Post merge updates for power costs, example

examples/scripts/maximising_power.py

@@ -47,16 +47,18 @@
 optim = pybop.XNES(
     cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10
 )
-x, final_cost = optim.run()
-print("Estimated parameters:", x)
+results = optim.run()
+print("Estimated parameters:", results.x)
 print(f"Initial gravimetric power density: {cost1(optim.x0):.2f} W.kg-1")
-print(f"Optimised gravimetric power density: {cost1(x):.2f} W.kg-1")
+print(f"Optimised gravimetric power density: {cost1(results.x):.2f} W.kg-1")
 print(f"Initial volumetric power density: {cost2(optim.x0):.2f} W.m-3")
-print(f"Optimised volumetric power density: {cost2(x):.2f} W.m-3")
-print(f"Optimised discharge rate: {x[-1]:.2f} A = {x[-1]/nominal_capacity:.2f} C")
+print(f"Optimised volumetric power density: {cost2(results.x):.2f} W.m-3")
+print(
+    f"Optimised discharge rate: {results.x[-1]:.2f} A = {results.x[-1]/nominal_capacity:.2f} C"
+)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison")
+pybop.quick_plot(problem, problem_inputs=results.x, title="Optimised Comparison")
 
 # Plot the cost landscape with optimisation path
 pybop.plot2d(optim, steps=5)

pybop/costs/design_costs.py

@@ -1,3 +1,5 @@
+from typing import Union
+
 import numpy as np
 
 from pybop.costs.base_cost import BaseCost
@@ -168,7 +170,7 @@ class GravimetricPowerDensity(DesignCost):
         The length of time (seconds) over which the power should be sustained.
     """
 
-    def __init__(self, problem, target_time: int = 3600):
+    def __init__(self, problem, target_time: Union[int, float] = 3600):
         super().__init__(problem)
         self.target_time = target_time
 
@@ -231,7 +233,7 @@ class VolumetricPowerDensity(DesignCost):
         The length of time (seconds) over which the power should be sustained.
     """
 
-    def __init__(self, problem, target_time: int = 3600):
+    def __init__(self, problem, target_time: Union[int, float] = 3600):
         super().__init__(problem)
         self.target_time = target_time