Spp

Spp#

Module Contents#

Classes Summary#

`BAAEnum`	Enum where members are also (and must be) strings
`SPP`	Southwest Power Pool (SPP)

Functions#

`add_interval`(→ pandas.DataFrame)	Adds Interval Start and Interval End columns to df
`fill_baa_column`(df, load_col)	Fill missing BAA values based on load magnitude.
`process_gen_mix`(→ pandas.DataFrame)	Parse SPP generation mix data from

Attributes Summary#

Contents#

gridstatus.spp.add_interval(df: pandas.DataFrame, interval_min: int) → pandas.DataFrame[source]#: Adds Interval Start and Interval End columns to df

gridstatus.spp.BAA_LOAD_THRESHOLD_MW = 5000[source]#

class gridstatus.spp.BAAEnum[source]#

Bases: enum.StrEnum

Enum where members are also (and must be) strings

Initialize self. See help(type(self)) for accurate signature.

Attributes

SPP	‘SPP’
SWPW	‘SWPW’

gridstatus.spp.BASE_LOAD_FORECAST_MID_TERM_URL = 'https://portal.spp.org/file-browser-api/download/mtlf-vs-actual?path='[source]#

gridstatus.spp.BASE_LOAD_FORECAST_SHORT_TERM_URL = 'https://portal.spp.org/file-browser-api/download/stlf-vs-actual?path='[source]#

gridstatus.spp.BASE_SOLAR_AND_WIND_MID_TERM_URL = 'https://portal.spp.org/file-browser-api/download/midterm-resource-forecast?path='[source]#

gridstatus.spp.BASE_SOLAR_AND_WIND_SHORT_TERM_URL = 'https://portal.spp.org/file-browser-api/download/shortterm-resource-forecast?path='[source]#

gridstatus.spp.DA_BINDING_CONSTRAINTS = 'da-binding-constraints'[source]#

gridstatus.spp.FILE_BROWSER_API_URL = 'https://portal.spp.org/file-browser-api/'[source]#

gridstatus.spp.FILE_BROWSER_DOWNLOAD_URL = 'https://portal.spp.org/file-browser-api/download'[source]#

gridstatus.spp.fill_baa_column(df, load_col)[source]#

Fill missing BAA values based on load magnitude.

If the BAA column doesn’t exist, creates it. If it exists but has NaN values, fills only the missing entries. Uses BAA_LOAD_THRESHOLD_MW to distinguish between SWPW (small loads) and SPP (large loads).

Parameters:

df – DataFrame with a load column to use for BAA inference.
load_col – Name of the column containing load values.

Returns:

The DataFrame with BAA column filled in-place.

gridstatus.spp.FS_DAM_LMP_BY_LOCATION = 'da-lmp-by-settlement-location'[source]#

gridstatus.spp.FS_RTBM_LMP_BY_LOCATION = 'rtbm-lmp-by-location'[source]#

gridstatus.spp.HOURLY_LOAD_WIDE_FORMAT_END_DATE[source]#

gridstatus.spp.LAST_UPDATED_KEYWORDS = ['last updated', 'as of'][source]#

gridstatus.spp.LMP_BY_SETTLEMENT_LOCATION_WEIS = 'lmp-by-settlement-location-weis'[source]#

gridstatus.spp.LMP_HUBS_AND_INTERFACES[source]#

gridstatus.spp.LOCATION_TYPE_ALL = 'ALL'[source]#

gridstatus.spp.LOCATION_TYPE_BUS = 'Bus'[source]#

gridstatus.spp.LOCATION_TYPE_HUB = 'Hub'[source]#

gridstatus.spp.LOCATION_TYPE_INTERFACE = 'Interface'[source]#

gridstatus.spp.LOCATION_TYPE_SETTLEMENT_LOCATION = 'Settlement Location'[source]#

gridstatus.spp.MARKETPLACE_BASE_URL = 'https://portal.spp.org'[source]#

gridstatus.spp.OPERATING_RESERVES = 'operating-reserves'[source]#

gridstatus.spp.process_gen_mix(df: pandas.DataFrame, detailed: bool = False) → pandas.DataFrame[source]#

Parse SPP generation mix data from https://marketplace.spp.org/pages/generation-mix-historical

Parameters:

df (pd.DataFrame) – raw data
detailed (bool) – whether to combine market and self columns

Returns:

processed data

Return type:

pd.DataFrame

gridstatus.spp.QUERY_RTM5_HUBS_URL = 'https://pricecontourmap.spp.org/arcgis/rest/services/MarketMaps/RTBM_FeatureData/MapServer/1/query'[source]#

gridstatus.spp.QUERY_RTM5_INTERFACES_URL = 'https://pricecontourmap.spp.org/arcgis/rest/services/MarketMaps/RTBM_FeatureData/MapServer/2/query'[source]#

gridstatus.spp.RELIABILITY_LEVELS = ['Normal Operations', 'Weather Advisory', 'Resource Advisory', 'Conservative Operations...[source]#

gridstatus.spp.RELIABILITY_LEVELS_ALIASES[source]#

gridstatus.spp.RTBM_BINDING_CONSTRAINTS = 'rtbm-binding-constraints'[source]#

gridstatus.spp.RTBM_LMP_BY_BUS = 'rtbm-lmp-by-bus'[source]#

gridstatus.spp.RTBM_MCP = 'rtbm-mcp'[source]#

class gridstatus.spp.SPP[source]#

Bases: gridstatus.base.ISOBase

Southwest Power Pool (SPP)

Attributes

default_timezone	‘US/Central’
interconnection_homepage	‘https://www.spp.org/engineering/generator-interconnection/’
iso_id	‘spp’
location_types	None
markets	None
name	‘Southwest Power Pool’
status_homepage	‘https://www.spp.org/markets-operations/current-grid-conditions/’

Methods

`get_as_prices_real_time_5_min`(→ pandas.DataFrame)	Provides Marginal Clearing Price information by Reserve Zone for each
`get_binding_constraints_day_ahead_hourly`(...)	Get Day-Ahead Binding Constraints
`get_binding_constraints_real_time_5_min`(→ pandas.DataFrame)	Get Real-Time Binding Constraints
`get_capacity_of_generation_on_outage`(→ pandas.DataFrame)	Get Capacity of Generation on Outage.
`get_capacity_of_generation_on_outage_annual`(...)	Get VER Curtailments for a year. Starting 2014.
`get_day_ahead_operating_reserve_prices`(→ pandas.DataFrame)	Provides Marginal Clearing Price information by Reserve Zone for each
`get_fuel_mix`(→ pandas.DataFrame)	Get combined fuel mix summed across SPP and SWPW BAAs
`get_fuel_mix_by_baa`(→ pandas.DataFrame)	Get fuel mix for both SPP and SWPW BAAs with a BAA column
`get_fuel_mix_by_baa_detailed`(→ pandas.DataFrame)	Get detailed fuel mix for both SPP and SWPW BAAs with a BAA column
`get_fuel_mix_detailed`(→ pandas.DataFrame)	Get combined detailed fuel mix summed across SPP and SWPW BAAs
`get_hourly_load`(→ pandas.DataFrame)	Get Hourly Load in the long format (on or after 2026-03-24).
`get_hourly_load_annual`(→ pandas.DataFrame)	Get Hourly Load for a year. Starting 2011.
`get_hourly_load_historical`(→ pandas.DataFrame)	Get Hourly Load in the legacy wide format (before 2026-03-24).
`get_interchange_real_time`(→ pandas.DataFrame)	Get real-time interchange (tie flow) data.
`get_interconnection_queue`(→ pandas.DataFrame)	Get interconnection queue
`get_lmp_day_ahead_hourly`(→ pandas.DataFrame)	Get day ahead hourly LMP data
`get_lmp_real_time_5_min_by_bus`(→ pandas.DataFrame)	Get LMP data by bus for the Real-Time 5 Minute Market
`get_lmp_real_time_5_min_by_location`(→ pandas.DataFrame)	Get LMP data by location for the Real-Time 5 Minute Market
`get_lmp_real_time_weis`(→ pandas.DataFrame)	Get LMP data for real time WEIS
`get_load`(→ pandas.DataFrame)	Returns total RTO load in 5 minute intervals from STLF data.
`get_load_by_baa`(→ pandas.DataFrame)	Returns actual load by BAA from short-term load forecast data.
`get_load_by_baa_hourly`(→ pandas.DataFrame \| None)	Returns hourly actual load by BAA from mid-term load forecast data.
`get_load_forecast`(→ pandas.DataFrame)	Returns total RTO load forecast in hourly intervals from MTLF data.
`get_load_forecast_by_baa`(→ pandas.DataFrame)	Returns hourly load forecast by BAA from MTLF data.
`get_load_forecast_mid_term`(→ pandas.DataFrame \| None)	Returns load forecast for +7 days in hourly intervals. Includes actual load
`get_load_forecast_short_term`(→ pandas.DataFrame \| None)	5-minute load forecast data for the SPP footprint (system-wide) for +/- 10
`get_market_clearing_day_ahead`(→ pandas.DataFrame)	Get Market Clearing Day Ahead
`get_market_clearing_real_time`(→ pandas.DataFrame)	Get Market Clearing Real Time
`get_operating_reserves`(→ pandas.DataFrame)
`get_raw_interconnection_queue`(→ BinaryIO)
`get_solar_and_wind_forecast_mid_term`(...)	Returns solar and wind generation forecast for +7 days in hourly intervals.
`get_solar_and_wind_forecast_short_term`(...)	Returns solar and wind generation forecast for +4 hours in 5 minute intervals.
`get_ver_curtailments`(→ pandas.DataFrame)	Get VER Curtailments summed across BAAs.
`get_ver_curtailments_annual`(→ pandas.DataFrame)	Get VER Curtailments summed across BAAs for a year. Starting 2014.
`get_ver_curtailments_by_baa`(→ pandas.DataFrame)	Get VER Curtailments broken down by BAA.
`get_ver_curtailments_by_baa_annual`(→ pandas.DataFrame)	Get VER Curtailments broken down by BAA for a year. Starting 2014.
`get_west_interchange_real_time`(→ pandas.DataFrame)	Get real-time interchange (tie flow) data for SPP West (SWPW).
`now`()

get_as_prices_real_time_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False, use_daily_files: bool = False) → pandas.DataFrame[source]#

Provides Marginal Clearing Price information by Reserve Zone for each Real-Time 5-minute Market solution.

Parameters:

date – date to get data for. Supports “latest” for most recent interval.
end – end date
verbose – print url
use_daily_files – if True, use daily files instead of 5 minute files.

Returns:

Real-Time 5-minute Marginal Clearing Prices

Return type:

pd.DataFrame

get_binding_constraints_day_ahead_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get Day-Ahead Binding Constraints

Parameters:

date – date to get data for. Supports “latest” for most recently available data.
end – end date
verbose – print url

Returns:

Day-Ahead Binding Constraints

Return type:

pd.DataFrame

get_binding_constraints_real_time_5_min(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get Real-Time Binding Constraints

Parameters:

date – date to get data for. Supports “latest” for most recent interval.
end – end date
verbose – print url

Returns:

Real-Time Binding Constraints

Return type:

pd.DataFrame

Get Capacity of Generation on Outage.

Published daily at 8am CT for next 7 days

Parameters:

date – start date
end – end date

get_capacity_of_generation_on_outage_annual(year: int, verbose: bool = True) → pandas.DataFrame[source]#

Get VER Curtailments for a year. Starting 2014. Recent data use get_capacity_of_generation_on_outage

Parameters:

year – year to get data for
verbose – print url

Returns:

VER Curtailments

Return type:

pd.DataFrame

Provides Marginal Clearing Price information by Reserve Zone for each Day-Ahead Market solution for each Operating Day. Posting is updated each day after the DA Market results are posted. Available at https://portal.spp.org/pages/da-mcp#

Parameters:

date – date to get data for
end – end date
verbose – print url

Returns:

Day Ahead Marginal Clearing Prices

Return type:

pd.DataFrame

get_fuel_mix(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get combined fuel mix summed across SPP and SWPW BAAs

Parameters:

date – “latest”, “today”, a timestamp, or a date range tuple
end – optional end date for range queries

Returns:

fuel mix summed across both BAAs

Return type:

pd.DataFrame

get_fuel_mix_by_baa(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get fuel mix for both SPP and SWPW BAAs with a BAA column

Parameters:

date – “latest”, “today”, a timestamp, or a date range tuple
end – optional end date for range queries

Returns:

fuel mix with BAA column differentiating SPP and SWPW

Return type:

pd.DataFrame

get_fuel_mix_by_baa_detailed(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get detailed fuel mix for both SPP and SWPW BAAs with a BAA column

Breaks out self scheduled and market scheduled generation.

Parameters:

date – “latest”, “today”, a timestamp, or a date range tuple
end – optional end date for range queries

Returns:

detailed fuel mix with BAA column

Return type:

pd.DataFrame

get_fuel_mix_detailed(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#

Get combined detailed fuel mix summed across SPP and SWPW BAAs

Breaks out self scheduled and market scheduled generation.

Parameters:

date – “latest”, “today”, a timestamp, or a date range tuple
end – optional end date for range queries

Returns:

detailed fuel mix summed across both BAAs

Return type:

pd.DataFrame

Get Hourly Load in the long format (on or after 2026-03-24).

Parameters:

date – start date (must be on or after 2026-03-24)
end – end date

Returns:

Hourly Load with columns Time, Interval Start,: Interval End, Balancing Area Name, Control Zone Name, Forecast Area Type, Load

Return type:

pd.DataFrame

get_hourly_load_annual(year: int, verbose: bool = True) → pandas.DataFrame[source]#

Get Hourly Load for a year. Starting 2011. For recent data use get_hourly_load

Parameters:

year – year to get data for
verbose – print url

Returns:

Hourly Load

Return type:

pd.DataFrame

Get Hourly Load in the legacy wide format (before 2026-03-24).

Deprecated: SPP changed the hourly load data format on 2026-03-24. Use get_hourly_load for data on or after 2026-03-24.

Parameters:

date – start date (must be before 2026-03-24)
end – end date

Returns:

Hourly Load in wide format

Return type:

pd.DataFrame

Get real-time interchange (tie flow) data.

For “latest” and “today”, returns ~2 days of 1-minute interchange data from the real-time endpoint.

For historical dates, downloads monthly CSV files from the historical tie flow archive.

Data from: - Real-time: https://portal.spp.org/pages/integrated-marketplace-interchange-trend - Historical: https://portal.spp.org/pages/historical-tie-flow

Parameters:

date – supports “latest”, “today”, or a historical date/date range
end – end date for historical range queries
verbose – print info

Returns:

interchange data

Return type:

pd.DataFrame

get_interconnection_queue(verbose: bool = False) → pandas.DataFrame[source]#

Get interconnection queue

Returns:: Interconnection queue
Return type:: pandas.DataFrame

get_lmp_day_ahead_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, location_type: str = LOCATION_TYPE_ALL, verbose: bool = False) → pandas.DataFrame[source]#

Get day ahead hourly LMP data

Supported Location Types:

Hub
Interface
ALL

Get LMP data by bus for the Real-Time 5 Minute Market

Parameters:

date – date to get data for
end – end date
verbose – print url

NOTE: does not take a location_type argument because it always returns LOCATION_TYPE_BUS.

get_lmp_real_time_5_min_by_location(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, location_type: str = LOCATION_TYPE_ALL, verbose: bool = False, use_daily_files: bool = False) → pandas.DataFrame[source]#

Get LMP data by location for the Real-Time 5 Minute Market

Parameters:

date – date to get data for
end – end date
location_type – location type to get data for. Options are: - ALL (LOCATION_TYPE_ALL) - Hub (LOCATION_TYPE_HUB) - Interface (LOCATION_TYPE_INTERFACE) - Settlement Location (LOCATION_TYPE_SETTLEMENT_LOCATION)
verbose – print url
use_daily_files – if True, use daily files instead of 5 minute files.

Get LMP data for real time WEIS

Parameters:

date – date to get data for. if end is not provided, will get data for 5 minute interval that date is in.
end – end date
verbose – print url

get_load(date: str | pandas.Timestamp, end: str | pandas.Timestamp | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Returns total RTO load in 5 minute intervals from STLF data.

get_load_by_baa(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Returns actual load by BAA from short-term load forecast data.

get_load_by_baa_hourly(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame | None[source]#: Returns hourly actual load by BAA from mid-term load forecast data.

get_load_forecast(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Returns total RTO load forecast in hourly intervals from MTLF data.

get_load_forecast_by_baa(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#: Returns hourly load forecast by BAA from MTLF data.

Returns load forecast for +7 days in hourly intervals. Includes actual load for the past 24 hours. Data from https://portal.spp.org/pages/mtlf-vs-actual

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

5-minute load forecast data for the SPP footprint (system-wide) for +/- 10 minutes. Also includes actual load.

Data from https://portal.spp.org/pages/stlf-vs-actual

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info
end (pd.Timestamp|str) – end date
drop_null_forecast_rows (bool) – if True, drop rows with null forecast values

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

Get Market Clearing Day Ahead

Parameters:

date – start date
end – end date

Returns:

Market Clearing Day Ahead

Return type:

pd.DataFrame

Get Market Clearing Real Time

Parameters:

date – start date
end – end date

Returns:

Market Clearing Real Time

Return type:

pd.DataFrame

get_operating_reserves(date: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp], end: str | pandas.Timestamp | tuple[pandas.Timestamp, pandas.Timestamp] | None = None, verbose: bool = False) → pandas.DataFrame[source]#

get_raw_interconnection_queue(verbose: bool = False) → BinaryIO[source]#

Returns solar and wind generation forecast for +7 days in hourly intervals.

Data from https://portal.spp.org/pages/midterm-resource-forecast.

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

Returns solar and wind generation forecast for +4 hours in 5 minute intervals. Include actuals for past day in 5 minute intervals.

Data from https://portal.spp.org/pages/shortterm-resource-forecast

Parameters:

date (pd.Timestamp|str) – date to get data for. Supports “latest” and “today”
verbose (bool) – print info

Returns:

forecast as dataframe.

Return type:

pd.DataFrame

Get VER Curtailments summed across BAAs.

Supports recent data. For historical annual data use get_ver_curtailments_annual. For data broken down by BAA use get_ver_curtailments_by_baa.

Parameters:

date – start date
end – end date

get_ver_curtailments_annual(year: int, verbose: bool = True) → pandas.DataFrame[source]#

Get VER Curtailments summed across BAAs for a year. Starting 2014.

Recent data use get_ver_curtailments. For data broken down by BAA use get_ver_curtailments_by_baa_annual.

Parameters:

year – year to get data for
verbose – print url

Returns:

VER Curtailments

Return type:

pd.DataFrame

Get VER Curtailments broken down by BAA.

Supports recent data. For historical annual data use get_ver_curtailments_by_baa_annual.

Parameters:

date – start date
end – end date

get_ver_curtailments_by_baa_annual(year: int, verbose: bool = True) → pandas.DataFrame[source]#

Get VER Curtailments broken down by BAA for a year. Starting 2014.

Recent data use get_ver_curtailments_by_baa.

Parameters:

year – year to get data for
verbose – print url

Returns:

VER Curtailments

Return type:

pd.DataFrame

Get real-time interchange (tie flow) data for SPP West (SWPW).

For “latest” and “today”, returns ~2 days of 1-minute interchange data from the real-time endpoint.

For historical dates, downloads monthly CSV files from the historical tie flow archive.

Data from: - Real-time: https://portal.spp.org/pages/integrated-marketplace-interchange-trend - Historical: https://portal.spp.org/pages/historical-tie-flow

Parameters:

date – supports “latest”, “today”, or a historical date/date range
end – end date for historical range queries
verbose – print info

Returns:

interchange data

Return type:

pd.DataFrame

static now()[source]#

gridstatus.spp.STATUS_STOP_WORDS = ['as', 'at', 'ct', 'eea', 'of', 'on'][source]#

`BAA_LOAD_THRESHOLD_MW`
`BASE_LOAD_FORECAST_MID_TERM_URL`
`BASE_LOAD_FORECAST_SHORT_TERM_URL`
`BASE_SOLAR_AND_WIND_MID_TERM_URL`
`BASE_SOLAR_AND_WIND_SHORT_TERM_URL`
`DA_BINDING_CONSTRAINTS`
`FILE_BROWSER_API_URL`
`FILE_BROWSER_DOWNLOAD_URL`
`FS_DAM_LMP_BY_LOCATION`
`FS_RTBM_LMP_BY_LOCATION`
`HOURLY_LOAD_WIDE_FORMAT_END_DATE`
`LAST_UPDATED_KEYWORDS`
`LMP_BY_SETTLEMENT_LOCATION_WEIS`
`LMP_HUBS_AND_INTERFACES`
`LOCATION_TYPE_ALL`
`LOCATION_TYPE_BUS`
`LOCATION_TYPE_HUB`
`LOCATION_TYPE_INTERFACE`
`LOCATION_TYPE_SETTLEMENT_LOCATION`
`MARKETPLACE_BASE_URL`
`OPERATING_RESERVES`
`QUERY_RTM5_HUBS_URL`
`QUERY_RTM5_INTERFACES_URL`
`RELIABILITY_LEVELS`
`RELIABILITY_LEVELS_ALIASES`
`RTBM_BINDING_CONSTRAINTS`
`RTBM_LMP_BY_BUS`
`RTBM_MCP`
`STATUS_STOP_WORDS`