Resource Assessment Instances¶

`ResourceAssessmentInstances(perfdb)` ¶

Class used for handling resource assessment instances. Can be accessed via perfdb.resourceassessments.instances.

Parameters:

perfdb ¶
(PerfDB) –

Top level object carrying all functionality and the connection handler.

Source code in echo_postgres/perfdb_root.py

def __init__(self, perfdb: e_pg.PerfDB) -> None:
    """Base class that all subclasses should inherit from.

    Parameters
    ----------
    perfdb : PerfDB
        Top level object carrying all functionality and the connection handler.

    """
    self._perfdb: e_pg.PerfDB = perfdb

`get(resource_assessment_names=None, resource_assessment_types=None, companies=None, filter_type='and', output_type='dict')` ¶

Gets all resource assessment instances definitions with detailed information.

The most useful keys/columns returned are:

id
description
resource_assessment_type_name
company_name

Parameters:

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

List of resource assessment instances to filter the query. If None, no filter will be applied. By default None
resource_assessment_types ¶
(list[str] | None, default: None ) –

List of resource assessment types to filter the query. If None, no filter will be applied. By default None
companies ¶
(list[str] | None, default: None ) –

List of companies to filter the query. If None, no filter will be applied. By default None
filter_type ¶
(Literal['and', 'or'], default: 'and' ) –

Type of filter to apply. Can be one of ["and", "or"]. By default "and"
output_type ¶
(Literal['dict', 'DataFrame'], default: 'dict' ) –

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

Returns:

dict[str, dict[str, Any]] –

In case output_instance is "dict", returns a dictionary in the format {name: {attribute: value, ...}, ...}
DataFrame –

In case output_instance is "DataFrame", returns a DataFrame with the following format: index = name, columns = [attribute, ...]

Source code in echo_postgres/resourceassessment_instances.py

@validate_call
def get(
    self,
    resource_assessment_names: list[str] | None = None,
    resource_assessment_types: list[str] | None = None,
    companies: list[str] | None = None,
    filter_type: Literal["and", "or"] = "and",
    output_type: Literal["dict", "DataFrame"] = "dict",
) -> dict[str, dict[str, Any]] | DataFrame:
    """Gets all resource assessment instances definitions with detailed information.

    The most useful keys/columns returned are:

    - id
    - description
    - resource_assessment_type_name
    - company_name

    Parameters
    ----------
    resource_assessment_names : list[str] | None
        List of resource assessment instances to filter the query. If None, no filter will be applied. By default None
    resource_assessment_types : list[str] | None
        List of resource assessment types to filter the query. If None, no filter will be applied. By default None
    companies : list[str] | None
        List of companies to filter the query. If None, no filter will be applied. By default None
    filter_type : Literal["and", "or"], optional
        Type of filter to apply. Can be one of ["and", "or"]. By default "and"
    output_type : Literal["dict", "DataFrame"], optional
        Output type of the data. Can be one of ["dict", "DataFrame"]
        By default "dict"

    Returns
    -------
    dict[str, dict[str, Any]]
        In case output_instance is "dict", returns a dictionary in the format {name: {attribute: value, ...}, ...}
    DataFrame
        In case output_instance is "DataFrame", returns a DataFrame with the following format: index = name, columns = [attribute, ...]
    """
    if output_type not in ["dict", "DataFrame"]:
        raise ValueError(f"output_type must be one of ['dict', 'DataFrame'], not {output_type}")

    # validating arguments and building WHERE clause
    where = self._check_get_args(
        resource_assessment_names=resource_assessment_names,
        resource_assessment_types=resource_assessment_types,
        companies=companies,
        filter_type=filter_type,
    )

    query = [sql.SQL("SELECT * FROM performance.v_resource_assessments ")]
    if where:
        query.append(where)
    query.append(sql.SQL(" ORDER BY name"))
    query = sql.Composed(query)

    with self._perfdb.conn.reconnect() as conn:
        df = conn.read_to_pandas(query)
    df = df.set_index("name")

    return df.to_dict(orient="index") if output_type == "dict" else df

`get_ids(resource_assessment_names=None, resource_assessment_types=None, companies=None, filter_type='and')` ¶

Gets all resource assessment instances and their respective ids.

Parameters:

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

List of resource assessment instances to filter the query. If None, no filter will be applied. By default None
resource_assessment_types ¶
(list[str] | None, default: None ) –

List of resource assessment types to filter the query. If None, no filter will be applied. By default None
companies ¶
(list[str] | None, default: None ) –

List of companies to filter the query. If None, no filter will be applied. By default None
filter_type ¶
(Literal['and', 'or'], default: 'and' ) –

Type of filter to apply. Can be one of ["and", "or"]. By default "and"

Returns:

dict[str, int] –

Dictionary with all resource assessment instances and their respective ids in the format {resource_assessment_instance: id, ...}.

Source code in echo_postgres/resourceassessment_instances.py

@validate_call
def get_ids(
    self,
    resource_assessment_names: list[str] | None = None,
    resource_assessment_types: list[str] | None = None,
    companies: list[str] | None = None,
    filter_type: Literal["and", "or"] = "and",
) -> dict[str, int]:
    """Gets all resource assessment instances and their respective ids.

    Parameters
    ----------
    resource_assessment_names : list[str] | None
        List of resource assessment instances to filter the query. If None, no filter will be applied. By default None
    resource_assessment_types : list[str] | None
        List of resource assessment types to filter the query. If None, no filter will be applied. By default None
    companies : list[str] | None
        List of companies to filter the query. If None, no filter will be applied. By default None
    filter_type : Literal["and", "or"], optional
        Type of filter to apply. Can be one of ["and", "or"]. By default "and"

    Returns
    -------
    dict[str, int]
        Dictionary with all resource assessment instances and their respective ids in the format {resource_assessment_instance: id, ...}.
    """
    # validating arguments and building WHERE clause
    where = self._check_get_args(
        resource_assessment_names=resource_assessment_names,
        resource_assessment_types=resource_assessment_types,
        companies=companies,
        filter_type=filter_type,
    )

    query = [sql.SQL("SELECT name, id FROM performance.v_resource_assessments ")]
    if where:
        query.append(where)
    query.append(sql.SQL(" ORDER BY name"))
    query = sql.Composed(query)

    with self._perfdb.conn.reconnect() as conn:
        df = conn.read_to_pandas(query)

    return df.set_index("name").to_dict()["id"]

Resource Assessment Instances¶