Add better time tracking

CHANGELOG.rst

@@ -7,6 +7,29 @@ Release Notes
 0.12 Series
 ...........
 
+0.12.19 (2026-07-24)
+--------------------
+
+General:
+
+* Add support for python 3.14
+* Add execution time profiling to the ABC-SMC run, reporting pure simulation time, parallel-pipeline setup, in-between-iterations
+  time, and within it the population size calculation and distance function
+  adaptation. The timings are also returned by ``run_generation``.
+
+Visualization:
+
+* ``plot_walltime`` and ``plot_total_walltime`` now report the actual
+  per-generation walltimes and no longer include the idle time that passed
+  between a stored analysis and a later resumed run.
+
+Storage:
+
+* Store the per-generation walltime in the database (new ``wall_time`` column,
+  database version 2). Databases created with older pyABC versions must be
+  migrated via ``abc-migrate`` before they can be resumed; for such databases
+  the walltime plots fall back to the previous, end-time-based behavior.
+
 0.12.18 (2026-04-14)
 --------------------
 

pyabc/distance/base.py

@@ -89,8 +89,8 @@ def __call__(
         self,
         x: dict,
         x_0: dict,
-        t: int = None,
-        par: dict = None,
+        t: int | None = None,
+        par: dict | None = None,
     ) -> float:
         """
         Evaluate at time point t the distance of the summary statistics of
@@ -183,8 +183,8 @@ def __call__(
         self,
         x: dict,  # noqa: ARG002
         x_0: dict,  # noqa: ARG002
-        t: int = None,  # noqa: ARG002
-        par: dict = None,  # noqa: ARG002
+        t: int | None = None,  # noqa: ARG002
+        par: dict | None = None,  # noqa: ARG002
     ) -> float:
         raise AssertionError(
             f'Distance {self.__class__.__name__} should not be called.'
@@ -203,8 +203,8 @@ def __call__(
         self,
         x: dict,  # noqa: ARG002
         x_0: dict,  # noqa: ARG002
-        t: int = None,  # noqa: ARG002
-        par: dict = None,  # noqa: ARG002
+        t: int | None = None,  # noqa: ARG002
+        par: dict | None = None,  # noqa: ARG002
     ) -> float:
         return -1
 
@@ -224,16 +224,16 @@ class FunctionDistance(Distance):
         statistics x and x_0. Returns the distance between both.
     """
 
-    def __init__(self, fun):
+    def __init__(self, fun: Callable):
         super().__init__()
         self.fun = fun
 
     def __call__(
         self,
         x: dict,
         x_0: dict,
-        t: int = None,  # noqa: ARG002
-        par: dict = None,  # noqa: ARG002
+        t: int | None = None,  # noqa: ARG002
+        par: dict | None = None,  # noqa: ARG002
     ) -> float:
         return self.fun(x, x_0)
 
@@ -250,7 +250,7 @@ def get_config(self):
         return conf
 
     @staticmethod
-    def to_distance(maybe_distance: Callable | Distance) -> Distance:
+    def to_distance(maybe_distance: Callable | Distance | None) -> Distance:
         """
         Parameters
         ----------

pyabc/distance/pnorm.py

@@ -61,7 +61,7 @@ def __init__(
         self,
         p: float = 1,
         fixed_weights: dict[str, float] | dict[int, dict[str, float]] = None,
-        sumstat: Sumstat = None,
+        sumstat: Sumstat | None = None,
     ):
         super().__init__()
 
@@ -201,8 +201,8 @@ def __call__(
         self,
         x: dict,
         x_0: dict,
-        t: int = None,
-        par: dict = None,  # noqa: ARG002
+        t: int | None = None,
+        par: dict | None = None,  # noqa: ARG002
     ) -> float:
         # extract weights for given time point
         weights = self.get_weights(t=t)
@@ -308,14 +308,14 @@ class AdaptivePNormDistance(PNormDistance):
     def __init__(
         self,
         p: float = 1,
-        initial_scale_weights: dict[str, float] = None,
-        fixed_weights: dict[str, float] = None,
+        initial_scale_weights: dict[str, float] | None = None,
+        fixed_weights: dict[str, float] | None = None,
         fit_scale_ixs: EventIxs | Collection[int] | int = np.inf,
-        scale_function: Callable = None,
-        max_scale_weight_ratio: float = None,
-        scale_log_file: str = None,
+        scale_function: Callable | None = None,
+        max_scale_weight_ratio: float | None = None,
+        scale_log_file: str | None = None,
         all_particles_for_scale: bool = True,
-        sumstat: Sumstat = None,
+        sumstat: Sumstat | None = None,
     ):
         # call p-norm constructor
         super().__init__(p=p, fixed_weights=fixed_weights, sumstat=sumstat)
@@ -505,25 +505,25 @@ def __init__(
         self,
         predictor: Predictor,
         p: float = 1,
-        initial_scale_weights: dict[str, float] = None,
-        initial_info_weights: dict[str, float] = None,
-        fixed_weights: dict[str, float] = None,
+        initial_scale_weights: dict[str, float] | None = None,
+        initial_info_weights: dict[str, float] | None = None,
+        fixed_weights: dict[str, float] | None = None,
         fit_scale_ixs: EventIxs | Collection | int = np.inf,
-        fit_info_ixs: EventIxs | Collection | int = None,
+        fit_info_ixs: EventIxs | Collection | int | None = None,
         normalize_by_par: bool = True,
-        scale_function: Callable = None,
-        max_scale_weight_ratio: float = None,
-        max_info_weight_ratio: float = None,
-        scale_log_file: str = None,
-        info_log_file: str = None,
-        info_sample_log_file: str = None,
-        sumstat: Sumstat = None,
-        fd_deltas: list[float] | float = None,
-        subsetter: Subsetter = None,
+        scale_function: Callable | None = None,
+        max_scale_weight_ratio: float | None = None,
+        max_info_weight_ratio: float | None = None,
+        scale_log_file: str | None = None,
+        info_log_file: str | None = None,
+        info_sample_log_file: str | None = None,
+        sumstat: Sumstat | None = None,
+        fd_deltas: list[float] | float | None = None,
+        subsetter: Subsetter | None = None,
         all_particles_for_scale: bool = True,
         all_particles_for_prediction: bool = True,
         feature_normalization: str = WEIGHTS,
-        par_trafo: ParTrafoBase = None,
+        par_trafo: ParTrafoBase | None = None,
     ):
         """
         Parameters
@@ -595,7 +595,9 @@ def __init__(
 
         self.predictor = predictor
 
-        self.initial_info_weights: dict[str, float] = initial_info_weights
+        self.initial_info_weights: dict[str, float] | None = (
+            initial_info_weights
+        )
         self.info_weights: dict[int, np.ndarray] = {}
 
         if fit_info_ixs is None: