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Add MultiFittingProblem, example and test
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import numpy as np | ||
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import pybop | ||
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# Parameter set and model definition | ||
parameter_set = pybop.ParameterSet.pybamm("Chen2020") | ||
model_1 = pybop.lithium_ion.SPM(parameter_set=parameter_set) | ||
model_2 = pybop.lithium_ion.SPM(parameter_set=parameter_set.copy()) | ||
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# Fitting parameters | ||
parameters = pybop.Parameters( | ||
pybop.Parameter( | ||
"Negative electrode active material volume fraction", | ||
prior=pybop.Gaussian(0.68, 0.05), | ||
true_value=parameter_set["Negative electrode active material volume fraction"], | ||
), | ||
pybop.Parameter( | ||
"Positive electrode active material volume fraction", | ||
prior=pybop.Gaussian(0.58, 0.05), | ||
true_value=parameter_set["Positive electrode active material volume fraction"], | ||
), | ||
) | ||
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# Generate a dataset | ||
sigma = 0.001 | ||
experiment_1 = pybop.Experiment([("Discharge at 0.5C for 2 minutes (4 second period)")]) | ||
values_1 = model_1.predict(experiment=experiment_1) | ||
dataset_1 = pybop.Dataset( | ||
{ | ||
"Time [s]": values_1["Time [s]"].data, | ||
"Current function [A]": values_1["Current [A]"].data, | ||
"Voltage [V]": values_1["Voltage [V]"].data | ||
+ np.random.normal(0, sigma, len(values_1["Voltage [V]"].data)), | ||
} | ||
) | ||
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# Generate a second dataset | ||
experiment_2 = pybop.Experiment([("Discharge at 1C for 2 minutes (4 second period)")]) | ||
values_2 = model_2.predict(experiment=experiment_2) | ||
dataset_2 = pybop.Dataset( | ||
{ | ||
"Time [s]": values_2["Time [s]"].data, | ||
"Current function [A]": values_2["Current [A]"].data, | ||
"Voltage [V]": values_2["Voltage [V]"].data | ||
+ np.random.normal(0, sigma, len(values_2["Voltage [V]"].data)), | ||
} | ||
) | ||
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# Generate a problem for each dataset and combine into one | ||
problem_1 = pybop.FittingProblem(model_1, parameters, dataset_1) | ||
problem_2 = pybop.FittingProblem(model_2, parameters, dataset_2) | ||
problem = pybop.MultiFittingProblem(problem_list=[problem_1, problem_2]) | ||
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# Generate the cost function and optimisation class | ||
cost = pybop.SumSquaredError(problem) | ||
optim = pybop.IRPropMin( | ||
cost, | ||
# sigma0=0.011, | ||
verbose=True, | ||
max_iterations=12, | ||
) | ||
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# Run optimisation | ||
x, final_cost = optim.run() | ||
print("Estimated parameters:", x) | ||
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# Plot the timeseries output | ||
pybop.quick_plot(problem_1, inputs=x, title="Optimised Comparison") | ||
pybop.quick_plot(problem_2, inputs=x, title="Optimised Comparison") | ||
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# Plot convergence | ||
pybop.plot_convergence(optim) | ||
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# Plot the parameter traces | ||
pybop.plot_parameters(optim) | ||
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# Plot the cost landscape with optimisation path | ||
bounds = np.array([[0.5, 0.8], [0.4, 0.7]]) | ||
pybop.plot2d(optim, bounds=bounds, steps=15) |
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