Suggesting changes to #338

examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb

@@ -1641,7 +1641,7 @@
    "source": [
     "optim = pybop.PSO(cost, max_unchanged_iterations=55, threshold=1e-6)\n",
     "x, final_cost = optim.run()\n",
-    "print(\"Initial parameters:\", cost.x0)\n",
+    "print(\"Initial parameters:\", optim.x0)\n",
     "print(\"Estimated parameters:\", x)"
    ]
   },
@@ -1679,7 +1679,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -1850,7 +1850,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Parameter Extrapolation\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Parameter Extrapolation\");"
    ]
   },
   {

examples/notebooks/equivalent_circuit_identification.ipynb

@@ -419,7 +419,7 @@
    "source": [
     "optim = pybop.CMAES(cost, max_iterations=300)\n",
     "x, final_cost = optim.run()\n",
-    "print(\"Initial parameters:\", cost.x0)\n",
+    "print(\"Initial parameters:\", optim.x0)\n",
     "print(\"Estimated parameters:\", x)"
    ]
   },
@@ -457,7 +457,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/multi_model_identification.ipynb

@@ -3904,9 +3904,7 @@
    ],
    "source": [
     "for optim, x in zip(optims, xs):\n",
-    "    pybop.quick_plot(\n",
-    "        optim.cost.problem, parameter_values=x, title=optim.cost.problem.model.name\n",
-    "    )"
+    "    pybop.quick_plot(optim.cost.problem, inputs=x, title=optim.cost.problem.model.name)"
    ]
   },
   {

examples/notebooks/multi_optimiser_identification.ipynb

@@ -599,7 +599,7 @@
    ],
    "source": [
     "for optim, x in zip(optims, xs):\n",
-    "    pybop.quick_plot(optim.cost.problem, parameter_values=x, title=optim.name())"
+    "    pybop.quick_plot(optim.cost.problem, inputs=x, title=optim.name())"
    ]
   },
   {

examples/notebooks/optimiser_calibration.ipynb

@@ -404,7 +404,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -723,7 +723,7 @@
    "source": [
     "optim = pybop.GradientDescent(cost, sigma0=0.0115)\n",
     "x, final_cost = optim.run()\n",
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/pouch_cell_identification.ipynb

@@ -517,7 +517,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -1539,7 +1539,7 @@
     }
    ],
    "source": [
-    "sol = problem.evaluate(x)\n",
+    "sol = problem.evaluate(parameters.as_dict(x))\n",
     "\n",
     "go.Figure(\n",
     "    [\n",

examples/notebooks/spm_AdamW.ipynb

@@ -437,7 +437,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/spm_electrode_design.ipynb

@@ -277,7 +277,7 @@
    "source": [
     "x, final_cost = optim.run()\n",
     "print(\"Estimated parameters:\", x)\n",
-    "print(f\"Initial gravimetric energy density: {-cost(cost.x0):.2f} Wh.kg-1\")\n",
+    "print(f\"Initial gravimetric energy density: {-cost(optim.x0):.2f} Wh.kg-1\")\n",
     "print(f\"Optimised gravimetric energy density: {-final_cost:.2f} Wh.kg-1\")"
    ]
   },
@@ -329,7 +329,7 @@
    "source": [
     "if cost.update_capacity:\n",
     "    problem._model.approximate_capacity(x)\n",
-    "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, inputs=x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/scripts/BPX_spm.py

@@ -51,7 +51,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/ecm_CMAES.py

@@ -89,7 +89,7 @@
 pybop.plot_dataset(dataset)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/exp_UKF.py

@@ -27,8 +27,9 @@
 # Make a prediction with measurement noise
 sigma = 1e-2
 t_eval = np.linspace(0, 20, 10)
-model.parameters = parameters
-values = model.predict(t_eval=t_eval, inputs=parameters.true_value())
+model.classify_and_update_parameters(parameters)
+true_inputs = parameters.as_dict("true")
+values = model.predict(t_eval=t_eval, inputs=true_inputs)
 values = values["2y"].data
 corrupt_values = values + np.random.normal(0, sigma, len(t_eval))
 
@@ -41,7 +42,7 @@
 model.build(parameters=parameters)
 simulator = pybop.Observer(parameters, model, signal=["2y"])
 simulator._time_data = t_eval
-measurements = simulator.evaluate(parameters.true_value())
+measurements = simulator.evaluate(true_inputs)
 
 # Verification step: Compare by plotting
 go = pybop.PlotlyManager().go
@@ -84,7 +85,7 @@
 )
 
 # Verification step: Find the maximum likelihood estimate given the true parameters
-estimation = observer.evaluate(parameters.true_value())
+estimation = observer.evaluate(true_inputs)
 
 # Verification step: Add the estimate to the plot
 line4 = go.Scatter(
@@ -102,7 +103,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output (requires model that returns Voltage)
-pybop.quick_plot(observer, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(observer, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/gitt.py

@@ -59,7 +59,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_AdamW.py

@@ -68,7 +68,7 @@ def noise(sigma):
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_CMAES.py

@@ -53,7 +53,7 @@
 pybop.plot_dataset(dataset)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_IRPropMin.py

@@ -42,7 +42,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_MAP.py

@@ -57,7 +57,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x[0:2], title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x[0:2], title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_MLE.py

@@ -56,7 +56,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x[0:2], title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x[0:2], title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_NelderMead.py

@@ -68,7 +68,7 @@ def noise(sigma):
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_SNES.py

@@ -42,7 +42,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_UKF.py

@@ -68,7 +68,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output (requires model that returns Voltage)
-pybop.quick_plot(observer, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(observer, inputs=x, title="Optimised Comparison")
 
 # # Plot convergence
 # pybop.plot_convergence(optim)

examples/scripts/spm_XNES.py

@@ -43,7 +43,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_descent.py

@@ -48,7 +48,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_pso.py

@@ -43,7 +43,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spm_scipymin.py

@@ -45,7 +45,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)

examples/scripts/spme_max_energy.py

@@ -12,7 +12,7 @@
 # NOTE: This script can be easily adjusted to consider the volumetric
 # (instead of gravimetric) energy density by changing the line which
 # defines the cost and changing the output to:
-# print(f"Initial volumetric energy density: {cost(cost.x0):.2f} Wh.m-3")
+# print(f"Initial volumetric energy density: {cost(optim.x0):.2f} Wh.m-3")
 # print(f"Optimised volumetric energy density: {final_cost:.2f} Wh.m-3")
 
 # Define parameter set and model
@@ -54,13 +54,13 @@
 # Run optimisation
 x, final_cost = optim.run()
 print("Estimated parameters:", x)
-print(f"Initial gravimetric energy density: {cost(cost.x0):.2f} Wh.kg-1")
+print(f"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1")
 print(f"Optimised gravimetric energy density: {final_cost:.2f} Wh.kg-1")
 
 # Plot the timeseries output
 if cost.update_capacity:
     problem._model.approximate_capacity(x)
-pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+pybop.quick_plot(problem, inputs=x, title="Optimised Comparison")
 
 # Plot the cost landscape with optimisation path
 if len(x) == 2:

examples/standalone/cost.py

@@ -43,7 +43,7 @@ def __init__(self, problem=None):
         )
         self.x0 = self.parameters.initial_value()
 
-    def _evaluate(self, x, grad=None):
+    def _evaluate(self, inputs, grad=None):
         """
         Calculate the cost for a given parameter value.
 
@@ -52,9 +52,8 @@ def _evaluate(self, x, grad=None):
 
         Parameters
         ----------
-        x : array-like
-            A one-element array containing the parameter value for which to
-            evaluate the cost.
+        inputs : Dict
+            The parameters for which to evaluate the cost.
         grad : array-like, optional
             Unused parameter, present for compatibility with gradient-based
             optimizers.
@@ -65,4 +64,4 @@ def _evaluate(self, x, grad=None):
             The calculated cost value for the given parameter.
         """
 
-        return x[0] ** 2 + 42
+        return inputs["x"] ** 2 + 42