fix: pre-commit errors

Author: cdc-as81

src/calibrationtools/async_runner.py

@@ -7,7 +7,7 @@
 
 import asyncio
 import threading
-from typing import Any, Callable
+from typing import Any, Callable, NoReturn
 
 
 def run_coroutine_from_sync(coroutine_factory: Callable[[], Any]) -> Any:
@@ -39,9 +39,12 @@ def runner() -> None:
         except BaseException as exc:  # pragma: no cover - passthrough
             error["value"] = exc
 
+    def raise_worker_error(exc: BaseException) -> NoReturn:
+        raise exc
+
     thread = threading.Thread(target=runner, daemon=True)
     thread.start()
     thread.join()
     if "value" in error:
-        raise error["value"]
+        raise_worker_error(error["value"])
     return result["value"]

src/calibrationtools/batch_generation_runner.py

@@ -7,7 +7,6 @@
 
 import asyncio
 import time
-from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass
 from functools import partial
 from typing import Any, Callable
@@ -28,7 +27,7 @@ class BatchGenerationConfig:
     stable across batched generations so the runner constructor stays small
     and execution methods operate on named fields.
 
-    Args:
+    Attributes:
         generation_particle_count (int): Number of accepted particles required
             to complete a generation.
         tolerance_values (list[float]): Acceptance tolerance for each
@@ -45,9 +44,6 @@ class BatchGenerationConfig:
             Callback that stores the finalized population on the sampler.
         reporter (SamplerReporter): Reporter used for progress and summary
             output.
-
-    Returns:
-        None
     """
 
     generation_particle_count: int
@@ -68,13 +64,10 @@ class BatchGenerationState:
     helper methods can share batch-generation state without long parameter
     lists.
 
-    Args:
+    Attributes:
         proposed_population (ParticlePopulation): Population being filled for
             the active generation.
         attempts (int): Total proposal attempts consumed so far.
-
-    Returns:
-        None
     """
 
     proposed_population: ParticlePopulation
@@ -93,32 +86,13 @@ class BatchGenerationRunner:
             across batched generations.
         run_state (SamplerRunState): Mutable bookkeeping for the active sampler
             run.
-
-    Returns:
-        None
     """
 
     def __init__(
         self,
         config: BatchGenerationConfig,
         run_state: SamplerRunState,
     ) -> None:
-        """Store the collaborators needed to execute batched generations.
-
-        This constructor keeps the runner lightweight by receiving a grouped
-        configuration object for stable dependencies and a separate run-state
-        object for per-run bookkeeping.
-
-        Args:
-            config (BatchGenerationConfig): Static settings and callbacks for
-                batched generation execution.
-            run_state (SamplerRunState): Mutable bookkeeping for the active
-                sampler run.
-
-        Returns:
-            None: This constructor does not return a value.
-        """
-
         self.config = config
         self.run_state = run_state
 
@@ -155,7 +129,9 @@ def resolve_settings(
             raise ValueError("batchsize must be positive")
         return batchsize, False
 
-    def run_generation(self, request: BatchGenerationRequest) -> GenerationStats:
+    def run_generation(
+        self, request: BatchGenerationRequest
+    ) -> GenerationStats:
         """Execute one batched generation and store its final population.
 
         This method coordinates adaptive proposal sizing, batch evaluation,
@@ -466,7 +442,10 @@ def _accept_particle_batch(
 
         considered = 0
         for err, proposed_particle in zip(errs, proposed_particles):
-            if proposed_population.size >= self.config.generation_particle_count:
+            if (
+                proposed_population.size
+                >= self.config.generation_particle_count
+            ):
                 break
 
             considered += 1

src/calibrationtools/particle_evaluator.py

@@ -20,42 +20,21 @@ class ParticleEvaluator:
     boundary.
 
     Args:
-        particles_to_params (Callable[[Particle], dict]): Function mapping a
+        particles_to_params (Callable[..., dict]): Function mapping a
             particle to model parameters.
         outputs_to_distance (Callable[..., float]): Function scoring simulated
             outputs against target data.
         target_data (Any): Observed data used for distance evaluation.
         model_runner (MRPModel): Model runner used to simulate outputs.
-
-    Returns:
-        None
     """
 
     def __init__(
         self,
-        particles_to_params: Callable[[Particle], dict],
+        particles_to_params: Callable[..., dict],
         outputs_to_distance: Callable[..., float],
         target_data: Any,
         model_runner: MRPModel,
     ) -> None:
-        """Store the collaborators needed to score one proposed particle.
-
-        This constructor keeps the particle-evaluation boundary explicit by
-        grouping the user-supplied mapping function, scoring function, target
-        data, and model runner in one object.
-
-        Args:
-            particles_to_params (Callable[[Particle], dict]): Function mapping
-                a particle to model parameters.
-            outputs_to_distance (Callable[..., float]): Function scoring
-                simulated outputs against target data.
-            target_data (Any): Observed data used for distance evaluation.
-            model_runner (MRPModel): Model runner used to simulate outputs.
-
-        Returns:
-            None
-        """
-
         self.particles_to_params = particles_to_params
         self.outputs_to_distance = outputs_to_distance
         self.target_data = target_data

src/calibrationtools/particlewise_generation_runner.py

@@ -31,7 +31,7 @@ class ParticlewiseGenerationConfig:
     execution methods can depend on named fields instead of long parameter
     lists.
 
-    Args:
+    Attributes:
         generation_particle_count (int): Number of accepted particles required
             to complete a generation.
         tolerance_values (list[float]): Acceptance tolerance for each
@@ -52,9 +52,6 @@ class ParticlewiseGenerationConfig:
             Callback that stores the finalized population on the sampler.
         reporter (SamplerReporter): Reporter used for progress and summary
             output.
-
-    Returns:
-        None
     """
 
     generation_particle_count: int
@@ -77,7 +74,7 @@ class ParticlewiseGenerationRequest:
     across sampler iterations, including executor access, timing markers, and
     keyword arguments forwarded into particle evaluation.
 
-    Args:
+    Attributes:
         generation (int): Zero-based generation index to execute.
         n_workers (int): Number of workers available to the generation.
         parallel_executor (ThreadPoolExecutor | None): Executor used for
@@ -88,9 +85,6 @@ class ParticlewiseGenerationRequest:
             generation.
         particle_kwargs (dict[str, Any]): Keyword arguments forwarded into
             particle evaluation.
-
-    Returns:
-        None
     """
 
     generation: int
@@ -109,16 +103,13 @@ class ParticlewiseGenerationState:
     and generation-specific sample method so helper methods can share that data
     without long positional argument lists.
 
-    Args:
+    Attributes:
         proposed_population (ParticlePopulation): Population being filled for
             the active generation.
         generator_slots (list[GeneratorSlot]): Proposal slots used to preserve
             deterministic ordering across execution modes.
         sample_method (Callable[[SeedSequence | None], Particle]): Proposal
             function for the active generation.
-
-    Returns:
-        None
     """
 
     proposed_population: ParticlePopulation
@@ -139,32 +130,13 @@ class ParticlewiseGenerationRunner:
             used across particlewise generations.
         run_state (SamplerRunState): Mutable bookkeeping for the active sampler
             run.
-
-    Returns:
-        None
     """
 
     def __init__(
         self,
         config: ParticlewiseGenerationConfig,
         run_state: SamplerRunState,
     ) -> None:
-        """Store the collaborators needed to execute particlewise generations.
-
-        This constructor keeps the runner lightweight by receiving a grouped
-        configuration object for stable dependencies and a separate run-state
-        object for per-run bookkeeping.
-
-        Args:
-            config (ParticlewiseGenerationConfig): Static settings and callbacks
-                for particlewise generation execution.
-            run_state (SamplerRunState): Mutable bookkeeping for the active
-                sampler run.
-
-        Returns:
-            None: This constructor does not return a value.
-        """
-
         self.config = config
         self.run_state = run_state
 
@@ -371,6 +343,10 @@ async def _collect_accepted_particles_async(
         Returns:
             tuple[list[AcceptedProposal], int]: Accepted proposals for the
                 generation and the total number of attempts consumed.
+
+        Raises:
+            BaseException: Re-raises any exception raised while collecting
+                proposals after cancelling the outstanding tasks.
         """
 
         assert request.parallel_executor is not None
@@ -569,6 +545,10 @@ def _finalize_generation(
 
         Returns:
             None: This helper does not return a value.
+
+        Raises:
+            UserWarning: Raised when a proposal slot exhausts all attempts
+                without producing an accepted particle.
         """
 
         with self.config.reporter.create_weight_progress() as progress:

src/calibrationtools/sampler.py

@@ -45,7 +45,7 @@ class ABCSampler:
         generation_particle_count (int): Number of particles to accept per generation for a complete population.
         tolerance_values (list[float]): List of tolerance values for each generation for evaluating acceptance criterion.
         priors (PriorDistribution | dict | Path): Prior distribution of the parameters being calibrated. Can be provided as a PriorDistribution object, a dictionary, or a path to a JSON file containing a valid priors schema.
-        particles_to_params (Callable[[Particle], dict]): Function to map particles to model parameters.
+        particles_to_params (Callable[..., dict]): Function to map particles to model parameters.
         outputs_to_distance (Callable[..., float]): Function to compute distance between model outputs and target data.
         target_data (Any): Observed data to compare against.
         model_runner (MRPModel): Model runner to simulate outputs given parameters.
@@ -92,7 +92,7 @@ def __init__(
         generation_particle_count: int,
         tolerance_values: list[float],
         priors: PriorDistribution | dict | Path,
-        particles_to_params: Callable[[Particle], dict],
+        particles_to_params: Callable[..., dict],
         outputs_to_distance: Callable[..., float],
         target_data: Any,
         model_runner: MRPModel,

src/calibrationtools/sampler_reporting.py

@@ -5,7 +5,6 @@
 """
 
 from dataclasses import dataclass
-from typing import Iterator
 
 from rich.console import Console
 from rich.progress import (
@@ -27,12 +26,9 @@ class ProgressHandle:
     This carrier keeps the `Progress` instance and the active task id together
     so runners can update progress through a single object.
 
-    Args:
+    Attributes:
         progress (Progress): Active Rich progress instance.
         task_id (TaskID): Task identifier within that progress instance.
-
-    Returns:
-        None
     """
 
     progress: Progress
@@ -48,30 +44,13 @@ class SamplerReporter:
     Args:
         verbose (bool): Whether progress and summary output should be visible.
         console (Console | None): Optional console override used for tests.
-
-    Returns:
-        None
     """
 
     def __init__(
         self,
         verbose: bool,
         console: Console | None = None,
     ) -> None:
-        """Store the console used for progress and summary output.
-
-        This constructor uses a hidden console when `verbose` is false so the
-        sampler can suppress all Rich output without changing execution flow.
-
-        Args:
-            verbose (bool): Whether progress and summary output should be
-                visible.
-            console (Console | None): Optional console override used for tests.
-
-        Returns:
-            None: This constructor does not return a value.
-        """
-
         self.console = (
             console if console is not None else formatting.get_console(verbose)
         )