`eds_scikit.biology.viz.wrapper`

plot_biology_summary

plot_biology_summary(data: Data, concepts_sets: List[ConceptsSet] = None, start_date: datetime = None, end_date: datetime = None, save_folder_path: str = 'Biology_summary', number_of_concept: Tuple[str, int] = None, limit_count: Tuple[str, int] = None, standard_terminologies: List[str] = default_standard_terminologies, standard_concept_regex: dict = default_standard_concept_regex, pd_limit_size: int = 100000, stats_only: bool = False) -> Union[AltChart, pd.DataFrame]

It aggregates, plots and saves all the concepts-sets in folders.

PARAMETER	DESCRIPTION
`data`	Instantiated `HiveData`, `PostgresData` or `PandasData` TYPE: `Data`
`concepts_sets`	List of concepts-sets to select TYPE: `List[str], optional` DEFAULT: `None`
`start_date`	EXAMPLE: `"2019-05-01"` TYPE: `datetime` DEFAULT: `None`
`end_date`	EXAMPLE: `"2021-11-01"` TYPE: `datetime, optional` DEFAULT: `None`
`save_folder_path`	Name of the folder where the plots will be saved TYPE: `str, optional` DEFAULT: `'Biology_summary'`
`number_of_concept`	The maximum number of concepts for a given terminology EXAMPLE: `("LOINC", 5)` TYPE: `Tuple[str, int], optional` DEFAULT: `None`
`limit_count`	The minimum number of observations per concepts for a given terminology EXAMPLE: `("LOINC", 5)` TYPE: `Tuple[str, int], optional` DEFAULT: `None`
`standard_terminologies`	EXAMPLE: `["LOINC", "AnaBio"]` TYPE: `List[str], optional` DEFAULT: `default_standard_terminologies`
`standard_concept_regex`	EXAMPLE: `{"LOINC": "[0-9]{2,5}[-][0-9]","AnaBio": "[A-Z][0-9]{4}"}` TYPE: `dict, optional` DEFAULT: `default_standard_concept_regex`
`pd_limit_size`	The limit number of rows to convert Koalas DatFrame into Pandas DataFrame TYPE: `int, optional` DEFAULT: `100000`
`stats_only`	If `True`, it will only aggregate the data for the summary table. TYPE: `bool, optional` DEFAULT: `False`

RETURNS	DESCRIPTION
`List[AltChart, pd.DataFrame]`	Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary

Source code in eds_scikit/biology/viz/wrapper.py

def plot_biology_summary(
    data: Data,
    concepts_sets: List[ConceptsSet] = None,
    start_date: datetime = None,
    end_date: datetime = None,
    save_folder_path: str = "Biology_summary",
    number_of_concept: Tuple[str, int] = None,
    limit_count: Tuple[str, int] = None,
    standard_terminologies: List[str] = default_standard_terminologies,
    standard_concept_regex: dict = default_standard_concept_regex,
    pd_limit_size: int = 100000,
    stats_only: bool = False,
) -> Union[AltChart, pd.DataFrame]:
    """It aggregates, plots and saves all the concepts-sets in folders.


    Parameters
    ----------
    data : Data
         Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData]
    concepts_sets : List[str], optional
        List of concepts-sets to select
    start_date : datetime
        **EXAMPLE**: `"2019-05-01"`
    end_date : datetime, optional
        **EXAMPLE**: `"2021-11-01"`
    save_folder_path : str, optional
        Name of the folder where the plots will be saved
    number_of_concept : Tuple[str, int], optional
        The maximum number of concepts for a given terminology
        **EXAMPLE**: `("LOINC", 5)`
    limit_count : Tuple[str, int], optional
        The minimum number of observations per concepts for a given terminology
        **EXAMPLE**: `("LOINC", 5)`
    standard_terminologies : List[str], optional
        **EXAMPLE**: `["LOINC", "AnaBio"]`
    standard_concept_regex : dict, optional
        **EXAMPLE**: `{"LOINC": "[0-9]{2,5}[-][0-9]","AnaBio": "[A-Z][0-9]{4}"}`
    pd_limit_size : int, optional
        The limit number of rows to convert [Koalas](https://koalas.readthedocs.io/en/latest/) DatFrame into [Pandas](https://pandas.pydata.org/) DataFrame
    stats_only : bool, optional
        If ``True``, it will only aggregate the data for the [summary table][summary-table].

    Returns
    -------
    List[AltChart, pd.DataFrame]
        Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary
    """

    if concepts_sets is None:
        concepts_sets = fetch_all_concepts_set()
    elif isinstance(concepts_sets, ConceptsSet):
        concepts_sets = [concepts_sets]

    if not os.path.isdir(save_folder_path):
        os.mkdir(save_folder_path)
        logger.info("{} folder has been created.", save_folder_path)

    if isinstance(concepts_sets, list) and all(
        isinstance(concepts_set, ConceptsSet) for concepts_set in concepts_sets
    ):
        for concepts_set in concepts_sets:
            concepts_set_path = "{}/{}".format(save_folder_path, concepts_set.name)
            rmtree(concepts_set_path, ignore_errors=True)
            os.mkdir(concepts_set_path)
            logger.info(
                "{}/{} folder has been created.",
                save_folder_path,
                concepts_set.name,
            )

            tables = aggregate_concepts_set(
                data=data,
                concepts_set=concepts_set,
                start_date=start_date,
                end_date=end_date,
                number_of_concept=number_of_concept,
                limit_count=limit_count,
                standard_terminologies=standard_terminologies,
                standard_concept_regex=standard_concept_regex,
                pd_limit_size=pd_limit_size,
                stats_only=stats_only,
            )

            for table_name, table in tables.items():
                table.to_pickle(
                    "{}/{}/{}.pkl".format(
                        save_folder_path, concepts_set.name, table_name
                    )
                )

            logger.info(
                "{} has been processed and saved in {}/{} folder.",
                concepts_set.name,
                save_folder_path,
                concepts_set.name,
            )

            plot_concepts_set(
                concepts_set_name=concepts_set.name, source_path=save_folder_path
            )
    else:
        logger.error(
            "concepts_set type is {} and must be a ConceptsSet object or a list of ConceptsSet objects",
            type(concepts_sets),
        )