Merge branch 'develop' into 238b-multi-fitting

pybop-team · Jul 23, 2024 · e51decc · e51decc
2 parents 6654b1b + e6788e4
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diff --git a/.gitignore b/.gitignore
@@ -314,3 +314,6 @@ $RECYCLE.BIN/
 # Airspeed Velocity
 *.asv/
 results/
+
+# Pycharm
+*.idea/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.5.1"
+    rev: "v0.5.4"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -3,7 +3,10 @@
 ## Features
 
 - [#364](https://github.com/pybop-team/PyBOP/pull/364) - Adds the MultiFittingProblem class and the multi_fitting example script.
+- [#327](https://github.com/pybop-team/PyBOP/issues/327) - Adds the `WeightedCost` subclass, defines when to evaluate a problem and adds the `spm_weighted_cost` example script.
+- [#393](https://github.com/pybop-team/PyBOP/pull/383) - Adds Minkowski and SumofPower cost classes, with an example and corresponding tests.
 - [#403](https://github.com/pybop-team/PyBOP/pull/403/) - Adds lychee link checking action.
+- [#313](https://github.com/pybop-team/PyBOP/pull/313/) - Fixes for PyBaMM v24.5, drops support for PyBaMM v23.9, v24.1
 
 ## Bug Fixes
 

diff --git a/examples/notebooks/comparing_cost_functions.ipynb b/examples/notebooks/comparing_cost_functions.ipynb
diff --git a/examples/scripts/gitt.py b/examples/scripts/gitt.py
@@ -36,10 +36,10 @@
 model = pybop.lithium_ion.WeppnerHuggins(parameter_set=parameter_set)
 
 parameters = pybop.Parameter(
-    "Positive electrode diffusivity [m2.s-1]",
+    "Positive particle 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]"],
+    true_value=parameter_set["Positive particle diffusivity [m2.s-1]"],
 )
 
 problem = pybop.FittingProblem(

diff --git a/examples/scripts/parameters/example_BPX.json b/examples/scripts/parameters/example_BPX.json
@@ -1,7 +1,7 @@
 {
     "Header": {
-          "BPX": 0.1,
-          "Title": "Parameterisation example of an LFP|graphite 2 Ah cylindrical 18650 cell.",
+          "BPX": 0.4,
+          "Title": "Parameterisation example of an LFP|graphite 2 Ah cylindrical 18650 cell. File downloaded on 19/3/24 from https://github.com/FaradayInstitution/BPX/blob/main/examples/lfp_18650_cell_BPX.json",
           "Description": "LFP|graphite 2 Ah cylindrical 18650 cell. Parameterisation by About:Energy Limited (aboutenergy.io), December 2022, based on cell cycling data, and electrode data gathered after cell teardown. Electrolyte properties from Nyman et al. 2008 (doi:10.1016/j.electacta.2008.04.023). Negative electrode entropic coefficient data are from O'Regan et al. 2022 (doi:10.1016/j.electacta.2022.140700). Positive electrode entropic coefficient data are from Gerver and Meyers 2011 (doi:10.1149/1.3591799). Other thermal properties are estimated.",
           "Model": "DFN"
     },
@@ -70,9 +70,6 @@
                 "Thickness [m]": 2e-05,
                 "Porosity": 0.47,
                 "Transport efficiency": 0.3222
-          },
-          "User-defined": {
-                "Source:": "An example BPX json file downloaded on 19/3/24 from https://github.com/FaradayInstitution/BPX/blob/main/examples/lfp_18650_cell_BPX.json"
           }
     }
 }
diff --git a/examples/scripts/spm_AdamW.py b/examples/scripts/spm_AdamW.py
@@ -53,7 +53,7 @@ def noise(sigma):
 problem = pybop.FittingProblem(
     model, parameters, dataset, signal=signal, init_soc=init_soc
 )
-cost = pybop.RootMeanSquaredError(problem)
+cost = pybop.Minkowski(problem, p=2)
 optim = pybop.AdamW(
     cost,
     verbose=True,

diff --git a/examples/scripts/spm_MAP.py b/examples/scripts/spm_MAP.py
@@ -2,12 +2,16 @@
 
 import pybop
 
+# Set variables
+sigma = 0.002
+init_soc = 0.7
+
 # Construct and update initial parameter values
 parameter_set = pybop.ParameterSet.pybamm("Chen2020")
 parameter_set.update(
     {
-        "Negative electrode active material volume fraction": 0.63,
-        "Positive electrode active material volume fraction": 0.51,
+        "Negative electrode active material volume fraction": 0.43,
+        "Positive electrode active material volume fraction": 0.56,
     }
 )
 
@@ -18,38 +22,51 @@
 parameters = pybop.Parameters(
     pybop.Parameter(
         "Negative electrode active material volume fraction",
-        prior=pybop.Gaussian(0.6, 0.05),
-        bounds=[0.5, 0.8],
+        prior=pybop.Uniform(0.3, 0.8),
+        bounds=[0.3, 0.8],
+        initial_value=0.653,
         true_value=parameter_set["Negative electrode active material volume fraction"],
     ),
     pybop.Parameter(
         "Positive electrode active material volume fraction",
-        prior=pybop.Gaussian(0.48, 0.05),
+        prior=pybop.Uniform(0.3, 0.8),
         bounds=[0.4, 0.7],
+        initial_value=0.657,
         true_value=parameter_set["Positive electrode active material volume fraction"],
     ),
 )
 
-# Generate data
-sigma = 0.005
-t_eval = np.arange(0, 900, 3)
-values = model.predict(t_eval=t_eval)
-corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval))
+# Generate data and corrupt it with noise
+experiment = pybop.Experiment(
+    [
+        (
+            "Discharge at 0.5C for 3 minutes (4 second period)",
+            "Charge at 0.5C for 3 minutes (4 second period)",
+        ),
+    ]
+)
+values = model.predict(
+    init_soc=init_soc, experiment=experiment, inputs=parameters.true_value()
+)
+corrupt_values = values["Voltage [V]"].data + np.random.normal(
+    0, sigma, len(values["Voltage [V]"].data)
+)
 
 # Form dataset
 dataset = pybop.Dataset(
     {
-        "Time [s]": t_eval,
+        "Time [s]": values["Time [s]"].data,
         "Current function [A]": values["Current [A]"].data,
         "Voltage [V]": corrupt_values,
     }
 )
 
 # Generate problem, cost function, and optimisation class
-problem = pybop.FittingProblem(model, parameters, dataset)
+problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)
 cost = pybop.MAP(problem, pybop.GaussianLogLikelihoodKnownSigma, sigma0=sigma)
 optim = pybop.AdamW(
     cost,
+    sigma0=0.05,
     max_unchanged_iterations=20,
     min_iterations=20,
     max_iterations=100,
@@ -61,7 +78,7 @@
 print("Estimated parameters:", x)
 
 # Plot the timeseries output
-pybop.quick_plot(problem, problem_inputs=x[0:2], title="Optimised Comparison")
+pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison")
 
 # Plot convergence
 pybop.plot_convergence(optim)
@@ -73,5 +90,5 @@
 pybop.plot2d(cost, steps=15)
 
 # Plot the cost landscape with optimisation path
-bounds = np.asarray([[0.55, 0.77], [0.48, 0.68]])
+bounds = np.asarray([[0.35, 0.7], [0.45, 0.625]])
 pybop.plot2d(optim, bounds=bounds, steps=15)
diff --git a/examples/scripts/spm_SNES.py b/examples/scripts/spm_SNES.py
@@ -35,7 +35,7 @@
 
 # Generate problem, cost function, and optimisation class
 problem = pybop.FittingProblem(model, parameters, dataset)
-cost = pybop.SumSquaredError(problem)
+cost = pybop.SumofPower(problem, p=2)
 optim = pybop.SNES(cost, max_iterations=100)
 
 x, final_cost = optim.run()

diff --git a/examples/scripts/spm_UKF.py b/examples/scripts/spm_UKF.py
@@ -22,7 +22,7 @@
 
 # Make a prediction with measurement noise
 sigma = 0.001
-t_eval = np.arange(0, 300, 2)
+t_eval = np.arange(0, 900, 0.5)
 values = model.predict(t_eval=t_eval)
 corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval))
 
@@ -42,8 +42,8 @@
 signal = ["Voltage [V]"]
 n_states = model.n_states
 n_signals = len(signal)
-covariance = np.diag([0] * 19 + [sigma**2] + [0] * 19 + [sigma**2])
-process_noise = np.diag([0] * 19 + [1e-6] + [0] * 19 + [1e-6])
+covariance = np.diag([0] * 20 + [sigma**2] + [0] * 20 + [sigma**2])
+process_noise = np.diag([0] * 20 + [1e-6] + [0] * 20 + [1e-6])
 measurement_noise = np.diag([sigma**2])
 observer = pybop.UnscentedKalmanFilterObserver(
     parameters,

diff --git a/examples/scripts/spm_weighted_cost.py b/examples/scripts/spm_weighted_cost.py
@@ -0,0 +1,61 @@
+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)
+
+# Fitting parameters
+parameters = pybop.Parameters(
+    pybop.Parameter(
+        "Negative electrode active material volume fraction",
+        prior=pybop.Gaussian(0.68, 0.05),
+        bounds=[0.5, 0.8],
+        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),
+        bounds=[0.4, 0.7],
+        true_value=parameter_set["Positive electrode active material volume fraction"],
+    ),
+)
+
+# Generate data
+sigma = 0.001
+t_eval = np.arange(0, 900, 3)
+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,
+    }
+)
+
+# Generate problem, cost function, and optimisation class
+problem = pybop.FittingProblem(model, parameters, dataset)
+cost1 = pybop.SumSquaredError(problem)
+cost2 = pybop.RootMeanSquaredError(problem)
+weighted_cost = pybop.WeightedCost(cost1, cost2, weights=[1, 100])
+
+for cost in [weighted_cost, cost1, cost2]:
+    optim = pybop.IRPropMin(cost, max_iterations=60)
+
+    # Run the optimisation
+    x, final_cost = optim.run()
+    print("True parameters:", parameters.true_value())
+    print("Estimated parameters:", x)
+
+    # Plot the timeseries output
+    pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison")
+
+    # Plot convergence
+    pybop.plot_convergence(optim)
+
+    # Plot the cost landscape with optimisation path
+    pybop.plot2d(optim, steps=15)
diff --git a/pybop/__init__.py b/pybop/__init__.py
@@ -85,6 +85,8 @@
 from .costs.fitting_costs import (
     RootMeanSquaredError,
     SumSquaredError,
+    Minkowski,
+    SumofPower,
     ObserverCost,
 )
 from .costs.design_costs import (
@@ -98,6 +100,7 @@
     GaussianLogLikelihoodKnownSigma,
     MAP,
 )
+from .costs._weighted_cost import WeightedCost
 
 #
 # Optimiser class

diff --git a/pybop/_experiment.py b/pybop/_experiment.py
@@ -49,6 +49,4 @@ def __init__(
             period,
             temperature,
             termination,
-            drive_cycles,
-            cccv_handling,
         )