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Subcomponent Models

SubcomponentModels(perfdb)

Class used for handling subcomponent models. Can be accessed via perfdb.components.subcomponents.models.

Parameters:

  • perfdb

    (PerfDB) –

    Top level object carrying all functionality and the connection handler.

Source code in echo_postgres/subcomponent_models.py
Python
def __init__(self, perfdb: e_pg.PerfDB) -> None:
    """Class used for handling subcomponent models. Can be accessed via `perfdb.components.subcomponents.models`.

    Parameters
    ----------
    perfdb : PerfDB
        Top level object carrying all functionality and the connection handler.
    """
    super().__init__(perfdb)

    from .subcomponent_model_attributes import SubcomponentModelAttributes

    # * subclasses

    self.attributes = SubcomponentModelAttributes(perfdb)

get(component_types=None, subcomponent_models=None, subcomponent_types=None, manufacturers=None, filter_type='and', get_attributes=False, output_type='dict')

Gets all subcomponent models definitions with detailed information.

The most useful keys/columns returned are:

  • id
  • description
  • component_type_id
  • subcomponent_type_id
  • manufacturer_id
  • manufacturer_name

Parameters:

  • component_types

    (list[str] | None, default: None ) –

    Name of the component types to filter the results.

  • subcomponent_types

    (list[str] | None, default: None ) –

    Name of the subcomponent types to filter the results, by default None

  • subcomponent_models

    (list[str] | None, default: None ) –

    Name of the subcomponent models to filter the results, by default None

  • manufacturers

    (list[str] | None, default: None ) –

    Name of the manufacturers to filter the results, by default None

  • filter_type

    (Literal['and', 'or'], default: 'and' ) –

    How to treat multiple filters. Can be one of ["and", "or"]. By default "and"

  • get_attributes

    (bool, default: False ) –

    If True, will also get the attributes of the subcomponent models.

  • output_type

    (Literal['dict', 'DataFrame', 'pl.DataFrame'], default: 'dict' ) –

    Output type of the data. Can be one of ["dict", "DataFrame", "pl.DataFrame"] By default "dict"

Returns:

  • dict[str, dict[str, Any]] –

    In case output_type is "dict", returns a dictionary in the format {component_type: {subcomponents_type: {subcomponent_model: {attribute: value, ...}, ...}, ...}, ...}

  • DataFrame –

    In case output_type is "DataFrame", returns a pandas DataFrame with the following format: index = MultiIndex[component_type_name, subcomponents_type_name, name], columns = [attribute, ...]

  • DataFrame –

    In case output_type is "pl.DataFrame", returns a Polars DataFrame

Source code in echo_postgres/subcomponent_models.py
Python
@validate_call
def get(
    self,
    component_types: list[str] | None = None,
    subcomponent_models: list[str] | None = None,
    subcomponent_types: list[str] | None = None,
    manufacturers: list[str] | None = None,
    filter_type: Literal["and", "or"] = "and",
    get_attributes: bool = False,
    output_type: Literal["dict", "DataFrame", "pl.DataFrame"] = "dict",
) -> dict[str, int]:
    """Gets all subcomponent models definitions with detailed information.

    The most useful keys/columns returned are:

    - id
    - description
    - component_type_id
    - subcomponent_type_id
    - manufacturer_id
    - manufacturer_name

    Parameters
    ----------
    component_types : list[str] | None
        Name of the component types to filter the results.
    subcomponent_types : list[str] | None, optional
        Name of the subcomponent types to filter the results, by default None
    subcomponent_models : list[str] | None, optional
        Name of the subcomponent models to filter the results, by default None
    manufacturers : list[str] | None, optional
        Name of the manufacturers to filter the results, by default None
    filter_type : Literal["and", "or"], optional
        How to treat multiple filters. Can be one of ["and", "or"]. By default "and"
    get_attributes : bool, optional
        If True, will also get the attributes of the subcomponent models.
    output_type : Literal["dict", "DataFrame", "pl.DataFrame"], optional
        Output type of the data. Can be one of ["dict", "DataFrame", "pl.DataFrame"]
        By default "dict"

    Returns
    -------
    dict[str, dict[str, Any]]
        In case output_type is "dict", returns a dictionary in the format {component_type: {subcomponents_type: {subcomponent_model: {attribute: value, ...}, ...}, ...}, ...}
    pd.DataFrame
        In case output_type is "DataFrame", returns a pandas DataFrame with the following format: index = MultiIndex[component_type_name, subcomponents_type_name, name], columns = [attribute, ...]
    pl.DataFrame
        In case output_type is "pl.DataFrame", returns a Polars DataFrame
    """
    where = self._check_get_args(
        component_types=component_types,
        subcomponent_models=subcomponent_models,
        subcomponent_types=subcomponent_types,
        filter_type=filter_type,
        manufacturers=manufacturers,
    )

    query = [
        sql.SQL("SELECT * FROM performance.v_subcomponent_models "),
        where,
        sql.SQL(" ORDER BY component_type_name, subcomponent_type_name, name"),
    ]
    query = sql.Composed(query)

    df = self._perfdb.conn.read_to_polars(query)

    # getting attributes
    if get_attributes:
        # names of the subcomponent models
        got_subcomponent_models = df["name"].to_list()
        attrs: pl.DataFrame = self._perfdb.components.subcomponents.models.attributes.get(
            subcomponent_models=got_subcomponent_models,
            output_type="pl.DataFrame",
            values_only=False,
        )
        # pivot the attributes
        attrs = attrs.pivot(index="subcomponent_model_id", on="attribute_name", values="attribute_value")
        # merging the attributes with the subcomponent models
        df = df.join(attrs, left_on="id", right_on="subcomponent_model_id", how="left")

    return convert_output(df, output_type, index_col=["component_type_name", "subcomponent_type_name", "name"], nest_by_index=True)

get_ids(component_types=None, subcomponent_types=None, subcomponent_models=None, manufacturers=None, filter_type='and')

Method used to get all subcomponent models and their respective ids.

Parameters:

  • component_types

    (list[str] | None, default: None ) –

    Name of the component types to filter the results.

  • subcomponent_types

    (list[str] | None, default: None ) –

    Name of the subcomponent types to filter the results, by default None

  • subcomponent_models

    (list[str] | None, default: None ) –

    Name of the subcomponent models to filter the results, by default None

  • manufacturers

    (list[str] | None, default: None ) –

    Name of the manufacturers to filter the results, by default None

  • filter_type

    (Literal['and', 'or'], default: 'and' ) –

    How to treat multiple filters. Can be one of ["and", "or"]. By default "and"

Returns:

  • dict[str, dict[str, dict[str, int]]] –

    Dictionary with all subcomponents types and their respective ids in the format {component_type: {subcomponents_type: {subcomponent_model: id, ...}, ...}, ...}.

Source code in echo_postgres/subcomponent_models.py
Python
@validate_call
def get_ids(
    self,
    component_types: list[str] | None = None,
    subcomponent_types: list[str] | None = None,
    subcomponent_models: list[str] | None = None,
    manufacturers: list[str] | None = None,
    filter_type: Literal["and", "or"] = "and",
) -> dict[str, dict[str, dict[str, int]]]:
    """Method used to get all subcomponent models and their respective ids.

    Parameters
    ----------
    component_types : list[str] | None
        Name of the component types to filter the results.
    subcomponent_types : list[str] | None, optional
        Name of the subcomponent types to filter the results, by default None
    subcomponent_models : list[str] | None, optional
        Name of the subcomponent models to filter the results, by default None
    manufacturers : list[str] | None, optional
        Name of the manufacturers to filter the results, by default None
    filter_type : Literal["and", "or"], optional
        How to treat multiple filters. Can be one of ["and", "or"]. By default "and"

    Returns
    -------
    dict[str, dict[str, dict[str, int]]]
        Dictionary with all subcomponents types and their respective ids in the format {component_type: {subcomponents_type: {subcomponent_model: id, ...}, ...}, ...}.
    """
    # checking inputs
    where = self._check_get_args(
        component_types=component_types,
        subcomponent_models=subcomponent_models,
        subcomponent_types=subcomponent_types,
        filter_type=filter_type,
        manufacturers=manufacturers,
    )

    query = [
        sql.SQL("SELECT component_type_name, subcomponent_type_name, name, id FROM performance.v_subcomponent_models "),
        where,
        sql.SQL(" ORDER BY component_type_name, subcomponent_type_name, name"),
    ]
    query = sql.Composed(query)

    df = self._perfdb.conn.read_to_polars(query)

    df = df.to_pandas(use_pyarrow_extension_array=True)
    df = df.set_index(["component_type_name", "subcomponent_type_name", "name"])

    result = df.to_dict(orient="index")
    final_result = {}
    for (component_type_name, subcomponent_type_name, name), values in result.items():
        if component_type_name not in final_result:
            final_result[component_type_name] = {}
        if subcomponent_type_name not in final_result[component_type_name]:
            final_result[component_type_name][subcomponent_type_name] = {}
        final_result[component_type_name][subcomponent_type_name][name] = values["id"]

    return final_result