Add assert_model_parameters_in_condition_or_parameter_table (#43)

petab/lint.py

@@ -7,6 +7,8 @@
 import re
 import copy
 import logging
+import libsbml
+import pandas as pd
 
 logger = logging.getLogger(__name__)
 
@@ -360,6 +362,18 @@ def lint_problem(problem: 'core.Problem'):
             logger.error(e)
             errors_occurred = True
 
+    if problem.sbml_model is not None and problem.condition_df is not None \
+            and problem.parameter_df is not None:
+        try:
+            assert_model_parameters_in_condition_or_parameter_table(
+                problem.sbml_model,
+                problem.condition_df,
+                problem.parameter_df
+            )
+        except AssertionError as e:
+            logger.error(e)
+            errors_occurred = True
+
     if errors_occurred:
         logger.error('Not OK')
     elif problem.measurement_df is None or problem.condition_df is None \
@@ -370,3 +384,44 @@ def lint_problem(problem: 'core.Problem'):
         logger.info('OK')
 
     return errors_occurred
+
+
+def assert_model_parameters_in_condition_or_parameter_table(
+        sbml_model: libsbml.Model,
+        condition_df: pd.DataFrame,
+        parameter_df: pd.DataFrame):
+    """Model non-placeholder model parameters must be either specified in the
+    condition or parameter table, unless they are AssignmentRule target, in
+    which case they must not occur in either.
+    Check that.
+
+    NOTE: SBML local parameters are ignored here"""
+
+    for parameter in sbml_model.getListOfParameters():
+        parameter_id = parameter.getId()
+
+        if parameter_id.startswith('observableParameter'):
+            continue
+        if parameter_id.startswith('noiseParameter'):
+            continue
+
+        is_assignee = \
+            sbml_model.getAssignmentRuleByVariable(parameter_id) is not None
+        in_parameter_df = parameter_id in parameter_df.index
+        in_condition_df = parameter_id in condition_df.columns
+
+        if is_assignee and (in_parameter_df or in_condition_df):
+            raise AssertionError(f"Model parameter '{parameter_id}' is target "
+                                 "of AssignmentRule, and thus, must not be "
+                                 "present in condition table or in parameter "
+                                 "table.")
+
+        if not is_assignee and not in_parameter_df and not in_condition_df:
+            raise AssertionError(f"Model parameter '{parameter_id}' neither "
+                                 "present in condition table nor in parameter "
+                                 "table.")
+
+        if in_parameter_df and in_condition_df:
+            raise AssertionError(f"Model parameter '{parameter_id}' present "
+                                 "in both condition table and parameter "
+                                 "table.")

tests/test_lint.py

@@ -5,7 +5,7 @@
 import pandas as pd
 
 sys.path.append(os.getcwd())
-from petab import lint  # noqa: E402
+from petab import (lint, sbml)  # noqa: E402
 
 
 def test_assert_measured_observables_present_in_model():
@@ -147,3 +147,40 @@ def test_assert_no_leading_trailing_whitespace():
 
     lint.assert_no_leading_trailing_whitespace(
         test_df['testNone'].values, "testNone")
+
+
+def test_assert_model_parameters_in_condition_or_parameter_table():
+    document = libsbml.SBMLDocument(3, 1)
+    model = document.createModel()
+    model.setTimeUnits("second")
+    model.setExtentUnits("mole")
+    model.setSubstanceUnits('mole')
+    sbml.add_global_parameter(model, 'parameter1')
+    sbml.add_global_parameter(model, 'noiseParameter1_')
+    sbml.add_global_parameter(model, 'observableParameter1_')
+
+    with pytest.raises(AssertionError):
+        lint.assert_model_parameters_in_condition_or_parameter_table(
+            model, pd.DataFrame(), pd.DataFrame())
+
+    lint.assert_model_parameters_in_condition_or_parameter_table(
+            model, pd.DataFrame(columns=['parameter1']), pd.DataFrame()
+    )
+
+    lint.assert_model_parameters_in_condition_or_parameter_table(
+            model, pd.DataFrame(), pd.DataFrame(index=['parameter1']))
+
+    with pytest.raises(AssertionError):
+        lint.assert_model_parameters_in_condition_or_parameter_table(
+            model,
+            pd.DataFrame(columns=['parameter1']),
+            pd.DataFrame(index=['parameter1']))
+
+    with pytest.raises(AssertionError):
+        lint.assert_model_parameters_in_condition_or_parameter_table(
+            model, pd.DataFrame(), pd.DataFrame())
+
+    sbml.create_assigment_rule(model, assignee_id='parameter1',
+                               formula='parameter2')
+    lint.assert_model_parameters_in_condition_or_parameter_table(
+        model, pd.DataFrame(), pd.DataFrame())