adds coverage, updates multi_fitting.py.

examples/scripts/multi_fitting.py

@@ -6,6 +6,13 @@
 parameter_set = pybop.ParameterSet.pybamm("Chen2020")
 model = pybop.lithium_ion.SPM(parameter_set=parameter_set)
 
+# Create initial SOC, experiment objects
+init_soc = [{"Initial SoC": 0.8}, {"Initial SoC": 0.6}]
+experiment = [
+    pybop.Experiment([("Discharge at 0.5C for 2 minutes (4 second period)")]),
+    pybop.Experiment([("Discharge at 1C for 1 minutes (4 second period)")]),
+]
+
 # Fitting parameters
 parameters = pybop.Parameters(
     pybop.Parameter(
@@ -21,9 +28,8 @@
 )
 
 # Generate a dataset and a fitting problem
-sigma = 0.001
-experiment = pybop.Experiment([("Discharge at 0.5C for 2 minutes (4 second period)")])
-values = model.predict(initial_state={"Initial SoC": 0.8}, experiment=experiment)
+sigma = 0.002
+values = model.predict(initial_state=init_soc[0], experiment=experiment[0])
 dataset_1 = pybop.Dataset(
     {
         "Time [s]": values["Time [s]"].data,
@@ -36,8 +42,7 @@
 
 # Generate a second dataset and problem
 model = model.new_copy()
-experiment = pybop.Experiment([("Discharge at 1C for 1 minutes (4 second period)")])
-values = model.predict(initial_state={"Initial SoC": 0.8}, experiment=experiment)
+values = model.predict(initial_state=init_soc[1], experiment=experiment[1])
 dataset_2 = pybop.Dataset(
     {
         "Time [s]": values["Time [s]"].data,
@@ -53,9 +58,11 @@
 
 # Generate the cost function and optimisation class
 cost = pybop.SumSquaredError(problem)
-optim = pybop.IRPropMin(
+optim = pybop.CuckooSearch(
     cost,
     verbose=True,
+    sigma0=0.05,
+    max_unchanged_iterations=20,
     max_iterations=100,
 )
 

pybop/samplers/base_sampler.py

@@ -4,7 +4,7 @@
 import numpy as np
 from pints import ParallelEvaluator
 
-from pybop import LogPosterior, Parameters
+from pybop import LogPosterior
 
 
 class BaseSampler:
@@ -43,8 +43,9 @@ def __init__(
             self.parameters = log_pdf[0].parameters
             self.n_parameters = log_pdf[0].n_parameters
         else:
-            self.parameters = Parameters()
-            self.n_parameters = 0
+            raise ValueError(
+                "log_pdf must be a LogPosterior or List[LogPosterior]"
+            )  # TODO: Update for more general sampling
 
         # Check initial conditions
         if x0 is not None and len(x0) != self.n_parameters:

tests/integration/test_monte_carlo_thevenin.py

@@ -98,6 +98,7 @@ def map_estimate(self, posterior):
         common_args = {
             "max_iterations": 100,
             "max_unchanged_iterations": 35,
+            "absolute_tolerance": 1e-7,
             "sigma0": [3e-4, 3e-4],
         }
         optim = pybop.CMAES(posterior, **common_args)
@@ -123,7 +124,7 @@ def map_estimate(self, posterior):
     )
     @pytest.mark.integration
     def test_sampling_thevenin(self, sampler, posterior, map_estimate):
-        x0 = np.clip(map_estimate + np.random.normal(0, 1e-3, size=2), 1e-3, 1e-1)
+        x0 = np.clip(map_estimate + np.random.normal(0, 1e-3, size=2), 1e-4, 1e-1)
         common_args = {
             "log_pdf": posterior,
             "chains": 1,

tests/unit/test_sampling.py

@@ -1,5 +1,6 @@
 import copy
 import logging
+import re
 from unittest.mock import call, patch
 
 import numpy as np
@@ -396,6 +397,12 @@ def test_base_sampler(self, log_posterior, x0):
         with pytest.raises(NotImplementedError):
             sampler.run()
 
+        with pytest.raises(
+            ValueError,
+            match=re.escape("log_pdf must be a LogPosterior or List[LogPosterior]"),
+        ):
+            pybop.BaseSampler(pybop.WeightedCost(log_posterior), x0, chains=1, cov0=0.1)
+
     @pytest.mark.unit
     def test_MCMC_sampler(self, log_posterior, x0, chains):
         with pytest.raises(TypeError):