Fix logging imports

src/batdetect2/cli/base.py

@@ -2,10 +2,6 @@
 
 import click
 
-from batdetect2.logging import enable_logging
-
-# from batdetect2.cli.ascii import BATDETECT_ASCII_ART
-
 __all__ = [
     "cli",
 ]
@@ -34,5 +30,7 @@ def cli(verbose: int = 0):
     """
     click.echo(INFO_STR)
 
+    from batdetect2.logging import enable_logging
+
     enable_logging(verbose)
     # click.echo(BATDETECT_ASCII_ART)

src/batdetect2/cli/inference.py

@@ -4,8 +4,6 @@ from typing import TYPE_CHECKING
 
 import click
 from loguru import logger
-from soundevent import io
-from soundevent.audio.files import get_audio_files
 
 from batdetect2.cli.base import cli
 
@@ -219,6 +217,8 @@ def predict_directory_command(
     Loads a checkpoint, scans `audio_dir` for supported audio files, runs
     inference, and saves predictions to `output_path`.
     """
+    from soundevent.audio.files import get_audio_files
+
     audio_files = list(get_audio_files(audio_dir))
     _run_prediction(
         model_path=model_path,
@@ -309,6 +309,8 @@ def predict_dataset_command(
     The dataset is read as a soundevent annotation set and unique recording
     paths are extracted before inference.
     """
+    from soundevent import io
+
     dataset_path = Path(dataset_path)
     dataset = io.load(dataset_path, type="annotation_set")
     audio_files = sorted(

src/batdetect2/logging.py

@@ -1,9 +1,12 @@
+from __future__ import annotations
+
 import io
 import sys
 from collections.abc import Callable
 from functools import partial
 from pathlib import Path
 from typing import (
+    TYPE_CHECKING,
     Annotated,
     Any,
     Dict,
@@ -13,21 +16,23 @@ from typing import (
     TypeVar,
 )
 
-import numpy as np
-import pandas as pd
-from lightning.pytorch.loggers import (
-    CSVLogger,
-    Logger,
-    MLFlowLogger,
-    TensorBoardLogger,
-)
 from loguru import logger
-from matplotlib.figure import Figure
 from pydantic import Field
-from soundevent import data
 
 from batdetect2.core.configs import BaseConfig
 
+if TYPE_CHECKING:
+    import numpy as np
+    import pandas as pd
+    from lightning.pytorch.loggers import (
+        CSVLogger,
+        Logger,
+        MLFlowLogger,
+        TensorBoardLogger,
+    )
+    from matplotlib.figure import Figure
+    from soundevent import data
+
 DEFAULT_LOGS_DIR: Path = Path("outputs") / "logs"
 
 __all__ = [
@@ -271,10 +276,16 @@ def build_logger(
     )
 
 
-PlotLogger = Callable[[str, Figure, int], None]
+PlotLogger = Callable[[str, "Figure", int], None]
 
 
 def get_image_logger(logger: Logger) -> PlotLogger | None:
+    from lightning.pytorch.loggers import (
+        CSVLogger,
+        MLFlowLogger,
+        TensorBoardLogger,
+    )
+
     if isinstance(logger, TensorBoardLogger):
         return logger.experiment.add_figure
 
@@ -296,10 +307,16 @@ def get_image_logger(logger: Logger) -> PlotLogger | None:
         return partial(save_figure, dir=Path(logger.log_dir))
 
 
-TableLogger = Callable[[str, pd.DataFrame, int], None]
+TableLogger = Callable[[str, "pd.DataFrame", int], None]
 
 
 def get_table_logger(logger: Logger) -> TableLogger | None:
+    from lightning.pytorch.loggers import (
+        CSVLogger,
+        MLFlowLogger,
+        TensorBoardLogger,
+    )
+
     if isinstance(logger, TensorBoardLogger):
         return partial(save_table, dir=Path(logger.log_dir))
 
@@ -337,6 +354,8 @@ def save_figure(name: str, fig: Figure, step: int, dir: Path) -> None:
 
 
 def _convert_figure_to_array(figure: Figure) -> np.ndarray:
+    import numpy as np
+
     with io.BytesIO() as buff:
         figure.savefig(buff, format="raw")
         buff.seek(0)

src/batdetect2/targets/terms.py

@@ -15,11 +15,11 @@ GENERIC_CLASS_KEY = "class"
 
 
 data_source = data.Term(
-    name="soundevent:data_source",
-    label="Data Source",
+    name="dcterms:source",
+    label="Source",
+    uri="http://purl.org/dc/terms/source",
     definition=(
-        "A unique identifier for the source of the data, typically "
-        "representing the project, site, or deployment context."
+        "A related resource from which the described resource is derived."
     ),
 )
 
@@ -45,6 +45,17 @@ individual = data.Term(
 )
 """Term used for tags identifying a specific individual animal."""
 
+dataset_split = data.Term(
+    name="batdetect2:split",
+    label="Dataset Split",
+    definition=(
+        "Identifies the specific data partition (e.g., 'train', 'test') "
+        "that the item belongs to within an experimental setup. "
+        "The expected value is a literal text string."
+    ),
+)
+"""Custom metadata term defining the machine learning partition of an item."""
+
 generic_class = data.Term(
     name="soundevent:class",
     label="Class",