"""
Nodes and variables.
"""
from __future__ import annotations
import weakref
from abc import ABC, abstractmethod
from collections.abc import Callable, Hashable, Iterable
from functools import wraps
from itertools import chain
from types import MappingProxyType
from typing import TYPE_CHECKING, Any, NamedTuple, TypeVar, Union
import tensorflow_probability.substrates.jax.bijectors as jb
import tensorflow_probability.substrates.jax.distributions as jd
import tensorflow_probability.substrates.numpy.bijectors as nb
import tensorflow_probability.substrates.numpy.distributions as nd
from deprecated.sphinx import deprecated
from ..distributions.nodist import NoDistribution
if TYPE_CHECKING:
from .model import Model
__all__ = [
"Array",
"Bijector",
"Calc",
"Data",
"Dist",
"Distribution",
"Group",
"InputGroup",
"Node",
"NodeState",
"Obs",
"Param",
"TransientCalc",
"TransientDist",
"TransientIdentity",
"TransientNode",
"Var",
"add_group",
]
Array = Any
Distribution = Union[jd.Distribution, nd.Distribution]
Bijector = Union[jb.Bijector, nb.Bijector]
T = TypeVar("T", bound=Hashable)
def _unique_tuple(*args: Iterable[T]) -> tuple[T, ...]:
return tuple(dict.fromkeys(chain(*args)))
def in_model_method(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if not self.model:
raise RuntimeError(
f"{repr(self)} is not part of a model, cannot call {fn.__name__}()"
)
return fn(self, *args, **kwargs)
return wrapped
def in_model_getter(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if not self.model:
raise RuntimeError(
f"{repr(self)} is not part of a model, cannot call '{fn.__name__}'"
)
return fn(self, *args, **kwargs)
return wrapped
def no_model_method(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if self.model:
raise RuntimeError(
f"{repr(self)} is part of a model, cannot call {fn.__name__}()"
)
return fn(self, *args, **kwargs)
return wrapped
def no_model_setter(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if self.model:
raise RuntimeError(
f"{repr(self)} is part of a model, cannot set '{fn.__name__}'"
)
return fn(self, *args, **kwargs)
return wrapped
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Nodes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]class NodeState(NamedTuple):
"""The state of a node."""
value: Any
"""The value of the node."""
outdated: bool
"""Whether the node is outdated."""
extra: Any = None
"""Optional extra information."""
[docs]class Node(ABC):
"""A node of a computational graph."""
def __init__(
self,
*inputs: Any,
_name: str = "",
_needs_seed: bool = False,
**kwinputs: Any,
):
self._groups: dict[str, Group] = {}
self._inputs = tuple(self._to_node(_input) for _input in inputs)
self._kwinputs = {kw: self._to_node(_input) for kw, _input in kwinputs.items()}
self._model: weakref.ref[Model] | Callable[[], None] = lambda: None
self._name = _name
self._needs_seed = _needs_seed
self._outdated = True
self._outputs: tuple[Node, ...] = ()
self._value: Any = None
self._var: Var | None = None
self.monitor = False
"""Whether the node should be monitored by an inference algorithm."""
def _add_output(self, output: Node) -> Node:
self._outputs = _unique_tuple(self._outputs, [output])
return self
def _clear_outputs(self) -> Node:
self._outputs = ()
return self
def _set_model(self, model: Model) -> Node:
if self.model:
raise RuntimeError(f"{repr(self)} can only be part of one model")
self._model = weakref.ref(model)
return self
def _set_var(self, var: Var) -> Node:
if self.var:
raise RuntimeError(f"{repr(self)} can only be part of one var")
self._var = var
return self
@staticmethod
def _to_node(x: Any) -> Node:
if isinstance(x, Var):
return x.var_value_node
if not isinstance(x, Node):
return Data(x)
return x
def _unset_model(self) -> Node:
self._model = lambda: None
return self
def _unset_var(self) -> Node:
self._var = None
return self
[docs] @in_model_method
def all_output_nodes(self) -> tuple[Node, ...]:
"""Returns all output nodes as a unique tuple."""
return self.outputs
[docs] def clear_state(self) -> Node:
"""Clears the state of the node."""
self.state = NodeState(None, True)
return self
[docs] @in_model_method
def flag_outdated(self) -> Node:
"""Flags the node and its recursive outputs as outdated."""
self._outdated = True
for node in self._outputs:
node.flag_outdated()
return self
@property
def groups(self) -> MappingProxyType[str, Group]:
"""The groups that this node is a part of."""
return MappingProxyType(self._groups)
@property
def inputs(self) -> tuple[Node, ...]:
"""The non-keyword input nodes."""
return self._inputs
@property
def kwinputs(self) -> MappingProxyType[str, Node]:
"""The keyword input nodes."""
return MappingProxyType(self._kwinputs)
@property
def model(self) -> Model | None:
"""The model the node is part of."""
return self._model()
@property
def name(self) -> str:
"""The name of the node."""
return self._name
@name.setter
@no_model_setter
def name(self, name: str):
self._name = name
@property
def needs_seed(self) -> bool:
"""Whether the node needs a seed / PRNG key."""
return self._needs_seed
@needs_seed.setter
@no_model_setter
def needs_seed(self, needs_seed: bool):
self._needs_seed = needs_seed
@property
def outdated(self) -> bool:
"""Whether the node is outdated."""
if not self.model:
return True
return self._outdated
@property
@in_model_getter
def outputs(self) -> tuple[Node, ...]:
"""The output nodes."""
return self._outputs
@property
def state(self) -> NodeState:
"""
The state of the node.
For the default node, a :class:`.NodeState` with the value and the
outdated flag, but subclasses can add extra information to the state.
"""
return NodeState(self.value, self.outdated)
@state.setter
def state(self, state: NodeState):
self._value = state.value
self._outdated = state.outdated
[docs] @abstractmethod
def update(self) -> Node:
"""Updates the value of the node."""
@property
def value(self) -> Any:
"""
The value of the node.
Can only be set for a :class:`.Data` node, but not a :class:`.Calc` or
:class:`.Dist` node. If the node is part of a :class:`.Model` ``m`` with
``m.auto_update == True``, setting the value of the node triggers an update
of the model. The auto-update can be disabled to improve the performance if
multiple model parameters are updated at once.
"""
return self._value
@property
def var(self) -> Var | None:
"""The variable the node is part of."""
return self._var
def __getstate__(self):
state = self.__dict__.copy()
state["_model"] = self._model()
return state
def __setstate__(self, state):
self.__dict__.update(state)
if self._model is not None:
self._model = weakref.ref(self._model)
else:
self._model = lambda: None
def __repr__(self) -> str:
return f"{type(self).__name__}<{self.name}>"
[docs]class TransientNode(Node):
"""
A node that does not cache its value.
A transient node is outdated if and only if at least one of its input nodes
is outdated. The :attr:`.outdated` property checks this condition on-the-fly.
"""
@property
def outdated(self) -> bool:
"""
Whether the node is outdated.
A transient node is outdated if and only if at least one of its input nodes
is outdated. This condition is checked on-the-fly.
"""
if not self.model:
return True
return any(_input.outdated for _input in self.all_input_nodes())
@property
def state(self) -> NodeState:
"""The state of the node with the value ``None``."""
return NodeState(None, self.outdated)
@state.setter
def state(self, state: NodeState):
self._value = state.value
self._outdated = state.outdated
[docs] def update(self):
"""Does nothing."""
return self
@property
@abstractmethod
def value(self) -> Any:
"""
The value of the node.
Computed on-the-fly.
"""
class ArgGroup(NamedTuple):
"""A group of arguments as a named tuple of ``args`` and ``kwargs``."""
args: list[Any]
"""The non-keyword arguments."""
kwargs: dict[str, Any]
"""The keyword arguments."""
[docs]class Data(Node):
"""A data node. Always up-to-date."""
def __init__(self, value: Any, _name: str = ""):
super().__init__(_name=_name)
self._value = value
[docs] def flag_outdated(self) -> Data:
"""Stops the recursion setting outdated flags."""
return self
@property
def outdated(self) -> bool:
return False
[docs] def update(self) -> Data:
"""Does nothing."""
return self
@property
def value(self) -> Any:
return self._value
@value.setter
def value(self, value: Any):
self._value = value
if self.model:
for node in self.outputs:
node.flag_outdated()
if self.model.auto_update:
self.model.update()
[docs]class Calc(Node):
"""A calculator node."""
def __init__(
self,
function: Callable[..., Any],
*inputs: Any,
_name: str = "",
_needs_seed: bool = False,
**kwinputs: Any,
):
super().__init__(*inputs, **kwinputs, _name=_name, _needs_seed=_needs_seed)
self._function = function
@property
def function(self) -> Callable[..., Any]:
"""The wrapped function."""
return self._function
@function.setter
@no_model_setter
def function(self, function: Callable[..., Any]):
self._function = function
[docs] def update(self) -> Calc:
args = [_input.value for _input in self.inputs]
kwargs = {kw: _input.value for kw, _input in self.kwinputs.items()}
self._value = self.function(*args, **kwargs)
self._outdated = False
return self
[docs]class TransientCalc(TransientNode, Calc):
"""A transient calculator node that does not cache its value."""
@property
def value(self) -> Any:
args = [_input.value for _input in self.inputs]
kwargs = {kw: _input.value for kw, _input in self.kwinputs.items()}
return self.function(*args, **kwargs)
[docs]class TransientIdentity(TransientCalc):
"""
A transient identity node that does not cache its value.
Essentially, this node "forwards" the value of its input to its outputs.
"""
def __init__(self, _input: Any, _name: str = ""):
super().__init__(lambda x: x, _input, _name=_name)
class VarValue(TransientIdentity):
"""
A proxy node for the value of a :class:`.Var`.
This node type is used to keep the references to a variable intact,
even if the underlying value node is replaced.
"""
[docs]class Dist(Node):
"""A distribution node."""
def __init__(
self,
distribution: Callable[..., Distribution],
*inputs: Any,
_name: str = "",
_needs_seed: bool = False,
**kwinputs: Any,
):
super().__init__(*inputs, **kwinputs, _name=_name, _needs_seed=_needs_seed)
self._at: Node | None = None
self._distribution = distribution
self._per_obs = True
@property
def at(self) -> Node | None:
"""Where to evaluate the distribution."""
return self._at
@at.setter
@no_model_setter
def at(self, at: Node | None):
if self.var and at is not self.var.var_value_node:
raise RuntimeError(
f"{repr(self)} is part of a var, cannot set property `at`"
)
self._at = at
@property
def distribution(self) -> Callable[..., Distribution]:
"""The wrapped distribution."""
return self._distribution
@distribution.setter
@no_model_setter
def distribution(self, distribution: Callable[..., Distribution]):
self._distribution = distribution
[docs] def init_dist(self) -> Distribution:
"""Initializes the distribution."""
args = [_input.value for _input in self.inputs]
kwargs = {kw: _input.value for kw, _input in self.kwinputs.items()}
dist = self.distribution(*args, **kwargs)
return dist
@property
def log_prob(self) -> Array:
"""The log-probability of the distribution."""
return self.value
@property
def per_obs(self) -> bool:
"""Whether the log-probability is stored per observation or summed up."""
return self._per_obs
@per_obs.setter
@no_model_setter
def per_obs(self, per_obs: bool):
self._per_obs = per_obs
[docs] def update(self) -> Dist:
if not self.at:
raise RuntimeError(
f"{repr(self)} cannot evaluate log-prob, property `at` not set"
)
log_prob = self.init_dist().log_prob(self.at.value)
if not self.per_obs and hasattr(log_prob, "sum"):
log_prob = log_prob.sum()
self._value = log_prob
self._outdated = False
return self
[docs]class TransientDist(TransientNode, Dist):
"""A transient distribution node that does not cache its value."""
@property
def value(self) -> Any:
if not self.at:
raise RuntimeError(
f"{repr(self)} cannot evaluate log-prob, property `at` not set"
)
log_prob = self.init_dist().log_prob(self.at.value)
if not self.per_obs and hasattr(log_prob, "sum"):
log_prob = log_prob.sum()
return log_prob
class NoDist(Dist):
def __init__(self):
super().__init__(NoDistribution)
def all_input_nodes(self) -> tuple[Node, ...]:
return ()
def all_output_nodes(self) -> tuple[Node, ...]:
return ()
@property
def outputs(self) -> tuple[Node, ...]:
return ()
def update(self) -> NoDist:
return self
@property
def value(self) -> float:
return 0.0
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Variable ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]class Var:
"""A variable wrapping a value and a distribution node."""
__slots__ = (
"_dist_node",
"_groups",
"_name",
"_observed",
"_parameter",
"_role",
"_value_node",
"_var_value_node",
"info",
)
def __init__(
self,
value: Any,
distribution: Dist | None = None,
name: str = "",
):
self._name = name
self._value_node: Node = Data(None)
self._dist_node: Dist = NoDist()
self._var_value_node: VarValue = VarValue(
self._value_node, _name=f"{self._name}_var_value"
)
self._var_value_node._set_var(self)
# use setters
self.value_node = value # type: ignore # unfrozen
self.dist_node = distribution # type: ignore # unfrozen
self._observed = False
self._parameter = False
self._role = ""
self._groups: dict[str, Group] = {}
self.info: dict[str, Any] = {}
"""Additional meta-information about the variable as a dict."""
[docs] @in_model_method
def all_output_nodes(self) -> tuple[Node, ...]:
"""Returns all output *nodes* as a unique tuple."""
nodes = list(self.value_node.all_output_nodes())
nodes.extend(self.var_value_node.all_output_nodes())
nodes.extend(self._dist_node.all_output_nodes())
nodes = [node for node in nodes if node is not self.var_value_node]
return _unique_tuple(nodes)
[docs] @in_model_method
def all_output_vars(self) -> tuple[Var, ...]:
"""
Returns all output *variables* as a unique tuple.
The returned tuple also contains output variables that are indirect outputs
of this variable through nodes without variables.
"""
nodes = list(self.all_output_nodes())
visited = []
_vars = []
while nodes:
node = nodes.pop()
if node not in visited:
if node.var and node.var is not self:
_vars.append(node.var)
else:
nodes.extend(node.all_output_nodes())
visited.append(node)
return _unique_tuple(_vars)
@property
def dist_node(self) -> Dist | None:
"""The distribution node of the variable."""
return self._dist_node if self.has_dist else None
@dist_node.setter
@no_model_setter
def dist_node(self, dist_node: Dist | None):
if not dist_node:
dist_node = NoDist()
if dist_node.model:
raise RuntimeError(
f"{repr(dist_node)} is part of a model, cannot be set as dist node"
)
if self.name and not dist_node.name:
dist_node.name = f"{self.name}_log_prob" # type: ignore # unfrozen
self._dist_node._unset_var()
dist_node._set_var(self)
dist_node.at = self.var_value_node # type: ignore # unfrozen
self._dist_node = dist_node
@property
def groups(self) -> MappingProxyType[str, Group]:
"""The groups that this variable is a part of."""
return MappingProxyType(self._groups)
@property
def has_dist(self) -> bool:
"""Whether the variable has a probability distribution."""
return not isinstance(self._dist_node, NoDist)
@property
def log_prob(self) -> Array:
"""
The log-probability of the variable.
A variable without a probability distribution has a log-probability of 0.0.
"""
return self._dist_node.value
@property
def model(self) -> Model | None:
"""The model the variable is part of."""
return self.value_node.model
@property
def name(self) -> str:
"""The name of the variable."""
return self._name
@name.setter
@no_model_setter
def name(self, name: str):
if name and self.value_node.name in ("", f"{self.name}_value"):
self.value_node.name = f"{name}_value" # type: ignore # unfrozen
self.var_value_node.name = f"{name}_var_value" # type: ignore # unfrozen
if name and self._dist_node.name in ("", f"{self.name}_log_prob"):
self._dist_node.name = f"{name}_log_prob" # type: ignore # unfrozen
self._name = name
@property
def nodes(self) -> list[Node]:
"""The nodes of the variable as a list."""
nodes = [self.value_node, self.var_value_node]
if self.dist_node:
nodes.append(self.dist_node)
return nodes
@property
def observed(self) -> bool:
"""Whether the variable is observed."""
return self._observed
@observed.setter
@no_model_setter
def observed(self, observed: bool):
self._observed = observed
@property
def parameter(self) -> bool:
"""Whether the variable is a parameter."""
return self._parameter
@parameter.setter
@no_model_setter
def parameter(self, parameter: bool):
self._parameter = parameter
@property
def role(self) -> str:
"""The role of the variable."""
return self._role
@role.setter
def role(self, role: str):
self._role = role
@property
def strong(self) -> bool:
"""Whether the variable is strong."""
return isinstance(self.value_node, Data)
[docs] def update(self) -> Var:
"""Updates the variable."""
self.value_node.update()
self._dist_node.update()
return self
@property
def value(self) -> Any:
"""
The value of the variable.
Can only be set if the variable is strong. If the variable is part of
a :class:`.Model` ``m`` with ``m.auto_update == True``, setting the value of
the variable triggers an update of the model. The auto-update can be disabled
to improve the performance if multiple model parameters are updated at once.
"""
return self.value_node.value
@value.setter
def value(self, value: Any):
if self.weak:
raise RuntimeError(f"{repr(self)} is weak, cannot set value")
self.value_node.value = value # type: ignore # data node
@property
def value_node(self) -> Node:
"""The value node of the variable."""
return self._value_node
@value_node.setter
@no_model_setter
def value_node(self, value_node: Any):
if isinstance(value_node, Var):
value_node = Calc(lambda x: x, value_node)
if not isinstance(value_node, Node):
value_node = Data(value_node)
if value_node.model:
raise RuntimeError(
f"{repr(value_node)} is part of a model, cannot be set as value node"
)
if self.name and not value_node.name:
value_node.name = f"{self.name}_value"
self.value_node._unset_var()
value_node._set_var(self)
self._value_node = value_node
self._var_value_node.set_inputs(self._value_node)
@property
def var_value_node(self) -> VarValue:
"""The proxy node for the value of the variable."""
return self._var_value_node
@property
def weak(self) -> bool:
"""Whether the variable is weak."""
return not self.strong
def __repr__(self) -> str:
return f"{type(self).__name__}<{self.name}>"
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Variable helpers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]def Obs(value: Any | Calc, distribution: Dist | None = None, name: str = "") -> Var:
"""Defines an observed variable."""
var = Var(value, distribution, name)
var.observed = True
return var
[docs]def Param(value: Any | Calc, distribution: Dist | None = None, name: str = "") -> Var:
"""Defines a parameter variable."""
var = Var(value, distribution, name)
var.value_node.monitor = True
var.parameter = True
return var
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Other helpers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]@deprecated(reason="Use the new `Group` object directly", version="0.2.2")
def add_group(name: str, **kwargs: Node | Var) -> Group:
"""
Assigns the nodes and variables to a group.
See :attr:`liesel.model.nodes.Node.groups`.
Parameters
----------
name
The name of the group.
kwargs
The nodes and variables in the group with their keys in the group
as keywords.
"""
return Group(name, **kwargs)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Group ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]class Group:
"""
A group holds a collection of related :class:`.Var` and/or :class:`.Node` objects.
They allow you to do three basic things:
1. Store related nodes together for easier access.
2. Access their member nodes and variables via ``group["name"]``, where ``"name"``
is the group-specific name, which can be different from the :attr:`.Var.name` /
:attr:`.Node.name`.
3. Easily retrieve a variable's or a node's value from a :attr:`.Model.state` based
on their group-specific name via :meth:`.value_from`.
Parameters
----------
name
The group's name. Must be unique among the groups of its members, and within
a model.
**nodes_and_vars
An arbitrary number of nodes or variables. The keywords will be used as the
group-specific names of the respective objects.
See Also
--------
* :attr:`.Node.groups` and :attr:`.Var.groups` are :obj:`MappingProxyType` objects
(basically read-only dictionaries) of the groups whose member the respective
object is.
* :meth:`.GraphBuilder.groups` and :meth:`.Model.groups` are methods that collect
and return all groups within the graph/model.
Notes
-----
Note the following:
- Groups can only be filled upon initialization.
- After initialization, variables and nodes cannot be removed from a group.
Examples
--------
Add a variable to a group:
>>> my_var = lsl.Var(0.0, name="long_unique_variable_name")
>>> grp = lsl.Group(name="demo_group", short_name=my_var)
>>> grp
Group<demo_group>
Access the variable by its group-specific name:
>>> grp["short_name"]
Var<long_unique_variable_name>
Retrieve the value of a variable from a model state:
>>> model_state = {my_var.value_node.name: lsl.NodeState(10., False)}
>>> grp.value_from(model_state, "short_name")
10.0
"""
def __init__(self, name: str, **nodes_and_vars: Node | Var) -> None:
self._name = name
self._nodes_and_vars = nodes_and_vars
for member in self._nodes_and_vars.values():
if name in member.groups:
raise RuntimeError(
f"{repr(member)} is already a member of a group "
f"with the name {repr(name)}"
)
member._groups[name] = self
self._nodes = {
name: obj
for name, obj in self._nodes_and_vars.items()
if isinstance(obj, Node)
}
self._vars = {
name: obj
for name, obj in self._nodes_and_vars.items()
if isinstance(obj, Var)
}
@property
def name(self) -> str:
"""The group's name."""
return self._name
[docs] def value_from(self, model_state: dict[str, NodeState], name: str) -> Array:
"""
Retrieves the value of a node or variable that is a member of the group from
a model state.
Parameters
----------
model_state
The state of a Liesel model, i.e. a :class:`~.Model.state`.
name
The name of the node or variable within this group.
Returns
-------
The value of the node or variable.
"""
member = self[name]
if isinstance(member, Var):
value_name = member.value_node.name
else:
value_name = member.name
return model_state[value_name].value
@property
def vars(self) -> MappingProxyType[str, Var]:
"""A mapping of the variables in the group with their names as keys."""
return MappingProxyType(self._vars)
@property
def nodes(self) -> MappingProxyType[str, Node]:
"""A mapping of the nodes in the group with their names as keys."""
return MappingProxyType(self._nodes)
@property
def nodes_and_vars(self) -> MappingProxyType[str, Node | Var]:
"""A mapping of all group members with their names as keys."""
return MappingProxyType(self._nodes_and_vars)
def __contains__(self, key) -> bool:
return key in self._nodes_and_vars
def __getitem__(self, key) -> Var | Node:
return self._nodes_and_vars[key]
def __repr__(self) -> str:
return f"{type(self).__name__}<{self.name}>"
