SexPhenotype

Bases: CategoricalPhenotype

SexPhenotype represents a sex-based phenotype. It returns the sex of individuals in the VALUE column and optionally filters based on identified sex. DATE is not defined for SexPhenotype.

Parameters:

Name	Type	Description	Default
name	str	Name of the phenotype, default is 'sex'.	'SEX'
domain	str	Domain of the phenotype, default is 'PERSON'.	'PERSON'
allowed_values		List of allowed values for the categorical variable.	required
column_name		Name of the column containing the required categorical variable. Default is 'SEX'.	required

Examples:

Example: Return the recorded sex of all patients.

from phenex.phenotypes import SexPhenotype
sex = SexPhenotype()

Example: Extract all male patients from the database.

from phenex.phenotypes import SexPhenotype
sex = SexPhenotype(
    allowed_values=['M'],
    column_name='GENDER_SOURCE_VALUE'
    )

Source code in phenex/phenotypes/sex_phenotype.py

class SexPhenotype(CategoricalPhenotype):
    """
    SexPhenotype represents a sex-based phenotype. It returns the sex of individuals in the VALUE column and optionally filters based on identified sex. DATE is not defined for SexPhenotype.

    Parameters:
        name: Name of the phenotype, default is 'sex'.
        domain: Domain of the phenotype, default is 'PERSON'.
        allowed_values: List of allowed values for the categorical variable.
        column_name: Name of the column containing the required categorical variable. Default is 'SEX'.

    Examples:

    Example: Return the recorded sex of all patients.
    ```python
    from phenex.phenotypes import SexPhenotype
    sex = SexPhenotype()
    ```

    Example: Extract all male patients from the database.
    ```python
    from phenex.phenotypes import SexPhenotype
    sex = SexPhenotype(
        allowed_values=['M'],
        column_name='GENDER_SOURCE_VALUE'
        )
    ```
    """

    def __init__(
        self,
        name: str = "SEX",
        domain: str = "PERSON",
        categorical_filter: "CategoricalFilter" = None,
        **kwargs
    ):
        if categorical_filter is None:
            categorical_filter = CategoricalFilter(column_name="SEX")
        else:
            if categorical_filter.column_name is None:
                categorical_filter.column_name = "SEX"

        super(SexPhenotype, self).__init__(
            name=name, domain=domain, categorical_filter=categorical_filter, **kwargs
        )

dependencies property

Recursively collect all dependencies of a node (including dependencies of dependencies).

Returns:

Type	Description
Set[Node]	List[Node]: A list of Node objects on which this Node depends.

dependency_graph property

Build a dependency graph where each node maps to its direct dependencies (children).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, Set[Node]: A mapping of Node's to their children Node's.

namespaced_table property

A PhenotypeTable has generic column names 'person_id', 'boolean', 'event_date', and 'value'. The namespaced_table prepends the phenotype name to all of these columns. This is useful when joining multiple phenotype tables together.

Returns:

Name	Type	Description
table	Table	The namespaced table for the current phenotype.

reverse_dependency_graph property

Build a reverse dependency graph where each node maps to nodes that depend on it (parents).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, List[Node]: A mapping of Node's to their parent Node's.

execute(tables=None, con=None, overwrite=False, lazy_execution=False, n_threads=1)

Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

Parameters:

Name	Type	Description	Default
tables	Dict[str, Table]	A dictionary mapping domains to Table objects.	None
con	Optional[object]	Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.	None
overwrite	bool	If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.	False
lazy_execution	bool	If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.	False
n_threads	int	Max number of Node's to execute simultaneously when this node has multiple children.	1

Returns:

Name	Type	Description
Table	Table	The resulting table for this node. Also accessible through self.table after calling self.execute().

Source code in phenex/node.py

def execute(
    self,
    tables: Dict[str, Table] = None,
    con: Optional[object] = None,
    overwrite: bool = False,
    lazy_execution: bool = False,
    n_threads: int = 1,
) -> Table:
    """
    Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

    Parameters:
        tables: A dictionary mapping domains to Table objects.
        con: Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.
        overwrite: If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.
        lazy_execution: If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.
        n_threads: Max number of Node's to execute simultaneously when this node has multiple children.

    Returns:
        Table: The resulting table for this node. Also accessible through self.table after calling self.execute().
    """
    # Handle None tables
    if tables is None:
        tables = {}

    # Use multithreaded execution if we have multiple children and n_threads > 1
    if len(self.children) > 1 and n_threads > 1:
        return self._execute_multithreaded(
            tables, con, overwrite, lazy_execution, n_threads
        )
    else:
        return self._execute_sequential(tables, con, overwrite, lazy_execution)

visualize_dependencies()

Create a text visualization of the dependency graph for this node and its dependencies.

Returns:

Name	Type	Description
str	str	A text representation of the dependency graph

Source code in phenex/node.py

def visualize_dependencies(self) -> str:
    """
    Create a text visualization of the dependency graph for this node and its dependencies.

    Returns:
        str: A text representation of the dependency graph
    """
    lines = [f"Dependencies for Node '{self.name}':"]

    # Get all dependencies
    all_deps = self.dependencies
    nodes = {node.name: node for node in all_deps}
    nodes[self.name] = self  # Add self to the nodes

    # Build dependency graph
    dependency_graph = self._build_dependency_graph(nodes)

    for node_name in sorted(nodes.keys()):
        dependencies = dependency_graph.get(node_name, set())
        if dependencies:
            deps_str = ", ".join(sorted(dependencies))
            lines.append(f"  {node_name} depends on: {deps_str}")
        else:
            lines.append(f"  {node_name} (no dependencies)")

    return "\n".join(lines)