remove superfluous function, make docstring better

Author: MaHaWo

src/sme_contrib/plot.py

@@ -4,11 +4,8 @@
 from matplotlib import pyplot as plt
 from matplotlib.colors import LinearSegmentedColormap as lscmap
 from matplotlib import animation
-from itertools import cycle
-import matplotlib.colors as mcolors
 import pyvista as pv
 from typing import Any, Callable
-import sme
 
 from .pyvista_utils import (
     find_layout,
@@ -37,28 +34,6 @@ def colormap(color, name="my colormap"):
     return lscmap.from_list(name, [(0, 0, 0), color], 256)
 
 
-def make_circular_colormap(
-    cmap: str = "tab10", values: np.ndarray = np.array([])
-) -> list[tuple]:
-    """Create a discrete colormap of potentially repeating colors of the same size as the `values` array.
-
-    Args:
-        cmap (str, optional): matplotlib colormap name. Defaults to "tab10".
-        values (np.array, optional): values to be mapped to colors. Defaults to [].
-
-    Returns:
-        list[tuple]: list of color in rgba format.
-    """
-    cm = [(0.0, 0.0, 0.0, 1.0)]
-    i = 0
-    for c in cycle(plt.get_cmap(cmap).colors):
-        cm.append(mcolors.to_rgba(c))
-        if len(cm) >= len(values):
-            break
-        i += 1
-    return cm
-
-
 def concentration_heatmap(
     simulation_result, species, z_slice: int = 0, title=None, ax=None, cmap=None
 ):

src/sme_contrib/pyvista_utils.py

@@ -7,7 +7,7 @@
 
 def rgb_to_scalar(img: np.ndarray) -> np.ndarray:
     """
-    Convert an RGB 3D image represented as a 4D tensor to a 3D image tensor where each unique RGB value is assigned a unique scalar, i.e., it contracts the dimension with the RGB values into scalars. This is useful because PyVista doesn't work well with RGB values directly and expects fields defined on a grid, usually given by the tensor shape.
+    Convert an RGB 3D image represented as a 4D tensor to a 3D image tensor where each unique RGB value is assigned a unique scalar, i.e., it contracts the dimension with the RGB values into scalars in such a way that 2 different colors are mapped to 2 different scalars, too. This is needed because PyVista doesn't work with RGB values directly and expects fields defined on a grid.
 
         img (np.ndarray): A 3D numpy array representing an RGB image with shape (height, width, 3).
 
@@ -25,7 +25,7 @@ def make_discrete_colormap(
     cmap: str = "tab10", values: np.ndarray = np.array([])
 ) -> pv.LookupTable:
     """
-    Create a discrete colormap for use with PyVista.
+    Create a discrete colormap for use with PyVista with as many colors as unique values in the `values`array based on a given matplotlbit colormap. The colors will possibly repeat if there are more unique values than colors in the colormap. In this case, the outcome is intended, e.g., for separability of regions in the visualization,
 
     Parameters:
     cmap (str): The name of the colormap to use. Default is 'tab10'.