Merge branch 'develop' into fixing_warnings

pypesto/hierarchical/base_problem.py

@@ -1,8 +1,9 @@
 """Inner optimization problem in hierarchical optimization."""
 
+from __future__ import annotations
+
 import copy
 import logging
-from typing import Union
 
 import numpy as np
 import pandas as pd
@@ -60,10 +61,10 @@ def __init__(self, xs: list[InnerParameter], data: list[np.ndarray]):
 
     @staticmethod
     def from_petab_amici(
-        petab_problem: "petab.Problem",
-        amici_model: "amici.Model",
-        edatas: list["amici.ExpData"],
-    ) -> "InnerProblem":
+        petab_problem: petab.Problem,
+        amici_model: amici.Model,
+        edatas: list[amici.ExpData],
+    ) -> InnerProblem:
         """Create an InnerProblem from a PEtab problem and AMICI objects."""
 
     def get_x_ids(self) -> list[str]:
@@ -219,9 +220,7 @@ def scale_value_dict(
     return scaled_dct
 
 
-def scale_value(
-    val: Union[float, np.array], scale: str
-) -> Union[float, np.array]:
+def scale_value(val: float | np.array, scale: str) -> float | np.array:
     """Scale a single value."""
     if scale == "lin":
         return val

pypesto/hierarchical/ordinal/calculator.py

@@ -1,5 +1,7 @@
 """Definition of an optimal scaling calculator class."""
 
+from __future__ import annotations
+
 import copy
 from collections.abc import Sequence
 
@@ -86,12 +88,12 @@ def __call__(
         mode: str,
         amici_model: AmiciModel,
         amici_solver: AmiciSolver,
-        edatas: list["amici.ExpData"],
+        edatas: list[amici.ExpData],
         n_threads: int,
         x_ids: Sequence[str],
         parameter_mapping: ParameterMapping,
         fim_for_hess: bool,
-        rdatas: list["amici.ReturnData"] = None,
+        rdatas: list[amici.ReturnData] = None,
     ):
         """Perform the actual AMICI call.
 

pypesto/hierarchical/ordinal/problem.py

@@ -1,5 +1,7 @@
 """Definition of an optimal scaling parameter class."""
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 
@@ -172,10 +174,10 @@ def initialize(self) -> None:
     @staticmethod
     def from_petab_amici(
         petab_problem: petab.Problem,
-        amici_model: "amici.Model",
-        edatas: list["amici.ExpData"],
+        amici_model: amici.Model,
+        edatas: list[amici.ExpData],
         method: str = None,
-    ) -> "OrdinalProblem":
+    ) -> OrdinalProblem:
         """Construct the inner problem from the `petab_problem`."""
         if not method:
             method = REDUCED
@@ -475,8 +477,8 @@ def get_inner_parameter_dictionary(self) -> dict:
 
 def optimal_scaling_inner_problem_from_petab_problem(
     petab_problem: petab.Problem,
-    amici_model: "amici.Model",
-    edatas: list["amici.ExpData"],
+    amici_model: amici.Model,
+    edatas: list[amici.ExpData],
     method: str,
 ):
     """Construct the inner problem from the `petab_problem`."""
@@ -511,7 +513,7 @@ def optimal_scaling_inner_problem_from_petab_problem(
 def optimal_scaling_inner_parameters_from_measurement_df(
     df: pd.DataFrame,
     method: str,
-    amici_model: "amici.Model",
+    amici_model: amici.Model,
 ) -> list[OrdinalParameter]:
     """Create list of inner free parameters from PEtab measurement table dependent on the method provided."""
     df = df.reset_index()
@@ -611,8 +613,8 @@ def get_estimate_for_method(method: str) -> tuple[bool, bool]:
 
 
 def optimal_scaling_ixs_for_measurement_specific_parameters(
-    petab_problem: "petab.Problem",
-    amici_model: "amici.Model",
+    petab_problem: petab.Problem,
+    amici_model: amici.Model,
     inner_parameters: list[OrdinalParameter],
 ) -> dict[str, list[tuple[int, int, int]]]:
     """Create mapping of parameters to measurements.

pypesto/hierarchical/ordinal/solver.py

@@ -39,7 +39,7 @@
 try:
     from amici.petab.parameter_mapping import ParameterMapping
 except ImportError:
-    pass
+    ParameterMapping = None
 
 
 class OrdinalInnerSolver(InnerSolver):

pypesto/hierarchical/relative/solver.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import copy
 from typing import Any
 

pypesto/hierarchical/semiquantitative/calculator.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import copy
 from collections.abc import Sequence
 
@@ -79,12 +81,12 @@ def __call__(
         mode: str,
         amici_model: AmiciModel,
         amici_solver: AmiciSolver,
-        edatas: list["amici.ExpData"],
+        edatas: list[amici.ExpData],
         n_threads: int,
         x_ids: Sequence[str],
         parameter_mapping: ParameterMapping,
         fim_for_hess: bool,
-        rdatas: list["amici.ReturnData"] = None,
+        rdatas: list[amici.ReturnData] = None,
     ):
         """Perform the actual AMICI call.
 

pypesto/hierarchical/semiquantitative/problem.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 
@@ -124,10 +126,10 @@ def initialize(self) -> None:
     @staticmethod
     def from_petab_amici(
         petab_problem: petab.Problem,
-        amici_model: "amici.Model",
-        edatas: list["amici.ExpData"],
+        amici_model: amici.Model,
+        edatas: list[amici.ExpData],
         spline_ratio: float = None,
-    ) -> "SemiquantProblem":
+    ) -> SemiquantProblem:
         """Construct the inner problem from the `petab_problem`."""
         if spline_ratio is None:
             spline_ratio = get_default_options()
@@ -289,8 +291,8 @@ def get_default_options() -> dict:
 
 def spline_inner_problem_from_petab_problem(
     petab_problem: petab.Problem,
-    amici_model: "amici.Model",
-    edatas: list["amici.ExpData"],
+    amici_model: amici.Model,
+    edatas: list[amici.ExpData],
     spline_ratio: float = None,
 ):
     """Construct the inner problem from the `petab_problem`."""
@@ -336,7 +338,7 @@ def spline_inner_problem_from_petab_problem(
 def spline_inner_parameters_from_measurement_df(
     df: pd.DataFrame,
     spline_ratio: float,
-    amici_model: "amici.Model",
+    amici_model: amici.Model,
 ) -> list[SplineInnerParameter]:
     """Create list of inner free spline parameters from PEtab measurement table."""
     df = df.reset_index()
@@ -382,8 +384,8 @@ def spline_inner_parameters_from_measurement_df(
 
 
 def noise_inner_parameters_from_parameter_df(
-    petab_problem: "petab.Problem",
-    amici_model: "amici.Model",
+    petab_problem: petab.Problem,
+    amici_model: amici.Model,
 ) -> list[SplineInnerParameter]:
     """Create list of inner free noise parameters from PEtab parameter table."""
     # Select the semiquantitative measurements.
@@ -432,8 +434,8 @@ def noise_inner_parameters_from_parameter_df(
 
 
 def spline_ixs_for_measurement_specific_parameters(
-    petab_problem: "petab.Problem",
-    amici_model: "amici.Model",
+    petab_problem: petab.Problem,
+    amici_model: amici.Model,
     inner_parameters: list[SplineInnerParameter],
 ) -> dict[str, list[tuple[int, int, int]]]:
     """Create mapping of parameters to measurements.

pypesto/hierarchical/semiquantitative/solver.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import warnings
 
 import numpy as np

pypesto/petab/__init__.py

@@ -24,9 +24,4 @@
 try:
     import amici
 except ImportError:
-    warnings.warn(
-        "PEtab import requires an installation of amici "
-        "(https://github.com/AMICI-dev/AMICI). "
-        "Install via `pip3 install amici`.",
-        stacklevel=1,
-    )
+    amici = None

setup.cfg

@@ -97,6 +97,8 @@ amici =
     amici >= 0.21.0
 roadrunner =
     libroadrunner >= 2.7.0
+    %(petab)s
+    python-libsbml >= 5.20.2
 petab =
     petab >= 0.4.0
 ipopt =