src/batdetect2/plotting/clip_annotations.py

@@ -4,9 +4,7 @@ from matplotlib.axes import Axes
 from soundevent import data, plot
 
 from batdetect2.plotting.clips import plot_clip
-from batdetect2.plotting.common import create_ax
 from batdetect2.typing.preprocess import PreprocessorProtocol
-from batdetect2.typing.targets import TargetProtocol
 
 __all__ = [
     "plot_clip_annotation",
@@ -45,31 +43,3 @@ def plot_clip_annotation(
         facecolor="none" if not fill else None,
     )
     return ax
-
-
-def plot_anchor_points(
-    clip_annotation: data.ClipAnnotation,
-    targets: TargetProtocol,
-    figsize: Optional[Tuple[int, int]] = None,
-    ax: Optional[Axes] = None,
-    size: int = 1,
-    color: str = "red",
-    marker: str = "x",
-    alpha: float = 1,
-) -> Axes:
-    ax = create_ax(ax=ax, figsize=figsize)
-
-    positions = []
-
-    for sound_event in clip_annotation.sound_events:
-        if not targets.filter(sound_event):
-            continue
-
-        sound_event = targets.transform(sound_event)
-
-        position, _ = targets.encode_roi(sound_event)
-        positions.append(position)
-
-    X, Y = zip(*positions)
-    ax.scatter(X, Y, s=size, c=color, marker=marker, alpha=alpha)
-    return ax

src/batdetect2/plotting/common.py

@@ -1,6 +1,6 @@
 """General plotting utilities."""
 
-from typing import Optional, Tuple, Union
+from typing import Optional, Tuple
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -25,7 +25,7 @@ def create_ax(
 
 
 def plot_spectrogram(
-    spec: Union[torch.Tensor, np.ndarray],
+    spec: torch.Tensor,
     start_time: float,
     end_time: float,
     min_freq: float,
@@ -34,15 +34,12 @@ def plot_spectrogram(
     figsize: Optional[Tuple[int, int]] = None,
     cmap="gray",
 ) -> axes.Axes:
-    if isinstance(spec, torch.Tensor):
-        spec = spec.numpy()
-
     ax = create_ax(ax=ax, figsize=figsize)
 
     ax.pcolormesh(
-        np.linspace(start_time, end_time, spec.shape[-1] + 1, endpoint=True),
-        np.linspace(min_freq, max_freq, spec.shape[-2] + 1, endpoint=True),
-        spec,
+        np.linspace(start_time, end_time, spec.shape[-1], endpoint=False),
+        np.linspace(min_freq, max_freq, spec.shape[-2], endpoint=False),
+        spec.numpy(),
         cmap=cmap,
     )
     return ax

src/batdetect2/train/augmentations.py

@@ -400,8 +400,6 @@ AugmentationConfig = Annotated[
 class AugmentationsConfig(BaseConfig):
     """Configuration for a sequence of data augmentations."""
 
-    enabled: bool = True
-
     steps: List[AugmentationConfig] = Field(default_factory=list)
 
 

src/batdetect2/train/config.py

@@ -67,8 +67,8 @@ class TrainingConfig(BaseConfig):
     t_max: int = 100
     dataloaders: LoadersConfig = Field(default_factory=LoadersConfig)
     loss: LossConfig = Field(default_factory=LossConfig)
-    augmentations: AugmentationsConfig = Field(
-        default_factory=lambda: DEFAULT_AUGMENTATION_CONFIG.model_copy()
+    augmentations: Optional[AugmentationsConfig] = Field(
+        default_factory=lambda: DEFAULT_AUGMENTATION_CONFIG
     )
     cliping: ClipingConfig = Field(default_factory=ClipingConfig)
     trainer: PLTrainerConfig = Field(default_factory=PLTrainerConfig)

src/batdetect2/train/labels.py

@@ -62,7 +62,7 @@ class LabelConfig(BaseConfig):
         diffuse targets.
     """
 
-    sigma: float = 2.0
+    sigma: float = 3.0
 
 
 def build_clip_labeler(
@@ -174,7 +174,7 @@ def generate_clip_label(
 
 
 def map_to_pixels(x, size, min_val, max_val) -> int:
-    return int(np.interp(x, [min_val, max_val], [0, size]))
+    return int(np.floor(np.interp(x, [min_val, max_val], [0, size])))
 
 
 def generate_heatmaps(

src/batdetect2/train/train.py

@@ -239,7 +239,7 @@ def build_train_dataset(
         clipper=clipper,
     )
 
-    if config.augmentations.enabled and config.augmentations.steps:
+    if config.augmentations and config.augmentations.steps:
         augmentations = build_augmentations(
             preprocessor,
             config=config.augmentations,