Costs dataclass

examples/introduction.ex.py

@@ -79,11 +79,10 @@
 # we have set some values on the `CostModel` instance and can print them.
 
 # %%
-import process.data_structure
 
 # Print some values on the CostModel instance
-print(f"Heat transport system: {process.data_structure.cost_variables.c226:.3e} M$")
-print(f"Electrical plant equipment: {process.data_structure.cost_variables.c24:.3e} M$")
+print(f"Heat transport system: {single_run.data.costs.c226:.3e} M$")
+print(f"Electrical plant equipment: {single_run.data.costs.c24:.3e} M$")
 
 # %%
 # Clean up

process/core/caller.py

@@ -97,7 +97,7 @@ def call_models(self, xc: np.ndarray, m: int) -> tuple[float, np.ndarray]:
         for _ in range(10):
             self._call_models_once(xc)
             # Evaluate objective function and constraints
-            objf = objective_function(data_structure.numerics.minmax)
+            objf = objective_function(data_structure.numerics.minmax, self.data)
             conf, _, _, _, _ = constraints.constraint_eqns(m, -1, self.data)
 
             if objf_prev is None and conf_prev is None:

process/core/final.py

@@ -57,7 +57,7 @@ def output_evaluation(data):
     po.oblnkl(constants.NOUT)
 
     # Evaluate objective function
-    norm_objf = objective_function(numerics.minmax)
+    norm_objf = objective_function(numerics.minmax, data)
     po.ovarre(constants.MFILE, "Normalised objective function", "(norm_objf)", norm_objf)
 
     # Print the residuals of the constraint equations

process/core/init.py

@@ -21,7 +21,6 @@
 from process.data_structure.buildings_variables import init_buildings_variables
 from process.data_structure.ccfe_hcpb_module import init_ccfe_hcpb_module
 from process.data_structure.constraint_variables import init_constraint_variables
-from process.data_structure.cost_variables import init_cost_variables
 from process.data_structure.current_drive_variables import init_current_drive_variables
 from process.data_structure.dcll_variables import init_dcll_module
 from process.data_structure.divertor_variables import init_divertor_variables
@@ -270,7 +269,6 @@ def init_all_module_vars():
     logging_model_handler.clear_logs()
     data_structure.numerics.init_numerics()
     init_buildings_variables()
-    init_cost_variables()
     init_divertor_variables()
     init_fwbs_variables()
     data_structure.global_variables.init_global_variables()

process/core/model.py

@@ -2,6 +2,7 @@
 from dataclasses import dataclass, fields
 
 from process.data_structure.cost_2015_variables import Cost2015Data
+from process.data_structure.cost_variables import CostData
 from process.data_structure.cs_fatigue_variables import CSFatigueData
 from process.data_structure.vacuum_variables import VacuumData
 from process.data_structure.water_usage_variables import WaterUseData
@@ -15,6 +16,7 @@ class DataStructure:
     costs_2015: Cost2015Data = initialise_later
     cs_fatigue: CSFatigueData = initialise_later
     vacuum: VacuumData = initialise_later
+    costs: CostData = initialise_later
 
     def __post_init__(self):
         for f in fields(self):

process/core/scan.py

@@ -15,7 +15,6 @@
 from process.data_structure import (
     build_variables,
     constraint_variables,
-    cost_variables,
     current_drive_variables,
     divertor_variables,
     fwbs_variables,
@@ -1103,11 +1102,11 @@ def scan_select(self, nwp, swp, iscn):
             case 20:
                 constraint_variables.t_burn_min = swp[iscn - 1]
             case 22:
-                if cost_variables.i_plant_availability == 1:
+                if self.data.costs.i_plant_availability == 1:
                     raise ProcessValueError(
                         "Do not scan f_t_plant_available if i_plant_availability=1"
                     )
-                cost_variables.f_t_plant_available = swp[iscn - 1]
+                self.data.costs.f_t_plant_available = swp[iscn - 1]
             case 24:
                 constraint_variables.p_fusion_total_max_mw = swp[iscn - 1]
             case 25:
@@ -1205,9 +1204,9 @@ def scan_select(self, nwp, swp, iscn):
             case 76:
                 heat_transport_variables.eta_turbine = swp[iscn - 1]
             case 77:
-                cost_variables.startupratio = swp[iscn - 1]
+                self.data.costs.startupratio = swp[iscn - 1]
             case 78:
-                cost_variables.fkind = swp[iscn - 1]
+                self.data.costs.fkind = swp[iscn - 1]
             case 79:
                 current_drive_variables.eta_ecrh_injector_wall_plug = swp[iscn - 1]
             case 80:

process/core/solver/constraints.py

@@ -1315,15 +1315,15 @@ def constraint_equation_60(constraint_registration, _data):
 
 
 @ConstraintManager.register_constraint(61, "", ">=")
-def constraint_equation_61(constraint_registration, _data):
+def constraint_equation_61(constraint_registration, data):
     """Equation for availability lower limit
 
     f_t_plant_available: Total plant availability fraction
     avail_min: Minimum availability
     """
     return geq(
-        data_structure.cost_variables.f_t_plant_available,
-        data_structure.cost_variables.avail_min,
+        data.costs.f_t_plant_available,
+        data.costs.avail_min,
         constraint_registration,
     )
 
@@ -1715,7 +1715,7 @@ def constraint_equation_84(constraint_registration, _data):
 
 
 @ConstraintManager.register_constraint(85, "years", "=")
-def constraint_equation_85(constraint_registration, _data):
+def constraint_equation_85(constraint_registration, data):
     """Equality constraint for the centerpost (CP) lifetime
 
     Depending on the chosen option i_cp_lifetime:
@@ -1731,20 +1731,20 @@ def constraint_equation_85(constraint_registration, _data):
         the CP lifetime must equate
     """
     # The CP lifetime is equal to the the divertor one
-    if data_structure.cost_variables.i_cp_lifetime == 0:
-        bound = data_structure.cost_variables.cplife_input
+    if data.costs.i_cp_lifetime == 0:
+        bound = data.costs.cplife_input
 
-    elif data_structure.cost_variables.i_cp_lifetime == 1:
-        bound = data_structure.cost_variables.life_div_fpy
+    elif data.costs.i_cp_lifetime == 1:
+        bound = data.costs.life_div_fpy
 
     # The CP lifetime is equal to the tritium breeding blankets / FW one
-    elif data_structure.cost_variables.i_cp_lifetime == 2:
+    elif data.costs.i_cp_lifetime == 2:
         bound = data_structure.fwbs_variables.life_blkt_fpy
 
-    elif data_structure.cost_variables.i_cp_lifetime == 3:
-        bound = data_structure.cost_variables.life_plant
+    elif data.costs.i_cp_lifetime == 3:
+        bound = data.costs.life_plant
 
-    return eq(data_structure.cost_variables.cplife, bound, constraint_registration)
+    return eq(data.costs.cplife, bound, constraint_registration)
 
 
 @ConstraintManager.register_constraint(86, "m", "<=")
@@ -1810,11 +1810,8 @@ def constraint_equation_90(constraint_registration, data):
     n_cycle: Allowable number of cycles for CS
     n_cycle_min: Minimum required cycles for CS
     """
-    if (
-        data_structure.cost_variables.ibkt_life == 1
-        and data.cs_fatigue.bkt_life_csf == 1
-    ):
-        data.cs_fatigue.n_cycle_min = data_structure.cost_variables.bktcycles
+    if data.costs.ibkt_life == 1 and data.cs_fatigue.bkt_life_csf == 1:
+        data.cs_fatigue.n_cycle_min = data.costs.bktcycles
 
     return geq(
         data.cs_fatigue.n_cycle,

process/core/solver/evaluators.py

@@ -4,7 +4,6 @@
 import numpy as np
 
 from process.core.caller import Caller
-from process.data_structure import cost_variables as cv
 from process.data_structure import global_variables as gv
 from process.data_structure import numerics
 from process.data_structure import physics_variables as pv
@@ -28,6 +27,7 @@ def __init__(self, models, data, _x):
         :type x: np.ndarray
         """
         self.caller = Caller(models, data)
+        self.data = data
 
     def fcnvmc1(self, _n, m, xv, ifail):
         """Function evaluator for VMCON.
@@ -75,7 +75,7 @@ def fcnvmc1(self, _n, m, xv, ifail):
             logger.debug(f"{(1 - (ifail % 7)) - 1 = }")
             logger.debug(f"{(numerics.nviter % 2) - 1 = }")
             logger.debug(f"{pv.temp_plasma_electron_vol_avg_kev = }")
-            logger.debug(f"{cv.coe = }")
+            logger.debug(f"{self.data.costs.coe = }")
             logger.debug(f"{pv.rmajor = }")
             logger.debug(f"{pv.p_fusion_total_mw = }")
             logger.debug(f"{pv.b_plasma_toroidal_on_axis = }")

process/core/solver/objectives.py

@@ -1,8 +1,8 @@
 import numpy as np
 
 from process.core.exceptions import ProcessValueError
+from process.core.model import DataStructure
 from process.data_structure import (
-    cost_variables,
     current_drive_variables,
     divertor_variables,
     heat_transport_variables,
@@ -32,7 +32,7 @@
 }
 
 
-def objective_function(minmax: int) -> float:
+def objective_function(minmax: int, data: DataStructure) -> float:
     """Calculate the specified objective function
 
     Parameters
@@ -57,6 +57,9 @@ def objective_function(minmax: int) -> float:
         * 17: Net electrical output
         * 18: NULL, f(x) = 1
         * 19: Major radius/burn time
+    data: DataStructure
+        data structure object for providing data to the
+        objective function
     """
     figure_of_merit = abs(minmax)
 
@@ -80,12 +83,12 @@ def objective_function(minmax: int) -> float:
                 + physics_variables.p_plasma_ohmic_mw
             )
         case 6:
-            objective_metric = cost_variables.coe / 100.0
+            objective_metric = data.costs.coe / 100.0
         case 7:
             objective_metric = (
-                cost_variables.cdirt / 1.0e3
-                if cost_variables.ireactor == 0
-                else cost_variables.concost / 1.0e4
+                data.costs.cdirt / 1.0e3
+                if data.costs.ireactor == 0
+                else data.costs.concost / 1.0e4
             )
         case 8:
             objective_metric = physics_variables.aspect
@@ -98,9 +101,9 @@ def objective_function(minmax: int) -> float:
         case 14:
             objective_metric = times_variables.t_plant_pulse_burn / 2.0e4
         case 15:
-            if cost_variables.i_plant_availability != 1:
+            if data.costs.i_plant_availability != 1:
                 raise ProcessValueError("minmax=15 requires i_plant_availability=1")
-            objective_metric = cost_variables.f_t_plant_available
+            objective_metric = data.costs.f_t_plant_available
         case 16:
             objective_metric = 0.95 * (physics_variables.rmajor / 9.0) - 0.05 * (
                 times_variables.t_plant_pulse_burn / 7200.0