Update changelog and documentation

CHANGELOG.md

@@ -19,13 +19,22 @@ All notable changes to this project will be documented in this file.
 
 ## [Unreleased]
 
+### Added
+
+- Added support for for sbml models to select parameters and species used in the inference
+- Added support for evaluating sbml models at multiple time points
+
+### Changed
+
+- Switched from using parameter names to using parameter ids in the sbml model
 
 ## [0.4.0]
 
 ### Added
 
 - Added a new example model, a 2d heat conduction equation
 - Added a function to model to specify more complex parameter domains
+
 ### Changed
 
 - Removed old functions from corona model

docs/source/examples/sbml_example.rst

@@ -22,12 +22,14 @@ Here's a code snippet to load your own sbml model and to do the parameter infere
 
     central_param = np.array([1.0, 1.0])  # initial guess, evaluation must have nonzero density
     param_limits = np.array([[0.0, 2.0], [0.0, 2.0]])
-    param_names = ['k1', 'k2']
+    param_ids = ['k1', 'k2']
+    timepoints = np.array([1.0])
 
-    model = SBMLModel('model.xml',
+    model = SBMLModel(sbml_file='model.xml',
                       central_param=central_param,
                       param_limits=param_limits,
-                      param_names=param_names)
+                      timepoints=timepoints,
+                      param_ids=param_ids)
     inference(model, 'data.csv')
 
 The attribute :py:attr:`~eulerpi.core.model.SBMLModel.param_names` contains the names of the parameters in the sbml model, for which the inference should be performed.

eulerpi/core/model.py

@@ -324,9 +324,9 @@ def data_dim(self):
     def __init__(
         self,
         sbml_file: str,
-        timepoints: list,
         central_param: np.ndarray,
         param_limits: np.ndarray,
+        timepoints: list,
         param_ids: Optional[list] = None,
         state_ids: Optional[list] = None,
         skip_creation: bool = False,

eulerpi/examples/sbml/sbml_caffeine_model.py

@@ -24,9 +24,9 @@ def __init__(
 
         super().__init__(
             sbml_file,
-            timepoints,
             central_param,
             param_limits,
+            timepoints,
             param_ids,
             **kwargs,
         )

eulerpi/examples/sbml/sbml_menten_model.py

@@ -24,17 +24,17 @@ def __init__(
         sbml_file = importlib.resources.path(
             "eulerpi.examples.sbml", "sbml_menten_model.xml"
         )
-        timepoints = np.array([0.5, 1.0, 2.0])
+        timepoints = np.array([0.5, 1.0])
         param_ids = ["Km", "kcat"]
         state_ids = ["s1"]
 
         super().__init__(
             sbml_file,
-            timepoints,
             central_param,
             param_limits,
+            timepoints,
             param_ids,
-            state_ids=state_ids,
+            state_ids,
             **kwargs,
         )
 

tests/test_examples.py

@@ -73,7 +73,20 @@ def get_example_name(example):
 
 @pytest.mark.parametrize("example", Examples(), ids=get_example_name)
 def test_model_requirements(example):
-    """Test the requirements of the inference for the example models."""
+    """Perform a simple sanity check on the model. It tests the following:
+    - The model has a positive parameter dimension
+    - The model has a positive data dimension
+    - The model has a valid combination of parameter and data dimension
+    - The central parameter has the correct shape
+    - The parameter limits have the correct shape
+    - The model can be instantiated
+    - The model forward pass can be calculated
+    - The model jacobi matrix can be calculated
+    - The return values of the forward pass and the jacobi matrix have the correct shape
+
+    Args:
+        example: Tuple of the form (module_location, className, dataFileName) or (module_location, className)
+    """
 
     # extract example parameters from tuple
     try: