Merge branch 'develop' into get_ores_by_id

.github/workflows/ci.yml

@@ -172,7 +172,7 @@ jobs:
       run: .github/workflows/install_deps.sh
 
     - name: Install PEtabJL dependencies
-      run: julia -e 'using Pkg; Pkg.add("PEtab"); Pkg.add("OrdinaryDiffEq"), Pkg.add("Sundials")'
+      run: julia -e 'using Pkg; Pkg.add(name="PEtab", version="1.4.2"); Pkg.add("OrdinaryDiffEq"), Pkg.add("Sundials")'
 
     - name: Run tests
       timeout-minutes: 25

pypesto/C.py

@@ -35,6 +35,9 @@
 ENSEMBLE_TYPE = 'ensemble_type'
 PREDICTIONS = 'predictions'
 
+SIMULTANEOUS = 'simultaneous'
+POINTWISE = 'pointwise'
+
 LOWER_BOUND = 'lower_bound'
 UPPER_BOUND = 'upper_bound'
 PREEQUILIBRATION_CONDITION_ID = 'preequilibrationConditionId'

pypesto/ensemble/ensemble.py

@@ -29,12 +29,14 @@
     OUTPUT,
     OUTPUT_SENSI,
     PERCENTILE,
+    POINTWISE,
     PREDICTION_ARRAYS,
     PREDICTION_ID,
     PREDICTION_RESULTS,
     PREDICTION_SUMMARY,
     PREDICTIONS,
     PREDICTOR,
+    SIMULTANEOUS,
     STANDARD_DEVIATION,
     SUMMARY,
     TIMEPOINTS,
@@ -1204,7 +1206,11 @@ def get_percentile_label(percentile: Union[float, int, str]) -> str:
     return f'{PERCENTILE} {percentile}'
 
 
-def calculate_cutoff(result: Result, percentile: float = 0.95):
+def calculate_cutoff(
+    result: Result,
+    percentile: float = 0.95,
+    cr_option: str = SIMULTANEOUS,
+):
     """
     Calculate the cutoff of the ensemble.
 
@@ -1220,6 +1226,10 @@ def calculate_cutoff(result: Result, percentile: float = 0.95):
         The percentile of the chi^2 distribution. Between 0 and 100.
         Higher values will result in a more lax cutoff. If the value is greater
         than 100, the cutoff will be returned as np.inf.
+    cr_option:
+        The type of confidence region, which determines the degree of freedom of
+        the chi^2 distribution for the cutoff value. It can take 'simultaneous' or
+        'pointwise'.
 
     Returns
     -------
@@ -1230,10 +1240,19 @@ def calculate_cutoff(result: Result, percentile: float = 0.95):
             f"percentile={percentile} is too large. Choose "
             f"0<=percentile<=100."
         )
+    if cr_option not in [SIMULTANEOUS, POINTWISE]:
+        raise ValueError(
+            "Confidence region must be either simultaneous or pointwise."
+        )
+
     # optimal point as base:
     fval_opt = result.optimize_result[0].fval
-    # degrees of freedom is equal to the number of parameters
-    df = result.problem.dim
-    range = chi2.ppf(q=percentile / 100, df=df)
+    if cr_option == SIMULTANEOUS:
+        # degrees of freedom is equal to the number of parameters
+        df = result.problem.dim
+    elif cr_option == POINTWISE:
+        # degrees of freedom is equal to 1
+        df = 1
 
+    range = chi2.ppf(q=percentile / 100, df=df)
     return fval_opt + range

pypesto/petab/importer.py

@@ -61,11 +61,15 @@
 
 class PetabImporter(AmiciObjectBuilder):
     """
-    Importer for Petab files.
+    Importer for PEtab files.
 
     Create an `amici.Model`, an `objective.AmiciObjective` or a
-    `pypesto.Problem` from Petab files.
-    """
+    `pypesto.Problem` from PEtab files. The created objective function is a
+    negative log-likelihood function and can thus be negative. The actual
+    form of the likelihood depends on the noise model specified in the provided PEtab problem.
+    For more information, see
+    [the PEtab documentation](https://petab.readthedocs.io/en/latest/documentation_data_format.html#noise-distributions)
+    """  # noqa
 
     MODEL_BASE_DIR = "amici_models"