# Copyright 2024 - present The PyMC Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from collections import defaultdict
from collections.abc import Callable, Iterable
from dataclasses import dataclass
from enum import Enum
from os import path
from typing import Any, cast
from pytensor import function
from pytensor.graph.basic import Variable, ancestors, walk
from pytensor.tensor.shape import Shape
from pymc.model.core import modelcontext
from pymc.pytensorf import _cheap_eval_mode
from pymc.util import VarName, get_default_varnames, get_var_name
__all__ = (
"ModelGraph",
"model_to_graphviz",
"model_to_networkx",
)
@dataclass
class DimInfo:
names: tuple[str | None, ...]
lengths: tuple[int, ...]
def __post_init__(self) -> None:
if len(self.names) != len(self.lengths):
raise ValueError("The number of names and lengths must be equal.")
def __hash__(self):
return hash((self.names, self.lengths))
def __bool__(self) -> bool:
return len(self.lengths) > 0 or len(self.names) > 0
PlateLabelFunc = Callable[[DimInfo], str]
def create_plate_label_without_dim_length(
dim_info: DimInfo,
) -> str:
return " x ".join(
f"{dname}" if dname else f"{dlen}"
for (dname, dlen) in zip(dim_info.names, dim_info.lengths)
)
def create_plate_label_with_dim_length(
dim_info: DimInfo,
) -> str:
return " x ".join(
f"{dname} ({dlen})" if dname else f"{dlen}"
for (dname, dlen) in zip(dim_info.names, dim_info.lengths)
)
def fast_eval(var):
return function([], var, mode=_cheap_eval_mode)()
class NodeType(str, Enum):
"""Enum for the types of nodes in the graph."""
POTENTIAL = "Potential"
FREE_RV = "Free Random Variable"
OBSERVED_RV = "Observed Random Variable"
DETERMINISTIC = "Deterministic"
DATA = "Data"
@dataclass
class NodeInfo:
var: Variable
node_type: NodeType
def __hash__(self):
return hash(self.var.name)
@dataclass
class Plate:
dim_info: DimInfo
variables: list[NodeInfo]
def __eq__(self, other) -> bool:
if not isinstance(other, Plate):
return False
return self.dim_info == other.dim_info and set(self.variables) == set(other.variables)
GraphvizNodeKwargs = dict[str, Any]
NodeFormatter = Callable[[Variable], GraphvizNodeKwargs]
def default_potential(var: Variable) -> GraphvizNodeKwargs:
"""Return default data for potential in the graph."""
return {
"shape": "octagon",
"style": "filled",
"label": f"{var.name}\n~\nPotential",
}
def random_variable_symbol(var: Variable) -> str:
"""Get the symbol of the random variable."""
op = var.owner.op
if name := getattr(op, "name", None):
symbol = name[0].upper() + name[1:]
else:
symbol = op.__class__.__name__.removesuffix("RV")
return symbol
def default_free_rv(var: Variable) -> GraphvizNodeKwargs:
"""Return default data for free RV in the graph."""
symbol = random_variable_symbol(var)
return {
"shape": "ellipse",
"style": None,
"label": f"{var.name}\n~\n{symbol}",
}
def default_observed_rv(var: Variable) -> GraphvizNodeKwargs:
"""Return default data for observed RV in the graph."""
symbol = random_variable_symbol(var)
return {
"shape": "ellipse",
"style": "filled",
"label": f"{var.name}\n~\n{symbol}",
}
def default_deterministic(var: Variable) -> GraphvizNodeKwargs:
"""Return default data for the deterministic in the graph."""
return {
"shape": "box",
"style": None,
"label": f"{var.name}\n~\nDeterministic",
}
def default_data(var: Variable) -> GraphvizNodeKwargs:
"""Return default data for the data in the graph."""
return {
"shape": "box",
"style": "rounded, filled",
"label": f"{var.name}\n~\nData",
}
def get_node_type(var_name: VarName, model) -> NodeType:
"""Return the node type of the variable in the model."""
v = model[var_name]
if v in model.deterministics:
return NodeType.DETERMINISTIC
elif v in model.free_RVs:
return NodeType.FREE_RV
elif v in model.observed_RVs:
return NodeType.OBSERVED_RV
elif v in model.data_vars:
return NodeType.DATA
else:
return NodeType.POTENTIAL
NodeTypeFormatterMapping = dict[NodeType, NodeFormatter]
DEFAULT_NODE_FORMATTERS: NodeTypeFormatterMapping = {
NodeType.POTENTIAL: default_potential,
NodeType.FREE_RV: default_free_rv,
NodeType.OBSERVED_RV: default_observed_rv,
NodeType.DETERMINISTIC: default_deterministic,
NodeType.DATA: default_data,
}
def update_node_formatters(node_formatters: NodeTypeFormatterMapping) -> NodeTypeFormatterMapping:
node_formatters = {**DEFAULT_NODE_FORMATTERS, **node_formatters}
unknown_keys = set(node_formatters.keys()) - set(NodeType)
if unknown_keys:
raise ValueError(
f"Node formatters must be of type NodeType. Found: {list(unknown_keys)}."
f" Please use one of {[node_type.value for node_type in NodeType]}."
)
return node_formatters
AddNode = Callable[[str, GraphvizNodeKwargs], None]
def _make_node(
node: NodeInfo,
*,
node_formatters: NodeTypeFormatterMapping,
add_node: AddNode,
cluster: str | None = None,
formatting: str = "plain",
):
"""Attaches the given variable to a graphviz or networkx Digraph."""
node_formatter = node_formatters[node.node_type]
kwargs = node_formatter(node.var)
if cluster is not None:
kwargs["cluster"] = cluster
var_name: str = cast(str, node.var.name)
add_node(var_name.replace(":", "&"), **kwargs) # type: ignore[call-arg]
class ModelGraph:
def __init__(self, model):
self.model = model
self._all_var_names = get_default_varnames(self.model.named_vars, include_transformed=False)
self._all_vars = {model[var_name] for var_name in self._all_var_names}
self.var_list = self.model.named_vars.values()
def get_parent_names(self, var: Variable) -> set[VarName]:
if var.owner is None:
return set()
named_vars = self._all_vars
def _expand(x):
if x in named_vars:
# Don't go beyond named_vars
return [x]
if x.owner is None:
return []
if isinstance(x.owner.op, Shape):
# Don't propagate shape-related dependencies
return []
# Continue walking the graph through the inputs
return x.owner.inputs
return {
cast(VarName, ancestor.name) # type: ignore[union-attr]
for ancestor in walk(nodes=var.owner.inputs, expand=_expand)
if ancestor in named_vars
}
def vars_to_plot(self, var_names: Iterable[VarName] | None = None) -> list[VarName]:
if var_names is None:
return self._all_var_names
selected_names = set(var_names)
# .copy() because sets cannot change in size during iteration
for var_name in selected_names.copy():
if var_name not in self._all_var_names:
raise ValueError(f"{var_name} is not in this model.")
for model_var in self.var_list:
if model_var in self.model.observed_RVs:
if self.model.rvs_to_values[model_var] == self.model[var_name]:
selected_names.add(model_var.name)
selected_ancestors = set(
filter(
lambda rv: rv.name in self._all_var_names,
list(ancestors([self.model[var_name] for var_name in selected_names])),
)
)
for var in selected_ancestors.copy():
if var in self.model.observed_RVs:
selected_ancestors.add(self.model.rvs_to_values[var])
# ordering of self._all_var_names is important
return [get_var_name(var) for var in selected_ancestors]
def make_compute_graph(
self, var_names: Iterable[VarName] | None = None
) -> dict[VarName, set[VarName]]:
"""Get map of var_name -> set(input var names) for the model."""
model = self.model
named_vars = self._all_vars
input_map: dict[VarName, set[VarName]] = defaultdict(set)
var_names_to_plot = self.vars_to_plot(var_names)
for var_name in var_names_to_plot:
parent_names = self.get_parent_names(model[var_name])
input_map[var_name].update(parent_names)
for var_name in var_names_to_plot:
if (var := model[var_name]) in model.observed_RVs:
# Make observed `Data` variables flow from the observed RV, and not the other way around
# (In the generative graph they usually inform shape of the observed RV)
# We have to iterate over the ancestors of the observed values because there can be
# deterministic operations in between the `Data` variable and the observed value.
obs_var = model.rvs_to_values[var]
for ancestor in ancestors([obs_var]):
if ancestor not in named_vars:
continue
obs_name = cast(VarName, ancestor.name)
input_map[var_name].discard(obs_name)
input_map[obs_name].add(var_name)
return input_map
def get_plates(
self,
var_names: Iterable[VarName] | None = None,
) -> list[Plate]:
"""Rough but surprisingly accurate plate detection.
Just groups by the shape of the underlying distribution. Will be wrong
if there are two plates with the same shape.
Returns
-------
dict
Maps plate labels to the set of ``VarName``s inside the plate.
"""
plates = defaultdict(set)
# TODO: Evaluate all RV shapes at once
# This should help find discrepancies, and
# avoids unnecessary function compiles for determining labels.
dim_lengths: dict[str, int] = {
dim_name: fast_eval(value).item() for dim_name, value in self.model.dim_lengths.items()
}
var_shapes: dict[str, tuple[int, ...]] = {
var_name: tuple(map(int, fast_eval(self.model[var_name].shape)))
for var_name in self.vars_to_plot(var_names)
}
for var_name in self.vars_to_plot(var_names):
shape: tuple[int, ...] = var_shapes[var_name]
if var_name in self.model.named_vars_to_dims:
# The RV is associated with `dims` information.
names = []
lengths = []
for d, dname in enumerate(self.model.named_vars_to_dims[var_name]):
names.append(dname)
lengths.append(dim_lengths.get(dname, shape[d]))
dim_info = DimInfo(
names=tuple(names),
lengths=tuple(lengths),
)
else:
# The RV has no `dims` information.
dim_size = len(shape)
dim_info = DimInfo(
names=tuple([None] * dim_size),
lengths=tuple(shape),
)
v = self.model[var_name]
node_type = get_node_type(var_name, self.model)
var = NodeInfo(var=v, node_type=node_type)
plates[dim_info].add(var)
return [
Plate(
dim_info=dim_info,
variables=list(variables),
)
for dim_info, variables in plates.items()
]
def edges(
self,
var_names: Iterable[VarName] | None = None,
) -> list[tuple[VarName, VarName]]:
"""Get edges between the variables in the model.
Parameters
----------
var_names : iterable of str, optional
Subset of variables to be plotted that identify a subgraph with respect to the entire model graph
Returns
-------
list of tuple
List of edges between the variables in the model.
"""
return [
(VarName(child.replace(":", "&")), VarName(parent.replace(":", "&")))
for child, parents in self.make_compute_graph(var_names=var_names).items()
for parent in parents
]
def nodes(self, plates: list[Plate] | None = None) -> list[NodeInfo]:
"""Get all nodes in the model graph."""
plates = plates or self.get_plates()
nodes = []
for plate in plates:
nodes.extend(plate.variables)
return nodes
def make_graph(
name: str,
plates: list[Plate],
edges: list[tuple[VarName, VarName]],
formatting: str = "plain",
save=None,
figsize=None,
dpi=300,
node_formatters: NodeTypeFormatterMapping | None = None,
graph_attr: dict[str, Any] | None = None,
create_plate_label: PlateLabelFunc = create_plate_label_with_dim_length,
):
"""Make graphviz Digraph of PyMC model.
Returns
-------
graphviz.Digraph
"""
try:
import graphviz
except ImportError:
raise ImportError(
"This function requires the python library graphviz, along with binaries. "
"The easiest way to install all of this is by running\n\n"
"\tconda install -c conda-forge python-graphviz"
)
node_formatters = node_formatters or {}
node_formatters = update_node_formatters(node_formatters)
graph = graphviz.Digraph(name, graph_attr=graph_attr)
for plate in plates:
if plate.dim_info:
# must be preceded by 'cluster' to get a box around it
plate_label = create_plate_label(plate.dim_info)
plate_name = f"cluster{plate_label}"
with graph.subgraph(name=plate_name) as sub:
for var in plate.variables:
_make_node(
node=var,
formatting=formatting,
node_formatters=node_formatters,
add_node=sub.node,
)
# plate label goes bottom right
sub.attr(label=plate_label, labeljust="r", labelloc="b", style="rounded")
else:
for var in plate.variables:
_make_node(
node=var,
formatting=formatting,
node_formatters=node_formatters,
add_node=graph.node,
)
for child, parent in edges:
graph.edge(parent, child)
if save is not None:
width, height = (None, None) if figsize is None else figsize
base, ext = path.splitext(save)
if ext:
ext = ext.replace(".", "")
else:
ext = "png"
graph_c = graph.copy()
graph_c.graph_attr.update(size=f"{width},{height}!")
graph_c.graph_attr.update(dpi=str(dpi))
graph_c.render(filename=base, format=ext, cleanup=True)
return graph
def make_networkx(
name: str,
plates: list[Plate],
edges: list[tuple[VarName, VarName]],
formatting: str = "plain",
node_formatters: NodeTypeFormatterMapping | None = None,
create_plate_label: PlateLabelFunc = create_plate_label_with_dim_length,
):
"""Make networkx Digraph of PyMC model.
Returns
-------
networkx.Digraph
"""
try:
import networkx
except ImportError:
raise ImportError(
"This function requires the python library networkx, along with binaries. "
"The easiest way to install all of this is by running\n\n"
"\tconda install networkx"
)
node_formatters = node_formatters or {}
node_formatters = update_node_formatters(node_formatters)
graphnetwork = networkx.DiGraph(name=name)
for plate in plates:
if plate.dim_info:
# # must be preceded by 'cluster' to get a box around it
plate_label = create_plate_label(plate.dim_info)
plate_name = f"cluster{plate_label}"
subgraphnetwork = networkx.DiGraph(name=plate_name, label=plate_label)
for var in plate.variables:
_make_node(
node=var,
node_formatters=node_formatters,
cluster=plate_name,
formatting=formatting,
add_node=subgraphnetwork.add_node,
)
for sgn in subgraphnetwork.nodes:
networkx.set_node_attributes(
subgraphnetwork,
{sgn: {"labeljust": "r", "labelloc": "b", "style": "rounded"}},
)
node_data = {
e[0]: e[1] for e in graphnetwork.nodes(data=True) & subgraphnetwork.nodes(data=True)
}
graphnetwork = networkx.compose(graphnetwork, subgraphnetwork)
networkx.set_node_attributes(graphnetwork, node_data)
graphnetwork.graph["name"] = name
else:
for var in plate.variables:
_make_node(
node=var,
formatting=formatting,
node_formatters=node_formatters,
add_node=graphnetwork.add_node,
)
for child, parents in edges:
graphnetwork.add_edge(parents, child)
return graphnetwork
[docs]
def model_to_networkx(
model=None,
*,
var_names: Iterable[VarName] | None = None,
formatting: str = "plain",
node_formatters: NodeTypeFormatterMapping | None = None,
include_dim_lengths: bool = True,
):
"""Produce a networkx Digraph from a PyMC model.
Requires networkx, which may be installed most easily with::
conda install networkx
Alternatively, you may install using pip with::
pip install networkx
See https://networkx.org/documentation/stable/ for more information.
Parameters
----------
model : Model
The model to plot. Not required when called from inside a modelcontext.
var_names : iterable of str, optional
Subset of variables to be plotted that identify a subgraph with respect to the entire model graph
formatting : str, optional
one of { "plain" }
node_formatters : dict, optional
A dictionary mapping node types to functions that return a dictionary of node attributes.
Check out the networkx documentation for more information
how attributes are added to nodes: https://networkx.org/documentation/stable/reference/classes/generated/networkx.Graph.add_node.html
include_dim_lengths : bool
Include the dim length in the plate label. Default is True.
Examples
--------
How to plot the graph of the model.
.. code-block:: python
import numpy as np
from pymc import HalfCauchy, Model, Normal, model_to_networkx
J = 8
y = np.array([28, 8, -3, 7, -1, 1, 18, 12])
sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])
with Model() as schools:
eta = Normal("eta", 0, 1, shape=J)
mu = Normal("mu", 0, sigma=1e6)
tau = HalfCauchy("tau", 25)
theta = mu + tau * eta
obs = Normal("obs", theta, sigma=sigma, observed=y)
model_to_networkx(schools)
Add custom attributes to Free Random Variables and Observed Random Variables nodes.
.. code-block:: python
node_formatters = {
"Free Random Variable": lambda var: {"shape": "circle", "label": var.name},
"Observed Random Variable": lambda var: {"shape": "square", "label": var.name},
}
model_to_networkx(schools, node_formatters=node_formatters)
"""
if "plain" not in formatting:
raise ValueError(f"Unsupported formatting for graph nodes: '{formatting}'. See docstring.")
if formatting != "plain":
warnings.warn(
"Formattings other than 'plain' are currently not supported.",
UserWarning,
stacklevel=2,
)
model = modelcontext(model)
graph = ModelGraph(model)
return make_networkx(
name=model.name,
plates=graph.get_plates(var_names=var_names),
edges=graph.edges(var_names=var_names),
formatting=formatting,
node_formatters=node_formatters,
create_plate_label=create_plate_label_with_dim_length
if include_dim_lengths
else create_plate_label_without_dim_length,
)
[docs]
def model_to_graphviz(
model=None,
*,
var_names: Iterable[VarName] | None = None,
formatting: str = "plain",
save: str | None = None,
figsize: tuple[int, int] | None = None,
dpi: int = 300,
node_formatters: NodeTypeFormatterMapping | None = None,
graph_attr: dict[str, Any] | None = None,
include_dim_lengths: bool = True,
):
"""Produce a graphviz Digraph from a PyMC model.
Requires graphviz, which may be installed most easily with
conda install -c conda-forge python-graphviz
Alternatively, you may install the `graphviz` binaries yourself,
and then `pip install graphviz` to get the python bindings. See
http://graphviz.readthedocs.io/en/stable/manual.html
for more information.
Parameters
----------
model : pm.Model
The model to plot. Not required when called from inside a modelcontext.
var_names : iterable of variable names, optional
Subset of variables to be plotted that identify a subgraph with respect to the entire model graph
formatting : str, optional
one of { "plain" }
save : str, optional
If provided, an image of the graph will be saved to this location. The format is inferred from
the file extension.
figsize : tuple[int, int], optional
Width and height of the figure in inches. If not provided, uses the default figure size. It only affect
the size of the saved figure.
dpi : int, optional
Dots per inch. It only affects the resolution of the saved figure. The default is 300.
graph_attr : dict, optional
A dictionary of top-level layout attributes for graphviz
Check out graphviz documentation for more information on available attributes
https://graphviz.org/doc/info/attrs.html
node_formatters : dict, optional
A dictionary mapping node types to functions that return a dictionary of node attributes.
Check out graphviz documentation for more information on available
attributes. https://graphviz.org/docs/nodes/
include_dim_lengths : bool
Include the dim lengths in the plate label. Default is True.
Examples
--------
How to plot the graph of the model.
.. code-block:: python
import numpy as np
from pymc import HalfCauchy, Model, Normal, model_to_graphviz
J = 8
y = np.array([28, 8, -3, 7, -1, 1, 18, 12])
sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])
with Model() as schools:
eta = Normal("eta", 0, 1, shape=J)
mu = Normal("mu", 0, sigma=1e6)
tau = HalfCauchy("tau", 25)
theta = mu + tau * eta
obs = Normal("obs", theta, sigma=sigma, observed=y)
model_to_graphviz(schools)
Note that this code automatically plots the graph if executed in a Jupyter notebook.
If executed non-interactively, such as in a script or python console, the graph
needs to be rendered explicitly:
.. code-block:: python
# creates the file `schools.pdf`
model_to_graphviz(schools).render("schools")
Display Free Random Variables and Observed Random Variables nodes with custom formatting.
.. code-block:: python
node_formatters = {
"Free Random Variable": lambda var: {"shape": "circle", "label": var.name},
"Observed Random Variable": lambda var: {"shape": "square", "label": var.name},
}
model_to_graphviz(schools, node_formatters=node_formatters)
"""
if "plain" not in formatting:
raise ValueError(f"Unsupported formatting for graph nodes: '{formatting}'. See docstring.")
if formatting != "plain":
warnings.warn(
"Formattings other than 'plain' are currently not supported.",
UserWarning,
stacklevel=2,
)
model = modelcontext(model)
graph = ModelGraph(model)
return make_graph(
model.name,
plates=graph.get_plates(var_names=var_names),
edges=graph.edges(var_names=var_names),
formatting=formatting,
save=save,
figsize=figsize,
dpi=dpi,
graph_attr=graph_attr,
node_formatters=node_formatters,
create_plate_label=create_plate_label_with_dim_length
if include_dim_lengths
else create_plate_label_without_dim_length,
)
def _create_mermaid_node_name(name: str) -> str:
return name.replace(":", "_").replace(" ", "_")
def _build_mermaid_node(node: NodeInfo) -> list[str]:
var = node.var
node_type = node.node_type
name = cast(str, var.name)
node_name = _create_mermaid_node_name(name)
if node_type == NodeType.DATA:
return [
f"{node_name}[{var.name} ~ Data]",
f"{node_name}@{{ shape: db }}",
]
elif node_type == NodeType.OBSERVED_RV:
return [
f"{node_name}([{name} ~ {random_variable_symbol(var)}])",
f"{node_name}@{{ shape: rounded }}",
f"style {node_name} fill:#757575",
]
elif node_type == NodeType.FREE_RV:
return [
f"{node_name}([{name} ~ {random_variable_symbol(var)}])",
f"{node_name}@{{ shape: rounded }}",
]
elif node_type == NodeType.DETERMINISTIC:
return [
f"{node_name}([{name} ~ Deterministic])",
f"{node_name}@{{ shape: rect }}",
]
elif node_type == NodeType.POTENTIAL:
return [
f"{node_name}([{name} ~ Potential])",
f"{node_name}@{{ shape: diam }}",
f"style {node_name} fill:#f0f0f0",
]
return []
def _build_mermaid_nodes(nodes) -> list[str]:
node_lines = []
for node in nodes:
node_lines.extend(_build_mermaid_node(node))
return node_lines
def _build_mermaid_edges(edges) -> list[str]:
"""Return a list of Mermaid edge definitions."""
edge_lines = []
for child, parent in edges:
child_id = _create_mermaid_node_name(child)
parent_id = _create_mermaid_node_name(parent)
edge_lines.append(f"{parent_id} --> {child_id}")
return edge_lines
def _build_mermaid_plates(plates, include_dim_lengths) -> list[str]:
plate_lines = []
for plate in plates:
if not plate.dim_info:
continue
plate_label_func = (
create_plate_label_with_dim_length
if include_dim_lengths
else create_plate_label_without_dim_length
)
plate_label = plate_label_func(plate.dim_info)
plate_name = f'subgraph "{plate_label}"'
plate_lines.append(plate_name)
for var in plate.variables:
plate_lines.append(f" {var.var.name}")
plate_lines.append("end")
return plate_lines
def model_to_mermaid(model=None, *, var_names=None, include_dim_lengths: bool = True) -> str:
"""Produce a Mermaid diagram string from a PyMC model.
Parameters
----------
model : pm.Model
The model to plot. Not required when called from inside a modelcontext.
var_names : iterable of variable names, optional
Subset of variables to be plotted that identify a subgraph with respect to the entire model graph
include_dim_lengths : bool
Include the dim lengths in the plate label. Default is True.
Returns
-------
str
Mermaid diagram string representing the model graph.
Examples
--------
Visualize a simple PyMC model
.. code-block:: python
import pymc as pm
with pm.Model() as model:
mu = pm.Normal("mu", mu=0, sigma=1)
sigma = pm.HalfNormal("sigma", sigma=1)
pm.Normal("obs", mu=mu, sigma=sigma, observed=[1, 2, 3])
print(pm.model_to_mermaid(model))
"""
model = modelcontext(model)
graph = ModelGraph(model)
plates = sorted(graph.get_plates(var_names=var_names), key=lambda plate: hash(plate.dim_info))
edges = sorted(graph.edges(var_names=var_names))
nodes = sorted(graph.nodes(plates=plates), key=lambda node: cast(str, node.var.name))
return "\n".join(
[
"graph TD",
"%% Nodes:",
*_build_mermaid_nodes(nodes),
"\n%% Edges:",
*_build_mermaid_edges(edges),
"\n%% Plates:",
*_build_mermaid_plates(plates, include_dim_lengths=include_dim_lengths),
]
)
