Run lint fixes

src/batdetect2/api.py

@@ -98,7 +98,7 @@ consult the API documentation in the code.
 """
 
 import warnings
-from typing import List, Optional, Tuple
+from typing import List, Tuple
 
 import numpy as np
 import torch

src/batdetect2/api_v2.py

@@ -1,6 +1,6 @@
 import json
 from pathlib import Path
-from typing import Dict, List, Optional, Sequence, Tuple
+from typing import Dict, List, Sequence, Tuple
 
 import numpy as np
 import torch

src/batdetect2/audio/clips.py

@@ -1,4 +1,4 @@
-from typing import Annotated, List, Literal, Optional, Union
+from typing import Annotated, List, Literal
 
 import numpy as np
 from loguru import logger

src/batdetect2/audio/loader.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 import numpy as np
 from numpy.typing import DTypeLike

src/batdetect2/cli/data.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import Optional
 
 import click
 

src/batdetect2/cli/evaluate.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import Optional
 
 import click
 from loguru import logger

src/batdetect2/cli/train.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import Optional
 
 import click
 from loguru import logger

src/batdetect2/config.py

@@ -1,4 +1,4 @@
-from typing import Literal, Optional
+from typing import Literal
 
 from pydantic import Field
 from soundevent.data import PathLike

src/batdetect2/core/arrays.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 import numpy as np
 import torch

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, Optional, Type, TypeVar
+from typing import Any, Type, TypeVar
 
 import yaml
 from deepmerge.merger import Merger

src/batdetect2/data/annotations/__init__.py

@@ -13,7 +13,7 @@ format-specific loading function to retrieve the annotations as a standard
 `soundevent.data.AnnotationSet`.
 """
 
-from typing import Annotated, Optional, Union
+from typing import Annotated
 
 from pydantic import Field
 from soundevent import data

src/batdetect2/data/annotations/aoef.py

@@ -12,7 +12,7 @@ that meet specific status criteria (e.g., completed, verified, without issues).
 """
 
 from pathlib import Path
-from typing import Literal, Optional
+from typing import Literal
 from uuid import uuid5
 
 from pydantic import Field

src/batdetect2/data/conditions.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable
-from typing import Annotated, List, Literal, Sequence, Union
+from typing import Annotated, List, Literal, Sequence
 
 from pydantic import Field
 from soundevent import data

src/batdetect2/data/datasets.py

@@ -19,7 +19,7 @@ The core components are:
 """
 
 from pathlib import Path
-from typing import List, Optional, Sequence
+from typing import List, Sequence
 
 from loguru import logger
 from pydantic import Field

src/batdetect2/data/iterators.py

@@ -1,5 +1,5 @@
 from collections.abc import Generator
-from typing import Optional, Tuple
+from typing import Tuple
 
 from soundevent import data
 

src/batdetect2/data/predictions/__init__.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Optional, Union
+from typing import Annotated
 
 from pydantic import Field
 from soundevent.data import PathLike
@@ -24,7 +24,10 @@ __all__ = [
 
 
 OutputFormatConfig = Annotated[
-    BatDetect2OutputConfig | ParquetOutputConfig | SoundEventOutputConfig | RawOutputConfig,
+    BatDetect2OutputConfig
+    | ParquetOutputConfig
+    | SoundEventOutputConfig
+    | RawOutputConfig,
     Field(discriminator="name"),
 ]
 

src/batdetect2/data/predictions/batdetect2.py

@@ -1,6 +1,6 @@
 import json
 from pathlib import Path
-from typing import List, Literal, Optional, Sequence, TypedDict
+from typing import List, Literal, Sequence, TypedDict
 
 import numpy as np
 from soundevent import data

src/batdetect2/data/predictions/parquet.py

@@ -1,6 +1,5 @@
-import json
 from pathlib import Path
-from typing import List, Literal, Optional, Sequence
+from typing import List, Literal, Sequence
 from uuid import UUID
 
 import numpy as np

src/batdetect2/data/predictions/raw.py

@@ -1,6 +1,6 @@
 from collections import defaultdict
 from pathlib import Path
-from typing import List, Literal, Optional, Sequence
+from typing import List, Literal, Sequence
 from uuid import UUID, uuid4
 
 import numpy as np

src/batdetect2/data/predictions/soundevent.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import List, Literal, Optional, Sequence
+from typing import List, Literal, Sequence
 
 import numpy as np
 from soundevent import data, io

src/batdetect2/data/split.py

@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Tuple
 
 from sklearn.model_selection import train_test_split
 

src/batdetect2/data/transforms.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable
-from typing import Annotated, Dict, List, Literal, Optional, Union
+from typing import Annotated, Dict, List, Literal
 
 from pydantic import Field
 from soundevent import data

src/batdetect2/detector/compute_features.py

@@ -1,6 +1,6 @@
 """Functions to compute features from predictions."""
 
-from typing import Dict, List, Optional
+from typing import Dict, List
 
 import numpy as np
 

src/batdetect2/detector/parameters.py

@@ -1,7 +1,7 @@
 import datetime
 import os
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import List
 
 from pydantic import BaseModel, Field, computed_field
 

src/batdetect2/detector/post_process.py

@@ -1,6 +1,6 @@
 """Post-processing of the output of the model."""
 
-from typing import List, Tuple, Union
+from typing import List, Tuple
 
 import numpy as np
 import torch

src/batdetect2/evaluate/affinity.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Literal, Optional, Union
+from typing import Annotated, Literal
 
 from pydantic import Field
 from soundevent import data

src/batdetect2/evaluate/config.py

@@ -1,4 +1,4 @@
-from typing import List, Optional
+from typing import List
 
 from pydantic import Field
 from soundevent import data

src/batdetect2/evaluate/dataset.py

@@ -1,4 +1,4 @@
-from typing import List, NamedTuple, Optional, Sequence
+from typing import List, NamedTuple, Sequence
 
 import torch
 from loguru import logger

src/batdetect2/evaluate/evaluator.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, Iterable, List, Sequence, Tuple
 
 from matplotlib.figure import Figure
 from soundevent import data
@@ -36,7 +36,7 @@ class Evaluator:
     def compute_metrics(self, eval_outputs: List[Any]) -> Dict[str, float]:
         results = {}
 
-        for task, outputs in zip(self.tasks, eval_outputs):
+        for task, outputs in zip(self.tasks, eval_outputs, strict=False):
             results.update(task.compute_metrics(outputs))
 
         return results
@@ -45,7 +45,7 @@ class Evaluator:
         self,
         eval_outputs: List[Any],
     ) -> Iterable[Tuple[str, Figure]]:
-        for task, outputs in zip(self.tasks, eval_outputs):
+        for task, outputs in zip(self.tasks, eval_outputs, strict=False):
             for name, fig in task.generate_plots(outputs):
                 yield name, fig
 

src/batdetect2/evaluate/legacy/evaluate_models.py

@@ -357,7 +357,7 @@ def train_rf_model(x_train, y_train, num_classes, seed=2001):
     clf = RandomForestClassifier(random_state=seed, n_jobs=-1)
     clf.fit(x_train, y_train)
     y_pred = clf.predict(x_train)
-    tr_acc = (y_pred == y_train).mean()
+    (y_pred == y_train).mean()
     # print('Train acc', round(tr_acc*100, 2))
     return clf, un_train_class
 
@@ -450,7 +450,7 @@ def add_root_path_back(data_sets, ann_path, wav_path):
 
 
 def check_classes_in_train(gt_list, class_names):
-    num_gt_total = np.sum([gg["start_times"].shape[0] for gg in gt_list])
+    np.sum([gg["start_times"].shape[0] for gg in gt_list])
     num_with_no_class = 0
     for gt in gt_list:
         for cc in gt["class_names"]:
@@ -569,7 +569,7 @@ if __name__ == "__main__":
     num_with_no_class = check_classes_in_train(gt_test, class_names)
     if total_num_calls == num_with_no_class:
         print("Classes from the test set are not in the train set.")
-        assert False
+        raise AssertionError()
 
     # only need the train data if evaluating Sonobat or Tadarida
     if args["sb_ip_dir"] != "" or args["td_ip_dir"] != "":
@@ -743,7 +743,7 @@ if __name__ == "__main__":
         # check if the class names are the same
         if params_bd["class_names"] != class_names:
             print("Warning: Class names are not the same as the trained model")
-            assert False
+            raise AssertionError()
 
         run_config = {
             **bd_args,
@@ -753,7 +753,7 @@ if __name__ == "__main__":
 
         preds_bd = []
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        for ii, gg in enumerate(gt_test):
+        for gg in gt_test:
             pred = du.process_file(
                 gg["file_path"],
                 model,

src/batdetect2/evaluate/lightning.py

@@ -47,7 +47,7 @@ class EvaluationModule(LightningModule):
                 ),
             )
             for clip_annotation, clip_dets in zip(
-                clip_annotations, clip_detections
+                clip_annotations, clip_detections, strict=False
             )
         ]
 

src/batdetect2/evaluate/match.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable, Iterable, Mapping
-from typing import Annotated, List, Literal, Optional, Sequence, Tuple, Union
+from typing import Annotated, List, Literal, Sequence, Tuple
 
 import numpy as np
 from pydantic import Field
@@ -94,7 +94,7 @@ def match(
                 class_name: score
                 for class_name, score in zip(
                     targets.class_names,
-                    prediction.class_scores,
+                    prediction.class_scores, strict=False,
                 )
             }
             if prediction is not None
@@ -563,7 +563,7 @@ def select_optimal_matches(
         maximize=True,
     )
 
-    for gt_idx, pred_idx in zip(assiged_rows, assigned_columns):
+    for gt_idx, pred_idx in zip(assiged_rows, assigned_columns, strict=False):
         affinity = float(affinity_matrix[gt_idx, pred_idx])
 
         if affinity <= affinity_threshold:

src/batdetect2/evaluate/metrics/classification.py

@@ -7,10 +7,8 @@ from typing import (
     List,
     Literal,
     Mapping,
-    Optional,
     Sequence,
     Tuple,
-    Union,
 )
 
 import numpy as np

src/batdetect2/evaluate/metrics/clip_classification.py

@@ -1,6 +1,6 @@
 from collections import defaultdict
 from dataclasses import dataclass
-from typing import Annotated, Callable, Dict, Literal, Sequence, Set, Union
+from typing import Annotated, Callable, Dict, Literal, Sequence, Set
 
 import numpy as np
 from pydantic import Field

src/batdetect2/evaluate/metrics/clip_detection.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Annotated, Callable, Dict, Literal, Sequence, Union
+from typing import Annotated, Callable, Dict, Literal, Sequence
 
 import numpy as np
 from pydantic import Field

src/batdetect2/evaluate/metrics/common.py

@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Tuple
 
 import numpy as np
 

src/batdetect2/evaluate/metrics/detection.py

@@ -5,9 +5,7 @@ from typing import (
     Dict,
     List,
     Literal,
-    Optional,
     Sequence,
-    Union,
 )
 
 import numpy as np

src/batdetect2/evaluate/metrics/top_class.py

@@ -1,4 +1,3 @@
-from collections import defaultdict
 from dataclasses import dataclass
 from typing import (
     Annotated,
@@ -6,9 +5,7 @@ from typing import (
     Dict,
     List,
     Literal,
-    Optional,
     Sequence,
-    Union,
 )
 
 import numpy as np

src/batdetect2/evaluate/plots/base.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure

src/batdetect2/evaluate/plots/classification.py

@@ -3,10 +3,8 @@ from typing import (
     Callable,
     Iterable,
     Literal,
-    Optional,
     Sequence,
     Tuple,
-    Union,
 )
 
 import matplotlib.pyplot as plt

src/batdetect2/evaluate/plots/clip_classification.py

@@ -3,10 +3,8 @@ from typing import (
     Callable,
     Iterable,
     Literal,
-    Optional,
     Sequence,
     Tuple,
-    Union,
 )
 
 import matplotlib.pyplot as plt

src/batdetect2/evaluate/plots/clip_detection.py

@@ -3,10 +3,8 @@ from typing import (
     Callable,
     Iterable,
     Literal,
-    Optional,
     Sequence,
     Tuple,
-    Union,
 )
 
 import pandas as pd

src/batdetect2/evaluate/plots/detection.py

@@ -4,10 +4,8 @@ from typing import (
     Callable,
     Iterable,
     Literal,
-    Optional,
     Sequence,
     Tuple,
-    Union,
 )
 
 import matplotlib.pyplot as plt

src/batdetect2/evaluate/plots/top_class.py

@@ -8,10 +8,8 @@ from typing import (
     Iterable,
     List,
     Literal,
-    Optional,
     Sequence,
     Tuple,
-    Union,
 )
 
 import matplotlib.pyplot as plt
@@ -405,7 +403,7 @@ def get_binned_sample(matches: List[MatchEval], n_examples: int = 5):
         return matches
 
     indices, pred_scores = zip(
-        *[(index, match.score) for index, match in enumerate(matches)]
+        *[(index, match.score) for index, match in enumerate(matches)], strict=False
     )
 
     bins = pd.qcut(pred_scores, q=n_examples, labels=False, duplicates="drop")

src/batdetect2/evaluate/tables.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Callable, Literal, Sequence, Union
+from typing import Annotated, Callable, Literal, Sequence
 
 import pandas as pd
 from pydantic import Field

src/batdetect2/evaluate/tasks/__init__.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Optional, Sequence, Union
+from typing import Annotated, Optional, Sequence
 
 from pydantic import Field
 from soundevent import data
@@ -26,7 +26,11 @@ __all__ = [
 
 
 TaskConfig = Annotated[
-    ClassificationTaskConfig | DetectionTaskConfig | ClipDetectionTaskConfig | ClipClassificationTaskConfig | TopClassDetectionTaskConfig,
+    ClassificationTaskConfig
+    | DetectionTaskConfig
+    | ClipDetectionTaskConfig
+    | ClipClassificationTaskConfig
+    | TopClassDetectionTaskConfig,
     Field(discriminator="name"),
 ]
 

src/batdetect2/evaluate/tasks/base.py

@@ -4,7 +4,6 @@ from typing import (
     Generic,
     Iterable,
     List,
-    Optional,
     Sequence,
     Tuple,
     TypeVar,
@@ -101,7 +100,7 @@ class BaseTask(EvaluatorProtocol, Generic[T_Output]):
     ) -> List[T_Output]:
         return [
             self.evaluate_clip(clip_annotation, preds)
-            for clip_annotation, preds in zip(clip_annotations, predictions)
+            for clip_annotation, preds in zip(clip_annotations, predictions, strict=False)
         ]
 
     def evaluate_clip(

src/batdetect2/finetune/finetune_model.py

@@ -1,7 +1,7 @@
 import argparse
 import os
 import warnings
-from typing import List, Optional
+from typing import List
 
 import torch
 import torch.utils.data
@@ -88,7 +88,7 @@ def select_device(warn=True) -> str:
     if warn:
         warnings.warn(
             "No GPU available, using the CPU instead. Please consider using a GPU "
-            "to speed up training."
+            "to speed up training.", stacklevel=2
         )
 
     return "cpu"

src/batdetect2/finetune/prep_data_finetune.py

@@ -2,7 +2,7 @@ import argparse
 import json
 import os
 from collections import Counter
-from typing import List, Optional, Tuple
+from typing import List, Tuple
 
 import numpy as np
 from sklearn.model_selection import StratifiedGroupKFold
@@ -162,7 +162,7 @@ def main():
         # change the names of the classes
         ip_names = args.input_class_names.split(";")
         op_names = args.output_class_names.split(";")
-        name_dict = dict(zip(ip_names, op_names))
+        name_dict = dict(zip(ip_names, op_names, strict=False))
 
     # load annotations
     data_all = tu.load_set_of_anns(

src/batdetect2/inference/dataset.py

@@ -1,4 +1,4 @@
-from typing import List, NamedTuple, Optional, Sequence
+from typing import List, NamedTuple, Sequence
 
 import torch
 from loguru import logger

src/batdetect2/inference/lightning.py

@@ -39,7 +39,7 @@ class InferenceModule(LightningModule):
                     targets=self.model.targets,
                 ),
             )
-            for clip, clip_dets in zip(clips, clip_detections)
+            for clip, clip_dets in zip(clips, clip_detections, strict=False)
         ]
 
         return predictions

src/batdetect2/logging.py

@@ -9,10 +9,8 @@ from typing import (
     Dict,
     Generic,
     Literal,
-    Optional,
     Protocol,
     TypeVar,
-    Union,
 )
 
 import numpy as np

src/batdetect2/models/__init__.py

@@ -26,7 +26,7 @@ for creating a standard BatDetect2 model instance is the `build_model` function
 provided here.
 """
 
-from typing import List, Optional
+from typing import List
 
 import torch
 

src/batdetect2/models/bottleneck.py

@@ -14,7 +14,7 @@ A factory function `build_bottleneck` constructs the appropriate bottleneck
 module based on the provided configuration.
 """
 
-from typing import Annotated, List, Optional, Union
+from typing import Annotated, List
 
 import torch
 from pydantic import Field

src/batdetect2/models/config.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 from soundevent import data
 

src/batdetect2/models/decoder.py

@@ -18,7 +18,7 @@ The `Decoder`'s `forward` method is designed to accept skip connection tensors
 at each stage.
 """
 
-from typing import Annotated, List, Optional, Union
+from typing import Annotated, List
 
 import torch
 from pydantic import Field
@@ -182,7 +182,7 @@ class Decoder(nn.Module):
                 f"but got {len(residuals)}."
             )
 
-        for layer, res in zip(self.layers, residuals[::-1]):
+        for layer, res in zip(self.layers, residuals[::-1], strict=False):
             x = layer(x + res)
 
         return x

src/batdetect2/models/detectors.py

@@ -14,7 +14,6 @@ logic for preprocessing inputs and postprocessing/decoding outputs resides in
 the `batdetect2.preprocess` and `batdetect2.postprocess` packages, respectively.
 """
 
-from typing import Optional
 
 import torch
 from loguru import logger

src/batdetect2/models/encoder.py

@@ -20,7 +20,7 @@ bottleneck output. A default configuration (`DEFAULT_ENCODER_CONFIG`) is also
 provided.
 """
 
-from typing import Annotated, List, Optional, Union
+from typing import Annotated, List
 
 import torch
 from pydantic import Field

src/batdetect2/plotting/clip_annotations.py

@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Tuple
 
 from matplotlib.axes import Axes
 from soundevent import data, plot
@@ -68,6 +68,6 @@ def plot_anchor_points(
         position, _ = targets.encode_roi(sound_event)
         positions.append(position)
 
-    X, Y = zip(*positions)
+    X, Y = zip(*positions, strict=False)
     ax.scatter(X, Y, s=size, c=color, marker=marker, alpha=alpha)
     return ax

src/batdetect2/plotting/clip_predictions.py

@@ -1,4 +1,4 @@
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, Tuple
 
 from matplotlib.axes import Axes
 from soundevent import data

src/batdetect2/plotting/clips.py

@@ -1,4 +1,4 @@
-from typing import Optional, Tuple
+from typing import Tuple
 
 import matplotlib.pyplot as plt
 import torch

src/batdetect2/plotting/common.py

@@ -1,6 +1,6 @@
 """General plotting utilities."""
 
-from typing import Optional, Tuple, Union
+from typing import Tuple
 
 import matplotlib.pyplot as plt
 import numpy as np

src/batdetect2/plotting/detections.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 from matplotlib import axes, patches
 from soundevent.plot import plot_geometry

src/batdetect2/plotting/gallery.py

@@ -1,4 +1,4 @@
-from typing import Optional, Sequence
+from typing import Sequence
 
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
@@ -36,7 +36,7 @@ def plot_match_gallery(
         sharey="row",
     )
 
-    for tp_ax, tp_match in zip(axes[0], true_positives[:n_examples]):
+    for tp_ax, tp_match in zip(axes[0], true_positives[:n_examples], strict=False):
         try:
             plot_true_positive_match(
                 tp_match,
@@ -53,7 +53,7 @@ def plot_match_gallery(
         ):
             continue
 
-    for fp_ax, fp_match in zip(axes[1], false_positives[:n_examples]):
+    for fp_ax, fp_match in zip(axes[1], false_positives[:n_examples], strict=False):
         try:
             plot_false_positive_match(
                 fp_match,
@@ -70,7 +70,7 @@ def plot_match_gallery(
         ):
             continue
 
-    for fn_ax, fn_match in zip(axes[2], false_negatives[:n_examples]):
+    for fn_ax, fn_match in zip(axes[2], false_negatives[:n_examples], strict=False):
         try:
             plot_false_negative_match(
                 fn_match,
@@ -87,7 +87,7 @@ def plot_match_gallery(
         ):
             continue
 
-    for ct_ax, ct_match in zip(axes[3], cross_triggers[:n_examples]):
+    for ct_ax, ct_match in zip(axes[3], cross_triggers[:n_examples], strict=False):
         try:
             plot_cross_trigger_match(
                 ct_match,

src/batdetect2/plotting/heatmaps.py

@@ -1,6 +1,6 @@
 """Plot heatmaps"""
 
-from typing import List, Optional, Tuple, Union
+from typing import List, Tuple
 
 import numpy as np
 import torch

src/batdetect2/plotting/legacy/plot.py

@@ -1,6 +1,6 @@
 """Plot functions to visualize detections and spectrograms."""
 
-from typing import List, Optional, Tuple, Union, cast
+from typing import List, Tuple, cast
 
 import matplotlib.ticker as tick
 import numpy as np

src/batdetect2/plotting/matches.py

@@ -1,4 +1,4 @@
-from typing import Optional, Protocol, Tuple, Union
+from typing import Protocol, Tuple
 
 from matplotlib.axes import Axes
 from soundevent import data, plot

src/batdetect2/plotting/metrics.py

@@ -1,4 +1,4 @@
-from typing import Dict, Optional, Tuple, Union
+from typing import Dict, Tuple
 
 import numpy as np
 import seaborn as sns

src/batdetect2/postprocess/config.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 from pydantic import Field
 from soundevent import data

src/batdetect2/postprocess/decoding.py

@@ -1,6 +1,6 @@
 """Decodes extracted detection data into standard soundevent predictions."""
 
-from typing import List, Optional
+from typing import List
 
 import numpy as np
 from soundevent import data
@@ -39,7 +39,7 @@ def to_raw_predictions(
         detections.times,
         detections.frequencies,
         detections.sizes,
-        detections.features,
+        detections.features, strict=False,
     ):
         highest_scoring_class = targets.class_names[class_scores.argmax()]
 

src/batdetect2/postprocess/extraction.py

@@ -15,7 +15,7 @@ precise time-frequency location of each detection. The final output aggregates
 all extracted information into a structured `xarray.Dataset`.
 """
 
-from typing import List, Optional
+from typing import List
 
 import torch
 

src/batdetect2/postprocess/nms.py

@@ -11,7 +11,7 @@ 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, Union
+from typing import Tuple
 
 import torch
 

src/batdetect2/postprocess/postprocessor.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple, Union
+from typing import List, Tuple
 
 import torch
 from loguru import logger

src/batdetect2/postprocess/remapping.py

@@ -12,7 +12,7 @@ classification probability maps, size prediction maps, and potentially
 intermediate features.
 """
 
-from typing import Dict, List, Optional, Union
+from typing import Dict, List
 
 import numpy as np
 import torch

src/batdetect2/preprocess/audio.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Literal, Union
+from typing import Annotated, Literal
 
 import torch
 from pydantic import Field

src/batdetect2/preprocess/config.py

@@ -1,4 +1,4 @@
-from typing import List, Optional
+from typing import List
 
 from pydantic import Field
 from soundevent.data import PathLike

src/batdetect2/preprocess/preprocessor.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 import torch
 from loguru import logger

src/batdetect2/preprocess/spectrogram.py

@@ -1,6 +1,6 @@
 """Computes spectrograms from audio waveforms with configurable parameters."""
 
-from typing import Annotated, Callable, Literal, Optional, Union
+from typing import Annotated, Callable, Literal
 
 import numpy as np
 import torch

src/batdetect2/targets/classes.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List
 
 from pydantic import Field, PrivateAttr, computed_field, model_validator
 from soundevent import data

src/batdetect2/targets/config.py

@@ -1,5 +1,5 @@
 from collections import Counter
-from typing import List, Optional
+from typing import List
 
 from pydantic import Field, field_validator
 from soundevent import data

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, Optional, Tuple, Union
+from typing import Annotated, Literal, Tuple
 
 import numpy as np
 from pydantic import Field

src/batdetect2/targets/targets.py

@@ -1,4 +1,4 @@
-from typing import Iterable, List, Optional, Tuple
+from typing import Iterable, List, Tuple
 
 from loguru import logger
 from soundevent import data

src/batdetect2/train/augmentations.py

@@ -2,7 +2,7 @@
 
 import warnings
 from collections.abc import Sequence
-from typing import Annotated, Callable, List, Literal, Optional, Tuple, Union
+from typing import Annotated, Callable, List, Literal, Tuple
 
 import numpy as np
 import torch
@@ -394,7 +394,7 @@ class MaskTime(torch.nn.Module):
             size=num_masks,
         )
         masks = [
-            (start, start + size) for start, size in zip(mask_start, mask_size)
+            (start, start + size) for start, size in zip(mask_start, mask_size, strict=False)
         ]
         return mask_time(spec, masks), clip_annotation
 
@@ -460,7 +460,7 @@ class MaskFrequency(torch.nn.Module):
             size=num_masks,
         )
         masks = [
-            (start, start + size) for start, size in zip(mask_start, mask_size)
+            (start, start + size) for start, size in zip(mask_start, mask_size, strict=False)
         ]
         return mask_frequency(spec, masks), clip_annotation
 

src/batdetect2/train/callbacks.py

@@ -107,7 +107,7 @@ class ValidationMetrics(Callback):
                 ),
             )
             for clip_annotation, clip_dets in zip(
-                clip_annotations, clip_detections
+                clip_annotations, clip_detections, strict=False
             )
         ]
 

src/batdetect2/train/checkpoints.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import Optional
 
 from lightning.pytorch.callbacks import Callback, ModelCheckpoint
 

src/batdetect2/train/config.py

@@ -1,4 +1,3 @@
-from typing import Optional, Union
 
 from pydantic import Field
 from soundevent import data

src/batdetect2/train/dataset.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Sequence
+from typing import List, Sequence
 
 import torch
 from loguru import logger

src/batdetect2/train/labels.py

@@ -6,7 +6,6 @@ the specific multi-channel heatmap formats required by the neural network.
 """
 
 from functools import partial
-from typing import Optional
 
 import numpy as np
 import torch

src/batdetect2/train/legacy/train.py

@@ -1,12 +1,12 @@
-from typing import NamedTuple, Optional
+from typing import NamedTuple
 
 import torch
+from batdetect2.models.types import DetectionModel
 from soundevent import data
 from torch.optim import Adam
 from torch.optim.lr_scheduler import CosineAnnealingLR
 from torch.utils.data import DataLoader
 
-from batdetect2.models.types import DetectionModel
 from batdetect2.train.dataset import LabeledDataset
 
 
@@ -26,7 +26,7 @@ def train_loop(
     learning_rate: float = 1e-4,
 ):
     train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-    validation_loader = DataLoader(validation_dataset, batch_size=32)
+    DataLoader(validation_dataset, batch_size=32)
 
     model.to(device)
 
@@ -36,8 +36,8 @@ def train_loop(
         num_epochs * len(train_loader),
     )
 
-    for epoch in range(num_epochs):
+    for _epoch in range(num_epochs):
-        train_loss = train_single_epoch(
+        train_single_epoch(
             model,
             train_loader,
             optimizer,
@@ -60,9 +60,9 @@ def train_single_epoch(
         optimizer.zero_grad()
 
         spec = batch.spec.to(device)
-        detection_heatmap = batch.detection_heatmap.to(device)
+        batch.detection_heatmap.to(device)
-        class_heatmap = batch.class_heatmap.to(device)
+        batch.class_heatmap.to(device)
-        size_heatmap = batch.size_heatmap.to(device)
+        batch.size_heatmap.to(device)
 
         outputs = model(spec)
 

src/batdetect2/train/legacy/train_model.py

@@ -2,6 +2,10 @@ import argparse
 import json
 import warnings
 
+import batdetect2.train.audio_dataloader as adl
+import batdetect2.train.evaluate as evl
+import batdetect2.train.train_split as ts
+import batdetect2.train.train_utils as tu
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
@@ -9,10 +13,6 @@ import torch.utils.data
 from torch.optim.lr_scheduler import CosineAnnealingLR
 
 import batdetect2.detector.post_process as pp
-import batdetect2.train.audio_dataloader as adl
-import batdetect2.train.evaluate as evl
-import batdetect2.train.train_split as ts
-import batdetect2.train.train_utils as tu
 import batdetect2.utils.plot_utils as pu
 from batdetect2.detector import models, parameters
 from batdetect2.train import losses

src/batdetect2/train/legacy/train_split.py

@@ -10,7 +10,7 @@ def get_train_test_data(ann_dir, wav_dir, split_name, load_extra=True):
         train_sets, test_sets = split_same(ann_dir, wav_dir, load_extra)
     else:
         print("Split not defined")
-        assert False
+        raise AssertionError()
 
     return train_sets, test_sets
 

src/batdetect2/train/legacy/train_utils.py

@@ -2,7 +2,7 @@ import json
 import sys
 from collections import Counter
 from pathlib import Path
-from typing import Dict, Generator, List, Optional, Tuple
+from typing import Dict, Generator, List, Tuple
 
 import numpy as np
 

src/batdetect2/train/lightning.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Optional, Tuple
+from typing import TYPE_CHECKING, Tuple
 
 import lightning as L
 import torch

src/batdetect2/train/losses.py

@@ -18,7 +18,6 @@ The primary entry points are:
 - `LossConfig`: The Pydantic model for configuring loss weights and parameters.
 """
 
-from typing import Optional
 
 import numpy as np
 import torch

src/batdetect2/types.py

@@ -1,6 +1,6 @@
 """Types used in the code base."""
 
-from typing import Any, List, NamedTuple, Optional, TypedDict
+from typing import Any, List, NamedTuple, TypedDict
 
 import numpy as np
 import torch

src/batdetect2/typing/data.py

@@ -1,4 +1,4 @@
-from typing import Generic, List, Optional, Protocol, Sequence, TypeVar
+from typing import Generic, List, Protocol, Sequence, TypeVar
 
 from soundevent.data import PathLike
 

src/batdetect2/typing/evaluate.py

@@ -4,7 +4,6 @@ from typing import (
     Generic,
     Iterable,
     List,
-    Optional,
     Protocol,
     Sequence,
     Tuple,

src/batdetect2/typing/postprocess.py

@@ -12,7 +12,7 @@ system that deal with model predictions.
 """
 
 from dataclasses import dataclass
-from typing import List, NamedTuple, Optional, Protocol, Sequence
+from typing import List, NamedTuple, Protocol, Sequence
 
 import numpy as np
 import torch

src/batdetect2/typing/preprocess.py

@@ -10,7 +10,7 @@ pipeline can interact consistently, regardless of the specific underlying
 implementation (e.g., different libraries or custom configurations).
 """
 
-from typing import Optional, Protocol
+from typing import Protocol
 
 import numpy as np
 import torch

src/batdetect2/typing/targets.py

@@ -13,7 +13,7 @@ throughout BatDetect2.
 """
 
 from collections.abc import Callable
-from typing import List, Optional, Protocol
+from typing import List, Protocol
 
 import numpy as np
 from soundevent import data