Merge branch 'develop' into ruff

doc/examples/example_cli_famos_helpers.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import List, Tuple
 
 import pandas as pd
 from more_itertools import one
@@ -43,7 +42,7 @@ def calibrate(
 
 def parse_summary_to_progress_list(
     summary_tsv: str,
-) -> List[Tuple[Method, set]]:
+) -> list[tuple[Method, set]]:
     """Get progress information from the summary file."""
     df_raw = pd.read_csv(summary_tsv, sep="\t")
     df = df_raw.loc[~pd.isnull(df_raw["predecessor change"])]

petab_select/candidate_space.py

@@ -6,8 +6,9 @@
 import csv
 import logging
 import warnings
+from collections.abc import Callable
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Type, Union
+from typing import Any
 
 import numpy as np
 from more_itertools import one
@@ -96,11 +97,11 @@ def __init__(
         method: Method,
         criterion: Criterion,
         # TODO add MODEL_TYPE = Union[str, Model], str for VIRTUAL_INITIAL_MODEL
-        predecessor_model: Optional[Model] = None,
-        excluded_hashes: Optional[list[ModelHash]] = None,
+        predecessor_model: Model | None = None,
+        excluded_hashes: list[ModelHash] | None = None,
         limit: TYPE_LIMIT = np.inf,
         summary_tsv: TYPE_PATH = None,
-        previous_predecessor_model: Optional[Model] = None,
+        previous_predecessor_model: Model | None = None,
         calibrated_models: dict[ModelHash, Model] = None,
     ):
         """See class attributes for arguments."""
@@ -132,7 +133,7 @@ def __init__(
         self.latest_iteration_calibrated_models = {}
 
     def set_iteration_user_calibrated_models(
-        self, user_calibrated_models: Optional[dict[str, Model]]
+        self, user_calibrated_models: dict[str, Model] | None
     ) -> None:
         """Hide previously-calibrated models from the calibration tool.
 
@@ -216,7 +217,7 @@ def get_iteration_calibrated_models(
             )
         return combined_calibrated_models
 
-    def write_summary_tsv(self, row: List[Any]):
+    def write_summary_tsv(self, row: list[Any]):
         """Write the summary of the last iteration to a TSV file.
 
         The destination is defined in ``self.summary_tsv``.
@@ -258,8 +259,8 @@ def _setup_summary_tsv(self):
     @classmethod
     def read_arguments_from_yaml_dict(
         cls,
-        yaml_dict: Dict[str, str],
-    ) -> Dict[str, Union[str, Model]]:
+        yaml_dict: dict[str, str],
+    ) -> dict[str, str | Model]:
         """Parse settings that were stored in YAML.
 
         Args:
@@ -304,7 +305,7 @@ def is_plausible(self, model: Model) -> bool:
         """
         return True
 
-    def distance(self, model: Model) -> Union[None, float, int]:
+    def distance(self, model: Model) -> None | float | int:
         """Compute the distance between two models that are neighbors.
 
         Args:
@@ -320,7 +321,7 @@ def distance(self, model: Model) -> Union[None, float, int]:
     def accept(
         self,
         model: Model,
-        distance: Union[None, float, int],
+        distance: None | float | int,
     ) -> None:
         """Add a candidate model to the candidate space.
 
@@ -349,7 +350,7 @@ def n_accepted(self) -> int:
 
     def excluded(
         self,
-        model_hash: Union[Model, ModelHash],
+        model_hash: Model | ModelHash,
     ) -> bool:
         """Check whether a model is excluded.
 
@@ -377,7 +378,7 @@ def _consider_method(self, model: Model) -> bool:
         """
         return True
 
-    def consider(self, model: Union[Model, None]) -> bool:
+    def consider(self, model: Model | None) -> bool:
         """Add a candidate model, if it should be added.
 
         Args:
@@ -420,9 +421,7 @@ def reset_accepted(self) -> None:
         self.models = []
         self.distances = []
 
-    def set_predecessor_model(
-        self, predecessor_model: Union[Model, str, None]
-    ):
+    def set_predecessor_model(self, predecessor_model: Model | str | None):
         """Set the predecessor model.
 
         See class attributes for arguments.
@@ -436,13 +435,13 @@ def set_predecessor_model(
                 f"A virtual initial model was requested for a method ({self.method}) that does not support them."
             )
 
-    def get_predecessor_model(self) -> Union[str, Model]:
+    def get_predecessor_model(self) -> str | Model:
         """Get the predecessor model."""
         return self.predecessor_model
 
     def set_excluded_hashes(
         self,
-        hashes: Union[Model, ModelHash, list[Union[Model, ModelHash]]],
+        hashes: Model | ModelHash | list[Model | ModelHash],
         extend: bool = False,
     ) -> None:
         """Set the excluded hashes.
@@ -453,7 +452,7 @@ def set_excluded_hashes(
             extend:
                 Whether to replace or extend the current excluded hashes.
         """
-        if isinstance(hashes, (Model, ModelHash)):
+        if isinstance(hashes, Model | ModelHash):
             hashes = [hashes]
         excluded_hashes = set()
         for potential_hash in hashes:
@@ -517,9 +516,9 @@ def wrapper():
 
     def reset(
         self,
-        predecessor_model: Optional[Union[Model, str, None]] = None,
+        predecessor_model: Model | str | None | None = None,
         # FIXME change `Any` to some `TYPE_MODEL_HASH` (e.g. union of str/int/float)
-        excluded_hashes: Optional[list[ModelHash]] = None,
+        excluded_hashes: list[ModelHash] | None = None,
         limit: TYPE_LIMIT = None,
     ) -> None:
         """Reset the candidate models, optionally reinitialize with a model.
@@ -549,8 +548,8 @@ def reset(
     def distances_in_estimated_parameters(
         self,
         model: Model,
-        predecessor_model: Optional[Model] = None,
-    ) -> Dict[str, Union[float, int]]:
+        predecessor_model: Model | None = None,
+    ) -> dict[str, float | int]:
         """Distance between two models in model space, using different metrics.
 
         All metrics are in terms of estimated parameters.
@@ -679,7 +678,7 @@ class ForwardCandidateSpace(CandidateSpace):
     def __init__(
         self,
         *args,
-        predecessor_model: Optional[Union[Model, str]] = None,
+        predecessor_model: Model | str | None = None,
         max_steps: int = None,
         **kwargs,
     ):
@@ -868,10 +867,10 @@ class FamosCandidateSpace(CandidateSpace):
     def __init__(
         self,
         *args,
-        predecessor_model: Optional[Union[Model, str, None]] = None,
-        critical_parameter_sets: List = [],
-        swap_parameter_sets: List = [],
-        method_scheme: Dict[tuple, str] = None,
+        predecessor_model: Model | str | None | None = None,
+        critical_parameter_sets: list = [],
+        swap_parameter_sets: list = [],
+        method_scheme: dict[tuple, str] = None,
         n_reattempts: int = 0,
         consecutive_laterals: bool = False,
         **kwargs,
@@ -1031,7 +1030,7 @@ def read_arguments_from_yaml_dict(cls, yaml_dict) -> dict:
     def update_after_calibration(
         self,
         *args,
-        iteration_calibrated_models: Dict[str, Model],
+        iteration_calibrated_models: dict[str, Model],
         **kwargs,
     ) -> None:
         """See `CandidateSpace.update_after_calibration`."""
@@ -1068,7 +1067,7 @@ def update_after_calibration(
 
     def update_from_iteration_calibrated_models(
         self,
-        iteration_calibrated_models: Dict[str, Model],
+        iteration_calibrated_models: dict[str, Model],
     ) -> bool:
         """Update ``self.best_models`` with the latest ``iteration_calibrated_models``
         and determine if there was a new best model. If so, return
@@ -1122,7 +1121,7 @@ def insert_model_into_best_models(self, model_to_insert: Model) -> None:
         )
         self.best_models.insert(insert_index, model_to_insert)
 
-    def consider(self, model: Union[Model, None]) -> bool:
+    def consider(self, model: Model | None) -> bool:
         """Re-define ``consider`` of FAMoS to be the ``consider`` method
         of the ``inner_candidate_space``. Update all the attributes
         changed in the ``consider`` method.
@@ -1392,7 +1391,7 @@ class LateralCandidateSpace(CandidateSpace):
     def __init__(
         self,
         *args,
-        predecessor_model: Union[Model, None],
+        predecessor_model: Model | None,
         max_steps: int = None,
         **kwargs,
     ):
@@ -1472,7 +1471,7 @@ def _consider_method(self, model):
 }
 
 
-def method_to_candidate_space_class(method: Method) -> Type[CandidateSpace]:
+def method_to_candidate_space_class(method: Method) -> type[CandidateSpace]:
     """Get a candidate space class, given its method name.
 
     Args:

petab_select/cli.py

@@ -1,7 +1,7 @@
 """The PEtab Select command-line interface."""
 
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any
 
 import click
 import dill
@@ -17,7 +17,7 @@
 from .problem import Problem
 
 
-def read_state(filename: str) -> Dict[str, Any]:
+def read_state(filename: str) -> dict[str, Any]:
     with open(filename, "rb") as f:
         state = dill.load(f)
 
@@ -28,17 +28,17 @@ def read_state(filename: str) -> Dict[str, Any]:
 
 
 def write_state(
-    state: Dict[str, Any],
+    state: dict[str, Any],
     filename: str,
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
     with open(filename, "wb") as f:
         dill.dump(state, f)
 
 
 def get_state(
     problem: Problem,
     candidate_space: CandidateSpace,
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
     state = {
         "problem": dill.dumps(problem),
         "candidate_space": dill.dumps(candidate_space),
@@ -164,8 +164,8 @@ def start_iteration(
     limit: float = np.inf,
     limit_sent: float = np.inf,
     relative_paths: bool = False,
-    excluded_model_files: List[str] = None,
-    excluded_model_hash_files: List[str] = None,
+    excluded_model_files: list[str] = None,
+    excluded_model_hash_files: list[str] = None,
 ) -> None:
     """Search for candidate models in the model space.
 
@@ -319,7 +319,7 @@ def end_iteration(
     state_dill: str,
     models_yaml: str,
     metadata_yaml: str,
-    calibrated_models_yamls: List[str] = None,
+    calibrated_models_yamls: list[str] = None,
     relative_paths: bool = False,
 ) -> None:
     """Finalize a model selection iteration.
@@ -398,7 +398,7 @@ def end_iteration(
     ),
 )
 def model_to_petab(
-    models_yamls: List[str],
+    models_yamls: list[str],
     output_path: str,
     model_id: str = None,
 ) -> None:
@@ -454,7 +454,7 @@ def model_to_petab(
     help="The directory where the PEtab files will be output. The PEtab files will be stored in a model-specific subdirectory.",
 )
 def models_to_petab(
-    models_yamls: List[str],
+    models_yamls: list[str],
     output_path_prefix: str,
 ) -> None:
     """Create a PEtab problem for each model in a PEtab Select model YAML file.
@@ -488,7 +488,7 @@ def models_to_petab(
     result_string = "\n".join(
         [
             "\t".join([model.model_id, result[PETAB_YAML]])
-            for model, result in zip(models, results)
+            for model, result in zip(models, results, strict=False)
         ]
     )
     print(result_string)
@@ -542,7 +542,7 @@ def models_to_petab(
 )
 def get_best(
     problem_yaml: str,
-    models_yamls: List[str],
+    models_yamls: list[str],
     output: str,
     state_filename: str = None,
     criterion: str = None,

petab_select/constants.py

@@ -4,7 +4,7 @@
 import sys
 from enum import Enum
 from pathlib import Path
-from typing import Dict, List, Literal, Union
+from typing import Literal
 
 # Zero-indexed column/row indices
 MODEL_ID_COLUMN = 0
@@ -21,7 +21,7 @@
 PETAB_ESTIMATE_TRUE = 1
 
 # TYPING_PATH = Union[str, Path]
-TYPE_PATH = Union[str, Path]
+TYPE_PATH = str | Path
 
 # Model space file columns
 # TODO ensure none of these occur twice in the column header (this would
@@ -100,12 +100,12 @@
 
 # Parameters can be fixed to a value, or estimated if indicated with the string
 # `ESTIMATE`.
-TYPE_PARAMETER = Union[float, int, Literal[ESTIMATE]]
-TYPE_PARAMETER_OPTIONS = List[TYPE_PARAMETER]
+TYPE_PARAMETER = float | int | Literal[ESTIMATE]
+TYPE_PARAMETER_OPTIONS = list[TYPE_PARAMETER]
 # Parameter ID -> parameter value mapping.
-TYPE_PARAMETER_DICT = Dict[str, TYPE_PARAMETER]
+TYPE_PARAMETER_DICT = dict[str, TYPE_PARAMETER]
 # Parameter ID -> multiple possible parameter values.
-TYPE_PARAMETER_OPTIONS_DICT = Dict[str, TYPE_PARAMETER_OPTIONS]
+TYPE_PARAMETER_OPTIONS_DICT = dict[str, TYPE_PARAMETER_OPTIONS]
 
 TYPE_CRITERION = float
 

petab_select/handlers.py

@@ -1,7 +1,7 @@
-from typing import Callable, Union
+from collections.abc import Callable
 
 # `float` for `np.inf`
-TYPE_LIMIT = Union[float, int]
+TYPE_LIMIT = float | int
 
 
 # TODO exclusions handler