build: trim default runtime dependencies

pyproject.toml

@@ -11,10 +11,9 @@ dependencies = [
   "deepmerge>=2.0",
   "hydra-core>=1.3.2",
   "librosa>=0.10.1",
-  "lightning[extra]==2.5.0",
+  "lightning==2.5.0",
   "loguru>=0.7.3",
   "matplotlib>=3.7.1",
-  "netcdf4>=1.6.5",
   "numpy>=1.23.5",
   "pandas>=1.5.3",
   "pydantic>=2.0.0",
@@ -24,9 +23,10 @@ dependencies = [
   "seaborn>=0.13.2",
   "soundevent[audio,geometry,plot]>=2.10.0",
   "soundfile>=0.12.1",
-  "tensorboard>=2.16.2",
+  "tabulate>=0.10.0",
   "torch>=2.0.0",
   "torchaudio>=2.0.0",
+  "tqdm>=4.70.0",
   "xarray>=2024.0.0",
 ]
 requires-python = ">=3.10,<3.14"
@@ -88,6 +88,7 @@ dev = [
   "deepdiff>=8.6.1",
   "pytest-xdist[psutil]>=3.8.0",
 ]
+tensorboard = ["tensorboard>=2.16.2"]
 dvclive = ["dvclive>=3.48.2"]
 mlflow = ["mlflow>=3.1.1"]
 gradio = [

src/batdetect2/logging.py

@@ -201,7 +201,14 @@ def create_tensorboard_logger(
     experiment_name: str | None = None,
     run_name: str | None = None,
 ) -> Logger:
-    from lightning.pytorch.loggers import TensorBoardLogger
+    try:
+        from lightning.pytorch.loggers import TensorBoardLogger
+    except ImportError as error:
+        raise ValueError(
+            "TensorBoard is not installed and cannot be used for logging. "
+            "Make sure you have it installed by running `pip install tensorboard` "
+            "or `uv add tensorboard`"
+        ) from error
 
     if log_dir is None:
         log_dir = Path(config.log_dir)

src/batdetect2/outputs/formats/parquet.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import List, Literal, Sequence
+from typing import List, Literal, Sequence, TypedDict
 from uuid import UUID
 
 import numpy as np
@@ -26,6 +26,11 @@ class ParquetOutputConfig(BaseConfig):
     include_geometry: bool = True
 
 
+class ClipInfo(TypedDict):
+    clip: data.Clip
+    preds: list[Detection]
+
+
 class ParquetFormatter(OutputFormatterProtocol[ClipDetections]):
     def __init__(
         self,
@@ -120,7 +125,7 @@ def load(self, path: data.PathLike) -> List[ClipDetections]:
         else:
             df = pd.read_parquet(path)
 
-        predictions_by_clip = {}
+        predictions_by_clip: dict[UUID, ClipInfo] = {}
 
         for _, row in df.iterrows():
             clip_uuid = row["clip_uuid"]

src/batdetect2/plotting/detections.py

@@ -185,25 +185,25 @@ def plot_clip_evaluation(
                     label="found GT",
                     edgecolor=gt_color,
                     facecolor="none" if not fill else gt_color,
-                    linestyle=gt_linestyle,
+                    linestyle=gt_linestyle,  # type: ignore
                 ),
                 patches.Patch(
                     label="missed GT",
                     edgecolor=missed_gt_color,
                     facecolor="none" if not fill else missed_gt_color,
-                    linestyle=missed_gt_linestyle,
+                    linestyle=missed_gt_linestyle,  # type: ignore
                 ),
                 patches.Patch(
                     label="true Det",
                     edgecolor=true_pred_color,
                     facecolor="none" if not fill else true_pred_color,
-                    linestyle=true_pred_linestyle,
+                    linestyle=true_pred_linestyle,  # type: ignore
                 ),
                 patches.Patch(
                     label="false Det",
                     edgecolor=false_pred_color,
                     facecolor="none" if not fill else false_pred_color,
-                    linestyle=false_pred_linestyle,
+                    linestyle=false_pred_linestyle,  # type: ignore
                 ),
             ]
         )

src/batdetect2/plotting/heatmaps.py

@@ -1,9 +1,9 @@
 """Plot heatmaps."""
 
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from matplotlib import axes, patches
-from matplotlib.cm import get_cmap
 from matplotlib.colors import Colormap, LinearSegmentedColormap, to_rgba
 
 from batdetect2.plotting.common import create_ax
@@ -80,7 +80,7 @@ def plot_classification_heatmap(
         raise ValueError("Inconsistent number of class names")
 
     if not isinstance(cmap, Colormap):
-        cmap = get_cmap(cmap)
+        cmap = plt.get_cmap(cmap)
 
     handles = []