Implement GITT example

examples/scripts/gitt.py

@@ -0,0 +1,80 @@
+import numpy as np
+import pybamm
+
+import pybop
+
+# Define model
+original_parameters = pybamm.ParameterValues("Xu2019")
+model = pybop.lithium_ion.SPM(
+    parameter_set=original_parameters, 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 = pybamm.ParameterValues(
+    {
+        "Reference OCP [V]": 4.1821,
+        "Derivative of the OCP wrt stoichiometry [V]": -1.38636,
+        "Current function [A]": original_parameters["Current function [A]"],
+        "Number of electrodes connected in parallel to make a cell": original_parameters[
+            "Number of electrodes connected in parallel to make a cell"
+        ],
+        "Electrode width [m]": original_parameters["Electrode width [m]"],
+        "Electrode height [m]": original_parameters["Electrode height [m]"],
+        "Positive electrode active material volume fraction": original_parameters[
+            "Positive electrode active material volume fraction"
+        ],
+        "Positive electrode porosity": original_parameters[
+            "Positive electrode porosity"
+        ],
+        "Positive particle radius [m]": original_parameters[
+            "Positive particle radius [m]"
+        ],
+        "Positive electrode thickness [m]": original_parameters[
+            "Positive electrode thickness [m]"
+        ],
+        "Positive electrode diffusivity [m2.s-1]": original_parameters[
+            "Positive electrode diffusivity [m2.s-1]"
+        ],
+        "Maximum concentration in positive electrode [mol.m-3]": original_parameters[
+            "Maximum concentration in positive electrode [mol.m-3]"
+        ],
+    }
+)
+
+# Define the cost to optimise
+signal = ["Voltage [V]"]
+problem = pybop.GITT(
+    model="Weppner & Huggins", parameter_set=parameter_set, dataset=dataset
+)
+cost = pybop.RootMeanSquaredError(problem)
+
+# Build the optimisation problem
+optim = pybop.Optimisation(cost=cost, optimiser=pybop.PSO, 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)

examples/standalone/problem.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from pybop._problem import BaseProblem
+from pybop import BaseProblem
 
 
 class StandaloneProblem(BaseProblem):

pybop/__init__.py

@@ -29,10 +29,12 @@
 from ._utils import is_numeric
 
 #
-# Cost class
-# Problem class
+# Problem classes
 #
-from ._problem import BaseProblem, FittingProblem, DesignProblem
+from .problems.base_problem import BaseProblem
+from .problems.fitting_problem import FittingProblem
+from .problems.design_problem import DesignProblem
+from .problems.gitt import GITT
 
 #
 # Cost function class