-remove index from unit initialization

-clean up index everywhere

assume/common/base.py

@@ -51,27 +51,24 @@ def __init__(
         unit_operator: str,
         technology: str,
         bidding_strategies: dict[str, BaseStrategy],
-        index: pd.DatetimeIndex,
+        forecaster: Forecaster,
         node: str = "node0",
-        forecaster: Forecaster = None,
         location: tuple[float, float] = (0.0, 0.0),
         **kwargs,
     ):
         self.id = id
         self.unit_operator = unit_operator
         self.technology = technology
+        self.bidding_strategies: dict[str, BaseStrategy] = bidding_strategies
+        self.forecaster = forecaster
+        self.index = forecaster.index
+
         self.node = node
         self.location = location
-        self.bidding_strategies: dict[str, BaseStrategy] = bidding_strategies
-        self.index = index
+
         self.outputs = defaultdict(lambda: FastSeries(value=0.0, index=self.index))
         # series does not like to convert from tensor to float otherwise
 
-        # RL data stored as lists to simplify storing to the buffer
-        self.outputs["rl_observations"] = []
-        self.outputs["rl_actions"] = []
-        self.outputs["rl_rewards"] = []
-
         # some data is stored as series to allow to store it in the outputs
         # check if any bidding strategy is using the RL strategy
         if any(
@@ -85,12 +82,10 @@ def __init__(
             )
             self.outputs["reward"] = FastSeries(value=0.0, index=self.index)
 
-        if forecaster:
-            self.forecaster = forecaster
-        else:
-            self.forecaster = defaultdict(
-                lambda: FastSeries(value=0.0, index=self.index)
-            )
+            # RL data stored as lists to simplify storing to the buffer
+            self.outputs["rl_observations"] = []
+            self.outputs["rl_actions"] = []
+            self.outputs["rl_rewards"] = []
 
     def calculate_bids(
         self,

assume/scenario/loader_amiris.py

@@ -135,6 +135,7 @@ def add_agent_to_world(
     base_path: str,
     markups: dict = {},
     supports: dict = {},
+    index: pd.DatetimeIndex = None,
 ):
     """
     Adds an agent from a amiris agent definition to the ASSUME world.
@@ -175,7 +176,7 @@ def add_agent_to_world(
                         "technology": "demand",
                         "price": value,
                     },
-                    NaiveForecast(world.index, demand=100000),
+                    NaiveForecast(index, demand=100000),
                 )
         case "EnergyExchange" | "DayAheadMarketSingleZone":
             clearing_section = agent["Attributes"].get("Clearing", agent["Attributes"])
@@ -223,7 +224,7 @@ def add_agent_to_world(
             co2_price = agent["Attributes"]["Co2Prices"]
             if isinstance(co2_price, str):
                 price_series = read_csv(base_path, co2_price)
-                co2_price = price_series.reindex(world.index).ffill().fillna(0)
+                co2_price = price_series.reindex(index).ffill().fillna(0)
             prices["co2"] = co2_price
         case "FuelsMarket":
             # fill prices for forecaster
@@ -235,7 +236,7 @@ def add_agent_to_world(
                     price_series.index = price_series.index.round("h")
                     if not price_series.index.is_unique:
                         price_series = price_series.groupby(level=0).last()
-                    price = price_series.reindex(world.index).ffill()
+                    price = price_series.reindex(index).ffill()
                 prices[fuel_type] = price * fuel["ConversionFactor"]
         case "DemandTrader":
             world.add_unit_operator(agent["Id"])
@@ -253,7 +254,7 @@ def add_agent_to_world(
                         "technology": "demand",
                         "price": load["ValueOfLostLoad"],
                     },
-                    NaiveForecast(world.index, demand=demand_series),
+                    NaiveForecast(index, demand=demand_series),
                 )
 
         case "StorageTrader":
@@ -270,7 +271,7 @@ def add_agent_to_world(
             forecast_price = prices.get("co2", 20)
             # TODO forecast should be calculated using calculate_EOM_price_forecast
             forecast = NaiveForecast(
-                world.index,
+                index,
                 availability=1,
                 co2_price=prices.get("co2", 2),
                 # price_forecast is used for price_EOM
@@ -336,11 +337,11 @@ def add_agent_to_world(
             availability = prototype["PlannedAvailability"]
             if isinstance(availability, str):
                 availability = read_csv(base_path, availability)
-                availability = availability.reindex(world.index).ffill()
+                availability = availability.reindex(index).ffill()
             availability *= prototype.get("UnplannedAvailabilityFactor", 1)
 
             forecast = NaiveForecast(
-                world.index,
+                index,
                 availability=availability,
                 fuel_price=fuel_price,
                 co2_price=prices.get("co2", 2),
@@ -387,7 +388,7 @@ def add_agent_to_world(
             max_power = attr["InstalledPowerInMW"]
             if isinstance(availability, str):
                 dispatch_profile = read_csv(base_path, availability)
-                availability = dispatch_profile.reindex(world.index).ffill().fillna(0)
+                availability = dispatch_profile.reindex(index).ffill().fillna(0)
 
                 if availability.max() > 1:
                     scale_value = availability.max()
@@ -398,7 +399,7 @@ def add_agent_to_world(
             fuel_price = prices.get(translate_fuel_type[attr["EnergyCarrier"]], 0)
             fuel_price += attr.get("OpexVarInEURperMWH", 0)
             forecast = NaiveForecast(
-                world.index,
+                index,
                 availability=availability,
                 fuel_price=fuel_price,
                 co2_price=prices.get("co2", 0),
@@ -488,7 +489,6 @@ def load_amiris(
         end=end,
         save_frequency_hours=save_interval,
         simulation_id=sim_id,
-        index=index,
     )
     # helper dict to map trader markups/markdowns to powerplants
     markups = {}
@@ -523,6 +523,7 @@ def load_amiris(
             base_path,
             markups,
             supports,
+            index,
         )
     # calculate market price before simulation
     world

assume/scenario/loader_csv.py

@@ -522,7 +522,6 @@ def load_config_and_create_forecaster(
         "path": path,
         "start": start,
         "end": end,
-        "index": forecaster.index,
         "powerplant_units": powerplant_units,
         "storage_units": storage_units,
         "demand_units": demand_units,
@@ -565,7 +564,6 @@ def setup_world(
     config = scenario_data["config"]
     start = scenario_data["start"]
     end = scenario_data["end"]
-    index = scenario_data["index"]
     powerplant_units = scenario_data["powerplant_units"]
     storage_units = scenario_data["storage_units"]
     demand_units = scenario_data["demand_units"]
@@ -627,7 +625,6 @@ def setup_world(
         simulation_id=sim_id,
         learning_config=learning_config,
         bidding_params=bidding_strategy_params,
-        index=index,
         forecaster=forecaster,
     )
 

assume/scenario/loader_oeds.py

@@ -61,7 +61,6 @@ def load_oeds(
         end=end,
         save_frequency_hours=48,
         simulation_id=sim_id,
-        index=index,
     )
     # setup eom market
 

assume/scenario/loader_pypsa.py

@@ -49,7 +49,6 @@ def load_pypsa(
         end=end,
         save_frequency_hours=save_frequency_hours,
         simulation_id=sim_id,
-        index=index,
     )
     # setup eom market
 

assume/units/demand.py

@@ -8,6 +8,7 @@
 
 from assume.common.base import SupportsMinMax
 from assume.common.fast_pandas import FastSeries
+from assume.common.forecasts import Forecaster
 
 
 class Demand(SupportsMinMax):
@@ -36,9 +37,9 @@ def __init__(
         unit_operator: str,
         technology: str,
         bidding_strategies: dict,
-        index: pd.DatetimeIndex,
         max_power: float,
         min_power: float,
+        forecaster: Forecaster,
         node: str = "node0",
         price: float | pd.Series = 3000.0,
         location: tuple[float, float] = (0.0, 0.0),
@@ -49,7 +50,7 @@ def __init__(
             unit_operator=unit_operator,
             technology=technology,
             bidding_strategies=bidding_strategies,
-            index=index,
+            forecaster=forecaster,
             node=node,
             location=location,
             **kwargs,
@@ -65,7 +66,7 @@ def __init__(
         volume = self.forecaster[self.id]
         self.volume = -volume  # demand is negative
         if isinstance(price, numbers.Real):
-            price = FastSeries(index=index, value=price)
+            price = FastSeries(index=self.index, value=price)
         self.price = price
 
     def execute_current_dispatch(

assume/units/powerplant.py

@@ -9,6 +9,7 @@
 import pandas as pd
 
 from assume.common.base import SupportsMinMax
+from assume.common.forecasts import Forecaster
 from assume.common.market_objects import MarketConfig, Orderbook
 from assume.common.utils import get_products_index
 
@@ -54,7 +55,7 @@ def __init__(
         unit_operator: str,
         technology: str,
         bidding_strategies: dict,
-        index: pd.DatetimeIndex,
+        forecaster: Forecaster,
         max_power: float,
         min_power: float = 0.0,
         efficiency: float = 1.0,
@@ -82,7 +83,7 @@ def __init__(
             unit_operator=unit_operator,
             technology=technology,
             bidding_strategies=bidding_strategies,
-            index=index,
+            forecaster=forecaster,
             node=node,
             location=location,
             **kwargs,

assume/units/steel_plant.py

@@ -14,6 +14,7 @@
 )
 
 from assume.common.base import SupportsMinMax
+from assume.common.forecasts import Forecaster
 from assume.common.market_objects import MarketConfig, Orderbook
 from assume.common.utils import get_products_index
 from assume.units.dsm_load_shift import DSMFlex
@@ -53,9 +54,9 @@ def __init__(
         id: str,
         unit_operator: str,
         bidding_strategies: dict,
+        forecaster: Forecaster,
         technology: str = "steel_plant",
         node: str = "node0",
-        index: pd.DatetimeIndex = None,
         location: tuple[float, float] = (0.0, 0.0),
         components: dict[str, dict] = None,
         objective: str = None,
@@ -70,7 +71,7 @@ def __init__(
             technology=technology,
             components=components,
             bidding_strategies=bidding_strategies,
-            index=index,
+            forecaster=forecaster,
             node=node,
             location=location,
             **kwargs,

assume/units/storage.py

@@ -11,6 +11,7 @@
 
 from assume.common.base import SupportsMinMaxCharge
 from assume.common.fast_pandas import FastSeries
+from assume.common.forecasts import Forecaster
 from assume.common.market_objects import MarketConfig, Orderbook
 from assume.common.utils import get_products_index
 
@@ -59,6 +60,7 @@ def __init__(
         unit_operator: str,
         technology: str,
         bidding_strategies: dict,
+        forecaster: Forecaster,
         max_power_charge: float | pd.Series,
         max_power_discharge: float | pd.Series,
         max_soc: float,
@@ -82,19 +84,18 @@ def __init__(
         min_down_time: float = 0,
         downtime_hot_start: int = 8,  # hours
         downtime_warm_start: int = 48,  # hours
-        index: pd.DatetimeIndex = None,
         location: tuple[float, float] = (0, 0),
         node: str = "node0",
         **kwargs,
     ):
         super().__init__(
             id=id,
+            unit_operator=unit_operator,
             technology=technology,
+            bidding_strategies=bidding_strategies,
+            forecaster=forecaster,
             node=node,
             location=location,
-            bidding_strategies=bidding_strategies,
-            index=index,
-            unit_operator=unit_operator,
             **kwargs,
         )
 

assume/world.py

@@ -9,7 +9,6 @@
 from datetime import datetime
 from pathlib import Path
 
-import pandas as pd
 from mango import (
     RoleAgent,
     activate,
@@ -157,7 +156,6 @@ def setup(
         start: datetime,
         end: datetime,
         simulation_id: str,
-        index: pd.Series,
         save_frequency_hours: int = 24,
         bidding_params: dict = {},
         learning_config: LearningConfig = {},
@@ -195,7 +193,6 @@ def setup(
         self.forecaster = forecaster
 
         self.bidding_params = bidding_params
-        self.index = index
 
         # create new container
         container_kwargs = {}
@@ -474,7 +471,6 @@ def create_unit(
         return unit_class(
             id=id,
             unit_operator=unit_operator_id,
-            index=self.index,
             forecaster=forecaster,
             **unit_params,
         )

examples/world_script.py

@@ -30,7 +30,6 @@ def init(world, n=1):
         end=end,
         save_frequency_hours=48,
         simulation_id=sim_id,
-        index=index,
     )
 
     marketdesign = [

examples/world_script_policy.py

@@ -40,7 +40,6 @@ def init(world: World):
         end=end,
         save_frequency_hours=48,
         simulation_id=sim_id,
-        index=index,
     )
 
     marketdesign = [