Removing legacy types

src/batdetect2/api.py

@@ -98,7 +98,6 @@ consult the API documentation in the code.
 """
 
 import warnings
-from typing import List, Tuple
 
 import numpy as np
 import torch
@@ -272,7 +271,7 @@ def process_spectrogram(
     samp_rate: int = TARGET_SAMPLERATE_HZ,
     model: DetectionModel = MODEL,
     config: ProcessingConfiguration | None = None,
-) -> Tuple[List[Annotation], np.ndarray]:
+) -> tuple[list[Annotation], np.ndarray]:
     """Process spectrogram with model.
 
     Parameters
@@ -314,7 +313,7 @@ def process_audio(
     model: DetectionModel = MODEL,
     config: ProcessingConfiguration | None = None,
     device: torch.device = DEVICE,
-) -> Tuple[List[Annotation], np.ndarray, torch.Tensor]:
+) -> tuple[list[Annotation], np.ndarray, torch.Tensor]:
     """Process audio array with model.
 
     Parameters
@@ -357,7 +356,7 @@ def postprocess(
     outputs: ModelOutput,
     samp_rate: int = TARGET_SAMPLERATE_HZ,
     config: ProcessingConfiguration | None = None,
-) -> Tuple[List[Annotation], np.ndarray]:
+) -> tuple[list[Annotation], np.ndarray]:
     """Postprocess model outputs.
 
     Convert model tensor outputs to predicted bounding boxes and

src/batdetect2/api_v2.py

@@ -1,6 +1,6 @@
 import json
 from pathlib import Path
-from typing import Dict, List, Sequence, Tuple
+from typing import Sequence
 
 import numpy as np
 import torch
@@ -110,7 +110,7 @@ class BatDetect2API:
         experiment_name: str | None = None,
         run_name: str | None = None,
         save_predictions: bool = True,
-    ) -> Tuple[Dict[str, float], List[List[Detection]]]:
+    ) -> tuple[dict[str, float], list[list[Detection]]]:
         return evaluate(
             self.model,
             test_annotations,
@@ -187,7 +187,7 @@ class BatDetect2API:
     def process_audio(
         self,
         audio: np.ndarray,
-    ) -> List[Detection]:
+    ) -> list[Detection]:
         spec = self.generate_spectrogram(audio)
         return self.process_spectrogram(spec)
 
@@ -195,7 +195,7 @@ class BatDetect2API:
         self,
         spec: torch.Tensor,
         start_time: float = 0,
-    ) -> List[Detection]:
+    ) -> list[Detection]:
         if spec.ndim == 4 and spec.shape[0] > 1:
             raise ValueError("Batched spectrograms not supported.")
 
@@ -214,7 +214,7 @@ class BatDetect2API:
     def process_directory(
         self,
         audio_dir: data.PathLike,
-    ) -> List[ClipDetections]:
+    ) -> list[ClipDetections]:
         files = list(get_audio_files(audio_dir))
         return self.process_files(files)
 
@@ -222,7 +222,7 @@ class BatDetect2API:
         self,
         audio_files: Sequence[data.PathLike],
         num_workers: int | None = None,
-    ) -> List[ClipDetections]:
+    ) -> list[ClipDetections]:
         return process_file_list(
             self.model,
             audio_files,
@@ -238,7 +238,7 @@ class BatDetect2API:
         clips: Sequence[data.Clip],
         batch_size: int | None = None,
         num_workers: int | None = None,
-    ) -> List[ClipDetections]:
+    ) -> list[ClipDetections]:
         return run_batch_inference(
             self.model,
             clips,
@@ -274,7 +274,7 @@ class BatDetect2API:
     def load_predictions(
         self,
         path: data.PathLike,
-    ) -> List[ClipDetections]:
+    ) -> list[ClipDetections]:
         return self.formatter.load(path)
 
     @classmethod

src/batdetect2/core/configs.py

@@ -8,7 +8,7 @@ configuration data from files, with optional support for accessing nested
 configuration sections.
 """
 
-from typing import Any, Type, TypeVar, Union, overload
+from typing import Any, Type, TypeVar, overload
 
 import yaml
 from deepmerge.merger import Merger
@@ -69,8 +69,7 @@ class BaseConfig(BaseModel):
 
 T = TypeVar("T")
 T_Model = TypeVar("T_Model", bound=BaseModel)
-
+Schema = Type[T_Model] | TypeAdapter[T]
-Schema = Union[Type[T_Model], TypeAdapter[T]]
 
 
 def get_object_field(obj: dict, current_key: str) -> Any:

src/batdetect2/data/iterators.py

@@ -1,5 +1,4 @@
 from collections.abc import Generator
-from typing import Tuple
 
 from soundevent import data
 
@@ -10,7 +9,7 @@ from batdetect2.typing.targets import TargetProtocol
 def iterate_over_sound_events(
     dataset: Dataset,
     targets: TargetProtocol,
-) -> Generator[Tuple[str | None, data.SoundEventAnnotation], None, None]:
+) -> Generator[tuple[str | None, data.SoundEventAnnotation], None, None]:
     """Iterate over sound events in a dataset.
 
     Parameters
@@ -24,7 +23,7 @@ def iterate_over_sound_events(
 
     Yields
     ------
-    Tuple[Optional[str], data.SoundEventAnnotation]
+    tuple[Optional[str], data.SoundEventAnnotation]
         A tuple containing:
         - The encoded class name (str) for the sound event, or None if it
           cannot be encoded to a specific class.

src/batdetect2/data/predictions/base.py

@@ -1,6 +1,5 @@
-from typing import Literal
-
 from pathlib import Path
+from typing import Literal
 
 from soundevent.data import PathLike
 

src/batdetect2/data/split.py

@@ -1,5 +1,3 @@
-from typing import Tuple
-
 from sklearn.model_selection import train_test_split
 
 from batdetect2.data.datasets import Dataset
@@ -15,7 +13,7 @@ def split_dataset_by_recordings(
     targets: TargetProtocol,
     train_size: float = 0.75,
     random_state: int | None = None,
-) -> Tuple[Dataset, Dataset]:
+) -> tuple[Dataset, Dataset]:
     recordings = extract_recordings_df(dataset)
 
     sound_events = extract_sound_events_df(

src/batdetect2/detector/post_process.py

@@ -1,7 +1,5 @@
 """Post-processing of the output of the model."""
 
-from typing import List, Tuple
-
 import numpy as np
 import torch
 from torch import nn
@@ -45,7 +43,7 @@ def run_nms(
     outputs: ModelOutput,
     params: NonMaximumSuppressionConfig,
     sampling_rate: np.ndarray,
-) -> Tuple[List[PredictionResults], List[np.ndarray]]:
+) -> tuple[list[PredictionResults], list[np.ndarray]]:
     """Run non-maximum suppression on the output of the model.
 
     Model outputs processed are expected to have a batch dimension.
@@ -73,8 +71,8 @@ def run_nms(
     scores, y_pos, x_pos = get_topk_scores(pred_det_nms, top_k)
 
     # loop over batch to save outputs
-    preds: List[PredictionResults] = []
+    preds: list[PredictionResults] = []
-    feats: List[np.ndarray] = []
+    feats: list[np.ndarray] = []
     for num_detection in range(pred_det_nms.shape[0]):
         # get valid indices
         inds_ord = torch.argsort(x_pos[num_detection, :])
@@ -151,7 +149,7 @@ def run_nms(
 
 def non_max_suppression(
     heat: torch.Tensor,
-    kernel_size: int | Tuple[int, int],
+    kernel_size: int | tuple[int, int],
 ):
     # kernel can be an int or list/tuple
     if isinstance(kernel_size, int):

src/batdetect2/evaluate/plots/top_class.py

@@ -4,12 +4,9 @@ from dataclasses import dataclass, field
 from typing import (
     Annotated,
     Callable,
-    Dict,
     Iterable,
-    List,
     Literal,
     Sequence,
-    Tuple,
 )
 
 import matplotlib.pyplot as plt
@@ -32,7 +29,7 @@ from batdetect2.plotting.metrics import plot_pr_curve, plot_roc_curve
 from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
 from batdetect2.typing import AudioLoader, PreprocessorProtocol, TargetProtocol
 
-TopClassPlotter = Callable[[Sequence[ClipEval]], Iterable[Tuple[str, Figure]]]
+TopClassPlotter = Callable[[Sequence[ClipEval]], Iterable[tuple[str, Figure]]]
 
 top_class_plots: Registry[TopClassPlotter, [TargetProtocol]] = Registry(
     name="top_class_plot"
@@ -73,7 +70,7 @@ class PRCurve(BasePlot):
     def __call__(
         self,
         clip_evaluations: Sequence[ClipEval],
-    ) -> Iterable[Tuple[str, Figure]]:
+    ) -> Iterable[tuple[str, Figure]]:
         y_true = []
         y_score = []
         num_positives = 0
@@ -140,7 +137,7 @@ class ROCCurve(BasePlot):
     def __call__(
         self,
         clip_evaluations: Sequence[ClipEval],
-    ) -> Iterable[Tuple[str, Figure]]:
+    ) -> Iterable[tuple[str, Figure]]:
         y_true = []
         y_score = []
 
@@ -223,7 +220,7 @@ class ConfusionMatrix(BasePlot):
     def __call__(
         self,
         clip_evaluations: Sequence[ClipEval],
-    ) -> Iterable[Tuple[str, Figure]]:
+    ) -> Iterable[tuple[str, Figure]]:
         cm, labels = compute_confusion_matrix(
             clip_evaluations,
             self.targets,
@@ -295,26 +292,26 @@ class ExampleClassificationPlot(BasePlot):
     def __call__(
         self,
         clip_evaluations: Sequence[ClipEval],
-    ) -> Iterable[Tuple[str, Figure]]:
+    ) -> Iterable[tuple[str, Figure]]:
         grouped = group_matches(clip_evaluations, threshold=self.threshold)
 
         for class_name, matches in grouped.items():
-            true_positives: List[MatchEval] = get_binned_sample(
+            true_positives: list[MatchEval] = get_binned_sample(
                 matches.true_positives,
                 n_examples=self.num_examples,
             )
 
-            false_positives: List[MatchEval] = get_binned_sample(
+            false_positives: list[MatchEval] = get_binned_sample(
                 matches.false_positives,
                 n_examples=self.num_examples,
             )
 
-            false_negatives: List[MatchEval] = random.sample(
+            false_negatives: list[MatchEval] = random.sample(
                 matches.false_negatives,
                 k=min(self.num_examples, len(matches.false_negatives)),
             )
 
-            cross_triggers: List[MatchEval] = get_binned_sample(
+            cross_triggers: list[MatchEval] = get_binned_sample(
                 matches.cross_triggers, n_examples=self.num_examples
             )
 
@@ -374,16 +371,16 @@ def build_top_class_plotter(
 
 @dataclass
 class ClassMatches:
-    false_positives: List[MatchEval] = field(default_factory=list)
+    false_positives: list[MatchEval] = field(default_factory=list)
-    false_negatives: List[MatchEval] = field(default_factory=list)
+    false_negatives: list[MatchEval] = field(default_factory=list)
-    true_positives: List[MatchEval] = field(default_factory=list)
+    true_positives: list[MatchEval] = field(default_factory=list)
-    cross_triggers: List[MatchEval] = field(default_factory=list)
+    cross_triggers: list[MatchEval] = field(default_factory=list)
 
 
 def group_matches(
     clip_evals: Sequence[ClipEval],
     threshold: float = 0.2,
-) -> Dict[str, ClassMatches]:
+) -> dict[str, ClassMatches]:
     class_examples = defaultdict(ClassMatches)
 
     for clip_eval in clip_evals:
@@ -412,7 +409,7 @@ def group_matches(
     return class_examples
 
 
-def get_binned_sample(matches: List[MatchEval], n_examples: int = 5):
+def get_binned_sample(matches: list[MatchEval], n_examples: int = 5):
     if len(matches) < n_examples:
         return matches
 

src/batdetect2/finetune/prep_data_finetune.py

@@ -2,7 +2,6 @@ import argparse
 import json
 import os
 from collections import Counter
-from typing import List, Tuple
 
 import numpy as np
 from sklearn.model_selection import StratifiedGroupKFold
@@ -12,8 +11,8 @@ from batdetect2 import types
 
 
 def print_dataset_stats(
-    data: List[types.FileAnnotation],
+    data: list[types.FileAnnotation],
-    classes_to_ignore: List[str] | None = None,
+    classes_to_ignore: list[str] | None = None,
 ) -> Counter[str]:
     print("Num files:", len(data))
     counts, _ = tu.get_class_names(data, classes_to_ignore)
@@ -22,7 +21,7 @@ def print_dataset_stats(
     return counts
 
 
-def load_file_names(file_name: str) -> List[str]:
+def load_file_names(file_name: str) -> list[str]:
     if not os.path.isfile(file_name):
         raise FileNotFoundError(f"Input file not found - {file_name}")
 
@@ -100,12 +99,12 @@ def parse_args():
 
 
 def split_data(
-    data: List[types.FileAnnotation],
+    data: list[types.FileAnnotation],
     train_file: str,
     test_file: str,
     n_splits: int = 5,
     random_state: int = 0,
-) -> Tuple[List[types.FileAnnotation], List[types.FileAnnotation]]:
+) -> tuple[list[types.FileAnnotation], list[types.FileAnnotation]]:
     if train_file != "" and test_file != "":
         # user has specifed the train / test split
         mapping = {

src/batdetect2/inference/dataset.py

@@ -1,4 +1,4 @@
-from typing import List, NamedTuple, Sequence
+from typing import NamedTuple, Sequence
 
 import torch
 from loguru import logger
@@ -29,7 +29,7 @@ class DatasetItem(NamedTuple):
 
 
 class InferenceDataset(Dataset[DatasetItem]):
-    clips: List[data.Clip]
+    clips: list[data.Clip]
 
     def __init__(
         self,
@@ -111,7 +111,7 @@ def build_inference_dataset(
     )
 
 
-def _collate_fn(batch: List[DatasetItem]) -> DatasetItem:
+def _collate_fn(batch: list[DatasetItem]) -> DatasetItem:
     max_width = max(item.spec.shape[-1] for item in batch)
     return DatasetItem(
         spec=torch.stack(

src/batdetect2/models/__init__.py

@@ -26,8 +26,6 @@ The primary entry point for building a full, ready-to-use BatDetect2 model
 is the ``build_model`` factory function exported from this module.
 """
 
-from typing import List
-
 import torch
 
 from batdetect2.models.backbones import (
@@ -142,7 +140,7 @@ class Model(torch.nn.Module):
         self.postprocessor = postprocessor
         self.targets = targets
 
-    def forward(self, wav: torch.Tensor) -> List[ClipDetectionsTensor]:
+    def forward(self, wav: torch.Tensor) -> list[ClipDetectionsTensor]:
         """Run the full detection pipeline on a waveform tensor.
 
         Converts the waveform to a spectrogram, passes it through the
@@ -157,7 +155,7 @@ class Model(torch.nn.Module):
 
         Returns
         -------
-        List[ClipDetectionsTensor]
+        list[ClipDetectionsTensor]
             One detection tensor per clip in the batch. Each tensor encodes
             the detected events (locations, class scores, sizes) for that
             clip.

src/batdetect2/models/backbones.py

@@ -23,7 +23,7 @@ output so that the output spatial dimensions always match the input spatial
 dimensions.
 """
 
-from typing import Annotated, Literal, Tuple, Union
+from typing import Annotated, Literal
 
 import torch
 import torch.nn.functional as F
@@ -58,6 +58,14 @@ from batdetect2.typing.models import (
     EncoderProtocol,
 )
 
+__all__ = [
+    "BackboneImportConfig",
+    "UNetBackbone",
+    "BackboneConfig",
+    "load_backbone_config",
+    "build_backbone",
+]
+
 
 class UNetBackboneConfig(BaseConfig):
     """Configuration for a U-Net-style encoder-decoder backbone.
@@ -110,15 +118,6 @@ class BackboneImportConfig(ImportConfig):
     name: Literal["import"] = "import"
 
 
-__all__ = [
-    "BackboneImportConfig",
-    "UNetBackbone",
-    "BackboneConfig",
-    "load_backbone_config",
-    "build_backbone",
-]
-
-
 class UNetBackbone(BackboneModel):
     """U-Net-style encoder-decoder backbone network.
 
@@ -262,7 +261,8 @@ class UNetBackbone(BackboneModel):
 
 
 BackboneConfig = Annotated[
-    Union[UNetBackboneConfig,], Field(discriminator="name")
+    UNetBackboneConfig | BackboneImportConfig,
+    Field(discriminator="name"),
 ]
 
 
@@ -292,7 +292,7 @@ def build_backbone(config: BackboneConfig | None = None) -> BackboneModel:
 def _pad_adjust(
     spec: torch.Tensor,
     factor: int = 32,
-) -> Tuple[torch.Tensor, int, int]:
+) -> tuple[torch.Tensor, int, int]:
     """Pad a tensor's height and width to be divisible by ``factor``.
 
     Adds zero-padding to the bottom and right edges of the tensor so that
@@ -308,7 +308,7 @@ def _pad_adjust(
 
     Returns
     -------
-    Tuple[torch.Tensor, int, int]
+    tuple[torch.Tensor, int, int]
         - Padded tensor.
         - Number of rows added to the height (``h_pad``).
         - Number of columns added to the width (``w_pad``).

src/batdetect2/models/blocks.py

@@ -46,7 +46,7 @@ configuration object (one of the ``*Config`` classes exported here), using
 a discriminated-union ``name`` field to dispatch to the correct class.
 """
 
-from typing import Annotated, List, Literal, Tuple, Union
+from typing import Annotated, Literal
 
 import torch
 import torch.nn.functional as F
@@ -687,7 +687,7 @@ class FreqCoordConvUpConfig(BaseConfig):
     up_mode: str = "bilinear"
     """Interpolation mode for upsampling (e.g., "nearest", "bilinear")."""
 
-    up_scale: Tuple[int, int] = (2, 2)
+    up_scale: tuple[int, int] = (2, 2)
     """Scaling factor for height and width during upsampling."""
 
 
@@ -706,22 +706,22 @@ class FreqCoordConvUpBlock(Block):
 
     Parameters
     ----------
-    in_channels : int
+    in_channels
         Number of channels in the input tensor (before upsampling).
-    out_channels : int
+    out_channels
         Number of output channels after the convolution.
-    input_height : int
+    input_height
         Height (H dimension, frequency bins) of the tensor *before* upsampling.
         Used to calculate the height for coordinate feature generation after
         upsampling.
-    kernel_size : int, default=3
+    kernel_size
         Size of the square convolutional kernel.
-    pad_size : int, default=1
+    pad_size
         Padding added before convolution.
-    up_mode : str, default="bilinear"
+    up_mode
         Interpolation mode for upsampling (e.g., "nearest", "bilinear",
         "bicubic").
-    up_scale : Tuple[int, int], default=(2, 2)
+    up_scale
         Scaling factor for height and width during upsampling
         (typically (2, 2)).
     """
@@ -734,7 +734,7 @@ class FreqCoordConvUpBlock(Block):
         kernel_size: int = 3,
         pad_size: int = 1,
         up_mode: str = "bilinear",
-        up_scale: Tuple[int, int] = (2, 2),
+        up_scale: tuple[int, int] = (2, 2),
     ):
         super().__init__()
         self.in_channels = in_channels
@@ -824,7 +824,7 @@ class StandardConvUpConfig(BaseConfig):
     up_mode: str = "bilinear"
     """Interpolation mode for upsampling (e.g., "nearest", "bilinear")."""
 
-    up_scale: Tuple[int, int] = (2, 2)
+    up_scale: tuple[int, int] = (2, 2)
     """Scaling factor for height and width during upsampling."""
 
 
@@ -839,17 +839,17 @@ class StandardConvUpBlock(Block):
 
     Parameters
     ----------
-    in_channels : int
+    in_channels
         Number of channels in the input tensor (before upsampling).
-    out_channels : int
+    out_channels
         Number of output channels after the convolution.
-    kernel_size : int, default=3
+    kernel_size
         Size of the square convolutional kernel.
-    pad_size : int, default=1
+    pad_size
         Padding added before convolution.
-    up_mode : str, default="bilinear"
+    up_mode
         Interpolation mode for upsampling (e.g., "nearest", "bilinear").
-    up_scale : Tuple[int, int], default=(2, 2)
+    up_scale
         Scaling factor for height and width during upsampling.
     """
 
@@ -860,7 +860,7 @@ class StandardConvUpBlock(Block):
         kernel_size: int = 3,
         pad_size: int = 1,
         up_mode: str = "bilinear",
-        up_scale: Tuple[int, int] = (2, 2),
+        up_scale: tuple[int, int] = (2, 2),
     ):
         super(StandardConvUpBlock, self).__init__()
         self.in_channels = in_channels
@@ -922,15 +922,14 @@ class StandardConvUpBlock(Block):
 
 
 LayerConfig = Annotated[
-    Union[
+    ConvConfig
-        ConvConfig,
+    | BlockImportConfig
-        FreqCoordConvDownConfig,
+    | FreqCoordConvDownConfig
-        StandardConvDownConfig,
+    | StandardConvDownConfig
-        FreqCoordConvUpConfig,
+    | FreqCoordConvUpConfig
-        StandardConvUpConfig,
+    | StandardConvUpConfig
-        SelfAttentionConfig,
+    | SelfAttentionConfig
-        "LayerGroupConfig",
+    | "LayerGroupConfig",
-    ],
     Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configuration models."""
@@ -952,7 +951,7 @@ class LayerGroupConfig(BaseConfig):
     """
 
     name: Literal["LayerGroup"] = "LayerGroup"
-    layers: List[LayerConfig]
+    layers: list[LayerConfig]
 
 
 class LayerGroup(nn.Module):

src/batdetect2/plotting/clip_annotations.py

@@ -1,5 +1,3 @@
-from typing import Tuple
-
 from matplotlib.axes import Axes
 from soundevent import data, plot
 
@@ -16,7 +14,7 @@ __all__ = [
 def plot_clip_annotation(
     clip_annotation: data.ClipAnnotation,
     preprocessor: PreprocessorProtocol | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     audio_dir: data.PathLike | None = None,
     add_points: bool = False,
@@ -50,7 +48,7 @@ def plot_clip_annotation(
 def plot_anchor_points(
     clip_annotation: data.ClipAnnotation,
     targets: TargetProtocol,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     size: int = 1,
     color: str = "red",

src/batdetect2/plotting/clip_predictions.py

@@ -1,4 +1,4 @@
-from typing import Iterable, Tuple
+from typing import Iterable
 
 from matplotlib.axes import Axes
 from soundevent import data
@@ -18,7 +18,7 @@ __all__ = [
 def plot_clip_prediction(
     clip_prediction: data.ClipPrediction,
     preprocessor: PreprocessorProtocol | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     audio_dir: data.PathLike | None = None,
     add_legend: bool = False,

src/batdetect2/plotting/clips.py

@@ -1,5 +1,3 @@
-from typing import Tuple
-
 import matplotlib.pyplot as plt
 import torch
 from matplotlib.axes import Axes
@@ -19,7 +17,7 @@ def plot_clip(
     clip: data.Clip,
     audio_loader: AudioLoader | None = None,
     preprocessor: PreprocessorProtocol | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     audio_dir: data.PathLike | None = None,
     spec_cmap: str = "gray",

src/batdetect2/plotting/common.py

@@ -1,7 +1,5 @@
 """General plotting utilities."""
 
-from typing import Tuple
-
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
@@ -14,7 +12,7 @@ __all__ = [
 
 def create_ax(
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     **kwargs,
 ) -> axes.Axes:
     """Create a new axis if none is provided"""
@@ -31,7 +29,7 @@ def plot_spectrogram(
     min_freq: float | None = None,
     max_freq: float | None = None,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_colorbar: bool = False,
     colorbar_kwargs: dict | None = None,
     vmin: float | None = None,

src/batdetect2/plotting/heatmaps.py

@@ -1,7 +1,5 @@
 """Plot heatmaps"""
 
-from typing import List, Tuple
-
 import numpy as np
 import torch
 from matplotlib import axes, patches
@@ -14,7 +12,7 @@ from batdetect2.plotting.common import create_ax
 def plot_detection_heatmap(
     heatmap: torch.Tensor | np.ndarray,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] = (10, 10),
+    figsize: tuple[int, int] = (10, 10),
     threshold: float | None = None,
     alpha: float = 1,
     cmap: str | Colormap = "jet",
@@ -50,8 +48,8 @@ def plot_detection_heatmap(
 def plot_classification_heatmap(
     heatmap: torch.Tensor | np.ndarray,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] = (10, 10),
+    figsize: tuple[int, int] = (10, 10),
-    class_names: List[str] | None = None,
+    class_names: list[str] | None = None,
     threshold: float | None = 0.1,
     alpha: float = 1,
     cmap: str | Colormap = "tab20",

src/batdetect2/plotting/legacy/plot.py

@@ -1,6 +1,6 @@
 """Plot functions to visualize detections and spectrograms."""
 
-from typing import List, Tuple, cast
+from typing import cast
 
 import matplotlib.ticker as tick
 import numpy as np
@@ -27,7 +27,7 @@ def spectrogram(
     spec: torch.Tensor | np.ndarray,
     config: ProcessingConfiguration | None = None,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     cmap: str = "plasma",
     start_time: float = 0,
 ) -> axes.Axes:
@@ -35,18 +35,18 @@ def spectrogram(
 
     Parameters
     ----------
-    spec (Union[torch.Tensor, np.ndarray]): Spectrogram to plot.
+    spec: Spectrogram to plot.
-    config (Optional[ProcessingConfiguration], optional): Configuration
+    config: Configuration
         used to compute the spectrogram. Defaults to None. If None,
         the default configuration will be used.
-    ax (Optional[axes.Axes], optional): Matplotlib axes object.
+    ax: Matplotlib axes object.
         Defaults to None. if provided, the spectrogram will be plotted
         on this axes.
-    figsize (Optional[Tuple[int, int]], optional): Figure size.
+    figsize: Figure size.
         Defaults to None. If `ax` is None, this will be used to create
         a new figure of the given size.
-    cmap (str, optional): Colormap to use. Defaults to "plasma".
+    cmap: Colormap to use. Defaults to "plasma".
-    start_time (float, optional): Start time of the spectrogram.
+    start_time: Start time of the spectrogram.
         Defaults to 0. This is useful if plotting a spectrogram
         of a segment of a longer audio file.
 
@@ -104,10 +104,10 @@ def spectrogram(
 
 def spectrogram_with_detections(
     spec: torch.Tensor | np.ndarray,
-    dets: List[Annotation],
+    dets: list[Annotation],
     config: ProcessingConfiguration | None = None,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     cmap: str = "plasma",
     with_names: bool = True,
     start_time: float = 0,
@@ -117,21 +117,21 @@ def spectrogram_with_detections(
 
     Parameters
     ----------
-    spec (Union[torch.Tensor, np.ndarray]): Spectrogram to plot.
+    spec: Spectrogram to plot.
-    detections (List[Annotation]): List of detections.
+    detections: List of detections.
-    config (Optional[ProcessingConfiguration], optional): Configuration
+    config: Configuration
         used to compute the spectrogram. Defaults to None. If None,
         the default configuration will be used.
-    ax (Optional[axes.Axes], optional): Matplotlib axes object.
+    ax: Matplotlib axes object.
         Defaults to None. if provided, the spectrogram will be plotted
         on this axes.
-    figsize (Optional[Tuple[int, int]], optional): Figure size.
+    figsize: Figure size.
         Defaults to None. If `ax` is None, this will be used to create
         a new figure of the given size.
-    cmap (str, optional): Colormap to use. Defaults to "plasma".
+    cmap: Colormap to use. Defaults to "plasma".
-    with_names (bool, optional): Whether to plot the name of the
+    with_names: Whether to plot the name of the
         predicted class next to the detection. Defaults to True.
-    start_time (float, optional): Start time of the spectrogram.
+    start_time: Start time of the spectrogram.
         Defaults to 0. This is useful if plotting a spectrogram
         of a segment of a longer audio file.
     **kwargs: Additional keyword arguments to pass to the
@@ -167,9 +167,9 @@ def spectrogram_with_detections(
 
 
 def detections(
-    dets: List[Annotation],
+    dets: list[Annotation],
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     with_names: bool = True,
     **kwargs,
 ) -> axes.Axes:
@@ -177,14 +177,14 @@ def detections(
 
     Parameters
     ----------
-    dets (List[Annotation]): List of detections.
+    dets: List of detections.
-    ax (Optional[axes.Axes], optional): Matplotlib axes object.
+    ax: Matplotlib axes object.
         Defaults to None. if provided, the spectrogram will be plotted
         on this axes.
-    figsize (Optional[Tuple[int, int]], optional): Figure size.
+    figsize: Figure size.
         Defaults to None. If `ax` is None, this will be used to create
         a new figure of the given size.
-    with_names (bool, optional): Whether to plot the name of the
+    with_names: Whether to plot the name of the
         predicted class next to the detection. Defaults to True.
     **kwargs: Additional keyword arguments to pass to the
         `plot.detection` function.
@@ -214,7 +214,7 @@ def detections(
 def detection(
     det: Annotation,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     linewidth: float = 1,
     edgecolor: str = "w",
     facecolor: str = "none",
@@ -224,19 +224,19 @@ def detection(
 
     Parameters
     ----------
-    det (Annotation): Detection to plot.
+    det: Detection to plot.
-    ax (Optional[axes.Axes], optional): Matplotlib axes object. Defaults
+    ax: Matplotlib axes object. Defaults
         to None. If provided, the spectrogram will be plotted on this axes.
-    figsize (Optional[Tuple[int, int]], optional): Figure size. Defaults
+    figsize: Figure size. Defaults
         to None. If `ax` is None, this will be used to create a new figure
         of the given size.
-    linewidth (float, optional): Line width of the detection.
+    linewidth: Line width of the detection.
         Defaults to 1.
-    edgecolor (str, optional): Edge color of the detection.
+    edgecolor: Edge color of the detection.
         Defaults to "w", i.e. white.
-    facecolor (str, optional): Face color of the detection.
+    facecolor: Face color of the detection.
         Defaults to "none", i.e. transparent.
-    with_name (bool, optional): Whether to plot the name of the
+    with_name: Whether to plot the name of the
         predicted class next to the detection. Defaults to True.
 
     Returns
@@ -277,22 +277,22 @@ def detection(
 
 
 def _compute_spec_extent(
-    shape: Tuple[int, int],
+    shape: tuple[int, int],
     params: SpectrogramParameters,
-) -> Tuple[float, float, float, float]:
+) -> tuple[float, float, float, float]:
     """Compute the extent of a spectrogram.
 
     Parameters
     ----------
-    shape (Tuple[int, int]): Shape of the spectrogram.
+    shape: Shape of the spectrogram.
         The first dimension is the frequency axis and the second
         dimension is the time axis.
-    params (SpectrogramParameters): Spectrogram parameters.
+    params: Spectrogram parameters.
         Should be the same as the ones used to compute the spectrogram.
 
     Returns
     -------
-    Tuple[float, float, float, float]: Extent of the spectrogram.
+    tuple[float, float, float, float]: Extent of the spectrogram.
         The first two values are the minimum and maximum time values,
         the last two values are the minimum and maximum frequency values.
     """

src/batdetect2/plotting/matches.py

@@ -1,4 +1,4 @@
-from typing import Protocol, Tuple
+from typing import Protocol
 
 from matplotlib.axes import Axes
 from soundevent import data, plot
@@ -40,7 +40,7 @@ def plot_false_positive_match(
     match: MatchProtocol,
     audio_loader: AudioLoader | None = None,
     preprocessor: PreprocessorProtocol | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     audio_dir: data.PathLike | None = None,
     duration: float = DEFAULT_DURATION,
@@ -111,7 +111,7 @@ def plot_false_negative_match(
     match: MatchProtocol,
     audio_loader: AudioLoader | None = None,
     preprocessor: PreprocessorProtocol | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     audio_dir: data.PathLike | None = None,
     duration: float = DEFAULT_DURATION,
@@ -171,7 +171,7 @@ def plot_true_positive_match(
     match: MatchProtocol,
     preprocessor: PreprocessorProtocol | None = None,
     audio_loader: AudioLoader | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     audio_dir: data.PathLike | None = None,
     duration: float = DEFAULT_DURATION,
@@ -259,7 +259,7 @@ def plot_cross_trigger_match(
     match: MatchProtocol,
     preprocessor: PreprocessorProtocol | None = None,
     audio_loader: AudioLoader | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     ax: Axes | None = None,
     audio_dir: data.PathLike | None = None,
     duration: float = DEFAULT_DURATION,

src/batdetect2/plotting/metrics.py

@@ -1,5 +1,3 @@
-from typing import Dict, Tuple
-
 import numpy as np
 import seaborn as sns
 from cycler import cycler
@@ -34,14 +32,14 @@ def plot_pr_curve(
     recall: np.ndarray,
     thresholds: np.ndarray,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
-    color: str | Tuple[float, float, float] | None = None,
+    color: str | tuple[float, float, float] | None = None,
     add_labels: bool = True,
     add_legend: bool = False,
-    marker: str | Tuple[int, int, float] | None = "o",
+    marker: str | tuple[int, int, float] | None = "o",
-    markeredgecolor: str | Tuple[float, float, float] | None = None,
+    markeredgecolor: str | tuple[float, float, float] | None = None,
     markersize: float | None = None,
-    linestyle: str | Tuple[int, ...] | None = None,
+    linestyle: str | tuple[int, ...] | None = None,
     linewidth: float | None = None,
     label: str = "PR Curve",
 ) -> axes.Axes:
@@ -76,9 +74,9 @@ def plot_pr_curve(
 
 
 def plot_pr_curves(
-    data: Dict[str, Tuple[np.ndarray, np.ndarray, np.ndarray]],
+    data: dict[str, tuple[np.ndarray, np.ndarray, np.ndarray]],
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_legend: bool = True,
     add_labels: bool = True,
     include_ap: bool = False,
@@ -119,7 +117,7 @@ def plot_threshold_precision_curve(
     threshold: np.ndarray,
     precision: np.ndarray,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_labels: bool = True,
 ):
     ax = create_ax(ax=ax, figsize=figsize)
@@ -139,9 +137,9 @@ def plot_threshold_precision_curve(
 
 
 def plot_threshold_precision_curves(
-    data: Dict[str, Tuple[np.ndarray, np.ndarray, np.ndarray]],
+    data: dict[str, tuple[np.ndarray, np.ndarray, np.ndarray]],
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_legend: bool = True,
     add_labels: bool = True,
 ):
@@ -177,7 +175,7 @@ def plot_threshold_recall_curve(
     threshold: np.ndarray,
     recall: np.ndarray,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_labels: bool = True,
 ):
     ax = create_ax(ax=ax, figsize=figsize)
@@ -197,9 +195,9 @@ def plot_threshold_recall_curve(
 
 
 def plot_threshold_recall_curves(
-    data: Dict[str, Tuple[np.ndarray, np.ndarray, np.ndarray]],
+    data: dict[str, tuple[np.ndarray, np.ndarray, np.ndarray]],
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_legend: bool = True,
     add_labels: bool = True,
 ):
@@ -236,7 +234,7 @@ def plot_roc_curve(
     tpr: np.ndarray,
     thresholds: np.ndarray,
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_labels: bool = True,
 ) -> axes.Axes:
     ax = create_ax(ax=ax, figsize=figsize)
@@ -260,9 +258,9 @@ def plot_roc_curve(
 
 
 def plot_roc_curves(
-    data: Dict[str, Tuple[np.ndarray, np.ndarray, np.ndarray]],
+    data: dict[str, tuple[np.ndarray, np.ndarray, np.ndarray]],
     ax: axes.Axes | None = None,
-    figsize: Tuple[int, int] | None = None,
+    figsize: tuple[int, int] | None = None,
     add_legend: bool = True,
     add_labels: bool = True,
 ) -> axes.Axes:

src/batdetect2/postprocess/nms.py

@@ -11,8 +11,6 @@ activations that have lower scores than a local maximum. This helps prevent
 multiple, overlapping detections originating from the same sound event.
 """
 
-from typing import Tuple
-
 import torch
 
 NMS_KERNEL_SIZE = 9
@@ -27,7 +25,7 @@ BatDetect2.
 
 def non_max_suppression(
     tensor: torch.Tensor,
-    kernel_size: int | Tuple[int, int] = NMS_KERNEL_SIZE,
+    kernel_size: int | tuple[int, int] = NMS_KERNEL_SIZE,
 ) -> torch.Tensor:
     """Apply Non-Maximum Suppression (NMS) to a tensor, typically a heatmap.
 
@@ -42,11 +40,11 @@ def non_max_suppression(
 
     Parameters
     ----------
-    tensor : torch.Tensor
+    tensor
         Input tensor, typically representing a detection heatmap. Must be a
         3D (C, H, W) or 4D (N, C, H, W) tensor as required by the underlying
         `torch.nn.functional.max_pool2d` operation.
-    kernel_size : Union[int, Tuple[int, int]], default=NMS_KERNEL_SIZE
+    kernel_size
         Size of the sliding window neighborhood used to find local maxima.
         If an integer `k` is provided, a square kernel of size `(k, k)` is used.
         If a tuple `(h, w)` is provided, a rectangular kernel of height `h`

src/batdetect2/postprocess/postprocessor.py

@@ -1,5 +1,3 @@
-from typing import List, Tuple
-
 import torch
 from loguru import logger
 
@@ -51,7 +49,7 @@ class Postprocessor(torch.nn.Module, PostprocessorProtocol):
         max_freq: float,
         top_k_per_sec: int = 200,
         detection_threshold: float = 0.01,
-        nms_kernel_size: int | Tuple[int, int] = NMS_KERNEL_SIZE,
+        nms_kernel_size: int | tuple[int, int] = NMS_KERNEL_SIZE,
     ):
         """Initialize the Postprocessor."""
         super().__init__()
@@ -66,8 +64,8 @@ class Postprocessor(torch.nn.Module, PostprocessorProtocol):
     def forward(
         self,
         output: ModelOutput,
-        start_times: List[float] | None = None,
+        start_times: list[float] | None = None,
-    ) -> List[ClipDetectionsTensor]:
+    ) -> list[ClipDetectionsTensor]:
         detection_heatmap = non_max_suppression(
             output.detection_probs.detach(),
             kernel_size=self.nms_kernel_size,

src/batdetect2/targets/rois.py

@@ -20,7 +20,7 @@ selecting and configuring the desired mapper. This module separates the
 *geometric* aspect of target definition from *semantic* classification.
 """
 
-from typing import Annotated, Literal, Tuple
+from typing import Annotated, Literal
 
 import numpy as np
 from pydantic import Field
@@ -144,7 +144,7 @@ class AnchorBBoxMapper(ROITargetMapper):
 
     Attributes
     ----------
-    dimension_names : List[str]
+    dimension_names : list[str]
         The output dimension names: `['width', 'height']`.
     anchor : Anchor
         The configured anchor point type (e.g., "center", "bottom-left").
@@ -177,7 +177,7 @@ class AnchorBBoxMapper(ROITargetMapper):
         self.time_scale = time_scale
         self.frequency_scale = frequency_scale
 
-    def encode(self, sound_event: data.SoundEvent) -> Tuple[Position, Size]:
+    def encode(self, sound_event: data.SoundEvent) -> tuple[Position, Size]:
         """Encode a SoundEvent into an anchor position and scaled box size.
 
         The position is determined by the configured anchor on the sound
@@ -190,7 +190,7 @@ class AnchorBBoxMapper(ROITargetMapper):
 
         Returns
         -------
-        Tuple[Position, Size]
+        tuple[Position, Size]
             A tuple of (anchor_position, [scaled_width, scaled_height]).
         """
         from soundevent import geometry
@@ -314,7 +314,7 @@ class PeakEnergyBBoxMapper(ROITargetMapper):
 
     Attributes
     ----------
-    dimension_names : List[str]
+    dimension_names : list[str]
         The output dimension names: `['left', 'bottom', 'right', 'top']`.
     preprocessor : PreprocessorProtocol
         The spectrogram preprocessor instance.
@@ -371,7 +371,7 @@ class PeakEnergyBBoxMapper(ROITargetMapper):
 
         Returns
         -------
-        Tuple[Position, Size]
+        tuple[Position, Size]
             A tuple of (peak_position, [l, b, r, t] distances).
         """
         from soundevent import geometry
@@ -519,14 +519,14 @@ def _build_bounding_box(
 
     Parameters
     ----------
-    pos : Tuple[float, float]
+    pos
         Reference position (time, frequency).
-    duration : float
+    duration
         The required *unscaled* duration (width) of the bounding box.
-    bandwidth : float
+    bandwidth
         The required *unscaled* frequency bandwidth (height) of the bounding
         box.
-    anchor : Anchor
+    anchor
         Specifies which part of the bounding box the input `pos` corresponds to.
 
     Returns

src/batdetect2/targets/targets.py

@@ -1,4 +1,4 @@
-from typing import Iterable, List, Tuple
+from typing import Iterable
 
 from loguru import logger
 from soundevent import data
@@ -33,20 +33,20 @@ class Targets(TargetProtocol):
 
     Attributes
     ----------
-    class_names : List[str]
+    class_names
         An ordered list of the unique names of the specific target classes
         defined in the configuration.
-    generic_class_tags : List[data.Tag]
+    generic_class_tags
         A list of `soundevent.data.Tag` objects representing the configured
         generic class category (used when no specific class matches).
-    dimension_names : List[str]
+    dimension_names
         The names of the size dimensions handled by the ROI mapper
         (e.g., ['width', 'height']).
     """
 
-    class_names: List[str]
+    class_names: list[str]
-    detection_class_tags: List[data.Tag]
+    detection_class_tags: list[data.Tag]
-    dimension_names: List[str]
+    dimension_names: list[str]
     detection_class_name: str
 
     def __init__(self, config: TargetConfig):
@@ -128,7 +128,7 @@ class Targets(TargetProtocol):
         """
         return self._encode_fn(sound_event)
 
-    def decode_class(self, class_label: str) -> List[data.Tag]:
+    def decode_class(self, class_label: str) -> list[data.Tag]:
         """Decode a predicted class name back into representative tags.
 
         Uses the configured mapping (based on `TargetClass.output_tags` or
@@ -142,7 +142,7 @@ class Targets(TargetProtocol):
 
         Returns
         -------
-        List[data.Tag]
+        list[data.Tag]
             The list of tags corresponding to the input class name.
         """
         return self._decode_fn(class_label)
@@ -161,7 +161,7 @@ class Targets(TargetProtocol):
 
         Returns
         -------
-        Tuple[float, float]
+        tuple[float, float]
             The reference position `(time, frequency)`.
 
         Raises
@@ -192,9 +192,9 @@ class Targets(TargetProtocol):
 
         Parameters
         ----------
-        pos : Tuple[float, float]
+        pos
             The reference position `(time, frequency)`.
-        dims : np.ndarray
+        dims
             NumPy array with size dimensions (e.g., from model prediction),
             matching the order in `self.dimension_names`.
 
@@ -292,7 +292,7 @@ def load_targets(
 def iterate_encoded_sound_events(
     sound_events: Iterable[data.SoundEventAnnotation],
     targets: TargetProtocol,
-) -> Iterable[Tuple[str | None, Position, Size]]:
+) -> Iterable[tuple[str | None, Position, Size]]:
     for sound_event in sound_events:
         if not targets.filter(sound_event):
             continue

src/batdetect2/train/lightning.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING
 
 import lightning as L
 import torch
@@ -97,7 +97,7 @@ class TrainingModule(L.LightningModule):
 
 def load_model_from_checkpoint(
     path: PathLike,
-) -> Tuple[Model, "BatDetect2Config"]:
+) -> tuple[Model, "BatDetect2Config"]:
     module = TrainingModule.load_from_checkpoint(path)  # type: ignore
     return module.model, module.config
 

src/batdetect2/types.py

@@ -1,6 +1,6 @@
 """Types used in the code base."""
 
-from typing import Any, List, NamedTuple, TypedDict
+from typing import Any, NamedTuple, TypedDict
 
 import numpy as np
 import torch
@@ -86,7 +86,7 @@ class ModelParameters(TypedDict):
     resize_factor: float
     """Resize factor."""
 
-    class_names: List[str]
+    class_names: list[str]
     """Class names.
 
     The model is trained to detect these classes.
@@ -158,7 +158,7 @@ class FileAnnotation(TypedDict):
     notes: str
     """Notes of file."""
 
-    annotation: List[Annotation]
+    annotation: list[Annotation]
     """List of annotations."""
 
 
@@ -168,26 +168,26 @@ class RunResults(TypedDict):
     pred_dict: FileAnnotation
     """Predictions in the format expected by the annotation tool."""
 
-    spec_feats: NotRequired[List[np.ndarray]]
+    spec_feats: NotRequired[list[np.ndarray]]
     """Spectrogram features."""
 
-    spec_feat_names: NotRequired[List[str]]
+    spec_feat_names: NotRequired[list[str]]
     """Spectrogram feature names."""
 
-    cnn_feats: NotRequired[List[np.ndarray]]
+    cnn_feats: NotRequired[list[np.ndarray]]
     """CNN features."""
 
-    cnn_feat_names: NotRequired[List[str]]
+    cnn_feat_names: NotRequired[list[str]]
     """CNN feature names."""
 
-    spec_slices: NotRequired[List[np.ndarray]]
+    spec_slices: NotRequired[list[np.ndarray]]
     """Spectrogram slices."""
 
 
 class ResultParams(TypedDict):
     """Result parameters."""
 
-    class_names: List[str]
+    class_names: list[str]
     """Class names."""
 
     spec_features: bool
@@ -234,7 +234,7 @@ class ProcessingConfiguration(TypedDict):
     scale_raw_audio: bool
     """Whether to scale the raw audio to be between -1 and 1."""
 
-    class_names: List[str]
+    class_names: list[str]
     """Names of the classes the model can detect."""
 
     detection_threshold: float
@@ -466,7 +466,7 @@ class FeatureExtractionParameters(TypedDict):
 class HeatmapParameters(TypedDict):
     """Parameters that control the heatmap generation function."""
 
-    class_names: List[str]
+    class_names: list[str]
 
     fft_win_length: float
     """Length of the FFT window in seconds."""
@@ -553,15 +553,15 @@ class AudioLoaderAnnotationGroup(TypedDict):
     individual_ids: np.ndarray
     x_inds: np.ndarray
     y_inds: np.ndarray
-    annotation: List[Annotation]
+    annotation: list[Annotation]
     annotated: bool
     class_id_file: int
     """ID of the class of the file."""
 
 
 class AudioLoaderParameters(TypedDict):
-    class_names: List[str]
+    class_names: list[str]
-    classes_to_ignore: List[str]
+    classes_to_ignore: list[str]
     target_samp_rate: int
     scale_raw_audio: bool
     fft_win_length: float

src/batdetect2/typing/evaluate.py

@@ -1,12 +1,9 @@
 from dataclasses import dataclass
 from typing import (
-    Dict,
     Generic,
     Iterable,
-    List,
     Protocol,
     Sequence,
-    Tuple,
     TypeVar,
 )
 
@@ -33,7 +30,7 @@ class MatchEvaluation:
     gt_geometry: data.Geometry | None
 
     pred_score: float
-    pred_class_scores: Dict[str, float]
+    pred_class_scores: dict[str, float]
     pred_geometry: data.Geometry | None
 
     affinity: float
@@ -66,7 +63,7 @@ class MatchEvaluation:
 @dataclass
 class ClipMatches:
     clip: data.Clip
-    matches: List[MatchEvaluation]
+    matches: list[MatchEvaluation]
 
 
 class MatcherProtocol(Protocol):
@@ -75,7 +72,7 @@ class MatcherProtocol(Protocol):
         ground_truth: Sequence[data.Geometry],
         predictions: Sequence[data.Geometry],
         scores: Sequence[float],
-    ) -> Iterable[Tuple[int | None, int | None, float]]: ...
+    ) -> Iterable[tuple[int | None, int | None, float]]: ...
 
 
 Geom = TypeVar("Geom", bound=data.Geometry, contravariant=True)
@@ -94,7 +91,7 @@ class MetricsProtocol(Protocol):
         self,
         clip_annotations: Sequence[data.ClipAnnotation],
         predictions: Sequence[Sequence[Detection]],
-    ) -> Dict[str, float]: ...
+    ) -> dict[str, float]: ...
 
 
 class PlotterProtocol(Protocol):
@@ -102,7 +99,7 @@ class PlotterProtocol(Protocol):
         self,
         clip_annotations: Sequence[data.ClipAnnotation],
         predictions: Sequence[Sequence[Detection]],
-    ) -> Iterable[Tuple[str, Figure]]: ...
+    ) -> Iterable[tuple[str, Figure]]: ...
 
 
 EvaluationOutput = TypeVar("EvaluationOutput")
@@ -119,8 +116,8 @@ class EvaluatorProtocol(Protocol, Generic[EvaluationOutput]):
 
     def compute_metrics(
         self, eval_outputs: EvaluationOutput
-    ) -> Dict[str, float]: ...
+    ) -> dict[str, float]: ...
 
     def generate_plots(
         self, eval_outputs: EvaluationOutput
-    ) -> Iterable[Tuple[str, Figure]]: ...
+    ) -> Iterable[tuple[str, Figure]]: ...

src/batdetect2/typing/train.py

@@ -1,4 +1,4 @@
-from typing import Callable, NamedTuple, Protocol, Tuple
+from typing import Callable, NamedTuple, Protocol
 
 import torch
 from soundevent import data
@@ -52,7 +52,7 @@ steps, and returns the final `Heatmaps` used for model training.
 
 Augmentation = Callable[
     [torch.Tensor, data.ClipAnnotation],
-    Tuple[torch.Tensor, data.ClipAnnotation],
+    tuple[torch.Tensor, data.ClipAnnotation],
 ]
 
 

src/batdetect2/utils/audio_utils.py

@@ -1,5 +1,4 @@
 import warnings
-from typing import Tuple
 
 import librosa
 import librosa.core.spectrum
@@ -147,7 +146,7 @@ def load_audio(
     target_samp_rate: int,
     scale: bool = False,
     max_duration: float | None = None,
-) -> Tuple[int, np.ndarray]:
+) -> tuple[int, np.ndarray]:
     """Load an audio file and resample it to the target sampling rate.
 
     The audio is also scaled to [-1, 1] and clipped to the maximum duration.

src/batdetect2/utils/detector_utils.py

@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Any, Iterator, List, Tuple
+from typing import Any, Iterator
 
 import librosa
 import numpy as np
@@ -60,7 +60,7 @@ def get_default_bd_args():
     return args
 
 
-def list_audio_files(ip_dir: str) -> List[str]:
+def list_audio_files(ip_dir: str) -> list[str]:
     """Get all audio files in directory.
 
     Args:
@@ -86,7 +86,7 @@ def load_model(
     load_weights: bool = True,
     device: torch.device | str | None = None,
     weights_only: bool = True,
-) -> Tuple[DetectionModel, ModelParameters]:
+) -> tuple[DetectionModel, ModelParameters]:
     """Load model from file.
 
     Args:
@@ -185,26 +185,28 @@ def _merge_results(predictions, spec_feats, cnn_feats, spec_slices):
 
 def get_annotations_from_preds(
     predictions: PredictionResults,
-    class_names: List[str],
+    class_names: list[str],
-) -> List[Annotation]:
+) -> list[Annotation]:
     """Get list of annotations from predictions."""
     # Get the best class prediction probability and index for each detection
     class_prob_best = predictions["class_probs"].max(0)
     class_ind_best = predictions["class_probs"].argmax(0)
 
     # Pack the results into a list of dictionaries
-    annotations: List[Annotation] = [
+    annotations: list[Annotation] = [
-        {
+        Annotation(
-            "start_time": round(float(start_time), 4),
+            {
-            "end_time": round(float(end_time), 4),
+                "start_time": round(float(start_time), 4),
-            "low_freq": int(low_freq),
+                "end_time": round(float(end_time), 4),
-            "high_freq": int(high_freq),
+                "low_freq": int(low_freq),
-            "class": str(class_names[class_index]),
+                "high_freq": int(high_freq),
-            "class_prob": round(float(class_prob), 3),
+                "class": str(class_names[class_index]),
-            "det_prob": round(float(det_prob), 3),
+                "class_prob": round(float(class_prob), 3),
-            "individual": "-1",
+                "det_prob": round(float(det_prob), 3),
-            "event": "Echolocation",
+                "individual": "-1",
-        }
+                "event": "Echolocation",
+            }
+        )
         for (
             start_time,
             end_time,
@@ -232,7 +234,7 @@ def format_single_result(
     time_exp: float,
     duration: float,
     predictions: PredictionResults,
-    class_names: List[str],
+    class_names: list[str],
 ) -> FileAnnotation:
     """Format results into the format expected by the annotation tool.
 
@@ -315,9 +317,9 @@ def convert_results(
         ]
 
     # combine into final results dictionary
-    results: RunResults = {
+    results: RunResults = RunResults({  # type: ignore
         "pred_dict": pred_dict,
-    }
+    })
 
     # add spectrogram features if they exist
     if len(spec_feats) > 0 and params["spec_features"]:
@@ -413,7 +415,7 @@ def compute_spectrogram(
     sampling_rate: int,
     params: SpectrogramParameters,
     device: torch.device,
-) -> Tuple[float, torch.Tensor]:
+) -> tuple[float, torch.Tensor]:
     """Compute a spectrogram from an audio array.
 
     Will pad the audio array so that it is evenly divisible by the
@@ -475,7 +477,7 @@ def iterate_over_chunks(
     audio: np.ndarray,
     samplerate: float,
     chunk_size: float,
-) -> Iterator[Tuple[float, np.ndarray]]:
+) -> Iterator[tuple[float, np.ndarray]]:
     """Iterate over audio in chunks of size chunk_size.
 
     Parameters
@@ -509,7 +511,7 @@ def _process_spectrogram(
     samplerate: float,
     model: DetectionModel,
     config: ProcessingConfiguration,
-) -> Tuple[PredictionResults, np.ndarray]:
+) -> tuple[PredictionResults, np.ndarray]:
     # evaluate model
     with torch.no_grad():
         outputs = model(spec)
@@ -546,7 +548,7 @@ def postprocess_model_outputs(
     outputs: ModelOutput,
     samp_rate: int,
     config: ProcessingConfiguration,
-) -> Tuple[List[Annotation], np.ndarray]:
+) -> tuple[list[Annotation], np.ndarray]:
     # run non-max suppression
     pred_nms_list, features = pp.run_nms(
         outputs,
@@ -585,7 +587,7 @@ def process_spectrogram(
     samplerate: int,
     model: DetectionModel,
     config: ProcessingConfiguration,
-) -> Tuple[List[Annotation], np.ndarray]:
+) -> tuple[list[Annotation], np.ndarray]:
     """Process a spectrogram with detection model.
 
     Will run non-maximum suppression on the output of the model.
@@ -604,9 +606,9 @@ def process_spectrogram(
 
     Returns
     -------
-    detections: List[Annotation]
+    detections
         List of detections predicted by the model.
-    features : np.ndarray
+    features
         An array of CNN features associated with each annotation.
         The array is of shape (num_detections, num_features).
         Is empty if `config["cnn_features"]` is False.
@@ -632,7 +634,7 @@ def _process_audio_array(
     model: DetectionModel,
     config: ProcessingConfiguration,
     device: torch.device,
-) -> Tuple[PredictionResults, np.ndarray, torch.Tensor]:
+) -> tuple[PredictionResults, np.ndarray, torch.Tensor]:
     # load audio file and compute spectrogram
     _, spec = compute_spectrogram(
         audio,
@@ -669,7 +671,7 @@ def process_audio_array(
     model: DetectionModel,
     config: ProcessingConfiguration,
     device: torch.device,
-) -> Tuple[List[Annotation], np.ndarray, torch.Tensor]:
+) -> tuple[list[Annotation], np.ndarray, torch.Tensor]:
     """Process a single audio array with detection model.
 
     Parameters
@@ -689,7 +691,7 @@ def process_audio_array(
 
     Returns
     -------
-    annotations : List[Annotation]
+    annotations : list[Annotation]
         List of annotations predicted by the model.
     features : np.ndarray
         Array of CNN features associated with each annotation.

tests/test_data/test_annotations/test_batdetect2.py

@@ -1,7 +1,7 @@
 import json
 import uuid
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any
 
 import pytest
 from soundevent import data
@@ -20,11 +20,11 @@ def create_legacy_file_annotation(
     duration: float = 5.0,
     time_exp: float = 1.0,
     class_name: str = "Myotis",
-    annotations: Optional[List[Dict[str, Any]]] = None,
+    annotations: list[dict[str, Any]] | None = None,
     annotated: bool = True,
     issues: bool = False,
     notes: str = "",
-) -> Dict[str, Any]:
+) -> dict[str, Any]:
     if annotations is None:
         annotations = [
             {
@@ -61,7 +61,7 @@ def create_legacy_file_annotation(
 @pytest.fixture
 def batdetect2_files_test_setup(
     tmp_path: Path, wav_factory
-) -> Tuple[Path, Path, List[Dict[str, Any]]]:
+) -> tuple[Path, Path, list[dict[str, Any]]]:
     """Sets up a directory structure for batdetect2 files format tests."""
     audio_dir = tmp_path / "audio"
     audio_dir.mkdir()
@@ -143,7 +143,7 @@ def batdetect2_files_test_setup(
 @pytest.fixture
 def batdetect2_merged_test_setup(
     tmp_path: Path, batdetect2_files_test_setup
-) -> Tuple[Path, Path, List[Dict[str, Any]]]:
+) -> tuple[Path, Path, list[dict[str, Any]]]:
     """Sets up a directory structure for batdetect2 merged file format tests."""
     audio_dir, _, files_data = batdetect2_files_test_setup
     merged_anns_path = tmp_path / "merged_anns.json"