Update python versions in worklfows

.github/workflows/copyright-test.yml

@@ -17,10 +17,10 @@ jobs:
     steps:
       - uses: actions/checkout@v1
 
-      - name: Set up Python 3.8
+      - name: Set up Python 3.13
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: 3.13
           architecture: x64
 
       - name: install chi

.github/workflows/coverage-test.yml

@@ -17,12 +17,12 @@ jobs:
     steps:
       - uses: actions/checkout@v1
 
-      - name: set up Python 3.8
+      - name: set up Python 3.13
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: 3.13
           architecture: x64
-      
+
       - name: install sundials
         run: |
           sudo apt-get update

.github/workflows/docs-test.yml

@@ -17,10 +17,10 @@ jobs:
     steps:
       - uses: actions/checkout@v1
 
-      - name: Set up Python 3.8
+      - name: Set up Python 3.13
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: 3.13
           architecture: x64
 
       - name: install sundials (ubuntu)

.github/workflows/style-test.yml

@@ -17,10 +17,10 @@ jobs:
     steps:
       - uses: actions/checkout@v1
 
-      - name: Set up Python 3.8
+      - name: Set up Python 3.13
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: 3.13
           architecture: x64
 
       - name: install chi

.github/workflows/unit-test-os-versions.yml

@@ -17,12 +17,12 @@ jobs:
         os: [ubuntu-latest, windows-latest, macos-latest]
 
     steps:
-      - uses: actions/checkout@v1
+      - uses: actions/checkout@v4
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.13
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.13
 
       - name: install sundials (ubuntu)
         if: ${{ matrix.os == 'ubuntu-latest' }}

.github/workflows/unit-test-python-versions.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11']
+        python-version: ['3.11', '3.12', '3.13']
 
     steps:
       - uses: actions/checkout@v1

LICENSE.md

@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2024, David Augustin
+Copyright (c) 2025, David Augustin
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

chi/_log_pdfs.py

@@ -818,7 +818,7 @@ def __call__(self, parameters):
                 'An error occured while solving the mechanistic model: \n'
                 + str(e) + '.\n A score of -infinity is returned.',
                 RuntimeWarning)
-            return -np.infty
+            return -np.inf
 
         # Remember only error parameters
         parameters = parameters[self._n_mechanistic_params:]
@@ -993,7 +993,7 @@ def evaluateS1(self, parameters):
                 + str(e) + '.\n A score of -infinity is returned.',
                 RuntimeWarning)
             n_parameters = len(parameters)
-            return -np.infty, np.full(shape=n_parameters, fill_value=np.infty)
+            return -np.inf, np.full(shape=n_parameters, fill_value=np.inf)
 
         # Remember only error parameters
         parameters = parameters[self._n_mechanistic_params:]
@@ -1606,7 +1606,7 @@ def __call__(self, parameters):
                     'An error occured while solving the mechanistic model: \n'
                     + str(e) + '.\n A score of -infinity is returned.',
                     RuntimeWarning)
-                return -np.infty
+                return -np.inf
 
         # Add noise to simulate measurements
         if self._error_on_log_scale:
@@ -1832,7 +1832,7 @@ def evaluateS1(self, parameters):
                     'An error occured while solving the mechanistic model: \n'
                     + str(e) + '.\n A score of -infinity is returned.',
                     RuntimeWarning)
-                return -np.infty, sensitivities[:self._n_parameters]
+                return -np.inf, sensitivities[:self._n_parameters]
 
         # Add noise to simulate measurements
         if self._error_on_log_scale:

chi/tests/test_inference.py

@@ -810,46 +810,6 @@ def test_run(self):
         self.assertEqual(runs[1], 2)
         self.assertEqual(runs[2], 3)
 
-    def test_run_catch_exception(self):
-        # Check failure of optimisation doesn't interrupt all runs
-        # (CMAES returns NAN for 1-dim problems)
-
-        # Get test data and model
-        problem = copy.deepcopy(self.problem)
-        problem.fix_parameters({
-            'global.drug_concentration': 1,
-            'global.kappa': 1,
-            'global.lambda_0': 1,
-            'global.lambda_1': 1,
-            'Sigma base': 1,
-            'Sigma rel.': 1})
-        problem.set_log_prior(pints.ComposedLogPrior(*[
-            pints.UniformLogPrior(1E-3, 1E1)]))
-        log_posterior = problem.get_log_posterior()
-
-        # Set up optmisation controller
-        optimiser = chi.OptimisationController(log_posterior)
-        optimiser.set_n_runs(3)
-        result = optimiser.run(n_max_iterations=10)
-
-        keys = result.keys()
-        self.assertEqual(len(keys), 5)
-        self.assertEqual(keys[0], 'ID')
-        self.assertEqual(keys[1], 'Parameter')
-        self.assertEqual(keys[2], 'Estimate')
-        self.assertEqual(keys[3], 'Score')
-        self.assertEqual(keys[4], 'Run')
-
-        parameters = result['Parameter'].unique()
-        self.assertEqual(len(parameters), 1)
-        self.assertEqual(parameters[0], 'global.tumour_volume')
-
-        runs = result['Run'].unique()
-        self.assertEqual(len(runs), 3)
-        self.assertEqual(runs[0], 1)
-        self.assertEqual(runs[1], 2)
-        self.assertEqual(runs[2], 3)
-
     def test_set_optmiser(self):
         optimiser = chi.OptimisationController(self.log_posterior_id_40)
         optimiser.set_optimiser(pints.PSO)

chi/tests/test_log_pdfs.py

@@ -1824,7 +1824,7 @@ def test_call_and_compute_pointwise_ll(self):
         self.assertAlmostEqual(score, ref_score)
         n_obs = 7
         self.assertEqual(pw_score.shape, (n_obs,))
-        self.assertEqual(np.sum(pw_score), score)
+        self.assertAlmostEqual(np.sum(pw_score), score)
 
         # Reset number of outputs
         self.model.set_outputs(['central.drug_amount', 'dose.drug_amount'])

docs/source/api/index.rst

@@ -29,46 +29,46 @@ Summary of all functions and classes in chi
 
 .. autosummary::
 
-    chi.AveragedPredictiveModel
-    chi.compute_pointwise_loglikelihood
-    chi.ConstantAndMultiplicativeGaussianErrorModel
-    chi.CovariateModel
-    chi.ErrorModel
-    chi.library.DataLibrary
-    chi.library.ModelLibrary
-    chi.LinearCovariateModel
-    chi.LogLikelihood
-    chi.LogNormalErrorModel
-    chi.LogNormalModel
-    chi.LogPosterior
-    chi.HeterogeneousModel
-    chi.HierarchicalLogLikelihood
-    chi.HierarchicalLogPosterior
-    chi.GaussianErrorModel
-    chi.GaussianModel
-    chi.InferenceController
-    chi.MechanisticModel
-    chi.MultiplicativeGaussianErrorModel
-    chi.OptimisationController
-    chi.PAMPredictiveModel
-    chi.PKPDModel
-    chi.plots.MarginalPosteriorPlot
-    chi.plots.ParameterEstimatePlot
-    chi.plots.PDTimeSeriesPlot
-    chi.plots.PKTimeSeriesPlot
-    chi.plots.PDPredictivePlot
-    chi.plots.PKPredictivePlot
-    chi.plots.ResidualPlot
-    chi.PooledModel
-    chi.PopulationModel
-    chi.PopulationPredictiveModel
-    chi.ReducedErrorModel
-    chi.PosteriorPredictiveModel
-    chi.PredictiveModel
-    chi.PriorPredictiveModel
-    chi.ProblemModellingController
-    chi.ReducedMechanisticModel
-    chi.ReducedPopulationModel
-    chi.SamplingController
-    chi.SBMLModel
-    chi.TruncatedGaussianModel
+    AveragedPredictiveModel
+    compute_pointwise_loglikelihood
+    ConstantAndMultiplicativeGaussianErrorModel
+    CovariateModel
+    ErrorModel
+    library.DataLibrary
+    library.ModelLibrary
+    LinearCovariateModel
+    LogLikelihood
+    LogNormalErrorModel
+    LogNormalModel
+    LogPosterior
+    HeterogeneousModel
+    HierarchicalLogLikelihood
+    HierarchicalLogPosterior
+    GaussianErrorModel
+    GaussianModel
+    InferenceController
+    MechanisticModel
+    MultiplicativeGaussianErrorModel
+    OptimisationController
+    PAMPredictiveModel
+    PKPDModel
+    plots.MarginalPosteriorPlot
+    plots.ParameterEstimatePlot
+    plots.PDTimeSeriesPlot
+    plots.PKTimeSeriesPlot
+    plots.PDPredictivePlot
+    plots.PKPredictivePlot
+    plots.ResidualPlot
+    PooledModel
+    PopulationModel
+    PopulationPredictiveModel
+    ReducedErrorModel
+    PosteriorPredictiveModel
+    PredictiveModel
+    PriorPredictiveModel
+    ProblemModellingController
+    ReducedMechanisticModel
+    ReducedPopulationModel
+    SamplingController
+    SBMLModel
+    TruncatedGaussianModel

docs/source/api/mechanistic_models.rst

@@ -9,14 +9,14 @@ Mechanistic Models
 Mechanistic models in chi refer to any deterministic model that describes the
 evolution of quantities of interest in time. In systems biology
 such models are often inspired by biological mechanisms, which is why we go
-with the name :class:`chi.MechanisticModel`. :class:`chi.MechanisticModel` by
+with the name :class:`MechanisticModel`. :class:`MechanisticModel` by
 no means have to be mechanism-based though, but may be any function of time
 that you may deem interesting.
 
 Chi provides two ways to specify mechanistic models: 1. you can use the
-:class:`chi.MechanisticModel` base class an implement its methods yourself;
+:class:`MechanisticModel` base class an implement its methods yourself;
 2. you can specify the mechanistic model using the System Biology Markup
-Language (SBML_) and instantiate the model using :class:`chi.SBMLModel`.
+Language (SBML_) and instantiate the model using :class:`SBMLModel`.
 For detailed examples how either of those can be done, we refer to the Getting
 started.
 

docs/source/getting_started/fitting_models_to_data.rst

@@ -712,15 +712,15 @@ Reference to ErrorModel, LogPDF and PredictiveModel API
 
 .. autosummary::
 
-    chi.ErrorModel
-    chi.GaussianErrorModel
-    chi.LogNormalErrorModel
-    chi.MultiplicativeGaussianErrorModel
-    chi.ConstantAndMultiplicativeGaussianErrorModel
-    chi.ReducedErrorModel
-    chi.LogLikelihood
-    chi.LogPosterior
-    chi.ProblemModellingController
-    chi.SamplingController
-    chi.PredictiveModel
-    chi.PosteriorPredictiveModel
+    ErrorModel
+    GaussianErrorModel
+    LogNormalErrorModel
+    MultiplicativeGaussianErrorModel
+    ConstantAndMultiplicativeGaussianErrorModel
+    ReducedErrorModel
+    LogLikelihood
+    LogPosterior
+    ProblemModellingController
+    SamplingController
+    PredictiveModel
+    PosteriorPredictiveModel

docs/source/getting_started/mechanistic_model.rst

@@ -940,7 +940,7 @@ Reference to MechanisticModel API
 
 .. autosummary::
 
-    chi.MechanisticModel
-    chi.SBMLModel
-    chi.PKPDModel
-    chi.ReducedMechanisticModel
+    MechanisticModel
+    SBMLModel
+    PKPDModel
+    ReducedMechanisticModel

setup.py

@@ -9,7 +9,7 @@
 setup(
     # Module name
     name='chi-drm',
-    version='1.0.1',
+    version='1.0.2',
     description='Package to model dose response dynamics',
     long_description=readme,
     long_description_content_type="text/markdown",
@@ -30,13 +30,13 @@
 
     # List of dependencies
     install_requires=[
-        'arviz>=0.11',
+        'arviz>=0.17',
         'myokit>=1.34',
         'numpy>=1.17',
         'pandas>=0.24',
         'pints>=0.4',
         'plotly>=4.8.1',
-        'scipy<=1.12',  # 07/2024 - ArviZ seems to not yet keep up with SciPy
+        'scipy>=1.11',  # 07/2024 - ArviZ seems to not yet keep up with SciPy
         'tqdm>=4.46.1',
         'xarray>=0.19',
     ],