_kwargs["y"].append(y)
ax.scatter(**_leaf_kwargs)
ax.scatter(**_node_kwargs)
_leaf_colors = []
_node_colors = []
_leaf_kwargs.update({"x": [], "y": []})
_node_kwargs.update({"x": [], "y": []})
for node, color in node_colors.items():
x, y = node_coords[node]
if is_polar:
x, y = utilities.polar_to_cartesian(x, y)
if tree.is_leaf(node):
_leaf_kwargs["x"].append(x)
_leaf_kwargs["y"].append(y)
_leaf_colors.append(color)
else:
_node_kwargs["x"].append(x)
_node_kwargs["y"].append(y)
_node_colors.append(color)
_leaf_kwargs["c"] = _leaf_colors
_node_kwargs["c"] = _node_colors
ax.scatter(**_leaf_kwargs)
ax.scatter(**_node_kwargs)
# Plot all branches
_branch_kwargs = dict(linewidth=1, c="black")
_branch_kwargs.update(branch_kwargs or {})
for branch, (xs, ys) in branch_coords.items():
if branch in branch_colors:
continue
if is_polar:
xs, ys = utilities.polars_to_cartesians(xs, ys)
ax.plot(xs, ys, **_branch_kwargs)
for branch, color in branch_colors.items():
_branch_kwargs["c"] = color
xs, ys = branch_coords[branch]
if is_polar:
xs, ys = utilities.polars_to_cartesians(xs, ys)
ax.plot(xs, ys, **_branch_kwargs)
# Colorstrips
_colorstrip_kwargs = dict(linewidth=0)
_colorstrip_kwargs.update(colorstrip_kwargs or {})
for colorstrip in colorstrips:
# Last element is text, but this can not be shown in static plotting.
for xs, ys, c, _ in colorstrip.values():
_colorstrip_kwargs["c"] = c
if is_polar:
xs, ys = utilities.polars_to_cartesians(xs, ys)
ax.fill(xs, ys, **_colorstrip_kwargs)
return (fig, ax) if fig is not None else ax
def place_tree_and_annotations(
tree: CassiopeiaTree,
depth_key: Optional[str] = None,
meta_data: Optional[List[str]] = None,
allele_table: Optional[pd.DataFrame] = None,
indel_colors: Optional[pd.DataFrame] = None,
indel_priors: Optional[pd.DataFrame] = None,
orient: Union[Literal["up", "down", "left", "right"], float] = 90.0,
extend_branches: bool = True,
angled_branches: bool = True,
add_root: bool = False,
colorstrip_width: Optional[float] = None,
colorstrip_spacing: Optional[float] = None,
clade_colors: Optional[Dict[str, Tuple[float, float, float]]] = None,
continuous_cmap: Union[str, mpl.colors.Colormap] = "viridis",
vmin: Optional[float] = None,
vmax: Optional[float] = None,
categorical_cmap: Union[str, mpl.colors.Colormap] = "tab10",
value_mapping: Optional[Dict[str, int]] = None,
random_state: Optional[np.random.RandomState] = None,
) -> Tuple[Dict, Dict, Dict, Dict, List]:
"""Helper function to place the tree and all requested annotations.
Args:
tree: The CassiopeiaTree to plot.
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`.
meta_data: Meta data to plot alongside the tree, which must be columns
in the CassiopeiaTree.cell_meta variable.
allele_table: Alleletable to plot alongside the tree.
indel_colors: Color mapping to use for plotting the alleles for each
cell. Only necessary if `allele_table` is specified.
indel_priors: Prior probabilities for each indel. Only useful if an
allele table is to be plotted and `indel_colors` is None.
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. Defaults to 90.
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.
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`.
colorstrip_width: Width of the colorstrip. Width is defined as the
length in the direction of the leaves. Defaults to 5% of the tree
depth.
colorstrip_spacing: Space between consecutive colorstrips. Defaults to
half of `colorstrip_width`.
clade_colors: Dictionary containing internal node-color mappings. These
colors will be used to color all the paths from this node to the
leaves the provided color.
continuous_cmap: Colormap to use for continuous variables. Defaults to
`viridis`.
vmin: Value representing the lower limit of the color scale. Only applied
to continuous variables.
vmax: Value representing the upper limit of the color scale. Only applied
to continuous variables.
categorical_cmap: Colormap to use for categorical variables. Defaults to
`tab10`.
value_mapping: An optional dictionary containing string values to their
integer mappings. These mappings are used to assign colors by
calling the `cmap` with the designated integer mapping. By default,
the values are assigned pseudo-randomly (whatever order the set()
operation returns). Only applied for categorical variables.
random_state: A random state for reproducibility
Returns:
Four dictionaries (node coordinates, branch coordinates, node
colors, branch colors) and a list of colorstrips.
"""
meta_data = meta_data or []
# Place tree on the appropriate coordinate system.
node_coords, branch_coords = utilities.place_tree(
tree,
depth_key=depth_key,
orient=orient,
extend_branches=extend_branches,
angled_branches=angled_branches,
add_root=add_root,
)
# Compute first set of anchor coords, which are just the coordinates of
# all the leaves.
anchor_coords = {
node: coords
for node, coords in node_coords.items() if tree.is_leaf(node)
}
is_polar = isinstance(orient, (float, int))
loc = "polar" if is_polar else orient
tight_width, tight_height = compute_colorstrip_size(
node_coords, anchor_coords, loc)
width = colorstrip_width or tight_width
spacing = colorstrip_spacing or tight_width / 2
# Place indel heatmap
colorstrips = []
if allele_table is not None:
heatmap, anchor_coords = create_indel_heatmap(
allele_table,
anchor_coords,
width,
tight_height,
spacing,
loc,
indel_colors,
indel_priors,
random_state,
)
colorstrips.extend(heatmap)
# Any other annotations
for meta_item in meta_data:
if meta_item not in tree.cell_meta.columns:
raise PlottingError(
"Meta data item not in CassiopeiaTree cell meta.")
values = tree.cell_meta[meta_item]
if pd.api.types.is_numeric_dtype(values):
colorstrip, anchor_coords = create_continuous_colorstrip(
values.to_dict(),
anchor_coords,
width,
tight_height,
spacing,
loc,
continuous_cmap,
vmin,
vmax,
)
if pd.api.types.is_string_dtype(values):
colorstrip, anchor_coords = create_categorical_colorstrip(
values.to_dict(),
anchor_coords,
width,
tight_height,
spacing,
loc,
categorical_cmap,
value_mapping,
)
colorstrips.append(colorstrip)
# Clade colors
node_colors = {}
branch_colors = {}
if clade_colors:
node_colors, branch_colors = create_clade_colors(tree, clade_colors)
return node_coords, branch_coords, node_colors, branch_colors, colorstrips