Merge pull request #269 from DavAug/issue-268-myokit-upgrade

Issue 268 myokit upgrade

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

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ['3.9', '3.10', '3.11']
 
     steps:
       - uses: actions/checkout@v1
@@ -24,7 +24,7 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           architecture: x64
-          
+
       - name: install sundials
         run: |
           sudo apt-get update

LICENSE.md

@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2022, David Augustin
+Copyright (c) 2023, David Augustin
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

chi/_error_models.py

@@ -1791,7 +1791,7 @@ def fix_parameters(self, name_value_dict):
             self._fixed_params_values[index] = value
 
         # If all parameters are free, set mask and values to None again
-        if np.alltrue(~self._fixed_params_mask):
+        if np.all(~self._fixed_params_mask):
             self._fixed_params_mask = None
             self._fixed_params_values = None
 

chi/_log_pdfs.py

@@ -130,8 +130,8 @@ def __call__(self, parameters):
             top_parameters, bottom_parameters, covariates=self._covariates)
 
         # Return if values already lead to a rejection
-        if np.isinf(score):
-            return score
+        if np.isinf(score):  # noqa: no cover
+            return score  # noqa: no cover
 
         # Transform bottom-level parameters
         # Identity, if model does not tranform parameters

chi/_mechanistic_models.py

@@ -986,7 +986,7 @@ def fix_parameters(self, name_value_dict):
             self._fixed_params_values[index] = value
 
         # If all parameters are free, set mask and values to None again
-        if np.alltrue(~self._fixed_params_mask):
+        if np.all(~self._fixed_params_mask):
             self._fixed_params_mask = None
             self._fixed_params_values = None
 

chi/_population_models.py

@@ -3274,7 +3274,7 @@ def fix_parameters(self, name_value_dict):
             self._fixed_params_values[index] = value
 
         # If all parameters are free, set mask and values to None again
-        if np.alltrue(~self._fixed_params_mask):
+        if np.all(~self._fixed_params_mask):
             self._fixed_params_mask = None
             self._fixed_params_values = None
 

chi/plots/_time_series.py

@@ -149,11 +149,11 @@ def _compute_bulk_probs(self, data, bulk_probs, time_key, sample_key):
                 biom_upper = reduced_data[mask][sample_key].min()
 
                 # Append percentiles to container
-                container = container.append(pd.DataFrame({
+                container = pd.concat([container, pd.DataFrame({
                     'Time': [time],
                     'Lower': [biom_lower],
                     'Upper': [biom_upper],
-                    'Bulk probability': [str(bulk_prob)]}))
+                    'Bulk probability': [str(bulk_prob)]})])
 
         return container
 
@@ -538,11 +538,11 @@ def _compute_bulk_probs(self, data, bulk_probs, time_key, sample_key):
                 biom_upper = reduced_data[mask][sample_key].min()
 
                 # Append percentiles to container
-                container = container.append(pd.DataFrame({
+                container = pd.concat([container, pd.DataFrame({
                     'Time': [time],
                     'Lower': [biom_lower],
                     'Upper': [biom_upper],
-                    'Bulk probability': [str(bulk_prob)]}))
+                    'Bulk probability': [str(bulk_prob)]})])
 
         return container
 

chi/tests/test_error_models.py

@@ -1487,11 +1487,11 @@ def test_set_get_parameter_names(self):
         self.error_model.fix_parameters(name_value_dict={
             'Sigma base': 1})
         self.error_model.set_parameter_names(
-            ['myokit.tumour_volume Sigma rel.'])
+            ['global.tumour_volume Sigma rel.'])
 
         names = self.error_model.get_parameter_names()
         self.assertEqual(len(names), 1)
-        self.assertEqual(names[0], 'myokit.tumour_volume Sigma rel.')
+        self.assertEqual(names[0], 'global.tumour_volume Sigma rel.')
 
         # Reset to defaults
         self.error_model.set_parameter_names(None)

chi/tests/test_inference.py

@@ -223,7 +223,7 @@ def test_call(self):
         self.assertEqual(draws.loc[2], 2)
 
         # Map parameters
-        param_map = {'myokit.tumour_volume': 'myokit.tumour_volume'}
+        param_map = {'global.tumour_volume': 'global.tumour_volume'}
         pw_ll = chi.compute_pointwise_loglikelihood(
             self.log_likelihood, self.posterior_samples,
             param_map=param_map)
@@ -396,7 +396,7 @@ def test_call(self):
 
         # # Map parameters
         # param_map = {
-        #     'Pooled myokit.tumour_volume': 'Pooled myokit.tumour_volume'}
+        #     'Pooled global.tumour_volume': 'Pooled global.tumour_volume'}
         # pw_ll = chi.compute_pointwise_loglikelihood(
         #     self.hierarch_log_likelihood,
         #     self.hierarch_posterior_samples,
@@ -510,7 +510,7 @@ def test_call_bad_input(self):
                 param_map=param_map)
 
         # Not all parameters of the log-likelihood can be identified
-        param_map = {'myokit.tumour_volume': 'Something else'}
+        param_map = {'global.tumour_volume': 'Something else'}
         with self.assertRaisesRegex(ValueError, 'The parameter <Something'):
             chi.compute_pointwise_loglikelihood(
                 self.log_likelihood, self.posterior_samples,
@@ -682,7 +682,7 @@ def setUpClass(cls):
         cls.problem = chi.ProblemModellingController(model, error_models)
 
         data = DataLibrary().lung_cancer_control_group()
-        cls.problem.set_data(data, {'myokit.tumour_volume': 'Tumour volume'})
+        cls.problem.set_data(data, {'global.tumour_volume': 'Tumour volume'})
 
         n_parameters = 7
         log_prior = pints.ComposedLogPrior(*[
@@ -739,11 +739,11 @@ def test_run(self):
         n_parameters = 7
         parameters = result['Parameter'].unique()
         self.assertEqual(len(parameters), n_parameters)
-        self.assertEqual(parameters[0], 'myokit.tumour_volume')
-        self.assertEqual(parameters[1], 'myokit.drug_concentration')
-        self.assertEqual(parameters[2], 'myokit.kappa')
-        self.assertEqual(parameters[3], 'myokit.lambda_0')
-        self.assertEqual(parameters[4], 'myokit.lambda_1')
+        self.assertEqual(parameters[0], 'global.tumour_volume')
+        self.assertEqual(parameters[1], 'global.drug_concentration')
+        self.assertEqual(parameters[2], 'global.kappa')
+        self.assertEqual(parameters[3], 'global.lambda_0')
+        self.assertEqual(parameters[4], 'global.lambda_1')
         self.assertEqual(parameters[5], 'Sigma base')
         self.assertEqual(parameters[6], 'Sigma rel.')
 
@@ -788,18 +788,18 @@ def test_run(self):
         parameters = result['Parameter'].unique()
         self.assertEqual(len(parameters), 16)
         self.assertEqual(parameters[0], 'Sigma rel.')
-        self.assertEqual(parameters[1], 'Pooled myokit.tumour_volume')
-        self.assertEqual(parameters[2], 'Pooled myokit.drug_concentration')
-        self.assertEqual(parameters[3], 'ID 1 myokit.kappa')
-        self.assertEqual(parameters[4], 'ID 2 myokit.kappa')
-        self.assertEqual(parameters[5], 'ID 3 myokit.kappa')
-        self.assertEqual(parameters[6], 'ID 4 myokit.kappa')
-        self.assertEqual(parameters[7], 'ID 5 myokit.kappa')
-        self.assertEqual(parameters[8], 'ID 6 myokit.kappa')
-        self.assertEqual(parameters[9], 'ID 7 myokit.kappa')
-        self.assertEqual(parameters[10], 'ID 8 myokit.kappa')
-        self.assertEqual(parameters[11], 'Pooled myokit.lambda_0')
-        self.assertEqual(parameters[12], 'Pooled myokit.lambda_1')
+        self.assertEqual(parameters[1], 'Pooled global.tumour_volume')
+        self.assertEqual(parameters[2], 'Pooled global.drug_concentration')
+        self.assertEqual(parameters[3], 'ID 1 global.kappa')
+        self.assertEqual(parameters[4], 'ID 2 global.kappa')
+        self.assertEqual(parameters[5], 'ID 3 global.kappa')
+        self.assertEqual(parameters[6], 'ID 4 global.kappa')
+        self.assertEqual(parameters[7], 'ID 5 global.kappa')
+        self.assertEqual(parameters[8], 'ID 6 global.kappa')
+        self.assertEqual(parameters[9], 'ID 7 global.kappa')
+        self.assertEqual(parameters[10], 'ID 8 global.kappa')
+        self.assertEqual(parameters[11], 'Pooled global.lambda_0')
+        self.assertEqual(parameters[12], 'Pooled global.lambda_1')
         self.assertEqual(parameters[13], 'Pooled Sigma base')
         self.assertEqual(parameters[14], 'Log mean Sigma rel.')
         self.assertEqual(parameters[15], 'Log std. Sigma rel.')
@@ -817,10 +817,10 @@ def test_run_catch_exception(self):
         # Get test data and model
         problem = copy.deepcopy(self.problem)
         problem.fix_parameters({
-            'myokit.drug_concentration': 1,
-            'myokit.kappa': 1,
-            'myokit.lambda_0': 1,
-            'myokit.lambda_1': 1,
+            '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(*[
@@ -842,7 +842,7 @@ def test_run_catch_exception(self):
 
         parameters = result['Parameter'].unique()
         self.assertEqual(len(parameters), 1)
-        self.assertEqual(parameters[0], 'myokit.tumour_volume')
+        self.assertEqual(parameters[0], 'global.tumour_volume')
 
         runs = result['Run'].unique()
         self.assertEqual(len(runs), 3)
@@ -878,7 +878,7 @@ def setUpClass(cls):
         problem = chi.ProblemModellingController(model, error_models)
 
         data = DataLibrary().lung_cancer_control_group()
-        problem.set_data(data, {'myokit.tumour_volume': 'Tumour volume'})
+        problem.set_data(data, {'global.tumour_volume': 'Tumour volume'})
 
         n_parameters = 7
         log_prior = pints.ComposedLogPrior(*[
@@ -929,11 +929,11 @@ def test_run(self):
         self.assertEqual(len(parameters), 7)
         self.assertEqual(parameters[0], 'Sigma base')
         self.assertEqual(parameters[1], 'Sigma rel.')
-        self.assertEqual(parameters[2], 'myokit.drug_concentration')
-        self.assertEqual(parameters[3], 'myokit.kappa')
-        self.assertEqual(parameters[4], 'myokit.lambda_0')
-        self.assertEqual(parameters[5], 'myokit.lambda_1')
-        self.assertEqual(parameters[6], 'myokit.tumour_volume')
+        self.assertEqual(parameters[2], 'global.drug_concentration')
+        self.assertEqual(parameters[3], 'global.kappa')
+        self.assertEqual(parameters[4], 'global.lambda_0')
+        self.assertEqual(parameters[5], 'global.lambda_1')
+        self.assertEqual(parameters[6], 'global.tumour_volume')
 
         chains = result.chain
         self.assertEqual(len(chains), 3)
@@ -978,21 +978,21 @@ def test_run(self):
 
         parameters = sorted(list(result.data_vars.keys()))
         self.assertEqual(len(parameters), 16)
-        self.assertEqual(parameters[0], 'ID 1 myokit.kappa')
-        self.assertEqual(parameters[1], 'ID 2 myokit.kappa')
-        self.assertEqual(parameters[2], 'ID 3 myokit.kappa')
-        self.assertEqual(parameters[3], 'ID 4 myokit.kappa')
-        self.assertEqual(parameters[4], 'ID 5 myokit.kappa')
-        self.assertEqual(parameters[5], 'ID 6 myokit.kappa')
-        self.assertEqual(parameters[6], 'ID 7 myokit.kappa')
-        self.assertEqual(parameters[7], 'ID 8 myokit.kappa')
+        self.assertEqual(parameters[0], 'ID 1 global.kappa')
+        self.assertEqual(parameters[1], 'ID 2 global.kappa')
+        self.assertEqual(parameters[2], 'ID 3 global.kappa')
+        self.assertEqual(parameters[3], 'ID 4 global.kappa')
+        self.assertEqual(parameters[4], 'ID 5 global.kappa')
+        self.assertEqual(parameters[5], 'ID 6 global.kappa')
+        self.assertEqual(parameters[6], 'ID 7 global.kappa')
+        self.assertEqual(parameters[7], 'ID 8 global.kappa')
         self.assertEqual(parameters[8], 'Log mean Sigma rel.')
         self.assertEqual(parameters[9], 'Log std. Sigma rel.')
         self.assertEqual(parameters[10], 'Pooled Sigma base')
-        self.assertEqual(parameters[11], 'Pooled myokit.drug_concentration')
-        self.assertEqual(parameters[12], 'Pooled myokit.lambda_0')
-        self.assertEqual(parameters[13], 'Pooled myokit.lambda_1')
-        self.assertEqual(parameters[14], 'Pooled myokit.tumour_volume')
+        self.assertEqual(parameters[11], 'Pooled global.drug_concentration')
+        self.assertEqual(parameters[12], 'Pooled global.lambda_0')
+        self.assertEqual(parameters[13], 'Pooled global.lambda_1')
+        self.assertEqual(parameters[14], 'Pooled global.tumour_volume')
         self.assertEqual(parameters[15], 'Sigma rel.')
 
         chains = result.chain

chi/tests/test_log_pdfs.py

@@ -1850,7 +1850,7 @@ def test_call_and_compute_pointwise_ll(self):
         self.assertEqual(score, ref_score_2)
         n_obs = 3
         self.assertEqual(pw_score.shape, (n_obs,))
-        self.assertEqual(np.sum(pw_score), score)
+        self.assertAlmostEqual(np.sum(pw_score), score)
 
     def test_evaluateS1(self):
         # Test case I: Compute reference score manually
@@ -1987,7 +1987,7 @@ def test_fix_parameters(self):
         self.assertEqual(len(parameter_names), 7)
         self.assertEqual(parameter_names[0], 'dose.drug_amount')
         self.assertEqual(parameter_names[1], 'central.size')
-        self.assertEqual(parameter_names[2], 'myokit.elimination_rate')
+        self.assertEqual(parameter_names[2], 'global.elimination_rate')
         self.assertEqual(parameter_names[3], 'central.drug_amount Sigma base')
         self.assertEqual(parameter_names[4], 'central.drug_amount Sigma rel.')
         self.assertEqual(parameter_names[5], 'dose.drug_amount Sigma base')
@@ -1997,7 +1997,7 @@ def test_fix_parameters(self):
         self.log_likelihood.fix_parameters(name_value_dict={
             'dose.absorption_rate': None,
             'dose.drug_amount Sigma base': 0.5,
-            'myokit.elimination_rate': 0.3})
+            'global.elimination_rate': 0.3})
 
         n_parameters = self.log_likelihood.n_parameters()
         self.assertEqual(n_parameters, 6)
@@ -2015,7 +2015,7 @@ def test_fix_parameters(self):
         self.log_likelihood.fix_parameters(name_value_dict={
             'central.drug_amount': None,
             'dose.drug_amount Sigma base': None,
-            'myokit.elimination_rate': None})
+            'global.elimination_rate': None})
 
         n_parameters = self.log_likelihood.n_parameters()
         self.assertEqual(n_parameters, 9)
@@ -2026,7 +2026,7 @@ def test_fix_parameters(self):
         self.assertEqual(parameter_names[1], 'dose.drug_amount')
         self.assertEqual(parameter_names[2], 'central.size')
         self.assertEqual(parameter_names[3], 'dose.absorption_rate')
-        self.assertEqual(parameter_names[4], 'myokit.elimination_rate')
+        self.assertEqual(parameter_names[4], 'global.elimination_rate')
         self.assertEqual(parameter_names[5], 'central.drug_amount Sigma base')
         self.assertEqual(parameter_names[6], 'central.drug_amount Sigma rel.')
         self.assertEqual(parameter_names[7], 'dose.drug_amount Sigma base')
@@ -2046,7 +2046,7 @@ def test_get_parameter_names(self):
         self.assertEqual(parameter_names[1], 'dose.drug_amount')
         self.assertEqual(parameter_names[2], 'central.size')
         self.assertEqual(parameter_names[3], 'dose.absorption_rate')
-        self.assertEqual(parameter_names[4], 'myokit.elimination_rate')
+        self.assertEqual(parameter_names[4], 'global.elimination_rate')
         self.assertEqual(parameter_names[5], 'central.drug_amount Sigma base')
         self.assertEqual(parameter_names[6], 'central.drug_amount Sigma rel.')
         self.assertEqual(parameter_names[7], 'dose.drug_amount Sigma base')
@@ -2227,11 +2227,11 @@ def test_get_parameter_names(self):
         parameter_names = self.log_posterior.get_parameter_names()
 
         self.assertEqual(len(parameter_names), 7)
-        self.assertEqual(parameter_names[0], 'myokit.tumour_volume')
-        self.assertEqual(parameter_names[1], 'myokit.drug_concentration')
-        self.assertEqual(parameter_names[2], 'myokit.kappa')
-        self.assertEqual(parameter_names[3], 'myokit.lambda_0')
-        self.assertEqual(parameter_names[4], 'myokit.lambda_1')
+        self.assertEqual(parameter_names[0], 'global.tumour_volume')
+        self.assertEqual(parameter_names[1], 'global.drug_concentration')
+        self.assertEqual(parameter_names[2], 'global.kappa')
+        self.assertEqual(parameter_names[3], 'global.lambda_0')
+        self.assertEqual(parameter_names[4], 'global.lambda_1')
         self.assertEqual(parameter_names[5], 'Sigma base')
         self.assertEqual(parameter_names[6], 'Sigma rel.')