Merge branch 'develop' into refactor_schema

Author: dweindl

petab/core.py

@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 __all__ = ['get_simulation_df', 'write_simulation_df', 'get_visualization_df',
            'write_visualization_df', 'get_notnull_columns',
-           'get_observable_id', 'flatten_timepoint_specific_output_overrides',
+           'flatten_timepoint_specific_output_overrides',
            'concat_tables', 'to_float_if_float', 'is_empty',
            'create_combine_archive', 'unique_preserve_order']
 
@@ -90,30 +90,6 @@ def get_notnull_columns(df: pd.DataFrame, candidates: Iterable):
             if col in df and not np.all(df[col].isnull())]
 
 
-def get_observable_id(parameter_id: str) -> str:
-    """Get PEtab observable ID from PEtab-style sigma or observable
-    `AssignmentRule`-target ``parameter_id``.
-
-    e.g. for 'observable_obs1' -> 'obs1', for 'sigma_obs1' -> 'obs1'
-
-    Arguments:
-        parameter_id: Some parameter ID
-
-    Returns:
-        Observable ID
-    """
-    warn("This function will be removed in future releases.",
-         DeprecationWarning)
-
-    if parameter_id.startswith(r'observable_'):
-        return parameter_id[len('observable_'):]
-
-    if parameter_id.startswith(r'sigma_'):
-        return parameter_id[len('sigma_'):]
-
-    raise ValueError('Cannot extract observable id from: ' + parameter_id)
-
-
 def flatten_timepoint_specific_output_overrides(
         petab_problem: 'petab.problem.Problem') -> None:
     """Flatten timepoint-specific output parameter overrides.

petab/sbml.py

@@ -11,22 +11,17 @@
 import petab
 
 logger = logging.getLogger(__name__)
-__all__ = ['add_global_parameter',
-           'add_model_output',
-           'add_model_output_sigma',
-           'add_model_output_with_sigma',
-           'create_assigment_rule',
-           'get_model_for_condition',
-           'get_model_parameters',
-           'get_sbml_model',
-           'globalize_parameters',
-           'is_sbml_consistent',
-           'load_sbml_from_file',
-           'load_sbml_from_string',
-           'log_sbml_errors',
-           'sbml_parameter_is_observable',
-           'sbml_parameter_is_sigma',
-           'write_sbml']
+__all__ = [
+    'get_model_for_condition',
+    'get_model_parameters',
+    'get_sbml_model',
+    'globalize_parameters',
+    'is_sbml_consistent',
+    'load_sbml_from_file',
+    'load_sbml_from_string',
+    'log_sbml_errors',
+    'write_sbml'
+]
 
 
 def is_sbml_consistent(sbml_document: libsbml.SBMLDocument,
@@ -123,178 +118,6 @@ def globalize_parameters(sbml_model: libsbml.Model,
             law.removeParameter(lp.getId())
 
 
-def add_global_parameter(sbml_model: libsbml.Model,
-                         parameter_id: str,
-                         parameter_name: str = None,
-                         constant: bool = False,
-                         units: str = 'dimensionless',
-                         value: float = 0.0) -> libsbml.Parameter:
-    """Add new global parameter to SBML model
-
-    Arguments:
-        sbml_model: SBML model
-        parameter_id: ID of the new parameter
-        parameter_name: Name of the new parameter
-        constant: Is parameter constant?
-        units: SBML unit ID
-        value: parameter value
-
-    Returns:
-        The created parameter
-    """
-
-    if parameter_name is None:
-        parameter_name = parameter_id
-
-    p = sbml_model.createParameter()
-    p.setId(parameter_id)
-    p.setName(parameter_name)
-    p.setConstant(constant)
-    p.setValue(value)
-    p.setUnits(units)
-    return p
-
-
-def create_assigment_rule(sbml_model: libsbml.Model,
-                          assignee_id: str,
-                          formula: str,
-                          rule_id: str = None,
-                          rule_name: str = None) -> libsbml.AssignmentRule:
-    """Create SBML AssignmentRule
-
-    Arguments:
-        sbml_model: Model to add output to
-        assignee_id: Target of assignment
-        formula: Formula string for model output
-        rule_id: SBML id for created rule
-        rule_name: SBML name for created rule
-
-    Returns:
-        The created ``AssignmentRule``
-    """
-    warn("This function will be removed in future releases.",
-         DeprecationWarning)
-
-    if rule_id is None:
-        rule_id = assignee_id
-
-    if rule_name is None:
-        rule_name = rule_id
-
-    rule = sbml_model.createAssignmentRule()
-    rule.setId(rule_id)
-    rule.setName(rule_name)
-    rule.setVariable(assignee_id)
-    rule.setFormula(formula)
-
-    return rule
-
-
-def add_model_output(sbml_model: libsbml.Model,
-                     observable_id: str,
-                     formula: str,
-                     observable_name: str = None) -> None:
-    """Add PEtab-style output to model
-
-    We expect that all formula parameters are added to the model elsewhere.
-
-    Arguments:
-        sbml_model: Model to add output to
-        formula: Formula string for model output
-        observable_id: ID without "observable\\_" prefix
-        observable_name: Any observable name
-    """
-    warn("This function will be removed in future releases.",
-         DeprecationWarning)
-
-    if observable_name is None:
-        observable_name = observable_id
-
-    prefixed_id = f'observable_{observable_id}'
-    add_global_parameter(sbml_model, prefixed_id, observable_name)
-    create_assigment_rule(sbml_model=sbml_model,
-                          assignee_id=prefixed_id,
-                          formula=formula)
-
-
-def add_model_output_sigma(sbml_model: libsbml.Model,
-                           observable_id: str,
-                           formula: str) -> None:
-    """Add PEtab-style sigma for the given observable id
-
-    We expect that all formula parameters are added to the model elsewhere.
-
-    Arguments:
-        sbml_model: Model to add to
-        observable_id: Observable id for which to add sigma
-        formula: Formula for sigma
-    """
-    warn("This function will be removed in future releases.",
-         DeprecationWarning)
-
-    add_global_parameter(sbml_model, f'sigma_{observable_id}')
-    create_assigment_rule(sbml_model, f'sigma_{observable_id}', formula)
-
-
-def add_model_output_with_sigma(
-        sbml_model: libsbml.Model,
-        observable_id: str,
-        observable_formula: str,
-        observable_name: str = None) -> None:
-    """Add PEtab-style output and corresponding sigma with single
-    (newly created) parameter
-
-    We expect that all formula parameters are added to the model elsewhere.
-
-    Arguments:
-        sbml_model:
-            Model to add output to
-        observable_formula:
-            Formula string for model output
-        observable_id:
-            ID without "observable\\_" prefix
-        observable_name:
-            Any name
-    """
-    warn("This function will be removed in future releases.",
-         DeprecationWarning)
-
-    add_model_output(sbml_model=sbml_model,
-                     observable_id=observable_id,
-                     observable_name=observable_name,
-                     formula=observable_formula)
-
-    noise_parameter_id = f'noiseParameter1_{observable_id}'
-    add_global_parameter(sbml_model=sbml_model,
-                         parameter_id=noise_parameter_id)
-
-    add_model_output_sigma(sbml_model=sbml_model,
-                           observable_id=observable_id,
-                           formula=noise_parameter_id)
-
-
-def sbml_parameter_is_observable(sbml_parameter: libsbml.Parameter) -> bool:
-    """
-    Returns whether the ``libsbml.Parameter`` ``sbml_parameter``
-    matches the defined observable format.
-    """
-    warn("This function will be removed in future releases.",
-         DeprecationWarning)
-
-    return sbml_parameter.getId().startswith('observable_')
-
-
-def sbml_parameter_is_sigma(sbml_parameter: libsbml.Parameter) -> bool:
-    """
-    Returns whether the ``libsbml.Parameter`` ``sbml_parameter``
-    matches the defined sigma format.
-    """
-    warn("This function will be removed in future releases.",
-         DeprecationWarning)
-
-    return sbml_parameter.getId().startswith('sigma_')
-
-
 def get_model_parameters(sbml_model: libsbml.Model, with_values=False
                          ) -> Union[List[str], Dict[str, float]]:
     """Return SBML model parameters which are not Rule targets

tests/test_petab.py

@@ -145,11 +145,6 @@ def test_serialization(petab_problem):
         == len(petab_problem.sbml_model.getListOfParameters())
 
 
-def test_get_observable_id():
-    assert petab.get_observable_id('observable_obs1') == 'obs1'
-    assert petab.get_observable_id('sigma_obs1') == 'obs1'
-
-
 def test_get_priors_from_df():
     """Check petab.get_priors_from_df."""
     parameter_df = pd.DataFrame({