OptimiseResult().__str__ across examples, adds OCP balancing script (#…

…536) * adds OCP balancing example, remove redundant code. * feat: add initial parameters to OptimisationResults * examples: updates for OptimisationResults __str__ * refactor: OptimisationResult prints with BaseOptimsiser verbose arg, verbose is True as default * style: pre-commit fixes --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
pybop-team · Oct 22, 2024 · 898b237 · 898b237
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diff --git a/examples/notebooks/adamw_identification.ipynb b/examples/notebooks/adamw_identification.ipynb
diff --git a/examples/notebooks/comparing_cost_functions.ipynb b/examples/notebooks/comparing_cost_functions.ipynb
diff --git a/examples/notebooks/ecm_trust-constr.ipynb b/examples/notebooks/ecm_trust-constr.ipynb
diff --git a/examples/notebooks/electrode_balancing.ipynb b/examples/notebooks/electrode_balancing.ipynb
diff --git a/examples/notebooks/energy_based_electrode_design.ipynb b/examples/notebooks/energy_based_electrode_design.ipynb
diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/examples/notebooks/equivalent_circuit_identification.ipynb
diff --git a/examples/notebooks/equivalent_circuit_identification_hppc.ipynb b/examples/notebooks/equivalent_circuit_identification_hppc.ipynb
diff --git a/examples/notebooks/equivalent_circuit_identification_multipulse.ipynb b/examples/notebooks/equivalent_circuit_identification_multipulse.ipynb
diff --git a/examples/notebooks/maximum_a_posteriori.ipynb b/examples/notebooks/maximum_a_posteriori.ipynb
diff --git a/examples/notebooks/multi_model_identification.ipynb b/examples/notebooks/multi_model_identification.ipynb
diff --git a/examples/notebooks/multi_optimiser_identification.ipynb b/examples/notebooks/multi_optimiser_identification.ipynb
diff --git a/examples/notebooks/optimiser_calibration.ipynb b/examples/notebooks/optimiser_calibration.ipynb
diff --git a/examples/notebooks/optimiser_interface.ipynb b/examples/notebooks/optimiser_interface.ipynb
@@ -159,7 +159,22 @@
    "execution_count": null,
    "id": "7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Halt: No significant change for 15 iterations.\n",
+      "OptimisationResult:\n",
+      "  Initial parameters: [0.00032648]\n",
+      "  Optimised parameters: [0.00099965]\n",
+      "  Final cost: 1.363466506257511e-09\n",
+      "  Optimisation time: 1.6153881549835205 seconds\n",
+      "  Number of iterations: 23\n",
+      "  SciPy result available: No\n"
+     ]
+    }
+   ],
    "source": [
     "optim_one = pybop.XNES(\n",
     "    cost, max_iterations=50\n",
@@ -183,7 +198,22 @@
    "execution_count": null,
    "id": "9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Halt: No significant change for 15 iterations.\n",
+      "OptimisationResult:\n",
+      "  Initial parameters: [0.00032648]\n",
+      "  Optimised parameters: [0.00099985]\n",
+      "  Final cost: 2.431998416403876e-10\n",
+      "  Optimisation time: 1.972628116607666 seconds\n",
+      "  Number of iterations: 25\n",
+      "  SciPy result available: No\n"
+     ]
+    }
+   ],
    "source": [
     "optim_two = pybop.Optimisation(\n",
     "    cost, optimiser=pybop.XNES, max_iterations=50\n",

diff --git a/examples/notebooks/pouch_cell_identification.ipynb b/examples/notebooks/pouch_cell_identification.ipynb
diff --git a/examples/notebooks/single_pulse_circuit_model.ipynb b/examples/notebooks/single_pulse_circuit_model.ipynb
diff --git a/examples/notebooks/solver_selection.ipynb b/examples/notebooks/solver_selection.ipynb
@@ -194,28 +194,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time Evaluate IDA KLU solver: 0.306\n"
+      "Time Evaluate IDA KLU solver: 0.320\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time Evaluate CasADi solver with 'safe' mode: 1.097\n"
+      "Time Evaluate CasADi solver with 'safe' mode: 1.176\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time Evaluate CasADi solver with 'fast' mode: 1.090\n"
+      "Time Evaluate CasADi solver with 'fast' mode: 0.912\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time Evaluate CasADi solver with 'fast with events' mode: 0.915\n"
+      "Time Evaluate CasADi solver with 'fast with events' mode: 0.943\n"
      ]
     }
    ],
@@ -248,28 +248,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time EvaluateS1 IDA KLU solver: 0.622\n"
+      "Time EvaluateS1 IDA KLU solver: 0.786\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time EvaluateS1 CasADi solver with 'safe' mode: 3.886\n"
+      "Time EvaluateS1 CasADi solver with 'safe' mode: 3.751\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time EvaluateS1 CasADi solver with 'fast' mode: 3.498\n"
+      "Time EvaluateS1 CasADi solver with 'fast' mode: 3.326\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time EvaluateS1 CasADi solver with 'fast with events' mode: 3.475\n"
+      "Time EvaluateS1 CasADi solver with 'fast with events' mode: 3.349\n"
      ]
     }
    ],

diff --git a/examples/notebooks/transformation_introduction.ipynb b/examples/notebooks/transformation_introduction.ipynb
diff --git a/examples/scripts/BPX_spm.py b/examples/scripts/BPX_spm.py
@@ -48,7 +48,6 @@
 # Run the optimisation
 results = optim.run()
 print("True parameters:", parameters.true_value())
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/cuckoo.py b/examples/scripts/cuckoo.py
@@ -61,7 +61,6 @@
 )
 
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/ecm_CMAES.py b/examples/scripts/ecm_CMAES.py
@@ -78,7 +78,6 @@
 optim = pybop.CMAES(cost, max_iterations=100)
 
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Export the parameters to JSON
 parameter_set.export_parameters(

diff --git a/examples/scripts/ecm_tau_constraints.py b/examples/scripts/ecm_tau_constraints.py
@@ -179,8 +179,6 @@ def check_params(
 )
 
 results = optim.run()
-print("Estimated parameters:", results.x)
-
 
 # Plot the time series
 pybop.plot.dataset(dataset)

diff --git a/examples/scripts/eis_fitting.py b/examples/scripts/eis_fitting.py
@@ -67,7 +67,6 @@ def noise(sigma, values):
 optim = pybop.CMAES(cost, max_iterations=100, sigma0=0.25, max_unchanged_iterations=30)
 
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the nyquist
 pybop.plot.nyquist(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/exp_UKF.py b/examples/scripts/exp_UKF.py
@@ -98,7 +98,6 @@
 
 # Run optimisation
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output (requires model that returns Voltage)
 pybop.plot.quick(observer, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/functional_parameters.py b/examples/scripts/functional_parameters.py
@@ -82,7 +82,6 @@ def positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T):
 
 # Run optimisation
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/gitt.py b/examples/scripts/gitt.py
@@ -56,7 +56,6 @@
 
 # Run the optimisation problem
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/linked_parameters.py b/examples/scripts/linked_parameters.py
@@ -56,7 +56,6 @@
     cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10
 )
 results = optim.run()
-print("Estimated parameters:", results.x)
 print(f"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1")
 print(f"Optimised gravimetric energy density: {cost(results.x):.2f} Wh.kg-1")
 

diff --git a/examples/scripts/maximising_energy.py b/examples/scripts/maximising_energy.py
@@ -56,7 +56,6 @@
     cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10
 )
 results = optim.run()
-print("Estimated parameters:", results.x)
 print(f"Initial gravimetric energy density: {cost1(optim.x0):.2f} Wh.kg-1")
 print(f"Optimised gravimetric energy density: {cost1(results.x):.2f} Wh.kg-1")
 print(f"Initial volumetric energy density: {cost2(optim.x0):.2f} Wh.m-3")

diff --git a/examples/scripts/maximising_power.py b/examples/scripts/maximising_power.py
@@ -48,7 +48,6 @@
     cost, verbose=True, allow_infeasible_solutions=False, max_iterations=10
 )
 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(results.x):.2f} W.kg-1")
 print(f"Initial volumetric power density: {cost2(optim.x0):.2f} W.m-3")

diff --git a/examples/scripts/maximum_a_posteriori.py b/examples/scripts/maximum_a_posteriori.py
@@ -74,7 +74,6 @@
 # Run the optimisation
 results = optim.run()
 print("True parameters:", parameters.true_value())
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/maximum_likelihood.py b/examples/scripts/maximum_likelihood.py
@@ -67,7 +67,6 @@ def noise(sigma):
 
 # Run the optimisation
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/multi_fitting.py b/examples/scripts/multi_fitting.py
@@ -62,7 +62,6 @@
 # Run optimisation
 results = optim.run()
 print("True parameters:", parameters.true_value())
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem_1, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/ocp_balancing.py b/examples/scripts/ocp_balancing.py
@@ -0,0 +1,100 @@
+import numpy as np
+
+import pybop
+
+# Parameter set and model definition
+parameter_set = pybop.ParameterSet.pybamm("Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
+
+# Set objects for initial conditions
+init_soc = {"Initial SoC": 1}
+parameter_set.set_initial_stoichiometries(initial_value=init_soc["Initial SoC"])
+cs_n_max = parameter_set["Maximum concentration in negative electrode [mol.m-3]"]
+cs_p_max = parameter_set["Maximum concentration in positive electrode [mol.m-3]"]
+v_min = parameter_set["Open-circuit voltage at 0% SOC [V]"]
+v_max = parameter_set["Open-circuit voltage at 100% SOC [V]"]
+
+# Define fitting parameters for OCP balancing
+parameters = pybop.Parameters(
+    pybop.Parameter(
+        "Open-circuit voltage at 0% SOC [V]",
+        prior=pybop.Gaussian(v_min, 1e-1),
+        bounds=[2.35, 2.65],
+        true_value=v_min,
+        transformation=pybop.LogTransformation(),
+    ),
+    pybop.Parameter(
+        "Open-circuit voltage at 100% SOC [V]",
+        prior=pybop.Gaussian(v_max, 1e-1),
+        bounds=[4.1, 4.3],
+        true_value=v_max,
+        transformation=pybop.LogTransformation(),
+    ),
+    pybop.Parameter(
+        "Maximum concentration in negative electrode [mol.m-3]",
+        prior=pybop.Gaussian(cs_n_max, 6e3),
+        bounds=[cs_n_max * 0.75, cs_n_max * 1.25],
+        true_value=cs_n_max,
+        transformation=pybop.LogTransformation(),
+    ),
+    pybop.Parameter(
+        "Maximum concentration in positive electrode [mol.m-3]",
+        prior=pybop.Gaussian(cs_p_max, 6e3),
+        bounds=[cs_p_max * 0.75, cs_p_max * 1.25],
+        true_value=cs_p_max,
+        transformation=pybop.LogTransformation(),
+    ),
+)
+
+# Generate synthetic data
+sigma = 5e-4  # Volts
+experiment = pybop.Experiment(
+    [
+        (
+            "Discharge at 0.1C until 2.5V (3 min period)",
+            "Charge at 0.1C until 4.2V (3 min period)",
+        ),
+    ]
+)
+values = model.predict(initial_state=init_soc, experiment=experiment)
+
+
+def noise(sigma):
+    return np.random.normal(0, sigma, len(values["Voltage [V]"].data))
+
+
+# Form dataset
+dataset = pybop.Dataset(
+    {
+        "Time [s]": values["Time [s]"].data,
+        "Current function [A]": values["Current [A]"].data,
+        "Voltage [V]": values["Voltage [V]"].data + noise(sigma),
+    }
+)
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.FittingProblem(model, parameters, dataset, initial_state=init_soc)
+cost = pybop.GaussianLogLikelihoodKnownSigma(problem, sigma0=sigma)
+optim = pybop.CMAES(
+    cost,
+    verbose=True,
+    sigma0=1e-1,
+    max_iterations=100,
+    max_unchanged_iterations=20,
+)
+
+# Run optimisation for Maximum Likelihood Estimate (MLE)
+results = optim.run()
+print("True parameters:", parameters.true_value())
+
+# Plot the timeseries output
+pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot.convergence(optim)
+
+# Plot the parameter traces
+pybop.plot.parameters(optim)
+
+# Plot the cost landscape with optimisation path
+pybop.plot.contour(optim, steps=5)
diff --git a/examples/scripts/spm_AdamW.py b/examples/scripts/spm_AdamW.py
@@ -64,7 +64,6 @@ def noise(sigma):
 
 # Run optimisation
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/spm_CMAES.py b/examples/scripts/spm_CMAES.py
@@ -49,7 +49,6 @@
 # Run the optimisation
 results = optim.run()
 print("True parameters:", parameters.true_value())
-print("Estimated parameters:", results.x)
 
 # Plot the time series
 pybop.plot.dataset(dataset)

diff --git a/examples/scripts/spm_IRPropMin.py b/examples/scripts/spm_IRPropMin.py
@@ -41,7 +41,6 @@
 optim = pybop.IRPropMin(cost, max_iterations=100)
 
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/spm_NelderMead.py b/examples/scripts/spm_NelderMead.py
@@ -62,7 +62,6 @@ def noise(sigma):
 
 # Run optimisation
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/spm_SNES.py b/examples/scripts/spm_SNES.py
@@ -39,8 +39,6 @@
 optim = pybop.SNES(cost, max_iterations=100)
 
 results = optim.run()
-print("Estimated parameters:", results.x)
-
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")
 

diff --git a/examples/scripts/spm_UKF.py b/examples/scripts/spm_UKF.py
@@ -61,7 +61,6 @@
 
 # Run optimisation
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output (requires model that returns Voltage)
 pybop.plot.quick(observer, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/spm_XNES.py b/examples/scripts/spm_XNES.py
@@ -40,7 +40,6 @@
 optim = pybop.XNES(cost, max_iterations=100)
 
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")

diff --git a/examples/scripts/spm_descent.py b/examples/scripts/spm_descent.py
@@ -47,7 +47,6 @@
 
 # Run optimisation
 results = optim.run()
-print("Estimated parameters:", results.x)
 
 # Plot the timeseries output
 pybop.plot.quick(problem, problem_inputs=results.x, title="Optimised Comparison")