Renamed TimeSeriesRaw to TImeSeriesData and .value to .data

flixOpt · Oct 21, 2024 · 3350a89 · 3350a89
1 parent 2ce5098
commit 3350a89
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Showing 9 changed files with 38 additions and 38 deletions.
diff --git a/examples/Ex03_full_seg_agg/Model_and_solve.py b/examples/Ex03_full_seg_agg/Model_and_solve.py
@@ -158,11 +158,11 @@
                     minimal_final_charge_state=137, maximal_final_charge_state=158,
                     eta_load=1, eta_unload=1, relative_loss_per_hour=0.001, prevent_simultaneous_charge_and_discharge=True)
 
-TS_Q_th_Last = TimeSeriesRaw(Q_th_Last)
+TS_Q_th_Last = TimeSeriesData(Q_th_Last)
 aWaermeLast = Sink('Wärmelast', sink=Flow('Q_th_Last', bus=Fernwaerme, size=1, fixed_relative_value=TS_Q_th_Last))
 
 # TS with explicit defined weight
-TS_P_el_Last = TimeSeriesRaw(P_el_Last, agg_weight=0.7)  # explicit defined weight
+TS_P_el_Last = TimeSeriesData(P_el_Last, agg_weight=0.7)  # explicit defined weight
 aStromLast = Sink('Stromlast', sink=Flow('P_el_Last', bus=Strom, size=1, fixed_relative_value=TS_P_el_Last))
 
 aKohleTarif = Source('Kohletarif',
@@ -171,8 +171,8 @@
 aGasTarif = Source('Gastarif', source=Flow('Q_Gas', bus=Gas, size=1000, effects_per_flow_hour={costs: gP, CO2: 0.3}))
 
 # 2 TS with same aggType (--> implicit defined weigth = 0.5)
-p_feed_in = TimeSeriesRaw(-(p_el - 0.5), agg_group='p_el')  # weight shared in group p_el
-p_sell = TimeSeriesRaw(p_el + 0.5, agg_group='p_el')
+p_feed_in = TimeSeriesData(-(p_el - 0.5), agg_group='p_el')  # weight shared in group p_el
+p_sell = TimeSeriesData(p_el + 0.5, agg_group='p_el')
 # p_feed_in = p_feed_in.value # only value
 # p_sell    = p_sell.value # only value
 aStromEinspeisung = Sink('Einspeisung', sink=Flow('P_el', bus=Strom, size=1000, effects_per_flow_hour=p_feed_in))

diff --git a/flixOpt/__init__.py b/flixOpt/__init__.py
@@ -12,5 +12,5 @@
 from flixOpt.calculation import FullCalculation, SegmentedCalculation, AggregatedCalculation
 from flixOpt.interface import InvestParameters, OnOffParameters
 from flixOpt.postprocessing import flix_results
-from flixOpt.core import setup_logging, change_logging_level, TimeSeriesRaw
+from flixOpt.core import setup_logging, change_logging_level, TimeSeriesData
 setup_logging('INFO')
diff --git a/flixOpt/aggregation.py b/flixOpt/aggregation.py
@@ -21,7 +21,7 @@
 from flixOpt.elements import Flow
 from flixOpt.flow_system import FlowSystem
 from flixOpt.components import Storage
-from flixOpt.core import TimeSeriesRaw
+from flixOpt.core import TimeSeriesData
 from flixOpt.structure import Element, SystemModel
 from flixOpt.math_modeling import Equation, Variable
 
@@ -372,8 +372,8 @@ def addPeak_Min_labels(self):
 
     def __init__(self,
                  time_series_list: List[TimeSeries],
-                 addPeakMax_TSraw: Optional[List[TimeSeriesRaw]] = None,
-                 addPeakMin_TSraw: Optional[List[TimeSeriesRaw]] = None):
+                 addPeakMax_TSraw: Optional[List[TimeSeriesData]] = None,
+                 addPeakMin_TSraw: Optional[List[TimeSeriesData]] = None):
         self.time_series_list = time_series_list
         self.addPeakMax_TSraw = addPeakMax_TSraw or []
         self.addPeakMin_TSraw = addPeakMin_TSraw or []
@@ -444,8 +444,8 @@ def _getWeight(self, aTS: TimeSeries):
     def _checkPeak_TSraw(self, aTSrawlist):
         if aTSrawlist is not None:
             for aTSraw in aTSrawlist:
-                if not isinstance(aTSraw, TimeSeriesRaw):
-                    raise Exception('addPeak_max/min must be list of TimeSeriesRaw-objects!')
+                if not isinstance(aTSraw, TimeSeriesData):
+                    raise Exception('addPeak_max/min must be list of TimeSeriesData-objects!')
 
     def __str__(self) -> str:
         result = f'{len(self.time_series_list)} TimeSeries used for aggregation:\n'

diff --git a/flixOpt/calculation.py b/flixOpt/calculation.py
@@ -17,7 +17,7 @@
 
 from flixOpt import utils
 from flixOpt.aggregation import TimeSeriesCollection
-from flixOpt.core import Skalar, Numeric, TimeSeriesRaw
+from flixOpt.core import Skalar, Numeric, TimeSeriesData
 from flixOpt.math_modeling import VariableTS
 from flixOpt.structure import SystemModel
 from flixOpt.flow_system import FlowSystem
@@ -195,10 +195,10 @@ def do_modeling(self, periodLengthInHours, nr_of_typical_periods, use_extreme_pe
         useOriginalTimeSeries : boolean.
             orginal or aggregated timeseries should
             be chosen for the calculation. default is False.
-        addPeakMax : list of TimeSeriesRaw
+        addPeakMax : list of TimeSeriesData
             list of data-timeseries. The period with the max-value are
             chosen as a explicitly period.
-        addPeakMin : list of TimeSeriesRaw
+        addPeakMin : list of TimeSeriesData
             list of data-timeseries. The period with the min-value are
             chosen as a explicitly period.
 

diff --git a/flixOpt/core.py b/flixOpt/core.py
@@ -20,12 +20,12 @@
 # Datatype Numeric_TS:
 #   Skalar      --> wird später dann in array ("Zeitreihe" mit length=nrOfTimeIndexe) übersetzt
 #   np.ndarray  --> muss length=nrOfTimeIndexe haben ("Zeitreihe")
-#   TimeSeriesRaw      --> wie obige aber zusätzliche Übergabe aggWeight (für Aggregation)
+#   TimeSeriesData      --> wie obige aber zusätzliche Übergabe aggWeight (für Aggregation)
 
 
-class TimeSeriesRaw:
+class TimeSeriesData:
     def __init__(self,
-                 value: Union[int, float, np.ndarray],
+                 data: Numeric,
                  agg_group: Optional[str] = None,
                  agg_weight: Optional[float] = None):
         """
@@ -34,18 +34,18 @@ def __init__(self,
             EXAMPLE solar:
             you have several solar timeseries. These should not be overweighted
             compared to the remaining timeseries (i.g. heat load, price)!
-            fixed_relative_value_solar1 = TimeSeriesRaw(sol_array_1, type = 'solar')
-            fixed_relative_value_solar2 = TimeSeriesRaw(sol_array_2, type = 'solar')
-            fixed_relative_value_solar3 = TimeSeriesRaw(sol_array_3, type = 'solar')
+            fixed_relative_value_solar1 = TimeSeriesData(sol_array_1, type = 'solar')
+            fixed_relative_value_solar2 = TimeSeriesData(sol_array_2, type = 'solar')
+            fixed_relative_value_solar3 = TimeSeriesData(sol_array_3, type = 'solar')
             --> this 3 series of same type share one weight, i.e. internally assigned each weight = 1/3
             (instead of standard weight = 1)
 
         Parameters
         ----------
-        value : Union[int, float, np.ndarray]
+        data : Union[int, float, np.ndarray]
             The timeseries data, which can be a scalar, array, or numpy array.
         agg_group : str, optional
-            The group this TimeSeriesRaw is a part of. agg_weight is split between members of a group. Default is None.
+            The group this TimeSeriesData is a part of. agg_weight is split between members of a group. Default is None.
         agg_weight : float, optional
             The weight for calculation_type 'aggregated', should be between 0 and 1. Default is None.
 
@@ -54,14 +54,14 @@ def __init__(self,
         Exception
             If both agg_group and agg_weight are set, an exception is raised.
         """
-        self.value = value
+        self.data = data
         self.agg_group = agg_group
         self.agg_weight = agg_weight
         if (agg_group is not None) and (agg_weight is not None):
             raise Exception('Either <agg_group> or explicit <agg_weigth> can be used. Not both!')
 
     def __repr__(self):
-        return f"TimeSeriesRaw(value={self.value}, agg_group={self.agg_group}, agg_weight={self.agg_weight})"
+        return f"TimeSeriesData(value={self.data}, agg_group={self.agg_group}, agg_weight={self.agg_weight})"
 
 
 class TimeSeries:
@@ -76,7 +76,7 @@ class TimeSeries:
     ----------
     label : str
         The label for the time series.
-    TSraw : Optional[TimeSeriesRaw]
+    TSraw : Optional[TimeSeriesData]
         The raw time series data if provided as cTSraw.
     data : Optional[Numeric]
         The actual data for the time series. Can be None.
@@ -94,8 +94,8 @@ def __init__(self, label: str, data: Optional[Numeric_TS]):
         self.explicit_active_data: Optional[Numeric] = None  # Shortcut fneeded for aggregation. TODO: Improve this!
         self.TSraw = None
 
-        if isinstance(data, TimeSeriesRaw):
-            self.data: Optional[Numeric] = self.make_scalar_if_possible(data.value)
+        if isinstance(data, TimeSeriesData):
+            self.data: Optional[Numeric] = self.make_scalar_if_possible(data.data)
             self.TSraw = data
         else:
             self.data: Optional[Numeric] = self.make_scalar_if_possible(data)

diff --git a/flixOpt/elements.py b/flixOpt/elements.py
@@ -115,7 +115,7 @@ def __init__(self,
         ----------
         label : str
             name.
-        excess_penalty_per_flow_hour : none or scalar, array or TimeSeriesRaw
+        excess_penalty_per_flow_hour : none or scalar, array or TimeSeriesData
             excess costs / penalty costs (bus balance compensation)
             (none/ 0 -> no penalty). The default is 1e5.
             (Take care: if you use a timeseries (no scalar), timeseries is aggregated if calculation_type = aggregated!)
@@ -187,9 +187,9 @@ def __init__(self,
             name of flow
         bus : Bus, optional
             bus to which flow is linked
-        relative_minimum : scalar, array, TimeSeriesRaw, optional
+        relative_minimum : scalar, array, TimeSeriesData, optional
             min value is relative_minimum multiplied by size
-        relative_maximum : scalar, array, TimeSeriesRaw, optional
+        relative_maximum : scalar, array, TimeSeriesData, optional
             max value is relative_maximum multiplied by size. If size = max then relative_maximum=1
         size : scalar. None if is a nominal value is a opt-variable, optional
             nominal value/ invest size (linked to relative_minimum, relative_maximum and others).
@@ -202,7 +202,7 @@ def __init__(self,
              def: :math:`load\_factor:= sumFlowHours/ (nominal\_val \cdot \Delta t_{tot})`
         load_factor_max : scalar, optional
             maximal load factor (see minimal load factor)
-        effects_per_flow_hour : scalar, array, TimeSeriesRaw, optional
+        effects_per_flow_hour : scalar, array, TimeSeriesData, optional
             operational costs, costs per flow-"work"
         can_be_off : OnOffParameters, optional
             flow can be "off", i.e. be zero (only relevant if relative_minimum > 0)
@@ -214,7 +214,7 @@ def __init__(self,
         flow_hours_total_min : TYPE, optional
             minimum flow-hours ("flow-work")
             (if size is not const, maybe load_factor_min fits better for you!)
-        fixed_relative_value : scalar, array, TimeSeriesRaw, optional
+        fixed_relative_value : scalar, array, TimeSeriesData, optional
             fixed relative values for flow (if given).
             val(t) := fixed_relative_value(t) * size(t)
             With this value, the flow-value is no opt-variable anymore;

diff --git a/flixOpt/interface.py b/flixOpt/interface.py
@@ -128,7 +128,7 @@ def __init__(self,
             (last off-time period of timeseries is not checked and can be shorter)
         consecutive_off_hours_max : scalar, optional
             max sum of non-operating hours in one piece
-        effects_per_switch_on : scalar, array, TimeSeriesRaw, optional
+        effects_per_switch_on : scalar, array, TimeSeriesData, optional
             cost of one switch from off (var_on=0) to on (var_on=1),
             unit i.g. in Euro
         switch_on_total_max : integer, optional

diff --git a/flixOpt/utils.py b/flixOpt/utils.py
@@ -11,7 +11,7 @@
 import numpy as np
 import math  # für nan
 
-from flixOpt.core import TimeSeriesRaw
+from flixOpt.core import TimeSeriesData
 from flixOpt.core import Numeric, Skalar
 
 logger = logging.getLogger('flixOpt')
@@ -46,12 +46,12 @@ def as_vector(value: Union[int, float, np.ndarray, List], length: int) -> np.nda
         return np.array(value)
 
 
-def check_bounds(value: Union[int, float, np.ndarray, TimeSeriesRaw],
+def check_bounds(value: Union[int, float, np.ndarray, TimeSeriesData],
                  label: str,
                  lower_bound: Numeric,
                  upper_bound: Numeric):
-    if isinstance(value, TimeSeriesRaw):
-        value = value.value
+    if isinstance(value, TimeSeriesData):
+        value = value.data
     if np.any(value < lower_bound):
         raise Exception(f'{label} is below its {lower_bound=}!')
     if np.any(value >= upper_bound):

diff --git a/tests/test_integration.py b/tests/test_integration.py
@@ -341,11 +341,11 @@ def calculate(self, modeling_type: Literal["full", "segmented", "aggregated"]):
         aKWK = CHP('BHKW2', eta_th=0.58, eta_el=0.22, effects_per_switch_on=24000, P_el=Flow('P_el', bus=Strom), Q_th=Flow('Q_th', bus=Fernwaerme), Q_fu=Flow('Q_fu', bus=Kohle, size=288, relative_minimum=87 / 288), on_values_before_begin=[0])
         aSpeicher = Storage('Speicher', charging=Flow('Q_th_load', size=137, bus=Fernwaerme), discharging=Flow('Q_th_unload', size=158, bus=Fernwaerme), capacity_in_flow_hours=684, initial_charge_state=137, minimal_final_charge_state=137, maximal_final_charge_state=158, eta_load=1, eta_unload=1, relative_loss_per_hour=0.001, prevent_simultaneous_charge_and_discharge=True)
 
-        TS_Q_th_Last, TS_P_el_Last = TimeSeriesRaw(Q_th_Last), TimeSeriesRaw(P_el_Last, agg_weight=0.7)
+        TS_Q_th_Last, TS_P_el_Last = TimeSeriesData(Q_th_Last), TimeSeriesData(P_el_Last, agg_weight=0.7)
         aWaermeLast, aStromLast = Sink('Wärmelast', sink=Flow('Q_th_Last', bus=Fernwaerme, size=1, fixed_relative_value=TS_Q_th_Last)), Sink('Stromlast', sink=Flow('P_el_Last', bus=Strom, size=1, fixed_relative_value=TS_P_el_Last))
         aKohleTarif, aGasTarif = Source('Kohletarif', source=Flow('Q_Kohle', bus=Kohle, size=1000, effects_per_flow_hour={costs: 4.6, CO2: 0.3})), Source('Gastarif', source=Flow('Q_Gas', bus=Gas, size=1000, effects_per_flow_hour={costs: gP, CO2: 0.3}))
 
-        p_feed_in, p_sell = TimeSeriesRaw(-(p_el - 0.5), agg_group='p_el'), TimeSeriesRaw(p_el + 0.5, agg_group='p_el')
+        p_feed_in, p_sell = TimeSeriesData(-(p_el - 0.5), agg_group='p_el'), TimeSeriesData(p_el + 0.5, agg_group='p_el')
         aStromEinspeisung, aStromTarif = Sink('Einspeisung', sink=Flow('P_el', bus=Strom, size=1000, effects_per_flow_hour=p_feed_in)), Source('Stromtarif', source=Flow('P_el', bus=Strom, size=1000, effects_per_flow_hour={costs: p_sell, CO2: 0.3}))
 
         es = FlowSystem(aTimeSeries, last_time_step_hours=None)