work on sensitivity analysis

Author: matthiaskoenig

src/sbmlsim/sensitivity/parameters.py

@@ -41,11 +41,9 @@ def parameters_for_sensitivity_analysis(
     def parameter_from_sbase(sbase: libsbml.SBase) -> SensitivityParameter:
         """Create parameter from SBase for sensitivity analysis."""
         uid = sbase.getId()
-        console.print(uid)
         name = sbase.getName() if sbase.isSetName() else uid
         udef: libsbml.UnitDefinition = sbase.getDerivedUnitDefinition()
         unit: str = libsbml.UnitDefinition.printUnits(ud=udef, compact=True)
-        console.print(unit)
 
         # FIXME: get bound information from SBML model or table
         parameter = SensitivityParameter(uid=uid, name=name, unit=unit)
@@ -96,11 +94,13 @@ def parameter_from_sbase(sbase: libsbml.SBase) -> SensitivityParameter:
     # remove excluded ids
     parameters_filtered: list[SensitivityParameter] = []
     parameters_excluded: list[SensitivityParameter] = []
-    for p in parameters:
-        if sid in exclude_ids:
-            parameters_excluded.append(p)
+
+    sp: SensitivityParameter
+    for sp in parameters:
+        if sp.uid in exclude_ids:
+            parameters_excluded.append(sp)
         else:
-            parameters_filtered.append(p)
+            parameters_filtered.append(sp)
 
     console.print(f"Excluded parameters: {parameters_excluded}")
 

src/sbmlsim/sensitivity/plots.py

@@ -33,13 +33,8 @@ def calculate_subset(df, cutoff=0.01):
         df_subset = calculate_subset(df, cutoff=cutoff)
         df_subset_mask = calculate_mask(df_subset, cutoff)
 
-
-
-    # yticklabels = ["{}".format(pid) for pid in X_subset.index]
-    # xticklabels = ["{}".format(pnames[pid]["label"]) for pid in X_subset.COLUMNS]
-
-    xticklabels = df.columns
-    yticklabels = df.index
+    yticklabels = [pid for pid in df_subset.index]
+    xticklabels = [pid for pid in df_subset.columns]
 
     # plot heatmap
     ax = sns.clustermap(