Updates example script file names

examples/scripts/comparison_examples/spm_UKF.py → examples/scripts/comparison_examples/unscented_kalman_filter.py

@@ -65,11 +65,11 @@
 # Plot the timeseries output (requires model that returns Voltage)
 pybop.plot.quick(observer, problem_inputs=results.x, title="Optimised Comparison")
 
-# # Plot convergence
-# pybop.plot.convergence(optim)
+# Plot convergence
+pybop.plot.convergence(optim)
 
-# # Plot the parameter traces
-# pybop.plot.parameters(optim)
+# Plot the parameter traces
+pybop.plot.parameters(optim)
 
-# # Plot the cost landscape with optimisation path
-# pybop.plot.surface(optim)
+# Plot the cost landscape with optimisation path
+pybop.plot.surface(optim)

examples/scripts/getting_started/ask-tell-interface.py

@@ -0,0 +1,87 @@
+import numpy as np
+import pybamm
+
+import pybop
+
+# Define model and use high-performant solver for sensitivities
+solver = pybamm.IDAKLUSolver()
+parameter_set = pybop.ParameterSet.pybamm("Chen2020")
+model = pybop.lithium_ion.SPM(parameter_set=parameter_set, solver=solver)
+
+# Fitting parameters
+parameters = pybop.Parameters(
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.55, 0.05),
+    ),
+    pybop.Parameter(
+        "Positive electrode active material volume fraction",
+        prior=pybop.Gaussian(0.55, 0.05),
+    ),
+)
+
+# Generate data
+sigma = 0.003
+experiment = pybop.Experiment(
+    [
+        (
+            "Discharge at 0.5C for 3 minutes (3 second period)",
+            "Charge at 0.5C for 3 minutes (3 second period)",
+        ),
+    ]
+    * 2
+)
+values = model.predict(initial_state={"Initial SoC": 0.5}, 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),
+        "Bulk open-circuit voltage [V]": values["Bulk open-circuit voltage [V]"].data
+        + noise(sigma),
+    }
+)
+
+signal = ["Voltage [V]", "Bulk open-circuit voltage [V]"]
+# Construct the problem and cost classes
+problem = pybop.FittingProblem(model, parameters, dataset, signal=signal)
+cost = pybop.Minkowski(problem, p=2)
+
+# We construct the optimiser class the same as normal
+# but will be using the `optimiser` attribute directly
+# for this example. This interface works for all
+# non SciPy-based optimisers.
+# Warning: not all arguments are supported via this
+# interface.
+optim = pybop.AdamW(cost)
+
+# Create storage vars
+x_best = []
+f_best = []
+
+# Run optimisation
+for i in range(100):
+    x = optim.optimiser.ask()
+    f = [cost(x[0], calculate_grad=True)]
+    optim.optimiser.tell(f)
+
+    # Store best solution so far
+    x_best.append(optim.optimiser.x_best())
+    f_best.append(optim.optimiser.x_best())
+
+    if i % 10 == 0:
+        print(
+            f"Iteration: {i} | Cost: {optim.optimiser.f_best()} | Parameters: {optim.optimiser.x_best()}"
+        )
+
+# Plot the timeseries output
+pybop.plot.quick(
+    problem, problem_inputs=optim.optimiser.x_best(), title="Optimised Comparison"
+)