Use colorcet for categorical colormaps (#1365)

Author: fepegar

pyproject.toml

@@ -49,7 +49,7 @@ dependencies = [
 
 [project.optional-dependencies]
 csv = ["pandas>=1"]
-plot = ["distinctipy>=1.3.4", "matplotlib>=3.4"]
+plot = ["colorcet", "matplotlib>=3.4"]
 video = ["ffmpeg-python>=0.2.0"]
 sklearn = [
     "scikit-learn>=1.6.1",

src/torchio/external/imports.py

@@ -26,8 +26,8 @@ def get_pandas() -> ModuleType:
     return _check_and_import(module='pandas', extra='csv')
 
 
-def get_distinctipy() -> ModuleType:
-    return _check_and_import(module='distinctipy', extra='plot')
+def get_colorcet() -> ModuleType:
+    return _check_and_import(module='colorcet', extra='plot')
 
 
 def get_ffmpeg() -> ModuleType:

src/torchio/visualization.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import warnings
+from itertools import cycle
 from pathlib import Path
 from typing import TYPE_CHECKING
 from typing import Any
@@ -47,6 +48,7 @@ def rotate(image: np.ndarray, *, radiological: bool = True, n: int = -1) -> np.n
 
 def _create_categorical_colormap(
     data: torch.Tensor,
+    cmap_name: str = 'glasbey_category10',
 ) -> tuple[ListedColormap, BoundaryNorm]:
     num_classes = int(data.max())
     mpl, _ = import_mpl_plt()
@@ -56,10 +58,12 @@ def _create_categorical_colormap(
         (1, 1, 1),  # white for class 1
     ]
     if num_classes > 1:
-        from .external.imports import get_distinctipy
+        from .external.imports import get_colorcet
 
-        distinctipy = get_distinctipy()
-        distinct_colors = distinctipy.get_colors(num_classes - 1, rng=0)
+        colorcet = get_colorcet()
+        cmap = getattr(colorcet.cm, cmap_name)
+        color_cycle = cycle(cmap.colors)
+        distinct_colors = [next(color_cycle) for _ in range(num_classes - 1)]
         colors.extend(distinct_colors)
     boundaries = np.arange(-0.5, num_classes + 1.5, 1)
     colormap = mpl.colors.ListedColormap(colors)