Add SbmlModel.from_antimony

petab/v1/models/sbml_model.py

@@ -1,4 +1,5 @@
 """Functions for handling SBML models"""
+from __future__ import annotations
 
 import itertools
 from collections.abc import Iterable
@@ -32,8 +33,25 @@ def __init__(
         sbml_document: libsbml.SBMLDocument = None,
         model_id: str = None,
     ):
+        """Constructor.
+
+        :param sbml_model: SBML model. Optional if `sbml_document` is given.
+        :param sbml_reader: SBML reader. Optional.
+        :param sbml_document: SBML document. Optional if `sbml_model` is given.
+        :param model_id: Model ID. Defaults to the SBML model ID."""
         super().__init__()
 
+        if sbml_model is None and sbml_document is None:
+            raise ValueError(
+                "Either sbml_model or sbml_document must be given."
+            )
+
+        if sbml_model is None:
+            sbml_model = sbml_document.getModel()
+
+        if sbml_document is None:
+            sbml_document = sbml_model.getSBMLDocument()
+
         self.sbml_reader: libsbml.SBMLReader | None = sbml_reader
         self.sbml_document: libsbml.SBMLDocument | None = sbml_document
         self.sbml_model: libsbml.Model | None = sbml_model
@@ -70,7 +88,7 @@ def __setstate__(self, state):
         self.__dict__.update(state)
 
     @staticmethod
-    def from_file(filepath_or_buffer, model_id: str = None):
+    def from_file(filepath_or_buffer, model_id: str = None) -> SbmlModel:
         sbml_reader, sbml_document, sbml_model = get_sbml_model(
             filepath_or_buffer
         )
@@ -82,7 +100,12 @@ def from_file(filepath_or_buffer, model_id: str = None):
         )
 
     @staticmethod
-    def from_string(sbml_string, model_id: str = None):
+    def from_string(sbml_string, model_id: str = None) -> SbmlModel:
+        """Create SBML model from an SBML string.
+
+        :param sbml_string: SBML model as string.
+        :param model_id: Model ID. Defaults to the SBML model ID.
+        """
         sbml_reader, sbml_document, sbml_model = load_sbml_from_string(
             sbml_string
         )
@@ -97,6 +120,18 @@ def from_string(sbml_string, model_id: str = None):
             model_id=model_id,
         )
 
+    @staticmethod
+    def from_antimony(ant_model: str | Path) -> SbmlModel:
+        """Create SBML model from an Antimony model.
+
+        Requires the `antimony` package (https://github.com/sys-bio/antimony).
+
+        :param ant_model: Antimony model as string or path to file.
+            Strings are interpreted as Antimony model strings.
+        """
+        sbml_str = antimony2sbml(ant_model)
+        return SbmlModel.from_string(sbml_str)
+
     @property
     def model_id(self):
         return self._model_id
@@ -238,3 +273,43 @@ def sympify_sbml(sbml_obj: libsbml.ASTNode | libsbml.SBase) -> sp.Expr:
     )
 
     return sp.sympify(formula_str, locals=_clash)
+
+
+def antimony2sbml(ant_model: str | Path) -> str:
+    """Convert Antimony model to SBML.
+
+    :param ant_model: Antimony model as string or path to file.
+        Strings are interpreted as Antimony model strings.
+
+    :returns:
+        The SBML model as string.
+    """
+    import antimony as ant
+
+    # Unload everything / free memory
+    ant.clearPreviousLoads()
+    ant.freeAll()
+
+    try:
+        # potentially fails because of too long file name
+        is_file = ant_model and Path(ant_model).exists()
+    except OSError:
+        is_file = False
+
+    if is_file:
+        status = ant.loadAntimonyFile(str(ant_model))
+    else:
+        status = ant.loadAntimonyString(ant_model)
+    if status < 0:
+        raise RuntimeError(
+            f"Antimony model could not be loaded: {ant.getLastError()}"
+        )
+
+    if (main_module_name := ant.getMainModuleName()) is None:
+        raise AssertionError("There is no Antimony module.")
+
+    sbml_str = ant.getSBMLString(main_module_name)
+    if not sbml_str:
+        raise ValueError("Antimony model could not be converted to SBML.")
+
+    return sbml_str

pyproject.toml

@@ -35,11 +35,11 @@ maintainers = [
 
 [project.optional-dependencies]
 tests = [
+    "antimony>=2.14.0",
+    "pysb",
     "pytest",
     "pytest-cov",
-    "simplesbml",
     "scipy",
-    "pysb",
 ]
 quality = [
     "pre-commit",
@@ -48,6 +48,9 @@ reports = [
     # https://github.com/spatialaudio/nbsphinx/issues/641
     "Jinja2==3.0.3",
 ]
+antimony = [
+    "antimony>=2.14.0",
+]
 combine = [
     "python-libcombine>=0.2.6",
 ]
@@ -61,6 +64,7 @@ doc = [
     # https://github.com/spatialaudio/nbsphinx/issues/687#issuecomment-1339271312
     "ipython>=7.21.0, !=8.7.0",
     "pysb",
+    "antimony>=2.14.0"
 ]
 vis = [
     "matplotlib>=3.6.0",

tests/v1/test_combine.py

@@ -4,8 +4,9 @@
 
 import pandas as pd
 
-import petab
+import petab.v1 as petab
 from petab.C import *
+from petab.v1.models.sbml_model import SbmlModel
 
 # import fixtures
 pytest_plugins = [
@@ -16,10 +17,7 @@
 def test_combine_archive():
     """Test `create_combine_archive` and `Problem.from_combine`"""
     # Create test files
-    import simplesbml
-
-    ss_model = simplesbml.SbmlModel()
-
+    model = SbmlModel.from_antimony("")
     # Create tables with arbitrary content
     measurement_df = pd.DataFrame(
         data={
@@ -80,7 +78,7 @@ def test_combine_archive():
     ) as tempdir:
         # Write test data
         outdir = Path(tempdir)
-        petab.write_sbml(ss_model.document, outdir / sbml_file_name)
+        model.to_file(outdir / sbml_file_name)
         petab.write_measurement_df(
             measurement_df, outdir / measurement_file_name
         )

tests/v1/test_deprecated.py

@@ -14,7 +14,7 @@ def test_problem_with_sbml_model():
     """Test that a problem can be correctly created from sbml model."""
     # retrieve test data
     (
-        ss_model,
+        model,
         condition_df,
         observable_df,
         measurement_df,
@@ -23,7 +23,7 @@ def test_problem_with_sbml_model():
 
     with pytest.deprecated_call():
         petab_problem = petab.Problem(  # noqa: F811
-            sbml_model=ss_model.model,
+            model=model,
             condition_df=condition_df,
             measurement_df=measurement_df,
             parameter_df=parameter_df,

tests/v1/test_lint.py

@@ -18,7 +18,6 @@
 
 def test_assert_measured_observables_present():
     # create test model
-
     measurement_df = pd.DataFrame(
         data={
             OBSERVABLE_ID: ["non-existing1"],
@@ -255,15 +254,15 @@ def test_assert_no_leading_trailing_whitespace():
 
 
 def test_assert_model_parameters_in_condition_or_parameter_table():
-    import simplesbml
-
     from petab.models.sbml_model import SbmlModel
 
-    ss_model = simplesbml.SbmlModel()
-    ss_model.addParameter("parameter1", 0.0)
-    ss_model.addParameter("noiseParameter1_", 0.0)
-    ss_model.addParameter("observableParameter1_", 0.0)
-    sbml_model = SbmlModel(sbml_model=ss_model.model)
+    ant_model = """
+    parameter1 = 0.0
+    noiseParameter1_ = 0.0
+    observableParameter1_ = 0.0
+    """
+    sbml_model = SbmlModel.from_antimony(ant_model)
+    assert sbml_model.is_valid()
 
     lint.assert_model_parameters_in_condition_or_parameter_table(
         sbml_model, pd.DataFrame(columns=["parameter1"]), pd.DataFrame()
@@ -284,7 +283,10 @@ def test_assert_model_parameters_in_condition_or_parameter_table():
         sbml_model, pd.DataFrame(), pd.DataFrame()
     )
 
-    ss_model.addAssignmentRule("parameter1", "parameter2")
+    sbml_model = SbmlModel.from_antimony(
+        ant_model + "\nparameter2 = 0\nparameter1 := parameter2"
+    )
+    assert sbml_model.is_valid()
     lint.assert_model_parameters_in_condition_or_parameter_table(
         sbml_model, pd.DataFrame(), pd.DataFrame()
     )
@@ -499,12 +501,11 @@ def test_assert_measurement_conditions_present_in_condition_table():
 
 def test_check_condition_df():
     """Check that we correctly detect errors in condition table"""
-    import simplesbml
 
     from petab.models.sbml_model import SbmlModel
 
-    ss_model = simplesbml.SbmlModel()
-    model = SbmlModel(sbml_model=ss_model.model)
+    model = SbmlModel.from_antimony("")
+
     condition_df = pd.DataFrame(
         data={
             CONDITION_ID: ["condition1"],
@@ -527,7 +528,7 @@ def test_check_condition_df():
     lint.check_condition_df(condition_df, model, observable_df)
 
     # fix by adding parameter
-    ss_model.addParameter("p1", 1.0)
+    model = SbmlModel.from_antimony("p1 = 1")
     lint.check_condition_df(condition_df, model)
 
     # species missing in model
@@ -536,7 +537,7 @@ def test_check_condition_df():
         lint.check_condition_df(condition_df, model)
 
     # fix:
-    ss_model.addSpecies("[s1]", 1.0)
+    model = SbmlModel.from_antimony("p1 = 1; species s1 = 1")
     lint.check_condition_df(condition_df, model)
 
     # compartment missing in model
@@ -545,7 +546,9 @@ def test_check_condition_df():
         lint.check_condition_df(condition_df, model)
 
     # fix:
-    ss_model.addCompartment(comp_id="c2", vol=1.0)
+    model = SbmlModel.from_antimony(
+        "p1 = 1; species s1 = 1; compartment c2 = 1"
+    )
     lint.check_condition_df(condition_df, model)
 
 

tests/v1/test_observables.py

@@ -69,14 +69,11 @@ def test_write_observable_df():
 
 def test_get_output_parameters():
     """Test measurements.get_output_parameters."""
-    # sbml model
-    import simplesbml
-
     from petab.models.sbml_model import SbmlModel
 
-    ss_model = simplesbml.SbmlModel()
-    ss_model.addParameter("fixedParameter1", 1.0)
-    ss_model.addParameter("observable_1", 1.0)
+    model = SbmlModel.from_antimony(
+        "fixedParameter1 = 1.0; observable_1 = 1.0"
+    )
 
     # observable file
     observable_df = pd.DataFrame(
@@ -88,9 +85,7 @@ def test_get_output_parameters():
         }
     ).set_index(OBSERVABLE_ID)
 
-    output_parameters = petab.get_output_parameters(
-        observable_df, SbmlModel(sbml_model=ss_model.model)
-    )
+    output_parameters = petab.get_output_parameters(observable_df, model)
 
     assert output_parameters == ["offset", "scaling"]
 
@@ -105,9 +100,7 @@ def test_get_output_parameters():
         }
     ).set_index(OBSERVABLE_ID)
 
-    output_parameters = petab.get_output_parameters(
-        observable_df, SbmlModel(sbml_model=ss_model.model)
-    )
+    output_parameters = petab.get_output_parameters(observable_df, model)
 
     assert output_parameters == ["N", "beta"]