Module phc.easy.summary.item_counts

Classes

class SummaryItemCounts

Expand source code

class SummaryItemCounts(PagingApiItem):
    @staticmethod
    def resource_path():
        return "analytics/summary/{project_id}/{summary_type}/{summary}/counts"

    @classmethod
    def process_params(cls, params: dict) -> dict:
        new_params = cls.params_class()(**params).dict()

        if SummaryClinicalType.has_value(new_params["summary"]):
            return {**new_params, "summary_type": "clinical"}
        elif SummaryOmicsType.has_value(new_params["summary"]):
            return {**new_params, "summary_type": "omics"}

        # Unknown summary_type
        return None

    @staticmethod
    def transform_results(data_frame: pd.DataFrame, **expand_args):
        if len(data_frame) == 0:
            return data_frame

        if expand_args.get("params", {}).get("summary") == "demographic":
            # Sort demographics results in a nice way
            return data_frame.sort_values(
                ["demographic_name", "count"], ascending=False
            ).reset_index(drop=True)

        if (
            "code_count" in data_frame.columns
            and "patient_count" in data_frame.columns
        ):
            return data_frame.sort_values(
                ["code_count", "patient_count"], ascending=False
            )

        return data_frame

    @staticmethod
    def execute_args() -> dict:
        return dict(ignore_cache=True)

    @staticmethod
    def params_class():
        return SummaryItemCountsOptions

    @classmethod
    def get_data_frame(
        cls,
        summary: Union[SummaryOmicsType, SummaryClinicalType],
        all_results: bool = True,
        auth_args: Auth = Auth.shared(),
        max_pages: Optional[int] = None,
        page_size: Optional[int] = None,
        log: bool = False,
        **kw_args,
    ):
        """Execute a request for summary counts across clinical and omics data

        NOTE: By default, demographic data is excluded since it is not
        technically counts of entities. If demographics-only data is desired,
        use this:

        >>> from phc.easy.summary.item_counts import SummaryItemCounts
        >>> SummaryItemCounts.get_data_frame(summary="demographics")

        ## Parameters

        Execution: `phc.easy.query.Query.execute_paging_api`
        """

        df = super().get_data_frame(
            **kw_args, **cls._get_current_args(inspect.currentframe(), locals())
        )

        return df

Ancestors

• PagingApiItem

Static methods

def execute_args() ‑> dict

Expand source code

@staticmethod
def execute_args() -> dict:
    return dict(ignore_cache=True)

def get_data_frame(summary: Union[SummaryOmicsType, SummaryClinicalType], all_results: bool = True, auth_args: Auth = <phc.easy.auth.Auth object>, max_pages: Optional[int] = None, page_size: Optional[int] = None, log: bool = False, **kw_args)

Execute a request for summary counts across clinical and omics data

NOTE: By default, demographic data is excluded since it is not technically counts of entities. If demographics-only data is desired, use this:

>>> from phc.easy.summary.item_counts import SummaryItemCounts
>>> SummaryItemCounts.get_data_frame(summary="demographics")

Parameters

Execution: Query.execute_paging_api()

Expand source code

@classmethod
def get_data_frame(
    cls,
    summary: Union[SummaryOmicsType, SummaryClinicalType],
    all_results: bool = True,
    auth_args: Auth = Auth.shared(),
    max_pages: Optional[int] = None,
    page_size: Optional[int] = None,
    log: bool = False,
    **kw_args,
):
    """Execute a request for summary counts across clinical and omics data

    NOTE: By default, demographic data is excluded since it is not
    technically counts of entities. If demographics-only data is desired,
    use this:

    >>> from phc.easy.summary.item_counts import SummaryItemCounts
    >>> SummaryItemCounts.get_data_frame(summary="demographics")

    ## Parameters

    Execution: `phc.easy.query.Query.execute_paging_api`
    """

    df = super().get_data_frame(
        **kw_args, **cls._get_current_args(inspect.currentframe(), locals())
    )

    return df

def params_class()

Inherited from: PagingApiItem.params_class

Returns a pydantic type that validates and transforms the params with dict()

Expand source code

@staticmethod
def params_class():
    return SummaryItemCountsOptions

def process_params(params: dict) ‑> dict

Inherited from: PagingApiItem.process_params

Validates and transforms the API query parameters

Expand source code

@classmethod
def process_params(cls, params: dict) -> dict:
    new_params = cls.params_class()(**params).dict()

    if SummaryClinicalType.has_value(new_params["summary"]):
        return {**new_params, "summary_type": "clinical"}
    elif SummaryOmicsType.has_value(new_params["summary"]):
        return {**new_params, "summary_type": "omics"}

    # Unknown summary_type
    return None

def resource_path()

Inherited from: PagingApiItem.resource_path

Returns the API url name for retrieval

Expand source code

@staticmethod
def resource_path():
    return "analytics/summary/{project_id}/{summary_type}/{summary}/counts"

def transform_results(data_frame: pandas.core.frame.DataFrame, **expand_args)

Inherited from: PagingApiItem.transform_results

Transform data frame batch

Expand source code

@staticmethod
def transform_results(data_frame: pd.DataFrame, **expand_args):
    if len(data_frame) == 0:
        return data_frame

    if expand_args.get("params", {}).get("summary") == "demographic":
        # Sort demographics results in a nice way
        return data_frame.sort_values(
            ["demographic_name", "count"], ascending=False
        ).reset_index(drop=True)

    if (
        "code_count" in data_frame.columns
        and "patient_count" in data_frame.columns
    ):
        return data_frame.sort_values(
            ["code_count", "patient_count"], ascending=False
        )

    return data_frame