#282 Restructure examples/ directory

examples/notebooks/Battery Parameterisation/diffusion_identification_from_gitt.py

@@ -0,0 +1,68 @@
+import numpy as np
+
+import pybop
+
+# Define model
+parameter_set = pybop.ParameterSet.pybamm("Xu2019")
+model = pybop.lithium_ion.SPM(
+    parameter_set=parameter_set, options={"working electrode": "positive"}
+)
+
+# Generate data
+sigma = 0.005
+t_eval = np.arange(0, 150, 2)
+values = model.predict(t_eval=t_eval)
+corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval))
+
+# Form dataset
+dataset = pybop.Dataset(
+    {
+        "Time [s]": t_eval,
+        "Current function [A]": values["Current [A]"].data,
+        "Voltage [V]": corrupt_values,
+    }
+)
+
+# Define parameter set
+parameter_set.update(
+    {
+        "Reference OCP [V]": 4.1821,
+        "Derivative of the OCP wrt stoichiometry [V]": -1.38636,
+    },
+    check_already_exists=False,
+)
+
+# Define the cost to optimise
+model = pybop.lithium_ion.WeppnerHuggins(parameter_set=parameter_set)
+
+parameters = pybop.Parameter(
+    "Positive electrode diffusivity [m2.s-1]",
+    prior=pybop.Gaussian(5e-14, 1e-13),
+    bounds=[1e-16, 1e-11],
+    true_value=parameter_set["Positive electrode diffusivity [m2.s-1]"],
+)
+
+problem = pybop.FittingProblem(
+    model,
+    parameters,
+    dataset,
+    signal=["Voltage [V]"],
+)
+
+cost = pybop.RootMeanSquaredError(problem)
+
+# Build the optimisation problem
+optim = pybop.PSO(cost=cost, verbose=True)
+
+# Run the optimisation problem
+x, final_cost = optim.run()
+print("Estimated parameters:", x)
+
+# Plot the timeseries output
+pybop.quick_plot(problem, parameter_values=x, title="Optimised Comparison")
+
+# Plot convergence
+pybop.plot_convergence(optim)
+
+# Plot the parameter traces
+pybop.plot_parameters(optim)