openego / eGon-data

    return dsm

def ind_osm_data_import(ind_vent_cool_share):

    """

    Import industry data per osm-area necessary to identify DSM-potential.

        ----------

    ind_share: float

        Share of considered application in industry demand

    """

    # import load data

    sources = config.datasets()["DSM_CTS_industry"]["sources"][

        "ind_osm_loadcurves"

    ]

    dsm = db.select_dataframe(

        f"""

        SELECT bus, scn_name, p_set FROM

        {sources['schema']}.{sources['table']}

        WHERE scn_name IN ('eGon2035', 'eGon100RE')

        """

    )

    # calculate share of timeseries for cooling and ventilation out of

    # industry-data

    timeseries = dsm["p_set"].copy()

    for index, liste in timeseries.items():

        share = [float(item) * ind_vent_cool_share for item in liste]

        timeseries.loc[index] = share

    dsm["p_set"] = timeseries.copy()

    return dsm

    return dsm

def ind_osm_data_import_individual(ind_vent_cool_share):

    """

    Import industry data per osm-area necessary to identify DSM-potential.

        ----------

    ind_share: float

        Share of considered application in industry demand

    """

    # import load data

    sources = config.datasets()["DSM_CTS_industry"]["sources"][

        "ind_osm_loadcurves_individual"

    ]

    dsm = db.select_dataframe(

        f"""

        SELECT osm_id, bus_id as bus, scn_name, p_set FROM

        {sources['schema']}.{sources['table']}

        WHERE scn_name in ('eGon2035', 'eGon100RE')

        """

    )

    # calculate share of timeseries for cooling and ventilation out of

    # industry-data

    timeseries = dsm["p_set"].copy()

    for index, liste in timeseries.items():

        share = [float(item) * ind_vent_cool_share for item in liste]

        timeseries.loc[index] = share

    dsm["p_set"] = timeseries.copy()

    return dsm

def ind_sites_vent_data_import(ind_vent_share, wz):