Merge branch 'develop' into 58-pre-commit

conftest.py

@@ -1,4 +1,7 @@
 import pytest
+import matplotlib
+
+matplotlib.use("Template")
 
 
 def pytest_addoption(parser):

examples/scripts/mle.py

@@ -41,7 +41,6 @@
 print("Estimated parameters:")
 print(x1)
 
-
 # Show the generated data
 simulated_values = problem.evaluate(x1[:2])
 

examples/scripts/rmse-estimisation.py → examples/scripts/rmse_estimation.py

@@ -1,5 +1,6 @@
 import pybop
 import pandas as pd
+import numpy as np
 
 # Form observations
 Measurements = pd.read_csv("examples/scripts/Chen_example.csv", comment="#").to_numpy()
@@ -27,7 +28,7 @@
     ),
 ]
 
-parameterisation = pybop.Parameterisation(
+parameterisation = pybop.Optimisation(
     model, observations=observations, fit_parameters=params
 )
 

pybop/__init__.py

@@ -20,30 +20,34 @@
 # Float format: a float can be converted to a 17 digit decimal and back without
 # loss of information
 FLOAT_FORMAT = "{: .17e}"
-# Absolute path to the PyBOP repo
-script_path = os.path.abspath(__file__)
+# Absolute path to the pybop module
+script_path = os.path.dirname(__file__)
 
 #
 # Model Classes
 #
-from .models import BaseModel, lithium_ion
+from .models import lithium_ion
+from .models.BaseModel import BaseModel
 
 #
 # Parameterisation class
 #
-from .identification import Parameterisation, ParameterSet, Parameter, Observed
+from .parameters.parameter_set import ParameterSet
+from .parameters.parameter import Parameter
+from .datasets.observations import Observed
 
 #
 # Priors class
 #
-from .priors import Gaussian, Uniform, Exponential
+from .parameters.priors import Gaussian, Uniform, Exponential
 
 #
 # Optimisation class
 #
-from .optimisation import BaseOptimisation
-from .optimisation.NLoptOptimize import NLoptOptimize
-from .optimisation.SciPyMinimize import SciPyMinimize
+from .optimisation import Optimisation
+from .optimisers import BaseOptimiser
+from .optimisers.NLoptOptimize import NLoptOptimize
+from .optimisers.SciPyMinimize import SciPyMinimize
 
 #
 # Remove any imported modules, so we don't expose them as part of pybop

pybop/models/BaseModel.py

@@ -1,7 +1,7 @@
 import pybamm
 
 
-class BaseModel:
+class BaseModel():
     """
     Base class for PyBOP models
     """
@@ -102,7 +102,7 @@ def simulate(self, inputs, t_eval):
         if self._built_model is None:
             raise ValueError("Model must be built before calling simulate")
         else:
-            if inputs is isinstance(dict):
+            if not isinstance(inputs, dict):
                 inputs_dict = {
                     key: inputs[i] for i, key in enumerate(self.fit_parameters)
                 }

pybop/identification/parameterisation.py → pybop/optimisation.py

@@ -2,9 +2,9 @@
 import numpy as np
 
 
-class Parameterisation:
+class Optimisation:
     """
-    Parameterisation class for pybop.
+    Optimisation class for PyBOP.
     """
 
     def __init__(

pybop/optimisation/BaseOptimisation.py → pybop/optimisers/BaseOptimiser.py

@@ -1,12 +1,12 @@
-class BaseOptimisation:
+class BaseOptimiser:
     """
 
     Base class for the optimisation methods.
 
     """
 
     def __init__(self):
-        self.name = "Base Optimisation"
+        self.name = "Base Optimiser"
 
     def optimise(self, cost_function, x0, bounds, method=None):
         """

pybop/optimisation/NLoptOptimize.py → pybop/optimisers/NLoptOptimize.py

@@ -1,15 +1,15 @@
 import nlopt
-from .BaseOptimisation import BaseOptimisation
+from .BaseOptimiser import BaseOptimiser
 
 
-class NLoptOptimize(BaseOptimisation):
+class NLoptOptimize(BaseOptimiser):
     """
-    Wrapper class for the NLOpt optimisation class. Extends the BaseOptimisation class.
+    Wrapper class for the NLOpt optimisation class. Extends the BaseOptimiser class.
     """
 
     def __init__(self, method=None, x0=None, xtol=None):
         super().__init__()
-        self.name = "NLOpt Optimisation"
+        self.name = "NLOpt Optimiser"
 
         if method is not None:
             self.opt = nlopt.opt(method, len(x0))

pybop/optimisation/SciPyMinimize.py → pybop/optimisers/SciPyMinimize.py

@@ -1,10 +1,10 @@
 from scipy.optimize import minimize
-from .BaseOptimisation import BaseOptimisation
+from .BaseOptimiser import BaseOptimiser
 
 
-class SciPyMinimize(BaseOptimisation):
+class SciPyMinimize(BaseOptimiser):
     """
-    Wrapper class for the Scipy optimisation class. Extends the BaseOptimisation class.
+    Wrapper class for the Scipy optimisation class. Extends the BaseOptimiser class.
     """
 
     def __init__(self, cost_function, x0, bounds=None, options=None):
@@ -14,7 +14,7 @@ def __init__(self, cost_function, x0, bounds=None, options=None):
         self.x0 = x0 or cost_function.x0
         self.bounds = bounds
         self.options = options
-        self.name = "Scipy Optimisation"
+        self.name = "Scipy Optimiser"
 
     def _runoptimise(self):
         """

setup.py

@@ -30,6 +30,7 @@
         "scipy>=1.3",
         "pandas>=1.0",
         "nlopt>=2.6",
+        "pints>=0.5",
     ],
     # https://pypi.org/classifiers/
     classifiers=[],

tests/unit/test_examples.py

@@ -0,0 +1,20 @@
+import pybop
+import numpy as np
+import pytest
+import runpy
+import os
+
+
+class TestExamples:
+    """
+    A class to test the example scripts.
+    """
+
+    @pytest.mark.unit
+    def test_example_scripts(self):
+        path_to_example_scripts = os.path.join(
+            pybop.script_path, "..", "examples", "scripts"
+        )
+        for example in os.listdir(path_to_example_scripts):
+            if example.endswith(".py"):
+                runpy.run_path(os.path.join(path_to_example_scripts, example))

tests/unit/test_models.py

@@ -0,0 +1,34 @@
+import pybop
+import numpy as np
+import pytest
+import runpy
+import os
+
+
+class TestModels:
+    """
+    A class to test the models.
+    """
+
+    @pytest.mark.unit
+    def test_simulate_without_build_model(self):
+        # Define model
+        model = pybop.lithium_ion.SPM()
+
+        with pytest.raises(
+            ValueError, match="Model must be built before calling simulate"
+        ):
+            model.simulate(None, None)
+
+    @pytest.mark.unit
+    def test_build(self):
+        model = pybop.lithium_ion.SPM()
+        model.build()
+        assert model.built_model is not None
+
+    @pytest.mark.unit
+    def test_n_parameters(self):
+        model = pybop.BaseModel()
+        n = model.n_outputs()
+        assert isinstance(n, int)
+

tests/unit/test_parameter_sets.py

@@ -0,0 +1,22 @@
+import pybop
+import numpy as np
+import pytest
+import runpy
+import os
+
+
+class TestParameterSets:
+    """
+    A class to test parameter sets.
+    """
+
+    @pytest.mark.unit
+    def test_parameter_set(self):
+        # Tests parameter set creation
+        with pytest.raises(ValueError):
+            pybop.ParameterSet("pybamms", "Chen2020")
+
+        parameter_test = pybop.ParameterSet("pybamm", "Chen2020")
+        np.testing.assert_allclose(
+            parameter_test["Negative electrode active material volume fraction"], 0.75
+        )

tests/unit/test_parameterisation.py → tests/unit/test_parameterisations.py

@@ -1,10 +1,14 @@
 import pybop
 import pybamm
-import numpy as np
 import pytest
+import numpy as np
+
 
+class TestModelParameterisation:
+    """
+    A class to test the model parameterisation methods.
+    """
 
-class TestParameterisation:
     @pytest.mark.unit
     def test_spm(self):
         # Define model
@@ -35,16 +39,17 @@ def test_spm(self):
             ),
         ]
 
-        parameterisation = pybop.Parameterisation(
+        parameterisation = pybop.Optimisation(
             model, observations=observations, fit_parameters=params
         )
 
         # get RMSE estimate using NLOpt
         results, last_optim, num_evals = parameterisation.rmse(
             signal="Voltage [V]", method="nlopt"
         )
-        # Assertions
-        np.testing.assert_allclose(last_optim, 1e-3, atol=1e-2)
+
+        # Assertions (for testing purposes only)
+        np.testing.assert_allclose(last_optim, 0, atol=1e-2)
         np.testing.assert_allclose(results, x0, atol=1e-1)
 
     @pytest.mark.unit
@@ -77,17 +82,18 @@ def test_spme(self):
             ),
         ]
 
-        parameterisation = pybop.Parameterisation(
+        parameterisation = pybop.Optimisation(
             model, observations=observations, fit_parameters=params
         )
 
         # get RMSE estimate using NLOpt
         results, last_optim, num_evals = parameterisation.rmse(
             signal="Voltage [V]", method="nlopt"
         )
-        # Assertions
-        np.testing.assert_allclose(last_optim, 1e-3, atol=1e-2)
-        np.testing.assert_allclose(results, x0, atol=1e-1)
+
+        # Assertions (for testing purposes only)
+        np.testing.assert_allclose(last_optim, 0, atol=1e-2)
+        np.testing.assert_allclose(results, x0, rtol=1e-1)
 
     def getdata(self, model, x0):
         model.parameter_set = model.pybamm_model.default_parameter_values

tests/unit/test_priors.py

@@ -0,0 +1,22 @@
+import pybop
+import numpy as np
+import pytest
+import runpy
+import os
+
+
+class TestPriors:
+    """
+    A class to test the priors.
+    """
+
+    @pytest.mark.unit
+    def test_priors(self):
+        # Tests priors
+        Gaussian = pybop.Gaussian(0.5, 1)
+        Uniform = pybop.Uniform(0, 1)
+        Exponential = pybop.Exponential(1)
+
+        np.testing.assert_allclose(Gaussian.pdf(0.5), 0.3989422804014327, atol=1e-4)
+        np.testing.assert_allclose(Uniform.pdf(0.5), 1, atol=1e-4)
+        np.testing.assert_allclose(Exponential.pdf(1), 0.36787944117144233, atol=1e-4)