feat(plotting): add size heatmap label plotting

src/batdetect2/plotting/__init__.py

@@ -6,6 +6,7 @@ from batdetect2.plotting.gallery import plot_match_gallery
 from batdetect2.plotting.heatmaps import (
     plot_classification_heatmap,
     plot_detection_heatmap,
+    plot_size_heatmap,
 )
 from batdetect2.plotting.matches import (
     plot_cross_trigger_match,
@@ -25,5 +26,6 @@ __all__ = [
     "plot_true_positive_match",
     "plot_detection_heatmap",
     "plot_classification_heatmap",
+    "plot_size_heatmap",
     "plot_match_gallery",
 ]

src/batdetect2/plotting/heatmaps.py

@@ -1,4 +1,4 @@
-"""Plot heatmaps"""
+"""Plot heatmaps."""
 
 import numpy as np
 import torch
@@ -8,6 +8,12 @@ from matplotlib.colors import Colormap, LinearSegmentedColormap, to_rgba
 
 from batdetect2.plotting.common import create_ax
 
+__all__ = [
+    "plot_detection_heatmap",
+    "plot_classification_heatmap",
+    "plot_size_heatmap",
+]
+
 
 def plot_detection_heatmap(
     heatmap: torch.Tensor | np.ndarray,
@@ -108,7 +114,91 @@ def plot_classification_heatmap(
     return ax
 
 
-def create_colormap(color: str) -> Colormap:
+def plot_size_heatmap(
+    heatmap: torch.Tensor | np.ndarray,
+    dimension_names: list[str],
+    ax: axes.Axes | None = None,
+    figsize: tuple[int, int] = (10, 10),
+    color: str = "crimson",
+    size: float = 20,
+    fontsize: float = 8,
+) -> axes.Axes:
+    """Plot sparse size labels from a size heatmap.
+
+    Parameters
+    ----------
+    heatmap : torch.Tensor | np.ndarray
+        Size heatmap with shape ``[num_dims, height, width]``. Entries are
+        expected to be zero everywhere except at labelled positions.
+    dimension_names : list[str]
+        Names corresponding to the first heatmap dimension.
+    ax : matplotlib.axes.Axes | None, default=None
+        Axis to plot on. If ``None``, a new axis is created.
+    figsize : tuple[int, int], default=(10, 10)
+        Figure size used when creating a new axis.
+    color : str, default="crimson"
+        Color used for scatter points and text labels.
+    size : float, default=20
+        Marker size for plotted points.
+    fontsize : float, default=8
+        Font size used for the text labels.
+
+    Returns
+    -------
+    matplotlib.axes.Axes
+        Axis containing the plotted size labels.
+    """
+    ax = create_ax(ax, figsize=figsize)
+
+    if isinstance(heatmap, torch.Tensor):
+        heatmap = heatmap.numpy()
+
+    if heatmap.ndim == 4:
+        heatmap = heatmap[0]
+
+    if heatmap.ndim != 3:
+        raise ValueError("Expecting a 3-dimensional array")
+
+    if len(dimension_names) != heatmap.shape[0]:
+        raise ValueError("Inconsistent number of dimension names")
+
+    point_mask = np.any(heatmap != 0, axis=0)
+    rows, cols = np.nonzero(point_mask)
+
+    if len(rows) == 0:
+        return ax
+
+    ax.scatter(cols, rows, c=color, s=size)
+
+    for row, col in zip(rows, cols, strict=False):
+        values = heatmap[:, row, col]
+        labels = [
+            f"{name}={value:.2f}"
+            for name, value in zip(
+                dimension_names,
+                values,
+                strict=False,
+            )
+            if value != 0
+        ]
+        ax.text(
+            float(col),
+            float(row),
+            "\n".join(labels),
+            fontsize=fontsize,
+            color=color,
+            va="bottom",
+            ha="left",
+        )
+
+    ax.set_xlim(0, heatmap.shape[2])
+    ax.set_ylim(0, heatmap.shape[1])
+    return ax
+
+
+def create_colormap(
+    color: str | tuple[float, float, float, float],
+) -> Colormap:
     (r, g, b, a) = to_rgba(color)
     return LinearSegmentedColormap.from_list(
         "cmap", colors=[(0, 0, 0, 0), (r, g, b, a)]