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graphcast.architecture.actor

Actor-based system for graph data transformation and processing.

This module implements a system for processing and transforming graph data. It provides a flexible framework for defining and executing data transformations through a tree of actors. The system supports various types of actors:

  • VertexActor: Processes and transforms vertex data
  • EdgeActor: Handles edge creation and transformation
  • TransformActor: Applies transformations to data
  • DescendActor: Manages hierarchical processing of nested data structures

The module uses an action context to maintain state during processing and supports both synchronous and asynchronous operations. It integrates with the graph database infrastructure to handle vertex and edge operations.

Example

wrapper = ActorWrapper(vertex="user", discriminant="id") ctx = ActionContext() result = wrapper(ctx, doc={"id": "123", "name": "John"})

Actor

Bases: ABC

Abstract base class for all actors in the system.

Actors are the fundamental processing units in the graph transformation system. Each actor type implements specific functionality for processing graph data.

Source code in graphcast/architecture/actor.py 
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class Actor(ABC):
    """Abstract base class for all actors in the system.

    Actors are the fundamental processing units in the graph transformation system.
    Each actor type implements specific functionality for processing graph data.

    Attributes:
        None (abstract class)
    """

    @abstractmethod
    def __call__(self, ctx: ActionContext, *nargs, **kwargs):
        """Execute the actor's main processing logic.

        Args:
            ctx: The action context containing the current processing state
            *nargs: Additional positional arguments
            **kwargs: Additional keyword arguments

        Returns:
            Updated action context
        """
        pass

    def fetch_important_items(self):
        """Get a dictionary of important items for string representation.

        Returns:
            dict: Dictionary of important items
        """
        return {}

    def finish_init(self, **kwargs):
        """Complete initialization of the actor.

        Args:
            **kwargs: Additional initialization parameters
        """
        pass

    def init_transforms(self, **kwargs):
        """Initialize transformations for the actor.

        Args:
            **kwargs: Transformation parameters
        """
        pass

    def count(self):
        """Get the count of items processed by this actor.

        Returns:
            int: Number of items
        """
        return 1

    def _filter_items(self, items):
        """Filter out None and empty items.

        Args:
            items: Dictionary of items to filter

        Returns:
            dict: Filtered dictionary
        """
        return {k: v for k, v in items.items() if v is not None and v}

    def _stringify_items(self, items):
        """Convert items to string representation.

        Args:
            items: Dictionary of items to stringify

        Returns:
            dict: Dictionary with stringified values
        """
        return {
            k: ", ".join(list(v)) if isinstance(v, (tuple, list)) else v
            for k, v in items.items()
        }

    def __str__(self):
        """Get string representation of the actor.

        Returns:
            str: String representation
        """
        d = self.fetch_important_items()
        d = self._filter_items(d)
        d = self._stringify_items(d)
        d_list = [[k, d[k]] for k in sorted(d)]
        d_list_b = [type(self).__name__] + [": ".join(x) for x in d_list]
        d_list_str = "\n".join(d_list_b)
        return d_list_str

    __repr__ = __str__

    def fetch_actors(self, level, edges):
        """Fetch actor information for tree representation.

        Args:
            level: Current level in the actor tree
            edges: List of edges in the actor tree

        Returns:
            tuple: (level, actor_type, string_representation, edges)
        """
        return level, type(self), str(self), edges

__call__(ctx, *nargs, **kwargs) abstractmethod

Execute the actor's main processing logic.

Parameters:

Name Type Description Default
ctx ActionContext

The action context containing the current processing state

 required
*nargs

Additional positional arguments

 ()
**kwargs

Additional keyword arguments

 {}

Returns:

Type Description

Updated action context
Source code in graphcast/architecture/actor.py 
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@abstractmethod
def __call__(self, ctx: ActionContext, *nargs, **kwargs):
    """Execute the actor's main processing logic.

    Args:
        ctx: The action context containing the current processing state
        *nargs: Additional positional arguments
        **kwargs: Additional keyword arguments

    Returns:
        Updated action context
    """
    pass

__str__()

Get string representation of the actor.

Returns:

Name Type Description
str

String representation
Source code in graphcast/architecture/actor.py 
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def __str__(self):
    """Get string representation of the actor.

    Returns:
        str: String representation
    """
    d = self.fetch_important_items()
    d = self._filter_items(d)
    d = self._stringify_items(d)
    d_list = [[k, d[k]] for k in sorted(d)]
    d_list_b = [type(self).__name__] + [": ".join(x) for x in d_list]
    d_list_str = "\n".join(d_list_b)
    return d_list_str

count()

Get the count of items processed by this actor.

Returns:

Name Type Description
int

Number of items
Source code in graphcast/architecture/actor.py 
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def count(self):
    """Get the count of items processed by this actor.

    Returns:
        int: Number of items
    """
    return 1

fetch_actors(level, edges)

Fetch actor information for tree representation.

Parameters:

Name Type Description Default
level

Current level in the actor tree

 required
edges

List of edges in the actor tree

 required

Returns:

Name Type Description
tuple

(level, actor_type, string_representation, edges)
Source code in graphcast/architecture/actor.py 
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def fetch_actors(self, level, edges):
    """Fetch actor information for tree representation.

    Args:
        level: Current level in the actor tree
        edges: List of edges in the actor tree

    Returns:
        tuple: (level, actor_type, string_representation, edges)
    """
    return level, type(self), str(self), edges

fetch_important_items()

Get a dictionary of important items for string representation.

Returns:

Name Type Description
dict

Dictionary of important items
Source code in graphcast/architecture/actor.py 
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def fetch_important_items(self):
    """Get a dictionary of important items for string representation.

    Returns:
        dict: Dictionary of important items
    """
    return {}

finish_init(**kwargs)

Complete initialization of the actor.

Parameters:

Name Type Description Default
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def finish_init(self, **kwargs):
    """Complete initialization of the actor.

    Args:
        **kwargs: Additional initialization parameters
    """
    pass

init_transforms(**kwargs)

Initialize transformations for the actor.

Parameters:

Name Type Description Default
**kwargs

Transformation parameters

 {}
Source code in graphcast/architecture/actor.py 
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def init_transforms(self, **kwargs):
    """Initialize transformations for the actor.

    Args:
        **kwargs: Transformation parameters
    """
    pass

ActorWrapper

Wrapper class for managing actor instances.

This class provides a unified interface for creating and managing different types of actors, handling initialization and execution.

Attributes:

Name Type Description
actor Actor

The wrapped actor instance
vertex_config VertexConfig

Vertex configuration
edge_config EdgeConfig

Edge configuration
Source code in graphcast/architecture/actor.py 
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class ActorWrapper:
    """Wrapper class for managing actor instances.

    This class provides a unified interface for creating and managing different types
    of actors, handling initialization and execution.

    Attributes:
        actor: The wrapped actor instance
        vertex_config: Vertex configuration
        edge_config: Edge configuration
    """

    def __init__(self, *args, **kwargs):
        """Initialize the actor wrapper.

        Args:
            *args: Positional arguments for actor initialization
            **kwargs: Keyword arguments for actor initialization

        Raises:
            ValueError: If unable to initialize an actor
        """
        self.actor: Actor
        self.vertex_config: VertexConfig
        self.edge_config: EdgeConfig
        if self._try_init_descend(*args, **kwargs):
            pass
        elif self._try_init_transform(**kwargs):
            pass
        elif self._try_init_vertex(**kwargs):
            pass
        elif self._try_init_edge(**kwargs):
            pass
        else:
            raise ValueError(f"Not able to init ActionNodeWrapper with {kwargs}")

    def init_transforms(self, **kwargs):
        """Initialize transforms for the wrapped actor.

        Args:
            **kwargs: Transform initialization parameters
        """
        self.actor.init_transforms(**kwargs)

    def finish_init(self, **kwargs):
        """Complete initialization of the wrapped actor.

        Args:
            **kwargs: Additional initialization parameters
        """
        kwargs["transforms"]: dict[str, ProtoTransform] = kwargs.get("transforms", {})
        self.actor.init_transforms(**kwargs)

        self.vertex_config = kwargs.get("vertex_config", VertexConfig(vertices=[]))
        kwargs["vertex_config"] = self.vertex_config
        self.edge_config = kwargs.get("edge_config", EdgeConfig())
        kwargs["edge_config"] = self.edge_config
        self.actor.finish_init(**kwargs)

    def count(self):
        """Get count of items processed by the wrapped actor.

        Returns:
            int: Number of items
        """
        return self.actor.count()

    def _try_init_descend(self, *args, **kwargs) -> bool:
        """Try to initialize a descend actor.

        Args:
            *args: Positional arguments
            **kwargs: Keyword arguments

        Returns:
            bool: True if successful, False otherwise
        """
        descend_key_candidates = [kwargs.pop(k, None) for k in DESCEND_KEY_VALUES]
        descend_key_candidates = [x for x in descend_key_candidates if x is not None]
        descend_key = descend_key_candidates[0] if descend_key_candidates else None
        ds = kwargs.pop("apply", None)
        if ds is not None:
            if isinstance(ds, list):
                descendants = ds
            else:
                descendants = [ds]
        elif len(args) > 0:
            descendants = list(args)
        else:
            return False
        self.actor = DescendActor(descend_key, descendants_kwargs=descendants, **kwargs)
        return True

    def _try_init_transform(self, **kwargs) -> bool:
        """Try to initialize a transform actor.

        Args:
            **kwargs: Keyword arguments

        Returns:
            bool: True if successful, False otherwise
        """
        try:
            self.actor = TransformActor(**kwargs)
            return True
        except Exception:
            return False

    def _try_init_vertex(self, **kwargs) -> bool:
        """Try to initialize a vertex actor.

        Args:
            **kwargs: Keyword arguments

        Returns:
            bool: True if successful, False otherwise
        """
        try:
            self.actor = VertexActor(**kwargs)
            return True
        except Exception:
            return False

    def _try_init_edge(self, **kwargs) -> bool:
        """Try to initialize an edge actor.

        Args:
            **kwargs: Keyword arguments

        Returns:
            bool: True if successful, False otherwise
        """
        try:
            self.actor = EdgeActor(**kwargs)
            return True
        except Exception:
            return False

    def __call__(self, ctx: ActionContext, *nargs, **kwargs) -> ActionContext:
        """Execute the wrapped actor.

        Args:
            ctx: Action context
            *nargs: Additional positional arguments
            **kwargs: Additional keyword arguments

        Returns:
            Updated action context
        """
        ctx = self.actor(ctx, *nargs, **kwargs)
        return ctx

    def normalize_ctx(self, ctx: ActionContext) -> defaultdict[GraphEntity, list]:
        """Normalize the action context.

        Args:
            ctx: Action context to normalize

        Returns:
            defaultdict[GraphEntity, list]: Normalized context
        """
        for vertex in list(ctx.acc_v_local):
            discriminant_dd: defaultdict[Optional[str], list] = ctx.acc_v_local.pop(
                vertex, defaultdict(list)
            )
            for discriminant in list(discriminant_dd):
                vertices = discriminant_dd.pop(discriminant, [])
                ctx.acc_vertex[vertex][discriminant] += vertices

        for edge_id, edge in self.edge_config.edges_items():
            s, t, _ = edge_id
            edges_ids = [k for k in ctx.acc_global if not isinstance(k, str)]
            if not any(s == sp and t == tp for sp, tp, _ in edges_ids):
                extra_edges = render_edge(
                    edge=edge,
                    vertex_config=self.vertex_config,
                    acc_vertex=ctx.acc_vertex,
                )
                extra_edges = render_weights(
                    edge,
                    self.vertex_config,
                    ctx.acc_v_local,
                    ctx.cdoc,
                    extra_edges,
                )

                for relation, v in extra_edges.items():
                    ctx.acc_global[edge_id] += v

        for vertex, dd in ctx.acc_vertex.items():
            for discriminant, vertex_list in dd.items():
                vertex_list = pick_unique_dict(vertex_list)
                vvv = merge_doc_basis(
                    vertex_list,
                    tuple(self.vertex_config.index(vertex).fields),
                    discriminant_key=None,
                )

                ctx.acc_global[vertex] += vvv

        ctx = add_blank_collections(ctx, self.vertex_config)

        return ctx.acc_global

    @classmethod
    def from_dict(cls, data: dict | list):
        """Create an actor wrapper from a dictionary or list.

        Args:
            data: Dictionary or list containing actor configuration

        Returns:
            ActorWrapper: New actor wrapper instance
        """
        if isinstance(data, list):
            return cls(*data)
        else:
            return cls(**data)

    def assemble_tree(self, fig_path: Optional[Path] = None):
        """Assemble and optionally visualize the actor tree.

        Args:
            fig_path: Optional path to save the visualization

        Returns:
            Optional[networkx.MultiDiGraph]: Graph representation of the actor tree
        """
        _, _, _, edges = self.fetch_actors(0, [])
        logger.info(f"{len(edges)}")
        try:
            import networkx as nx
        except ImportError as e:
            logger.error(f"not able to import networks {e}")
            return None
        nodes = {}
        g = nx.MultiDiGraph()
        for ha, hb, pa, pb in edges:
            nodes[ha] = pa
            nodes[hb] = pb
        from graphcast.plot.plotter import fillcolor_palette

        map_class2color = {
            DescendActor: fillcolor_palette["green"],
            VertexActor: "orange",
            EdgeActor: fillcolor_palette["violet"],
            TransformActor: fillcolor_palette["blue"],
        }

        for n, props in nodes.items():
            nodes[n]["fillcolor"] = map_class2color[props["class"]]
            nodes[n]["style"] = "filled"
            nodes[n]["color"] = "brown"

        edges = [(ha, hb) for ha, hb, _, _ in edges]
        g.add_edges_from(edges)
        g.add_nodes_from(nodes.items())

        if fig_path is not None:
            ag = nx.nx_agraph.to_agraph(g)
            ag.draw(
                fig_path,
                "pdf",
                prog="dot",
            )
            return None
        else:
            return g

    def fetch_actors(self, level, edges):
        """Fetch actor information for tree representation.

        Args:
            level: Current level in the actor tree
            edges: List of edges in the actor tree

        Returns:
            tuple: (level, actor_type, string_representation, edges)
        """
        return self.actor.fetch_actors(level, edges)

__call__(ctx, *nargs, **kwargs)

Execute the wrapped actor.

Parameters:

Name Type Description Default
ctx ActionContext

Action context

 required
*nargs

Additional positional arguments

 ()
**kwargs

Additional keyword arguments

 {}

Returns:

Type Description
ActionContext

Updated action context
Source code in graphcast/architecture/actor.py 
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def __call__(self, ctx: ActionContext, *nargs, **kwargs) -> ActionContext:
    """Execute the wrapped actor.

    Args:
        ctx: Action context
        *nargs: Additional positional arguments
        **kwargs: Additional keyword arguments

    Returns:
        Updated action context
    """
    ctx = self.actor(ctx, *nargs, **kwargs)
    return ctx

__init__(*args, **kwargs)

Initialize the actor wrapper.

Parameters:

Name Type Description Default
*args

Positional arguments for actor initialization

 ()
**kwargs

Keyword arguments for actor initialization

 {}

Raises:

Type Description
ValueError

If unable to initialize an actor
Source code in graphcast/architecture/actor.py 
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def __init__(self, *args, **kwargs):
    """Initialize the actor wrapper.

    Args:
        *args: Positional arguments for actor initialization
        **kwargs: Keyword arguments for actor initialization

    Raises:
        ValueError: If unable to initialize an actor
    """
    self.actor: Actor
    self.vertex_config: VertexConfig
    self.edge_config: EdgeConfig
    if self._try_init_descend(*args, **kwargs):
        pass
    elif self._try_init_transform(**kwargs):
        pass
    elif self._try_init_vertex(**kwargs):
        pass
    elif self._try_init_edge(**kwargs):
        pass
    else:
        raise ValueError(f"Not able to init ActionNodeWrapper with {kwargs}")

assemble_tree(fig_path=None)

Assemble and optionally visualize the actor tree.

Parameters:

Name Type Description Default
fig_path Optional[Path]

Optional path to save the visualization

 None

Returns:

Type Description

Optional[networkx.MultiDiGraph]: Graph representation of the actor tree
Source code in graphcast/architecture/actor.py 
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def assemble_tree(self, fig_path: Optional[Path] = None):
    """Assemble and optionally visualize the actor tree.

    Args:
        fig_path: Optional path to save the visualization

    Returns:
        Optional[networkx.MultiDiGraph]: Graph representation of the actor tree
    """
    _, _, _, edges = self.fetch_actors(0, [])
    logger.info(f"{len(edges)}")
    try:
        import networkx as nx
    except ImportError as e:
        logger.error(f"not able to import networks {e}")
        return None
    nodes = {}
    g = nx.MultiDiGraph()
    for ha, hb, pa, pb in edges:
        nodes[ha] = pa
        nodes[hb] = pb
    from graphcast.plot.plotter import fillcolor_palette

    map_class2color = {
        DescendActor: fillcolor_palette["green"],
        VertexActor: "orange",
        EdgeActor: fillcolor_palette["violet"],
        TransformActor: fillcolor_palette["blue"],
    }

    for n, props in nodes.items():
        nodes[n]["fillcolor"] = map_class2color[props["class"]]
        nodes[n]["style"] = "filled"
        nodes[n]["color"] = "brown"

    edges = [(ha, hb) for ha, hb, _, _ in edges]
    g.add_edges_from(edges)
    g.add_nodes_from(nodes.items())

    if fig_path is not None:
        ag = nx.nx_agraph.to_agraph(g)
        ag.draw(
            fig_path,
            "pdf",
            prog="dot",
        )
        return None
    else:
        return g

count()

Get count of items processed by the wrapped actor.

Returns:

Name Type Description
int

Number of items
Source code in graphcast/architecture/actor.py 
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def count(self):
    """Get count of items processed by the wrapped actor.

    Returns:
        int: Number of items
    """
    return self.actor.count()

fetch_actors(level, edges)

Fetch actor information for tree representation.

Parameters:

Name Type Description Default
level

Current level in the actor tree

 required
edges

List of edges in the actor tree

 required

Returns:

Name Type Description
tuple

(level, actor_type, string_representation, edges)
Source code in graphcast/architecture/actor.py 
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def fetch_actors(self, level, edges):
    """Fetch actor information for tree representation.

    Args:
        level: Current level in the actor tree
        edges: List of edges in the actor tree

    Returns:
        tuple: (level, actor_type, string_representation, edges)
    """
    return self.actor.fetch_actors(level, edges)

finish_init(**kwargs)

Complete initialization of the wrapped actor.

Parameters:

Name Type Description Default
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def finish_init(self, **kwargs):
    """Complete initialization of the wrapped actor.

    Args:
        **kwargs: Additional initialization parameters
    """
    kwargs["transforms"]: dict[str, ProtoTransform] = kwargs.get("transforms", {})
    self.actor.init_transforms(**kwargs)

    self.vertex_config = kwargs.get("vertex_config", VertexConfig(vertices=[]))
    kwargs["vertex_config"] = self.vertex_config
    self.edge_config = kwargs.get("edge_config", EdgeConfig())
    kwargs["edge_config"] = self.edge_config
    self.actor.finish_init(**kwargs)

from_dict(data) classmethod

Create an actor wrapper from a dictionary or list.

Parameters:

Name Type Description Default
data dict | list

Dictionary or list containing actor configuration

 required

Returns:

Name Type Description
ActorWrapper

New actor wrapper instance
Source code in graphcast/architecture/actor.py 
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@classmethod
def from_dict(cls, data: dict | list):
    """Create an actor wrapper from a dictionary or list.

    Args:
        data: Dictionary or list containing actor configuration

    Returns:
        ActorWrapper: New actor wrapper instance
    """
    if isinstance(data, list):
        return cls(*data)
    else:
        return cls(**data)

init_transforms(**kwargs)

Initialize transforms for the wrapped actor.

Parameters:

Name Type Description Default
**kwargs

Transform initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def init_transforms(self, **kwargs):
    """Initialize transforms for the wrapped actor.

    Args:
        **kwargs: Transform initialization parameters
    """
    self.actor.init_transforms(**kwargs)

normalize_ctx(ctx)

Normalize the action context.

Parameters:

Name Type Description Default
ctx ActionContext

Action context to normalize

 required

Returns:

Type Description
defaultdict[GraphEntity, list]

defaultdict[GraphEntity, list]: Normalized context
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def normalize_ctx(self, ctx: ActionContext) -> defaultdict[GraphEntity, list]:
    """Normalize the action context.

    Args:
        ctx: Action context to normalize

    Returns:
        defaultdict[GraphEntity, list]: Normalized context
    """
    for vertex in list(ctx.acc_v_local):
        discriminant_dd: defaultdict[Optional[str], list] = ctx.acc_v_local.pop(
            vertex, defaultdict(list)
        )
        for discriminant in list(discriminant_dd):
            vertices = discriminant_dd.pop(discriminant, [])
            ctx.acc_vertex[vertex][discriminant] += vertices

    for edge_id, edge in self.edge_config.edges_items():
        s, t, _ = edge_id
        edges_ids = [k for k in ctx.acc_global if not isinstance(k, str)]
        if not any(s == sp and t == tp for sp, tp, _ in edges_ids):
            extra_edges = render_edge(
                edge=edge,
                vertex_config=self.vertex_config,
                acc_vertex=ctx.acc_vertex,
            )
            extra_edges = render_weights(
                edge,
                self.vertex_config,
                ctx.acc_v_local,
                ctx.cdoc,
                extra_edges,
            )

            for relation, v in extra_edges.items():
                ctx.acc_global[edge_id] += v

    for vertex, dd in ctx.acc_vertex.items():
        for discriminant, vertex_list in dd.items():
            vertex_list = pick_unique_dict(vertex_list)
            vvv = merge_doc_basis(
                vertex_list,
                tuple(self.vertex_config.index(vertex).fields),
                discriminant_key=None,
            )

            ctx.acc_global[vertex] += vvv

    ctx = add_blank_collections(ctx, self.vertex_config)

    return ctx.acc_global

DescendActor

Bases: Actor

Actor for processing hierarchical data structures.

This actor manages the processing of nested data structures by coordinating the execution of child actors.

Attributes:

Name Type Description
key

Optional key for accessing nested data
_descendants list[ActorWrapper]

List of child actor wrappers
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class DescendActor(Actor):
    """Actor for processing hierarchical data structures.

    This actor manages the processing of nested data structures by coordinating
    the execution of child actors.

    Attributes:
        key: Optional key for accessing nested data
        _descendants: List of child actor wrappers
    """

    def __init__(self, key: Optional[str], descendants_kwargs: list, **kwargs):
        """Initialize the descend actor.

        Args:
            key: Optional key for accessing nested data
            descendants_kwargs: List of child actor configurations
            **kwargs: Additional initialization parameters
        """
        self.key = key
        self._descendants: list[ActorWrapper] = []
        for descendant_kwargs in descendants_kwargs:
            self._descendants += [ActorWrapper(**descendant_kwargs, **kwargs)]

    def fetch_important_items(self):
        """Get important items for string representation.

        Returns:
            dict: Dictionary of important items
        """
        sd = self.__dict__
        sm = {k: sd[k] for k in ["key"]}
        return {**sm}

    def add_descendant(self, d: ActorWrapper):
        """Add a child actor wrapper.

        Args:
            d: Actor wrapper to add
        """
        self._descendants += [d]

    def count(self):
        """Get total count of items processed by all descendants.

        Returns:
            int: Total count
        """
        return sum(d.count() for d in self.descendants)

    @property
    def descendants(self) -> list[ActorWrapper]:
        """Get sorted list of descendant actors.

        Returns:
            list[ActorWrapper]: Sorted list of descendant actors
        """
        return sorted(self._descendants, key=lambda x: _NodeTypePriority[type(x.actor)])

    def init_transforms(self, **kwargs):
        """Initialize transforms for all descendants.

        Args:
            **kwargs: Transform initialization parameters
        """
        for an in self.descendants:
            an.init_transforms(**kwargs)

    def finish_init(self, **kwargs):
        """Complete initialization of the descend actor and its descendants.

        Args:
            **kwargs: Additional initialization parameters
        """
        self.vertex_config: VertexConfig = kwargs.get(
            "vertex_config", VertexConfig(vertices=[])
        )

        for an in self.descendants:
            an.finish_init(**kwargs)

        available_fields = set()
        for anw in self.descendants:
            actor = anw.actor
            if isinstance(actor, TransformActor):
                available_fields |= set(list(actor.t.output))

        present_vertices = [
            anw.actor.name
            for anw in self.descendants
            if isinstance(anw.actor, VertexActor)
        ]

        for v in present_vertices:
            available_fields -= set(self.vertex_config.fields(v))

        for v in self.vertex_config.vertex_list:
            intersection = available_fields & set(v.fields)
            if intersection and v.name not in present_vertices:
                new_descendant = ActorWrapper(vertex=v.name)
                new_descendant.finish_init(**kwargs)
                self.add_descendant(new_descendant)

        logger.debug(
            f"""type, priority: {
                [
                    (t.__name__, _NodeTypePriority[t])
                    for t in (type(x.actor) for x in self.descendants)
                ]
            }"""
        )

    def __call__(self, ctx: ActionContext, **kwargs):
        """Process hierarchical data structure.

        Args:
            ctx: Action context
            **kwargs: Additional keyword arguments including 'doc'

        Returns:
            Updated action context

        Raises:
            ValueError: If no document is provided
        """
        doc = kwargs.pop("doc")

        if doc is None:
            raise ValueError(f"{type(self).__name__}: doc should be provided")

        if not doc:
            return ctx

        if self.key is not None:
            if isinstance(doc, dict) and self.key in doc:
                doc = doc[self.key]
            else:
                logging.error(f"doc {doc} was expected to have level {self.key}")
                return ctx

        doc_level = doc if isinstance(doc, list) else [doc]

        logger.debug(f"{len(doc_level)}")

        for i, sub_doc in enumerate(doc_level):
            logger.debug(f"docs: {i + 1}/{len(doc_level)}")
            if isinstance(sub_doc, dict):
                nargs: tuple = tuple()
                kwargs["doc"] = sub_doc
            else:
                nargs = (sub_doc,)
            ctx.cdoc = {}

            for j, anw in enumerate(self.descendants):
                logger.debug(
                    f"{type(anw.actor).__name__}: {j + 1}/{len(self.descendants)}"
                )
                ctx = anw(ctx, *nargs, **kwargs)
        return ctx

    def fetch_actors(self, level, edges):
        """Fetch actor information for tree representation.

        Args:
            level: Current level in the actor tree
            edges: List of edges in the actor tree

        Returns:
            tuple: (level, actor_type, string_representation, edges)
        """
        label_current = str(self)
        cname_current = type(self)
        hash_current = hash((level, cname_current, label_current))
        logger.info(f"{hash_current}, {level, cname_current, label_current}")
        props_current = {"label": label_current, "class": cname_current, "level": level}
        for d in self.descendants:
            level_a, cname, label_a, edges_a = d.fetch_actors(level + 1, edges)
            hash_a = hash((level_a, cname, label_a))
            props_a = {"label": label_a, "class": cname, "level": level_a}
            edges = [(hash_current, hash_a, props_current, props_a)] + edges_a
        return level, type(self), str(self), edges

descendants property

Get sorted list of descendant actors.

Returns:

Type Description
list[ActorWrapper]

list[ActorWrapper]: Sorted list of descendant actors

__call__(ctx, **kwargs)

Process hierarchical data structure.

Parameters:

Name Type Description Default
ctx ActionContext

Action context

 required
**kwargs

Additional keyword arguments including 'doc'

 {}

Returns:

Type Description

Updated action context

Raises:

Type Description
ValueError

If no document is provided
Source code in graphcast/architecture/actor.py 
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def __call__(self, ctx: ActionContext, **kwargs):
    """Process hierarchical data structure.

    Args:
        ctx: Action context
        **kwargs: Additional keyword arguments including 'doc'

    Returns:
        Updated action context

    Raises:
        ValueError: If no document is provided
    """
    doc = kwargs.pop("doc")

    if doc is None:
        raise ValueError(f"{type(self).__name__}: doc should be provided")

    if not doc:
        return ctx

    if self.key is not None:
        if isinstance(doc, dict) and self.key in doc:
            doc = doc[self.key]
        else:
            logging.error(f"doc {doc} was expected to have level {self.key}")
            return ctx

    doc_level = doc if isinstance(doc, list) else [doc]

    logger.debug(f"{len(doc_level)}")

    for i, sub_doc in enumerate(doc_level):
        logger.debug(f"docs: {i + 1}/{len(doc_level)}")
        if isinstance(sub_doc, dict):
            nargs: tuple = tuple()
            kwargs["doc"] = sub_doc
        else:
            nargs = (sub_doc,)
        ctx.cdoc = {}

        for j, anw in enumerate(self.descendants):
            logger.debug(
                f"{type(anw.actor).__name__}: {j + 1}/{len(self.descendants)}"
            )
            ctx = anw(ctx, *nargs, **kwargs)
    return ctx

__init__(key, descendants_kwargs, **kwargs)

Initialize the descend actor.

Parameters:

Name Type Description Default
key Optional[str]

Optional key for accessing nested data

 required
descendants_kwargs list

List of child actor configurations

 required
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def __init__(self, key: Optional[str], descendants_kwargs: list, **kwargs):
    """Initialize the descend actor.

    Args:
        key: Optional key for accessing nested data
        descendants_kwargs: List of child actor configurations
        **kwargs: Additional initialization parameters
    """
    self.key = key
    self._descendants: list[ActorWrapper] = []
    for descendant_kwargs in descendants_kwargs:
        self._descendants += [ActorWrapper(**descendant_kwargs, **kwargs)]

add_descendant(d)

Add a child actor wrapper.

Parameters:

Name Type Description Default
d ActorWrapper

Actor wrapper to add

 required
Source code in graphcast/architecture/actor.py 
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def add_descendant(self, d: ActorWrapper):
    """Add a child actor wrapper.

    Args:
        d: Actor wrapper to add
    """
    self._descendants += [d]

count()

Get total count of items processed by all descendants.

Returns:

Name Type Description
int

Total count
Source code in graphcast/architecture/actor.py 
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def count(self):
    """Get total count of items processed by all descendants.

    Returns:
        int: Total count
    """
    return sum(d.count() for d in self.descendants)

fetch_actors(level, edges)

Fetch actor information for tree representation.

Parameters:

Name Type Description Default
level

Current level in the actor tree

 required
edges

List of edges in the actor tree

 required

Returns:

Name Type Description
tuple

(level, actor_type, string_representation, edges)
Source code in graphcast/architecture/actor.py 
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def fetch_actors(self, level, edges):
    """Fetch actor information for tree representation.

    Args:
        level: Current level in the actor tree
        edges: List of edges in the actor tree

    Returns:
        tuple: (level, actor_type, string_representation, edges)
    """
    label_current = str(self)
    cname_current = type(self)
    hash_current = hash((level, cname_current, label_current))
    logger.info(f"{hash_current}, {level, cname_current, label_current}")
    props_current = {"label": label_current, "class": cname_current, "level": level}
    for d in self.descendants:
        level_a, cname, label_a, edges_a = d.fetch_actors(level + 1, edges)
        hash_a = hash((level_a, cname, label_a))
        props_a = {"label": label_a, "class": cname, "level": level_a}
        edges = [(hash_current, hash_a, props_current, props_a)] + edges_a
    return level, type(self), str(self), edges

fetch_important_items()

Get important items for string representation.

Returns:

Name Type Description
dict

Dictionary of important items
Source code in graphcast/architecture/actor.py 
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def fetch_important_items(self):
    """Get important items for string representation.

    Returns:
        dict: Dictionary of important items
    """
    sd = self.__dict__
    sm = {k: sd[k] for k in ["key"]}
    return {**sm}

finish_init(**kwargs)

Complete initialization of the descend actor and its descendants.

Parameters:

Name Type Description Default
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def finish_init(self, **kwargs):
    """Complete initialization of the descend actor and its descendants.

    Args:
        **kwargs: Additional initialization parameters
    """
    self.vertex_config: VertexConfig = kwargs.get(
        "vertex_config", VertexConfig(vertices=[])
    )

    for an in self.descendants:
        an.finish_init(**kwargs)

    available_fields = set()
    for anw in self.descendants:
        actor = anw.actor
        if isinstance(actor, TransformActor):
            available_fields |= set(list(actor.t.output))

    present_vertices = [
        anw.actor.name
        for anw in self.descendants
        if isinstance(anw.actor, VertexActor)
    ]

    for v in present_vertices:
        available_fields -= set(self.vertex_config.fields(v))

    for v in self.vertex_config.vertex_list:
        intersection = available_fields & set(v.fields)
        if intersection and v.name not in present_vertices:
            new_descendant = ActorWrapper(vertex=v.name)
            new_descendant.finish_init(**kwargs)
            self.add_descendant(new_descendant)

    logger.debug(
        f"""type, priority: {
            [
                (t.__name__, _NodeTypePriority[t])
                for t in (type(x.actor) for x in self.descendants)
            ]
        }"""
    )

init_transforms(**kwargs)

Initialize transforms for all descendants.

Parameters:

Name Type Description Default
**kwargs

Transform initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def init_transforms(self, **kwargs):
    """Initialize transforms for all descendants.

    Args:
        **kwargs: Transform initialization parameters
    """
    for an in self.descendants:
        an.init_transforms(**kwargs)

EdgeActor

Bases: Actor

Actor for processing edge data.

This actor handles the creation and transformation of edges between vertices, including weight calculations and relationship management.

Attributes:

Name Type Description
edge

Edge configuration
vertex_config VertexConfig

Vertex configuration
Source code in graphcast/architecture/actor.py 
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class EdgeActor(Actor):
    """Actor for processing edge data.

    This actor handles the creation and transformation of edges between vertices,
    including weight calculations and relationship management.

    Attributes:
        edge: Edge configuration
        vertex_config: Vertex configuration
    """

    def __init__(
        self,
        **kwargs,
    ):
        """Initialize the edge actor.

        Args:
            **kwargs: Edge configuration parameters
        """
        self.edge = Edge.from_dict(kwargs)
        self.vertex_config: VertexConfig

    def fetch_important_items(self):
        """Get important items for string representation.

        Returns:
            dict: Dictionary of important items
        """
        sd = self.edge.__dict__
        return {
            k: sd[k]
            for k in ["source", "target", "source_discriminant", "target_discriminant"]
        }

    def finish_init(self, **kwargs):
        """Complete initialization of the edge actor.

        Args:
            **kwargs: Additional initialization parameters
        """
        self.vertex_config: VertexConfig = kwargs.pop("vertex_config")
        edge_config: Optional[EdgeConfig] = kwargs.pop("edge_config", None)
        if edge_config is not None and self.vertex_config is not None:
            self.edge.finish_init(vertex_config=self.vertex_config)
            edge_config.update_edges(self.edge, vertex_config=self.vertex_config)

    def __call__(self, ctx: ActionContext, *nargs, **kwargs):
        """Process edge data.

        Args:
            ctx: Action context
            *nargs: Additional positional arguments
            **kwargs: Additional keyword arguments

        Returns:
            Updated action context
        """
        edges = render_edge(self.edge, self.vertex_config, ctx.acc_v_local)

        edges = render_weights(
            self.edge, self.vertex_config, ctx.acc_v_local, ctx.cdoc, edges
        )

        for relation, v in edges.items():
            ctx.acc_global[self.edge.source, self.edge.target, relation] += v

        ctx.acc_vertex[self.edge.source][self.edge.source_discriminant] += (
            ctx.acc_v_local[self.edge.source].pop(self.edge.source_discriminant, [])
        )
        ctx.acc_vertex[self.edge.target][self.edge.target_discriminant] += (
            ctx.acc_v_local[self.edge.target].pop(self.edge.target_discriminant, [])
        )

        return ctx

__call__(ctx, *nargs, **kwargs)

Process edge data.

Parameters:

Name Type Description Default
ctx ActionContext

Action context

 required
*nargs

Additional positional arguments

 ()
**kwargs

Additional keyword arguments

 {}

Returns:

Type Description

Updated action context
Source code in graphcast/architecture/actor.py 
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def __call__(self, ctx: ActionContext, *nargs, **kwargs):
    """Process edge data.

    Args:
        ctx: Action context
        *nargs: Additional positional arguments
        **kwargs: Additional keyword arguments

    Returns:
        Updated action context
    """
    edges = render_edge(self.edge, self.vertex_config, ctx.acc_v_local)

    edges = render_weights(
        self.edge, self.vertex_config, ctx.acc_v_local, ctx.cdoc, edges
    )

    for relation, v in edges.items():
        ctx.acc_global[self.edge.source, self.edge.target, relation] += v

    ctx.acc_vertex[self.edge.source][self.edge.source_discriminant] += (
        ctx.acc_v_local[self.edge.source].pop(self.edge.source_discriminant, [])
    )
    ctx.acc_vertex[self.edge.target][self.edge.target_discriminant] += (
        ctx.acc_v_local[self.edge.target].pop(self.edge.target_discriminant, [])
    )

    return ctx

__init__(**kwargs)

Initialize the edge actor.

Parameters:

Name Type Description Default
**kwargs

Edge configuration parameters

 {}
Source code in graphcast/architecture/actor.py 
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def __init__(
    self,
    **kwargs,
):
    """Initialize the edge actor.

    Args:
        **kwargs: Edge configuration parameters
    """
    self.edge = Edge.from_dict(kwargs)
    self.vertex_config: VertexConfig

fetch_important_items()

Get important items for string representation.

Returns:

Name Type Description
dict

Dictionary of important items
Source code in graphcast/architecture/actor.py 
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def fetch_important_items(self):
    """Get important items for string representation.

    Returns:
        dict: Dictionary of important items
    """
    sd = self.edge.__dict__
    return {
        k: sd[k]
        for k in ["source", "target", "source_discriminant", "target_discriminant"]
    }

finish_init(**kwargs)

Complete initialization of the edge actor.

Parameters:

Name Type Description Default
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def finish_init(self, **kwargs):
    """Complete initialization of the edge actor.

    Args:
        **kwargs: Additional initialization parameters
    """
    self.vertex_config: VertexConfig = kwargs.pop("vertex_config")
    edge_config: Optional[EdgeConfig] = kwargs.pop("edge_config", None)
    if edge_config is not None and self.vertex_config is not None:
        self.edge.finish_init(vertex_config=self.vertex_config)
        edge_config.update_edges(self.edge, vertex_config=self.vertex_config)

TransformActor

Bases: Actor

Actor for applying transformations to data.

This actor handles the application of transformations to input data, supporting both simple and complex transformation scenarios.

Attributes:

Name Type Description
_kwargs

Original initialization parameters
vertex Optional[str]

Optional target vertex
transforms dict

Dictionary of available transforms
name

Transform name
params

Transform parameters
t Transform

Transform instance
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class TransformActor(Actor):
    """Actor for applying transformations to data.

    This actor handles the application of transformations to input data, supporting
    both simple and complex transformation scenarios.

    Attributes:
        _kwargs: Original initialization parameters
        vertex: Optional target vertex
        transforms: Dictionary of available transforms
        name: Transform name
        params: Transform parameters
        t: Transform instance
    """

    def __init__(self, **kwargs):
        """Initialize the transform actor.

        Args:
            **kwargs: Transform configuration parameters
        """
        self._kwargs = kwargs
        self.vertex: Optional[str] = kwargs.pop("target_vertex", None)
        self.transforms: dict
        self.name = kwargs.get("name", None)
        self.params = kwargs.get("params", {})
        self.t: Transform = Transform(**kwargs)

    def fetch_important_items(self):
        """Get important items for string representation.

        Returns:
            dict: Dictionary of important items
        """
        sd = self.__dict__
        sm = {k: sd[k] for k in ["name", "vertex"]}
        smb = {"t.input": self.t.input, "t.output": self.t.output}
        return {**sm, **smb}

    def init_transforms(self, **kwargs):
        """Initialize available transforms.

        Args:
            **kwargs: Transform initialization parameters
        """
        self.transforms = kwargs.pop("transforms", {})
        try:
            pt = ProtoTransform(
                **{
                    k: self._kwargs[k]
                    for k in ProtoTransform.get_fields_members()
                    if k in self._kwargs
                }
            )
            if pt.name is not None and pt._foo is not None:
                if pt.name not in self.transforms:
                    self.transforms[pt.name] = pt
                elif pt.params:
                    self.transforms[pt.name] = pt
        except Exception:
            pass

    def finish_init(self, **kwargs):
        """Complete initialization of the transform actor.

        Args:
            **kwargs: Additional initialization parameters
        """
        self.transforms: dict[str, ProtoTransform] = kwargs.pop("transforms", {})

        if self.name is not None:
            pt = self.transforms.get(self.name, None)
            if pt is not None:
                self.t._foo = pt._foo
                self.t.module = pt.module
                self.t.foo = pt.foo
                if pt.params and not self.t.params:
                    self.t.params = pt.params
                    if (
                        pt.input
                        and not self.t.input
                        and pt.output
                        and not self.t.output
                    ):
                        self.t.input = pt.input
                        self.t.output = pt.output
                        self.t.__post_init__()

    def __call__(self, ctx: ActionContext, *nargs, **kwargs):
        """Apply transformation to input data.

        Args:
            ctx: Action context
            *nargs: Additional positional arguments
            **kwargs: Additional keyword arguments including 'doc'

        Returns:
            Updated action context

        Raises:
            ValueError: If no document is provided
        """
        logging.debug(f"transforms : {id(self.transforms)} {len(self.transforms)}")

        if kwargs:
            doc: Optional[dict] = kwargs.get("doc")
        elif nargs:
            doc = nargs[0]
        else:
            raise ValueError(f"{type(self).__name__}: doc should be provided")

        _update_doc: dict
        if isinstance(doc, dict):
            _update_doc = self.t(doc)
        else:
            value = self.t(doc)
            if isinstance(value, tuple):
                _update_doc = {
                    f"{DRESSING_TRANSFORMED_VALUE_KEY}#{j}": v
                    for j, v in enumerate(value)
                }
            elif isinstance(value, dict):
                _update_doc = value
            else:
                _update_doc = {f"{DRESSING_TRANSFORMED_VALUE_KEY}#0": value}

        if self.vertex is None:
            ctx.cdoc.update(_update_doc)
        else:
            ctx.buffer_vertex[self.vertex] = _update_doc
        return ctx

__call__(ctx, *nargs, **kwargs)

Apply transformation to input data.

Parameters:

Name Type Description Default
ctx ActionContext

Action context

 required
*nargs

Additional positional arguments

 ()
**kwargs

Additional keyword arguments including 'doc'

 {}

Returns:

Type Description

Updated action context

Raises:

Type Description
ValueError

If no document is provided
Source code in graphcast/architecture/actor.py 
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def __call__(self, ctx: ActionContext, *nargs, **kwargs):
    """Apply transformation to input data.

    Args:
        ctx: Action context
        *nargs: Additional positional arguments
        **kwargs: Additional keyword arguments including 'doc'

    Returns:
        Updated action context

    Raises:
        ValueError: If no document is provided
    """
    logging.debug(f"transforms : {id(self.transforms)} {len(self.transforms)}")

    if kwargs:
        doc: Optional[dict] = kwargs.get("doc")
    elif nargs:
        doc = nargs[0]
    else:
        raise ValueError(f"{type(self).__name__}: doc should be provided")

    _update_doc: dict
    if isinstance(doc, dict):
        _update_doc = self.t(doc)
    else:
        value = self.t(doc)
        if isinstance(value, tuple):
            _update_doc = {
                f"{DRESSING_TRANSFORMED_VALUE_KEY}#{j}": v
                for j, v in enumerate(value)
            }
        elif isinstance(value, dict):
            _update_doc = value
        else:
            _update_doc = {f"{DRESSING_TRANSFORMED_VALUE_KEY}#0": value}

    if self.vertex is None:
        ctx.cdoc.update(_update_doc)
    else:
        ctx.buffer_vertex[self.vertex] = _update_doc
    return ctx

__init__(**kwargs)

Initialize the transform actor.

Parameters:

Name Type Description Default
**kwargs

Transform configuration parameters

 {}
Source code in graphcast/architecture/actor.py 
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def __init__(self, **kwargs):
    """Initialize the transform actor.

    Args:
        **kwargs: Transform configuration parameters
    """
    self._kwargs = kwargs
    self.vertex: Optional[str] = kwargs.pop("target_vertex", None)
    self.transforms: dict
    self.name = kwargs.get("name", None)
    self.params = kwargs.get("params", {})
    self.t: Transform = Transform(**kwargs)

fetch_important_items()

Get important items for string representation.

Returns:

Name Type Description
dict

Dictionary of important items
Source code in graphcast/architecture/actor.py 
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def fetch_important_items(self):
    """Get important items for string representation.

    Returns:
        dict: Dictionary of important items
    """
    sd = self.__dict__
    sm = {k: sd[k] for k in ["name", "vertex"]}
    smb = {"t.input": self.t.input, "t.output": self.t.output}
    return {**sm, **smb}

finish_init(**kwargs)

Complete initialization of the transform actor.

Parameters:

Name Type Description Default
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def finish_init(self, **kwargs):
    """Complete initialization of the transform actor.

    Args:
        **kwargs: Additional initialization parameters
    """
    self.transforms: dict[str, ProtoTransform] = kwargs.pop("transforms", {})

    if self.name is not None:
        pt = self.transforms.get(self.name, None)
        if pt is not None:
            self.t._foo = pt._foo
            self.t.module = pt.module
            self.t.foo = pt.foo
            if pt.params and not self.t.params:
                self.t.params = pt.params
                if (
                    pt.input
                    and not self.t.input
                    and pt.output
                    and not self.t.output
                ):
                    self.t.input = pt.input
                    self.t.output = pt.output
                    self.t.__post_init__()

init_transforms(**kwargs)

Initialize available transforms.

Parameters:

Name Type Description Default
**kwargs

Transform initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def init_transforms(self, **kwargs):
    """Initialize available transforms.

    Args:
        **kwargs: Transform initialization parameters
    """
    self.transforms = kwargs.pop("transforms", {})
    try:
        pt = ProtoTransform(
            **{
                k: self._kwargs[k]
                for k in ProtoTransform.get_fields_members()
                if k in self._kwargs
            }
        )
        if pt.name is not None and pt._foo is not None:
            if pt.name not in self.transforms:
                self.transforms[pt.name] = pt
            elif pt.params:
                self.transforms[pt.name] = pt
    except Exception:
        pass

VertexActor

Bases: Actor

Actor for processing vertex data.

This actor handles the processing and transformation of vertex data, including field selection and discriminant handling.

Attributes:

Name Type Description
name

Name of the vertex
discriminant Optional[str]

Optional discriminant field
keep_fields Optional[tuple[str]]

Optional tuple of fields to keep
vertex_config VertexConfig

Configuration for the vertex
Source code in graphcast/architecture/actor.py 
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class VertexActor(Actor):
    """Actor for processing vertex data.

    This actor handles the processing and transformation of vertex data, including
    field selection and discriminant handling.

    Attributes:
        name: Name of the vertex
        discriminant: Optional discriminant field
        keep_fields: Optional tuple of fields to keep
        vertex_config: Configuration for the vertex
    """

    def __init__(
        self,
        vertex: str,
        discriminant: Optional[str] = None,
        keep_fields: Optional[tuple[str]] = None,
        **kwargs,
    ):
        """Initialize the vertex actor.

        Args:
            vertex: Name of the vertex
            discriminant: Optional discriminant field
            keep_fields: Optional tuple of fields to keep
            **kwargs: Additional initialization parameters
        """
        self.name = vertex
        self.discriminant: Optional[str] = discriminant
        self.keep_fields: Optional[tuple[str]] = keep_fields
        self.vertex_config: VertexConfig

    def fetch_important_items(self):
        """Get important items for string representation.

        Returns:
            dict: Dictionary of important items
        """
        sd = self.__dict__
        return {k: sd[k] for k in ["name", "discriminant", "keep_fields"]}

    def finish_init(self, **kwargs):
        """Complete initialization of the vertex actor.

        Args:
            **kwargs: Additional initialization parameters
        """
        self.vertex_config: VertexConfig = kwargs.pop("vertex_config")
        self.vertex_config.discriminant_chart[self.name] = True

    def __call__(self, ctx: ActionContext, *nargs, **kwargs):
        """Process vertex data.

        Args:
            ctx: Action context
            *nargs: Additional positional arguments
            **kwargs: Additional keyword arguments including 'doc'

        Returns:
            Updated action context
        """
        doc: dict = kwargs.pop("doc", {})

        vertex_keys = self.vertex_config.fields(self.name, with_aux=True)
        custom_transform = ctx.buffer_vertex.pop(self.name, {})

        _doc: dict = {
            k: custom_transform[k] for k in vertex_keys if k in custom_transform
        }

        n_value_keys = len(
            [k for k in ctx.cdoc if k.startswith(DRESSING_TRANSFORMED_VALUE_KEY)]
        )
        for j in range(n_value_keys):
            vkey = self.vertex_config.index(self.name).fields[j]
            v = ctx.cdoc.pop(f"{DRESSING_TRANSFORMED_VALUE_KEY}#{j}")
            _doc[vkey] = v

        for vkey in set(vertex_keys) - set(_doc):
            v = ctx.cdoc.get(vkey, None)
            if v is not None:
                _doc[vkey] = v

        for vkey in set(vertex_keys) - set(_doc):
            v = doc.get(vkey, None)
            if v is not None:
                _doc[vkey] = v

        if all(cfilter(doc) for cfilter in self.vertex_config.filters(self.name)):
            ctx.acc_v_local[self.name][self.discriminant] += [_doc]

        return ctx

__call__(ctx, *nargs, **kwargs)

Process vertex data.

Parameters:

Name Type Description Default
ctx ActionContext

Action context

 required
*nargs

Additional positional arguments

 ()
**kwargs

Additional keyword arguments including 'doc'

 {}

Returns:

Type Description

Updated action context
Source code in graphcast/architecture/actor.py 
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def __call__(self, ctx: ActionContext, *nargs, **kwargs):
    """Process vertex data.

    Args:
        ctx: Action context
        *nargs: Additional positional arguments
        **kwargs: Additional keyword arguments including 'doc'

    Returns:
        Updated action context
    """
    doc: dict = kwargs.pop("doc", {})

    vertex_keys = self.vertex_config.fields(self.name, with_aux=True)
    custom_transform = ctx.buffer_vertex.pop(self.name, {})

    _doc: dict = {
        k: custom_transform[k] for k in vertex_keys if k in custom_transform
    }

    n_value_keys = len(
        [k for k in ctx.cdoc if k.startswith(DRESSING_TRANSFORMED_VALUE_KEY)]
    )
    for j in range(n_value_keys):
        vkey = self.vertex_config.index(self.name).fields[j]
        v = ctx.cdoc.pop(f"{DRESSING_TRANSFORMED_VALUE_KEY}#{j}")
        _doc[vkey] = v

    for vkey in set(vertex_keys) - set(_doc):
        v = ctx.cdoc.get(vkey, None)
        if v is not None:
            _doc[vkey] = v

    for vkey in set(vertex_keys) - set(_doc):
        v = doc.get(vkey, None)
        if v is not None:
            _doc[vkey] = v

    if all(cfilter(doc) for cfilter in self.vertex_config.filters(self.name)):
        ctx.acc_v_local[self.name][self.discriminant] += [_doc]

    return ctx

__init__(vertex, discriminant=None, keep_fields=None, **kwargs)

Initialize the vertex actor.

Parameters:

Name Type Description Default
vertex str

Name of the vertex

 required
discriminant Optional[str]

Optional discriminant field

 None
keep_fields Optional[tuple[str]]

Optional tuple of fields to keep

 None
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def __init__(
    self,
    vertex: str,
    discriminant: Optional[str] = None,
    keep_fields: Optional[tuple[str]] = None,
    **kwargs,
):
    """Initialize the vertex actor.

    Args:
        vertex: Name of the vertex
        discriminant: Optional discriminant field
        keep_fields: Optional tuple of fields to keep
        **kwargs: Additional initialization parameters
    """
    self.name = vertex
    self.discriminant: Optional[str] = discriminant
    self.keep_fields: Optional[tuple[str]] = keep_fields
    self.vertex_config: VertexConfig

fetch_important_items()

Get important items for string representation.

Returns:

Name Type Description
dict

Dictionary of important items
Source code in graphcast/architecture/actor.py 
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def fetch_important_items(self):
    """Get important items for string representation.

    Returns:
        dict: Dictionary of important items
    """
    sd = self.__dict__
    return {k: sd[k] for k in ["name", "discriminant", "keep_fields"]}

finish_init(**kwargs)

Complete initialization of the vertex actor.

Parameters:

Name Type Description Default
**kwargs

Additional initialization parameters

 {}
Source code in graphcast/architecture/actor.py 
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def finish_init(self, **kwargs):
    """Complete initialization of the vertex actor.

    Args:
        **kwargs: Additional initialization parameters
    """
    self.vertex_config: VertexConfig = kwargs.pop("vertex_config")
    self.vertex_config.discriminant_chart[self.name] = True