formatted

src/soundevent/arrays/dimensions.py

@@ -493,9 +493,7 @@ def get_dim_step(
         return attrs[DimAttrs.step.value]
 
     if not estimate_step:
-        raise ValueError(
-            f"Step size not found in the '{dim}' dimension attributes."
-        )
+        raise ValueError(f"Step size not found in the '{dim}' dimension attributes.")
 
     return estimate_dim_step(
         coord.data,

src/soundevent/arrays/operations.py

@@ -7,11 +7,7 @@
 from numpy.typing import DTypeLike
 from xarray.core.types import InterpOptions
 
-from soundevent.arrays.dimensions import (
-    create_range_dim,
-    get_dim_range,
-    get_dim_step,
-)
+from soundevent.arrays.dimensions import create_range_dim, get_dim_range, get_dim_step
 
 __all__ = [
     "center",
@@ -92,9 +88,7 @@ def crop_dim(
         stop = current_stop
 
     if start > stop:
-        raise ValueError(
-            f"Start value {start} must be less than stop value {stop}"
-        )
+        raise ValueError(f"Start value {start} must be less than stop value {stop}")
 
     if start < current_start or stop > current_stop:
         raise ValueError(
@@ -180,9 +174,7 @@ def extend_dim(
         stop = current_stop
 
     if start > stop:
-        raise ValueError(
-            f"Start value {start} must be less than stop value {stop}"
-        )
+        raise ValueError(f"Start value {start} must be less than stop value {stop}")
 
     step = get_dim_step(arr, dim)
 
@@ -312,9 +304,7 @@ def set_value_at_pos(
         start, stop = get_dim_range(array, dim)
 
         if coord < start or coord > stop:
-            raise KeyError(
-                f"Position {coord} is outside the range of dimension {dim}."
-            )
+            raise KeyError(f"Position {coord} is outside the range of dimension {dim}.")
 
         index = array.indexes[dim].get_slice_bound(coord, "right")
         indexer[dims[dim]] = index - 1

src/soundevent/audio/chunks.py

@@ -111,8 +111,7 @@ def _read_chunk(riff: BinaryIO) -> Optional[Chunk]:
 
     if chunk_id in CHUNKS_WITH_SUBCHUNKS:
         chunk.subchunks = {
-            subchunk.chunk_id: subchunk
-            for subchunk in _get_subchunks(riff, size - 4)
+            subchunk.chunk_id: subchunk for subchunk in _get_subchunks(riff, size - 4)
         }
     else:
         riff.seek(size, os.SEEK_CUR)

src/soundevent/audio/filter.py

@@ -18,9 +18,7 @@ def _get_filter(
     order: int = 5,
 ) -> np.ndarray:
     if low_freq is None and high_freq is None:
-        raise ValueError(
-            "At least one of low_freq and high_freq must be specified."
-        )
+        raise ValueError("At least one of low_freq and high_freq must be specified.")
 
     if low_freq is None:
         # Low pass filter

src/soundevent/audio/media_info.py

@@ -156,9 +156,7 @@ def get_media_info(path: PathLike) -> MediaInfo:
         # chunk is the size of the data subchunk divided by the number
         # of channels and the bit depth.
         data_chunk = chunk.subchunks["data"]
-        samples = (
-            8 * data_chunk.size // (fmt_info.channels * fmt_info.bit_depth)
-        )
+        samples = 8 * data_chunk.size // (fmt_info.channels * fmt_info.bit_depth)
         duration = samples / fmt_info.samplerate
 
         return MediaInfo(

src/soundevent/audio/spectrum.py

@@ -120,9 +120,7 @@ def pcen_core(
         raise ValueError(f"eps={eps} must be strictly positive")
 
     if time_constant <= 0:
-        raise ValueError(
-            f"time_constant={time_constant} must be strictly positive"
-        )
+        raise ValueError(f"time_constant={time_constant} must be strictly positive")
 
     if b is None:
         t_frames = time_constant * sr / float(hop_length)
@@ -146,9 +144,7 @@ def pcen_core(
         if max_size == 1:
             ref = S
         elif S.ndim == 1:
-            raise ValueError(
-                "Max-filtering cannot be applied to 1-dimensional input"
-            )
+            raise ValueError("Max-filtering cannot be applied to 1-dimensional input")
         else:
             if max_axis is None:
                 if S.ndim != 2:

src/soundevent/data/annotation_sets.py

@@ -28,6 +28,4 @@ class AnnotationSet(BaseModel):
         default_factory=list,
         repr=False,
     )
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)

src/soundevent/data/annotation_tasks.py

@@ -60,15 +60,11 @@ class StatusBadge(BaseModel):
 
     state: AnnotationState
     owner: Optional[User] = None
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)
 
 
 class AnnotationTask(BaseModel):
     uuid: UUID = Field(default_factory=uuid4, repr=False)
     clip: Clip
     status_badges: List[StatusBadge] = Field(default_factory=list)
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)

src/soundevent/data/clip_annotations.py

@@ -54,6 +54,4 @@ class ClipAnnotation(BaseModel):
     sequences: List[SequenceAnnotation] = Field(default_factory=list)
     tags: List[Tag] = Field(default_factory=list)
     notes: List[Note] = Field(default_factory=list)
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)

src/soundevent/data/clip_evaluations.py

@@ -95,17 +95,13 @@ def _check_matches(self):
         }
 
         match_targets = [
-            match.target.uuid
-            for match in self.matches
-            if match.target is not None
+            match.target.uuid for match in self.matches if match.target is not None
         ]
 
         match_targets_set = set(match_targets)
 
         match_sources = [
-            match.source.uuid
-            for match in self.matches
-            if match.source is not None
+            match.source.uuid for match in self.matches if match.source is not None
         ]
 
         match_sources_set = set(match_sources)

src/soundevent/data/evaluations.py

@@ -25,9 +25,7 @@ class Evaluation(BaseModel):
     """Evaluation Class."""
 
     uuid: UUID = Field(default_factory=uuid4, repr=False)
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)
     evaluation_task: str
     clip_evaluations: Sequence[ClipEvaluation] = Field(default_factory=list)
     metrics: Sequence[Feature] = Field(default_factory=list)

src/soundevent/data/geometries.py

@@ -252,9 +252,7 @@ def _validate_time_interval(cls, v: List[Time]) -> List[Time]:
                 after the end time).
         """
         if len(v) != 2:
-            raise ValueError(
-                "The time interval must have exactly two time stamps."
-            )
+            raise ValueError("The time interval must have exactly two time stamps.")
 
         if v[0] > v[1]:
             raise ValueError("The start time must be before the end time.")
@@ -325,9 +323,7 @@ def _validate_coordinates(cls, v: List[float]) -> List[float]:
             raise ValueError("The time must be positive.")
 
         if frequency < 0 or frequency > MAX_FREQUENCY:
-            raise ValueError(
-                f"The frequency must be between 0 and {MAX_FREQUENCY}."
-            )
+            raise ValueError(f"The frequency must be between 0 and {MAX_FREQUENCY}.")
 
         return v
 
@@ -473,8 +469,7 @@ def _validate_coordinates(
 
                 if frequency < 0 or frequency > MAX_FREQUENCY:
                     raise ValueError(
-                        f"The frequency must be between 0 and "
-                        f"{MAX_FREQUENCY}."
+                        f"The frequency must be between 0 and " f"{MAX_FREQUENCY}."
                     )
 
         return v
@@ -532,9 +527,7 @@ def _validate_coordinates(cls, v: List[float]) -> List[float]:
                 negative or the frequency is outside the valid range).
         """
         if len(v) != 4:
-            raise ValueError(
-                "The bounding box must have exactly four coordinates."
-            )
+            raise ValueError("The bounding box must have exactly four coordinates.")
 
         start_time, low_freq, end_time, high_freq = v
 
@@ -558,9 +551,7 @@ def _validate_coordinates(cls, v: List[float]) -> List[float]:
             raise ValueError("The start time must be before the end time.")
 
         if low_freq > high_freq:
-            raise ValueError(
-                "The start frequency must be before the end frequency."
-            )
+            raise ValueError("The start frequency must be before the end frequency.")
 
         return v
 
@@ -771,28 +762,23 @@ def _validate_coordinates(
                 negative or the frequency is outside the valid range).
         """
         if len(v) < 1:
-            raise ValueError(
-                "The multipolygon must have at least one polygon."
-            )
+            raise ValueError("The multipolygon must have at least one polygon.")
 
         for polygon in v:
             if len(polygon) < 1:
                 raise ValueError("Each polygon must have at least one ring.")
 
             for ring in polygon:
                 if len(ring) < 3:
-                    raise ValueError(
-                        "Each ring must have at least three points."
-                    )
+                    raise ValueError("Each ring must have at least three points.")
 
                 for time, frequency in ring:
                     if time < 0:
                         raise ValueError("The time must be positive.")
 
                     if frequency < 0 or frequency > MAX_FREQUENCY:
                         raise ValueError(
-                            f"The frequency must be between 0 and "
-                            f"{MAX_FREQUENCY}."
+                            f"The frequency must be between 0 and " f"{MAX_FREQUENCY}."
                         )
 
         return v
@@ -921,6 +907,4 @@ def geometry_validate(
             from_attributes=mode == "attributes",
         )
     except ValidationError as error:
-        raise ValueError(
-            f"Object {obj} is not a valid {geom_type}."
-        ) from error
+        raise ValueError(f"Object {obj} is not a valid {geom_type}.") from error

src/soundevent/data/notes.py

@@ -95,9 +95,7 @@ class Note(BaseModel):
     message: str
     created_by: Optional[User] = None
     is_issue: bool = False
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)
 
     def __hash__(self):
         """Hash the Note object."""

src/soundevent/data/prediction_sets.py

@@ -73,6 +73,4 @@ class PredictionSet(BaseModel):
 
     uuid: UUID = Field(default_factory=uuid4)
     clip_predictions: List[ClipPrediction] = Field(default_factory=list)
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)

src/soundevent/data/recording_sets.py

@@ -12,6 +12,4 @@
 class RecordingSet(BaseModel):
     uuid: UUID = Field(default_factory=uuid4)
     recordings: List[Recording] = Field(default_factory=list, repr=False)
-    created_on: datetime.datetime = Field(
-        default_factory=datetime.datetime.now
-    )
+    created_on: datetime.datetime = Field(default_factory=datetime.datetime.now)

src/soundevent/data/recordings.py

@@ -193,10 +193,7 @@ def from_file(
         Recording
             The recording object.
         """
-        from soundevent.audio.media_info import (
-            compute_md5_checksum,
-            get_media_info,
-        )
+        from soundevent.audio.media_info import compute_md5_checksum, get_media_info
 
         media_info = get_media_info(path)
 

src/soundevent/evaluation/affinity.py

@@ -1,11 +1,7 @@
 """Measures of affinity between sound events geometries."""
 
 from soundevent import data
-from soundevent.geometry import (
-    buffer_geometry,
-    compute_bounds,
-    geometry_to_shapely,
-)
+from soundevent.geometry import buffer_geometry, compute_bounds, geometry_to_shapely
 
 __all__ = [
     "compute_affinity",
@@ -88,10 +84,7 @@ def compute_affinity(
     geometry1 = _prepare_geometry(geometry1, time_buffer, freq_buffer)
     geometry2 = _prepare_geometry(geometry2, time_buffer, freq_buffer)
 
-    if (
-        geometry1.type in TIME_GEOMETRY_TYPES
-        or geometry2.type in TIME_GEOMETRY_TYPES
-    ):
+    if geometry1.type in TIME_GEOMETRY_TYPES or geometry2.type in TIME_GEOMETRY_TYPES:
         return compute_affinity_in_time(geometry1, geometry2)
 
     shp1 = geometry_to_shapely(geometry1)
@@ -114,12 +107,8 @@ def compute_affinity_in_time(
     start_time1, _, end_time1, _ = compute_bounds(geometry1)
     start_time2, _, end_time2, _ = compute_bounds(geometry2)
 
-    intersection = max(
-        0, min(end_time1, end_time2) - max(start_time1, start_time2)
-    )
-    union = (
-        (end_time1 - start_time1) + (end_time2 - start_time2) - intersection
-    )
+    intersection = max(0, min(end_time1, end_time2) - max(start_time1, start_time2))
+    union = (end_time1 - start_time1) + (end_time2 - start_time2) - intersection
 
     if union == 0:
         return 0

src/soundevent/evaluation/metrics.py

@@ -43,9 +43,7 @@ def balanced_accuracy(
     y_score: np.ndarray,
 ) -> float:
     num_classes = y_score.shape[1]
-    y_true_array = np.array(
-        [y if y is not None else num_classes for y in y_true]
-    )
+    y_true_array = np.array([y if y is not None else num_classes for y in y_true])
     y_score = np.c_[y_score, 1 - y_score.sum(axis=1, keepdims=True)]
     y_pred = y_score.argmax(axis=1)
     return metrics.balanced_accuracy_score(
@@ -59,9 +57,7 @@ def accuracy(
     y_score: np.ndarray,
 ) -> float:
     num_classes = y_score.shape[1]
-    y_true_array = np.array(
-        [y if y is not None else num_classes for y in y_true]
-    )
+    y_true_array = np.array([y if y is not None else num_classes for y in y_true])
     y_score = np.c_[y_score, 1 - y_score.sum(axis=1, keepdims=True)]
     y_pred = y_score.argmax(axis=1)
     return metrics.accuracy_score(  # type: ignore
@@ -75,9 +71,7 @@ def top_3_accuracy(
     y_score: np.ndarray,
 ) -> float:
     num_classes = y_score.shape[1]
-    y_true_array = np.array(
-        [y if y is not None else num_classes for y in y_true]
-    )
+    y_true_array = np.array([y if y is not None else num_classes for y in y_true])
     y_score = np.c_[y_score, 1 - y_score.sum(axis=1, keepdims=True)]
     return metrics.top_k_accuracy_score(  # type: ignore
         y_true=y_true_array,

src/soundevent/evaluation/tasks/__init__.py

@@ -5,9 +5,7 @@
 from soundevent.evaluation.tasks.sound_event_classification import (
     sound_event_classification,
 )
-from soundevent.evaluation.tasks.sound_event_detection import (
-    sound_event_detection,
-)
+from soundevent.evaluation.tasks.sound_event_detection import sound_event_detection
 
 __all__ = [
     "clip_classification",