Isone Api =================== .. py:module:: isone_api Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: isone_api.ISONEAPI Attributes Summary ~~~~~~~~~~~~~~~~~~~ .. autoapisummary:: isone_api.DEFAULT_PAGE_SIZE isone_api.EXCLUDE_FROM_REALTIME_HOURLY_DEMAND isone_api.ZONE_LOCATIONID_MAP Contents ~~~~~~~~~~~~~~~~~~~ .. py:data:: DEFAULT_PAGE_SIZE :value: 1000 .. py:data:: EXCLUDE_FROM_REALTIME_HOURLY_DEMAND :value: ['NEPOOL AREA', '.H.INTERNALHUB'] .. py:class:: ISONEAPI(sleep_seconds: float = 5, max_retries: int = 3) Class to authenticate with and make requests to the ISO New England API. To authenticate, you need a username and password. To register, create an account here: https://www.iso-ne.com/participate/applications-status-changes/access-software-systems#ws-api **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **default_timezone** - US/Eastern **Methods** .. autoapisummary:: :nosignatures: isone_api.ISONEAPI.get_dayahead_hourly_demand isone_api.ISONEAPI.get_fuel_mix isone_api.ISONEAPI.get_hourly_load_forecast isone_api.ISONEAPI.get_load_hourly isone_api.ISONEAPI.get_location_by_id isone_api.ISONEAPI.get_locations isone_api.ISONEAPI.get_locations_all isone_api.ISONEAPI.get_realtime_hourly_demand isone_api.ISONEAPI.get_reliability_region_load_forecast isone_api.ISONEAPI.make_api_call isone_api.ISONEAPI.parse_problematic_datetime .. py:method:: get_dayahead_hourly_demand(date: str | pandas.Timestamp = 'latest', end: str | pandas.Timestamp | None = None, locations: list[str] = None, verbose: bool = False) -> pandas.DataFrame Get the day-ahead hourly demand data for specified locations and date range. :param date: The start date for the data request. Use "latest" for most recent data. :type date: str :param end_date: The end date for the data request. Only used if date is not "latest". :type end_date: str | None :param locations: List of specific location names to request data for. If None, data for all locations will be retrieved. :type locations: list[str], optional :returns: A DataFrame containing the day-ahead hourly demand data for all requested locations. :rtype: pandas.DataFrame .. py:method:: get_fuel_mix(date: str | pandas.Timestamp = 'latest', end: str | pandas.Timestamp | None = None, verbose: bool = False) -> pandas.DataFrame Return fuel mix data for the specified date range :param date: The start date for the data request. Use "latest" for most recent data. :type date: str | pd.Timestamp :param end: The end date for the data request. Only used if date is not "latest". :type end: str | pd.Timestamp | None :param verbose: Whether to print verbose logging information. :type verbose: bool :returns: DataFrame containing fuel mix data with timestamps and generation by fuel type :rtype: pd.DataFrame .. py:method:: get_hourly_load_forecast(date: str | pandas.Timestamp = 'latest', end: str | pandas.Timestamp | None = None, vintage: Literal[latest, all] = 'all', verbose: bool = False) -> pandas.DataFrame Get the hourly load forecast data for specified locations and date range. NB: ISO NE publishes load forecasts roughly every 30 minutes for the next 48-72 future intervals. Getting all forecasts (all "vintages") can be a lot of data, potentially thousands of rows for a single day. Sometimes you may want this, and that's why ISO NE provides the option to get all vintages, but you may be most interested in the most recent forecast for a given historical interval, essentially the shortest vintages, most accurate forecast, which they also provide. All vintages is typically 5x to 20x more data than latest, so it's something to consider when making a request. Giving the option for just the "latest" forecast (aka shortest horizon, aka most recent publish time/vintage) for a given historical interval avoids this large data pull and collation since ISO NE API has done that work for you already. :param date: The start date for the data request. Use "latest" for most recent data. :type date: str :param end_date: The end date for the data request. Only used if date is not "latest". :type end_date: str | None :param vintage: The vintage for the data request. Options are "latest" or "all", defaults to "all". :type vintage: Literal["latest", "all"] :returns: A DataFrame containing the hourly load forecast data for the system. :rtype: pandas.DataFrame .. py:method:: get_load_hourly(date: str | pandas.Timestamp, end: str | pandas.Timestamp = None, verbose: bool = False) -> pandas.DataFrame Return hourly load data for a given date or date range :param date: The start date for the data request. Use "latest" for most recent data. :type date: str | pd.Timestamp :param end: The end date for the data request. Only used if date is not "latest". :type end: str | pd.Timestamp, optional :param locations: List of specific location names to request data for. If None, data for all locations will be retrieved. :type locations: list[str], optional :param verbose: Whether to print verbose logging information. :type verbose: bool, optional :returns: DataFrame containing load data with timestamps and load :rtype: pd.DataFrame .. py:method:: get_location_by_id(location_id: int) -> pandas.DataFrame Get information for a specific location by its ID. :param location_id: The ID of the location to retrieve. :type location_id: int :returns: A DataFrame containing the location information. :rtype: pandas.DataFrame .. py:method:: get_locations() -> pandas.DataFrame Get a list of core hub and zone locations. :returns: A DataFrame containing location information. :rtype: pandas.DataFrame .. py:method:: get_locations_all() -> pandas.DataFrame Get detailed information for all locations. :returns: A DataFrame containing detailed information for all locations. :rtype: pandas.DataFrame .. py:method:: get_realtime_hourly_demand(date: str | pandas.Timestamp = 'latest', end: str | pandas.Timestamp | None = None, locations: list[str] = None, verbose: bool = False) -> pandas.DataFrame Get the real-time hourly demand data for specified locations and date range. :param date: The start date for the data request. Use "latest" for most recent data. :type date: str :param end_date: The end date for the data request. Only used if date is not "latest". :type end_date: str | None :param locations: List of specific location names to request data for. If None, data for all locations will be retrieved. :type locations: list[str], optional :returns: A DataFrame containing the real-time hourly demand data for all requested locations. :rtype: pandas.DataFrame .. py:method:: get_reliability_region_load_forecast(date: str | pandas.Timestamp = 'latest', end: str | pandas.Timestamp | None = None, vintage: Literal[latest, all] = 'all', verbose: bool = False) -> pandas.DataFrame Get the regional load forecast data for specified date range and vintages. :param date: The start date for the data request. Use "latest" for most recent data. :type date: str :param end: The end date for the data request. Only used if date is not "latest". :type end: str | None :param vintages: The vintage for the data request. Options are "latest" or "all". :type vintages: Literal["latest", "all"] :returns: A DataFrame containing the regional load forecast data for all requested locations. :rtype: pandas.DataFrame .. py:method:: make_api_call(url: str, api_params: dict = None, parse_json: bool = True, verbose: bool = False) .. py:method:: parse_problematic_datetime(date_string: str | pandas.Timestamp) -> datetime.datetime .. py:data:: ZONE_LOCATIONID_MAP