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SexPhenotype

Bases: Phenotype

SexPhenotype is a class that represents a sex-based phenotype. It is able to identify the sex of individuals and filter them based on identified sex.

Parameters:

Name Type Description Default
name str

Name of the phenotype, default is 'sex'.

 'sex'
allowed_values Optional[List[Union[str, int, float]]]

List of allowed values for the sex column.

 None
domain str

Domain of the phenotype, default is 'PERSON'.

 'PERSON'
Source code in phenex/phenotypes/sex_phenotype.py 
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class SexPhenotype(Phenotype):
    """
    SexPhenotype is a class that represents a sex-based phenotype. It is able to identify the sex of individuals and filter them based on identified sex.

    Parameters:
        name: Name of the phenotype, default is 'sex'.
        allowed_values: List of allowed values for the sex column.
        domain: Domain of the phenotype, default is 'PERSON'.
    """

    def __init__(
        self,
        name: str = "sex",
        allowed_values: Optional[List[Union[str, int, float]]] = None,
        domain: str = "PERSON",
    ):
        self.name = name
        self.allowed_values = allowed_values
        self.domain = domain
        self.children = []
        super(SexPhenotype, self).__init__()

    def _execute(self, tables: Dict[str, Table]) -> PhenotypeTable:
        person_table = tables[self.domain]
        assert is_phenex_person_table(person_table)

        if self.allowed_values is not None:
            sex_filter = CategoricalFilter(
                column_name="SEX", allowed_values=self.allowed_values
            )
            person_table = sex_filter._filter(person_table)

        return person_table.mutate(VALUE=person_table.SEX, EVENT_DATE=ibis.null())

namespaced_table property

A PhenotypeTable has generic column names 'person_id', 'boolean', 'event_date', and 'value'. The namespaced_table appends 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.

execute(tables)

Executes the phenotype computation for the current object and its children. This method recursively iterates over the children of the current object and calls their execute method if their table attribute is None.

Parameters:

Name Type Description Default
tables Dict[str, PhenexTable]

A dictionary mapping table names to PhenexTable objects. See phenex.mappers.DomainsDictionary.get_mapped_tables().

 required

Returns:

Name Type Description
table PhenotypeTable

The resulting phenotype table containing the required columns. The PhenotypeTable will contain the columns: PERSON_ID, EVENT_DATE, VALUE. DATE is determined by the return_date parameter. VALUE is different for each phenotype. For example, AgePhenotype will return the age in the VALUE column. A MeasurementPhenotype will return the observed value for the measurement. See the specific phenotype of interest to understand more.
Source code in phenex/phenotypes/phenotype.py 
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def execute(self, tables: Dict[str, Table]) -> PhenotypeTable:
    """
    Executes the phenotype computation for the current object and its children. This method recursively iterates over the children of the current object and calls their execute method if their table attribute is None.

    Args:
        tables (Dict[str, PhenexTable]): A dictionary mapping table names to PhenexTable objects. See phenex.mappers.DomainsDictionary.get_mapped_tables().

    Returns:
        table (PhenotypeTable): The resulting phenotype table containing the required columns. The PhenotypeTable will contain the columns: PERSON_ID, EVENT_DATE, VALUE. DATE is determined by the return_date parameter. VALUE is different for each phenotype. For example, AgePhenotype will return the age in the VALUE column. A MeasurementPhenotype will return the observed value for the measurement. See the specific phenotype of interest to understand more.
    """
    logger.info(f"Phenotype '{self.name}': executing...")
    for child in self.children:
        if child.table is None:
            logger.debug(
                f"Phenotype {self.name}: executing child phenotype '{child.name}'..."
            )
            child.execute(tables)
        else:
            logger.debug(
                f"Phenotype {self.name}: skipping already computed child phenotype '{child.name}'."
            )

    table = self._execute(tables).mutate(BOOLEAN=True)

    if not set(PHENOTYPE_TABLE_COLUMNS) <= set(table.columns):
        raise ValueError(
            f"Phenotype {self.name} must return columns {PHENOTYPE_TABLE_COLUMNS}. Found {table.columns}."
        )

    self.table = table.select(PHENOTYPE_TABLE_COLUMNS)
    # for some reason, having NULL datatype screws up writing the table to disk; here we make explicit cast
    if type(self.table.schema()["VALUE"]) == ibis.expr.datatypes.core.Null:
        self.table = self.table.cast({"VALUE": "float64"})

    assert is_phenex_phenotype_table(self.table)
    logger.info(f"Phenotype '{self.name}': execution completed.")
    return self.table